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91	Instrumentation and Computational Physics	ABS-76
Development of an Integrated E-Nose System with Controlled Sample Heating Chamber for Coffee Aroma Discrimination B H Iswanto1, M Rosyid, H Suhendar, F Hardoyono, M Delina Universitas Negeri Jakarta Abstract Keywords: Electronic Nose, Coffee Aroma, Sample Heating, Classification, Machine Learning Share Link | Plain Format | Corresponding Author (Bambang Heru Iswanto)

92	Instrumentation and Computational Physics	ABS-77
Optimization of Support Vector Machines and Window Sampling for E-Nose-Based Classification of Black and Green Tea Aroma Bambang Heru Iswanto, Huffaz Muhammad Abdurrofi Baith, Abdullah Mu^adz Muflih, Haris Suhendar Universitas Negeri Jakarta Abstract Aroma classification is critical for quality assurance and authentication in the global tea industry, where traditional sensory evaluation remains limited by subjectivity and inefficiency. This study aims to optimize both sampling window duration and Support Vector Machine (SVM) classifier performance for the objective, rapid classification of black and green tea aromas using a low-cost electronic nose (E-Nose). The custom-built E-Nose, integrating eight MQ-series gas sensors, measured volatile profiles from Indonesian black and green tea samples across three sampling windows-30, 60, and 90 seconds. For each sample, statistical features were extracted from sensor responses, and classification was performed using SVM models with linear and radial basis function (RBF) kernels. Model selection and validation employed leave-one-out cross-validation and grid-based hyperparameter tuning. Results show that a 60-second sampling window is sufficient for near-perfect classification (accuracy &#8805-97.5%), with the linear SVM achieving perfect separation at 90 seconds. Principal Component Analysis (PCA) confirmed clear feature-based separation of tea classes. These findings demonstrate that rapid, objective, and reliable tea aroma authentication can be achieved using simple machine learning models and short sampling durations with a low-cost E-Nose Keywords: Electronic nose- tea aroma- classification- sampling window- Support Vector Machine Share Link | Plain Format | Corresponding Author (Bambang Heru Iswanto)

93	Instrumentation and Computational Physics	ABS-78
Enhancing E-Nose Performance for Ginger Extract Aroma Classification through Sample Heating and Machine Learning Integration Bambang Heru Iswanto (a*), Muhammad Rosyid Suseno (a) a) Department of Physics, Universitas Negeri Jakarta Jl. Rawamangun Muka, Jakarta 13220, Indonesia *bhi[at]unj.ac.id Abstract Accurate aroma detection plays a vital role in the classification of ginger extracts, particularly for applications requiring rapid and cost-effective quality control. This study explores the impact of sample heating on the performance of a low-cost electronic nose (e-nose) system designed for aroma-based classification of ginger extracts. The system utilizes a sensor array composed of metal oxide gas sensors from the MQ series, integrated with machine learning algorithms including Support Vector Machine (SVM) and Artificial Neural Network (ANN). Ginger extract samples were tested under various thermal preconditioning conditions to assess how temperature influences sensor responsiveness and classification accuracy. The experimental results indicate that moderate heating significantly enhances the release of volatile organic compounds, resulting in stronger sensor responses and improved discrimination between different ginger extract types. Under optimal heating conditions, classification accuracy improved by up to 15% compared to ambient-temperature analysis, with the ANN model delivering the best performance. These findings suggest that incorporating thermal treatment is an effective strategy to improve the sensitivity and reliability of low-cost aroma detection systems. The integration of controlled heating with machine learning techniques offers a promising solution for advancing ginger extract aroma classification in both research and industrial quality assessment contexts. Keywords: Electronic Nose, Ginger Classification, MQ-Sensor, Machine Learning Share Link | Plain Format | Corresponding Author (Muhammad Rosyid Suseno)

94	Instrumentation and Computational Physics	ABS-85
Theoretical Study of \(CO_{2}\) Hydrogenation to HCOOH on Subnanometer PdZn Cluster Marleni Wirmas\(^{(a)}\), Muhammad Haris Mahyuddin\(^{(b,c*)}\), Mohammad Kemal Agusta\(^{(b,c)}\), and Hermawan Kresno Dipojono\(^{(b,c)}\) (a) Doctoral Program of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia (b) Quantum and Nano Technology Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia (c) Research Center for Nanoscience and Nanotechnology, Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia *mahyuddin133[at]itb.ac.id Abstract PdZn alloys have been potentially used as catalysts in various hydrogenation reactions, with each atom having significant roles in binding hydrogen and \(CO_{2}\) molecules. In this work, we conducted density functional theory (DFT) calculations to investigate the catalytic performance and reaction mechanism of \(CO_{2}\) hydrogenation to formic acid on a small, unsupported \(Pd_{5}Zn\) subnanocluster. Two reaction pathways are examined: the formate (HCOO) and carboxyl (COOH) routes. In the initial stage of \(CO_{2}\) as well as \(H_{2}\) adsorption, the molecules are adsorbed strongly with notable orbital hybridization between \(CO_{2}\), hydrogen, and PdZn atoms, suggesting an activated and well-suited condition for subsequent hydrogenation process. The preferred adsorption of all intermediates in the elementary reactions were investigated. Overall, intermediates in the formate pathway exhibit more stable adsorption and lower activation energies compared to those in the carboxyl pathway, making it the more favourable route in this system. These findings offer insights for designing efficient catalysts by utilizing subnanometer clusters to promote appropriate reaction intermediates. Keywords: subnanometer cluster, \(CO_{2}\) hydrogenation, Density Functional Theory, heterogeneous catalyst Share Link | Plain Format | Corresponding Author (Marleni Wirmas)

95	Instrumentation and Computational Physics	ABS-93
YOLOv11 dan CLAHE untuk Optimalisasi Deteksi Kendaraan Parkir Ilegal di Jalan Raya Berbasis Video CCTV Salma Mardhiyah, Bambang Heru Iswanto, dan Med Irzal Program Studi Fisika, Universitas Negeri Jakarta, Jl. Rawamangun Muka, Jakarta 13220, Indonesia Program Studi Ilmu Komputer, Universitas Negeri Jakarta. Jl. Rawamangun Muka, Jakarta 13220, Indonesia Abstract Deteksi parkir ilegal di jalan raya merupakan tantangan serius dalam pengelolaan lalu lintas dan ketertiban kota, khususnya di area publik dengan kepadatan kendaraan yang tinggi. Deteksi otomatis menggunakan kecerdasan buatan (AI) dapat menjadi solusi potensial untuk meningkatkan efisiensi dan akurasi pengawasan lalu lintas. Penelitian ini bertujuan untuk mengoptimalkan deteksi parkir ilegal dengan menggabungkan algoritma deteksi objek You Only Look Once versi 11 (YOLOv11) dan metode peningkatan kualitas citra Contrast Limited Adaptive Histogram Equalization (CLAHE). Metode yang diusulkan terdiri dari dua tahap utama. Tahap pertama adalah klasifikasi kondisi waktu dari citra CCTV dengan menganalisis 30% area teratas dari gambar. Citra dengan tingkat pencahayaan rendah akan diproses menggunakan metode CLAHE guna meningkatkan kontras serta visibilitas objek. Kedua, YOLOv11 diterapkan pada citra untuk mendeteksi kendaraan yang melakukan parkir ilegal di area bahu jalan yang telah diinisiasikan. Kendaraan dikategorikan sebagai pelanggaran parkir ilegal apabila berada pada posisi yang sama tanpa pergerakan signifikan selama interval waktu 5 detik dan ditandai dengan kotak merah oleh sistem. Hasil pengujian menunjukkan bahwa kombinasi CLAHE dan YOLOv11 mampu meningkatkan performa deteksi dengan akurasi mencapai 95,9%, presisi 94,7%, dan recall 90,1%. Kecepatan inferensi rata-rata sebesar 15 FPS memungkinkan integrasi dengan sistem pemantauan lalu lintas berbasis CCTV. Sementara itu, performa YOLOv11 tanpa CLAHE menghasilkan akurasi sebesar 91,2%, presisi 90,5%, dan recall 87,8%. Peningkatan performa secara signifikan terutama terjadi pada objek yang berada jauh dari kamera CCTV atau dalam kondisi pencahayaan yang rendah maupun buram. CLAHE terbukti membantu menjaga konsistensi bounding box dan mengurangi kesalahan deteksi terhadap objek non-kendaraan. Temuan ini mengindikasikan bahwa kombinasi metode YOLOv11 dan CLAHE dapat menjadi solusi efektif untuk deteksi kendaraan yang parkir secara ilegal di jalan raya serta berpotensi diimplementasikan secara langsung dalam sistem pengawasan lalu lintas berbasis CCTV. Penggunaan sistem ini diharapkan dapat meningkatkan kepatuhan pengguna jalan dan memperkuat penegakan hukum lalu lintas berbasis teknologi. Keywords: Deteksi Objek, YOLOv11, CLAHE, Parkir Liar, CCTV Share Link | Plain Format | Corresponding Author (Salma Mardhiyah)

96	Instrumentation and Computational Physics	ABS-94
SSD-MobileNet v2 dan SSD-EfficientDet dengan Partisi Ukuran Dataset untuk Meningkatkan Akurasi Deteksi Kendaraan Parkir Ilegal Berbasis Video CCTV Khansa Farras Callista, Bambang Heru Iswanto, Haris Suhendar Program Studi Fisika, FMIPA Universitas Negeri Jakarta, Jl. Rawamangun Muka No. 01, Rawamangun 13220, Indonesia. Abstract Parkir liar di pinggir jalan menjadi salah satu sumber kemacetan dan gangguan ketertiban lalu lintas. Pengawasan secara manual melalui CCTV tidak efisien. Oleh karena itu, penelitian ini bertujuan mengembangkan sistem otomatis untuk mendeteksi kendaraan parkir liar dengan membandingkan performa dua model deteksi objek berbasis Single Shot Multibox Detector (SSD), yaitu MobileNet v2 dan EfficientDet-D0, serta mengkaji pengaruh strategi pemisahan dataset berdasarkan ukuran objek terhadap akurasi deteksi. Metode yang digunakan mencakup pengumpulan dan anotasi data dari video CCTV publik, diikuti proses pelatihan dua model SSD pada dua subset data yang dibagi berdasarkan luas area bounding box kendaraan: objek kecil dan objek besar. EfficientDet-D0 dilatih untuk mendeteksi objek kecil, sementara MobileNet v2 difokuskan pada objek besar. Evaluasi dilakukan dengan metrik mean Average Precision (mAP) pada berbagai tingkat Intersection over Union (IoU), serta pengukuran kecepatan inferensi (FPS) untuk menilai kelayakan implementasi waktu nyata. Hasil penelitian menunjukkan bahwa MobileNet v2 memberikan performa terbaik pada subset objek besar, dengan tingkat ketepatan rata-rata sebesar 94,9% dan skor sempurna 100% pada tingkat tumpang tindih IoU 0.50 dan 0.75. Model ini juga menunjukkan kecepatan inferensi tinggi yaitu 25 FPS, menjadikannya cocok untuk sistem real-time. Di sisi lain, EfficientDet-D0 pada subset objek kecil mencatat rata-rata ketepatan sebesar 58,6%, menandakan keterbatasan dalam mendeteksi objek kecil secara akurat. Sebagai perbandingan, ketika seluruh dataset dilatih hanya menggunakan MobileNet v2 tanpa pemisahan, performa menurun menjadi mAP 73,7% pada IoU 0.50. Hal ini menegaskan bahwa strategi pemisahan dataset berdasarkan ukuran objek dan pelatihan model yang disesuaikan dapat meningkatkan akurasi deteksi secara signifikan, khususnya pada kondisi visual ekstrem. Temuan ini menunjukkan bahwa dengan memanfaatkan MobileNet v2 untuk objek besar dan EfficientDet-D0 untuk objek kecil, dapat membuka peluang pengembangan sistem pemantauan lalu lintas yang lebih cerdas dan efisien, yang mampu beroperasi secara otomatis, sehingga dapat membantu mengurangi pelanggaran parkir dan meningkatkan kelancaran arus lalu lintas. Keywords: Deteksi Objek, MobileNet v2, EfficientDet-D0, Parkir Liar, CCTV Share Link | Plain Format | Corresponding Author (Khansa Farras Callista)

97	Instrumentation and Computational Physics	ABS-103
Estimation of Gravitational Lens Parameters at Intermediate Redshifts Using Convolutional Neural Networks (CNN) Muhammad Doni Setiawan (a*), Anton Timur Jaelani (b,c) a) Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia *10321003[at]mahasiswa.itb.ac.id b) Astronomy Research Group and Bosscha Observatory, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia c) U-CoE AI-VLB, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia Abstract Strong gravitational lensing serves as a powerful astrophysical probe, enabling studies of dark matter, galaxy structure, and cosmological parameters. The number of strong gravitational lensing candidates at the galaxy scale is expected to reach O~10^{5} with ongoing and future wide-field galaxy surveys. Current modeling techniques largely depend on conventional fitting-such as least squares or maximum likelihood using Markov Chain Monte Carlo (MCMC)-which, despite their effectiveness, are computationally expensive and demand manual inspection. This motivates the development of faster yet accurate parameter estimation techniques. In this work, we construct a representative training dataset and develop an efficient Convolutional Neural Network (CNN) to estimate crucial lens parameters: the Einstein radius, axis ratio, and position angle. We utilize data from Public Data Release 3 (PDR3) of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Lens galaxies are selected in the range 0.3<z<0.9 following the strong-lens probability distribution. Preliminary results indicate that both the selection of the loss function and the regularization strategy markedly influence model performance. SpatialDropout outperforms standard dropout for regularization. Furthermore, prediction accuracy and convergence speed depend heavily on the distribution of the training data, thereby informing the optimal choice of loss function. Keywords: Strong gravitational lensing, Convolutional Neural Networkc (CNN), Lens parameter estimation Share Link | Plain Format | Corresponding Author (Muhammad Doni Setiawan)

98	Instrumentation and Computational Physics	ABS-106
Hybrid DAE-GAN Model with U-Net Architecture for Seismic Signal Denoising Eko Priyatno(12*), Ahmad Kadarisman (12), Santoso Soekirno(2), and Martarizal (2) 1) Directorate of Instrumentation and Calibration BMKG, Indonesia *eko.priyatno[at]ui.ac.id 2) Department of Physics Faculty of Mathematics and Natural Sciences, Universitas Indonesia Abstract Seismic information is crucial in geophysical research, yet its quality is often affected by various types of disturbances that complicate the analysis of subsurface structures. This study introduces a novel solution using deep learning to reduce noise in three-component seismic data. The proposed architecture is a combination of a Denoising Autoencoder (DAE) and a Generative Adversarial Network (GAN). A U-Net model is used as the Generator to reconstruct a noise-free signal from noise-affected data. On the other hand, a CNN-based Discriminator model serves to distinguish between the reconstructed signals and the original clean signals. The loss function for the Generator is a combination of Mean Squared Error (MSE) to ensure accurate reconstruction and an Adversarial Loss to maintain realistic statistical characteristics. Thus, the resulting signal is not only free from disturbances but also retains the original characteristics of seismic data. This model was trained and tested using data from the STEAD (STanford EArthquake Dataset). The model^s quality was evaluated using quantitative metrics such as Signal-to-Noise Ratio (SNR), RMSE, and PRD on a separate test set. The test results show that this model can significantly increase the SNR and produce a clean signal that is visually and spectrally (using STFT) very similar to the original signal. This method holds great potential for enhancing automation and efficiency in the seismic data pre-processing workflow. Keywords: Deep Learning, Denoising, Seismic Signal, U-Net, Generative Adversarial Network (GAN), Denoising Autoencoder (DAE), STEAD Share Link | Plain Format | Corresponding Author (Eko Priyatno)

99	Instrumentation and Computational Physics	ABS-117
Assessment of Working Fluid Efficacy in Shell-and-Tube Heat Exchangers using OpenFOAM Rida SN Mahmudah, Restu Widiatmono, Denny Darmawan Universitas Negeri Yogyakarta Abstract The choice of working fluid significantly influences the thermal performance of heat exchangers, especially in high-temperature energy systems such as those used in nuclear reactors. This study compares four candidate fluids-water, molten salt, lead-bismuth eutectic (LBE), and FLiBe-used in a shell-and-tube heat exchanger model. Transient conjugate heat transfer simulations were performed using OpenFOAM v12 for each fluid at four different mass flow rates (0.01, 0.05, 0.25, and 0.5 kg/s) while maintaining a constant temperature difference between shell and tube inlets. The results show that water has the highest heat transmission rate due to its large specific heat capacity- however, its effectiveness decreases at higher flow rates. In contrast, owing to its excellent thermal conductivity, LBE provides superior thermal efficiency, particularly at low flow rates. FLiBe demonstrated a strong balance between thermal transfer capabilities and efficiency under a variety of flow conditions. The study emphasizes the trade-offs between energy transmission and utilization, demonstrating that higher heat transfer rates do not necessarily equate to greater effectiveness. These findings support a more informed selection of working fluids for thermally demanding systems such as nuclear heat exchangers. Keywords: conjugate heat transfer, OpenFOAM, multi-region heat exchanger, molten salt, transient heat transfer Share Link | Plain Format | Corresponding Author (Rida SN Mahmudah)

100	Instrumentation and Computational Physics	ABS-133
Reconstructing Seismic Integrity from Urban Noise: A Deep Learning Approach for Improving Event Detection Martarizal 1, Eko Priyatno 12, Ahmad Kadarisman 12, Santoso Soekirno 1 1. Department of Physics, Universitas Indonesia, Depok, Indonesia 2. Directorate of Instrumentation and Calibration, BMKG, Jakarta, Indonesia Abstract Urban-induced noise remains a critical barrier to reliable seismic event detection, particularly in stations located in densely populated environments. This study presents a deep learning-based framework employing a seismic denoising autoencoder (SeisDAE) to reconstruct high-fidelity earthquake signals from noise-contaminated recordings. The model is trained on paired, time-synchronized waveforms from a low-noise reference station and a high-noise urban station, with data segmented into non-seismic intervals and confirmed earthquake events. This structure allows the autoencoder to jointly learn background suppression and event feature preservation. Preliminary evaluations demonstrate that the proposed approach effectively restores the visibility of seismic events in noisy station recordings, aligning them closely with their clean station counterparts. The results suggest that SeisDAE offers a promising direction for enhancing the operational performance of urban seismic networks, particularly in early warning and microseismic monitoring contexts. Keywords: Seismic denoising, urban noise suppression, deep autoencoder, SeisDAE, earthquake signal reconstruction, time-synchronized seismic data, seismic event detection Share Link | Plain Format | Corresponding Author (Martarizal Martarizal)

101	Instrumentation and Computational Physics	ABS-139
Perubahan Efek Nernst Anomali dan Seebeck pada Nickel (III) Iodide (&#119821-&#119842-&#119816-&#120785-) Terhadap Medan Magnet Menggunakan Density Functional Theory Fadhli Rahman, Dr. Teguh Budi Prayitno, M.Si., Dr. Edi Suprayoga, M. Si. Universitas Negeri Jakarta Abstract Penelitian ini bertujuan untuk menganalisis pengaruh medan magnet terhadap efek Seebeck dan efek Nernst anomali pada monolayer Nickel Triiodide (NiI3) menggunakan pendekatan teori fungsional kerapatan (Density Functional Theory/DFT). Studi ini dimotivasi oleh potensi material dua dimensi seperti NiI3 dalam aplikasi termoelektrik, yang sangat bergantung pada respons listrik terhadap gradien suhu dan medan magnet. Dengan menggunakan perangkat lunak OpenMX, simulasi dilakukan melalui beberapa tahap, termasuk relaksasi struktur, perhitungan densitas keadaan (Density of States), dan perhitungan sifat transport menggunakan pendekatan Boltzmann. Hasil menunjukkan bahwa medan magnet secara signifikan memengaruhi parameter termoelektrik seperti koefisien Seebeck dan efek Nernst anomali, khususnya dalam kondisi spin terpolarisasi. Temuan ini memberikan wawasan penting mengenai pemanfaatan NiI3 sebagai kandidat material termoelektrik berbasis spin, serta kontribusinya terhadap pengembangan perangkat energi berkelanjutan berbasis teknologi spintronik. Keywords: Nickel Triiodide (NiI3), Density Functional Theory (DFT), Efek Seebeck, Efek Nernst Anomali, Termoelektrik, Medan Magnet, Material 2D, Spintronik. Share Link | Plain Format | Corresponding Author (Fadhli Rahman)

102	Instrumentation and Computational Physics	ABS-141
An Internet of Things system design and implementation for monitoring carbon dioxide, methane, humidity, and temperature Iwan Sugriwan, Melania Suweni Muntini, Yono Hadi Pramono Department of Physics, Institute of Technology Sepuluh Nopember, Surabaya, Indonesia Abstract An Internet of Things (IoT) system was designed and fabricated as a data acquisition system for monitoring the concentrations of carbon dioxide gas, methane gas, humidity, and temperature. An MQ-135 sensor was utilized to measure carbon dioxide concentration, a TGS2611 sensor for methane, and an SHT11 sensor for humidity and temperature. These three sensors were connected to a NodeMCU ESP32 microcontroller and Wi-Fi module. Specifically, the MQ-135 sensor was connected via input 1, the TGS2611 sensor via input 2, and the SHT11 sensor was connected to the data and clock pins. Data from the three sensors was acquired and processed by the NodeMCU ESP32. The results were then transmitted to three destinations: a serial monitor, a 20x4 I2C LCD, and the Wi-Fi module for transmission to a server computer. On the server computer, software was developed, including programming for the NodeMCU ESP32 using the Arduino IDE, database management with MySQL, and a web application for displaying the measurement results on a website in real-time. A comparative test was conducted between the IoT system and a standard measuring instrument to ensure that the developed system could accurately measure the concentrations of carbon dioxide gas, methane gas, humidity, and temperature. Keywords: IoT, MQ-134, NodeMCU ESP32, SHT11, TGS2611. Share Link | Plain Format | Corresponding Author (Iwan Sugriwan)

103	Instrumentation and Computational Physics	ABS-145
Rancang Bangun Sistem Otomasi untuk Pengendalian Lingkungan dan Penyiraman Otomatis pada Aeroponik Hashifah Dewianty Putri1, a), Umiatin2, b), Ahmad Zatnika Purwalaksana3, c) 1Fisika/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia 2Fisika/ Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia 3Fisika/ Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia Abstract Pertanian konvensional di perkotaan menghadapi keterbatasan lahan dan inefisiensi air. Aeroponik menjadi solusi alternatif melalui budidaya tanpa tanah dengan penyemprotan nutrisi, namun membutuhkan kontrol lingkungan yang akurat. Penelitian ini mengembangkan sistem otomasi berbasis Arduino Uno yang mengintegrasikan sensor DHT22, BH1750, HC-SR04, dan RTC. Metode Research and Development (R&D) diaplikasikan melalui tahap analisis, perancangan, pengembangan, dan pengujian sistem pada budidaya selada butterhead. Hasil karakterisasi dari sensor DHT22 untuk parameter suhu menunjukkan nilai koefisien determinasi (R^2 ) sebesar 0,99565 dan memiliki nilai sensitivitas sebesar -0,26985. Sensor DHT22 untuk parameter kelembaban menunjukkan nilai koefisien determinasi (R^2 ) sebesar 0,33614 dan memiliki nilai sensitivitas sebesar -224,87850. Sensor HC-SR04 menunjukkan nilai (R^2 ) sebesar 1 dan memiliki nilai sensitivitas sebesar 0. Sensor BH1750 menunjukkan nilai (R^2 ) sebesar 0,98484 dan memiliki nilai sensitivitas sebesar -51,40617. Implikasi penelitian mencakup potensi pengembangan sistem serupa untuk komoditas hortikultura lainnya dengan modifikasi parameter pertumbuhan. Keywords: Aeroponik, Otomasi, Sensor, Penyiraman Otomatis. Share Link | Plain Format | Corresponding Author (Hashifah Dewianty Putri)

104	Instrumentation and Computational Physics	ABS-146
Pengembangan Sistem Otomasi Untuk Pengendalian Kualitas Larutan Nutrisi Pada Tandon Nutrisi Hidroponik Muhammad Adam Al Kautsar1, a) Umiatin2, b), Pinta Omas Pasaribu3, c) 1 Fisika/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia 2 Fisika/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia 3 Biologi/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia Abstract Pada tahun 2023, jumlah rumah tangga pertanian diperkirakan meningkat sebesar 8,74% menjadi 28,42 juta jiwa. Pertumbuhan pertanian ini dapat mengalami gangguan akibat konversi lahan disebabkan pertumbuhan penduduk. Teknik hidroponik merupakan solusi potensial karena hemat lahan dan mendorong pertumbuhan tanaman yang optimal. Penelitian ini bertujuan untuk melakukan pengembangan sistem otomasi untuk pengendalian kualitas larutan nutrisi pada tandon hidroponik. Sistem otomasi dirancang menggunakan metode Research and Development (R&D). Sistem dirancang untuk mengontrol parameter penting seperti ketinggian air, suhu dan Parts Per Million (PPM) larutan nutrisi secara otomatis pada tandon dan kolam, menggunakan berbagai sensor dengan jumlah dua untuk tiap sensornya seperti sensor Ultrasonik HC-SR04, sensor DS18B20, dan sensor Total Dissolved Solids (TDS). Pengujian dilakukan untuk memastikan efektivitas sistem dalam kondisi nyata, dengan pengumpulan data ketinggian air, suhu, PPM, dan respons sistem. Hasil karakterisasi dari dua sensor Ultrasonik HC-SR04 menunjukkan nilai koefisien determinasi (R^{2}) sebesar 0,9989 dan 0,9985 dengan nilai sensitivitas sebesar -0,1779 dan -0,2374. Dua Sensor DS18B20 menunjukkan nilai (R^{2}) sebesar 0,9968 dan 0,9995 dengan nilai sensitivitas sebesar -1,389 dan 0,4267. Dua Sensor TDS menunjukkan nilai (R^{2}) sebesar 0,9784 dan 0,9699 dengan nilai sensitivitas sebesar 323,0 dan 269,3. Sistem ini menghasilkan pertanian modern yang lebih optimal dan efisien. Keywords: 1st Hidroponik, 2nd Otomasi, 3rd Kualitas larutan. Share Link | Plain Format | Corresponding Author (Muhammad Adam Al Kautsar)

105	Instrumentation and Computational Physics	ABS-147
Sensor Characterization for Self Service Medical Check Up Machine Umiatin1, a), Pinkan Amanda Putri1, b), Ahmad Zatnika Purwalaksana1, c) 1Department of Physics Education, Universitas Negeri Jakarta Jl. Rawamangun Muka, Jakarta 13220, Indonesia Abstract Cardiovascular disease is the leading cause of death in the world, causing 41 million deaths each year, or 74% of global deaths from non-communicable diseases (NCDs) such as hypertension, diabetes, and obesity, therefore, early detection and monitoring of NCD risk factors are very important. This study aims to develop an integrated device called ATKM, which combines a tensiometer, glucometer, and BMI-fat analyzer. The study was conducted in three stages, starting with the characterization of sensors to measure blood pressure, blood glucose, and body fat composition. The sensors used include the MPX5050GP pressure sensor, the BPW34 optical sensor with 940 nm NIR-LED, the HC-SR04 and JSN-SR04T proximity sensors, and the load cell sensor. The MPX5050GP sensor showed a coefficient of determination (R^2) of 1 and a sensitivity of 0.012. The BPW34 sensor showed an (R^2) of 0.8736 with a sensitivity of -0.0013. The HC-SR04 and JSN-SR04T proximity sensors showed (R^2) values of 0.9996 and 0.9997, and sensitivities of 0.9943 and 0.9831, respectively. The load cell sensor achieved an (R^2) of 1 and a sensitivity of 1.0056. Overall, the results show that all sensors have good sensitivity and accuracy, making them suitable for integration into ATKM devices. Keywords: Tensiometer, Glucometer and BMI-Fat Analyzer Share Link | Plain Format | Corresponding Author (Pinkan Amanda Putri)

106	Instrumentation and Computational Physics	ABS-148
Modeling Granular Mixtures with Simple Rules: A Student Project in Agent-Based Simulation Muhammad Farhan Shadiq, Aurellyallodia Faiza Kusuma, Fifi Fitriyah Masduki, Sevi Nurafni, Putri Mustika Widartiningsih, Sparisoma Viridi Pradita Dirgantara High School Institut Teknologi Bandung IKOPIN University The University of Tokyo Abstract In this project, we used simple computer rules to simulate how mixtures of small solid particles (like sand or grains) behave. These mixtures can include different shapes and sizes of particles, and their behavior can be hard to predict. Our model uses a method called agent-based modeling, where each particle is treated like a small ^agent^ that follows rules based on its properties-such as size, shape, and surface roughness. Instead of using complicated physics equations, we used probabilities to decide how particles interact. We tested our model using real data from experiments with mixed particles, focusing on how they form piles. Our results show that even with simple rules, the model can still show realistic particle behavior. This approach could help create faster simulations for science and engineering. Keywords: Agent-Based Model, Granular Materials, Computation, Angle of Repose Share Link | Plain Format | Corresponding Author (Sparisoma Viridi)

107	Instrumentation and Computational Physics	ABS-149
Pengembangan Sistem Otomasi Aquaponik untuk Pengendalian Kualitas Air dan Pemeliharaan Ikan Secara Real-Time Berbasis Mikrokontroler Sayyid Abdul Matin1, a), Umiatin2, b), Ahmad Zatnika Purwalaksana3, c) 1Fisika/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia 2Fisika/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia 3Fisika/Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, DKI Jakarta, Indonesia Abstract Urbanisasi dan degradasi lahan pertanian yang terjadi di wilayah perkotaan menuntut akan adanya penerapan akan sistem produksi pangan berkelanjutan seperti akuaponik. Penelitian ini mengembangkan sistem otomasi akuaponik berbasis mikrokontroler untuk pengendalian kualitas air dan pemeliharaan ikan secara real-time. Sistem ini mengintegrasikan sensor suhu, pH, dan gas untuk memantau parameter lingkungan yang kritis, dan menggunakan aktuator seperti pompa air, pompa peristaltik, dan kompresor untuk menjaga stabilitas ekosistem. Komponen utamanya meliputi Arduino Uno, sensor suhu DS18B20, sensor pH-4502C, sensor gas MQ-135, dan LCD HMI Nextion untuk tampilan data secara real-time. Algoritma sistem dirancang untuk mengaktifkan sistem pendingin saat suhu air mencapai 23 derajat Celcius atau lebih dari sama dengan 26 derajat Celcius dan untuk memulai sirkulasi air saat pH berada di luar kisaran 6,0-7,0. Sistem ini dikembangkan menggunakan model ADDIE, dan validasi sensor dilakukan melalui proses kalibrasi dan analisis statistik. Hasil karakterisasi dari sensor pH-4502C menunjukkan nilai koefisien determinasi sebesar 0,9997 dan memiliki nilai sensitivitas sebesar -0,05352. Sensor DS18B20 menunjukkan nilai koefisiensi determinasi sebesar 0,9926 dan memiliki nilai sensitivitas sebesar -0,8952. Sistem ini meningkatkan efisiensi pengendalian kualitas air dalam akuaponik dan dapat menjadi referensi yang praktis untuk diterapkan pada teknologi monitoring yang berbasis mikrokontroler di bidang pertanian. Keywords: Akuaponik, Otomasi, Monitoring Real-time. Share Link | Plain Format | Corresponding Author (Sayyid Abdul Matin)

108	Instrumentation and Computational Physics	ABS-165
Early Detection of Seismic Signal Anomalies Using Raspberry Pi 5 and Lightweight Machine Learning Models Ahmad Kadarisman 1,2, Ayu Widowati 1, Marrissa Arlinkha 1, Rina Yuniarty 1, Rini Anggraeni 1, Santoso Soekirno 1, Martarizal 1, Hanif Andi Nugraha 2 1. Department of Physics, Universias Indonesia, Depok, Indonesia 2. Directorate of Instrumentation and Calibration, BMKG, Jakarta, Indonesia Abstract Real-time detection of anomalous seismic signals is critical for maintaining the reliability of monitoring systems, particularly in environments prone to anthropogenic interference and instrumental instability. This paper presents a lightweight edge computing framework built on the Raspberry Pi 5 and AI-Kit, designed to identify signal anomalies onsite. Seismic data streams in MiniSEED format were segmented and processed to extract statistical and spectral features (e.g., RMS, kurtosis, and Power Spectral Density). Unsupervised learning models, including Isolation Forest and a lightweight Autoencoder, were deployed to detect deviations without the need for labeled datasets. Experimental evaluations using data from the TOJI station showed effective anomaly identification with low inference latency and minimal resource consumption, underscoring the systems suitability for deployment in resource-constrained seismic networks. Keywords: Seismic anomaly detection, Raspberry Pi 5, real-time monitoring, AI-Kit, unsupervised learning, anthropogenic noise, embedded system, Isolation Forest, Autoencoder Share Link | Plain Format | Corresponding Author (Ahmad Kadarisman)

109	Instrumentation and Computational Physics	ABS-172
Effect of magnetic anisotropy on spin-current driven by resonant dynamics of skyrmion lattices Seno Aji (a*), Muhammad Anin Nabail Azhiim (a), Nur Ika Puji Ayu (a), Adam Badra Cahaya (a), Koichi Kusakabe (b), and Muhammad Aziz Majidi (a) a) Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia *senji77[at]sci.ui.ac.id b) School of Science, Graduate School of Science, University of Hyogo, 3-2-1 Kouto, Kamigori-cho, Ako-gun, Hyogo, 678-1297, Japan Abstract We study the generation of spin currents in a skyrmion-hosting material lacking inversion symmetry through a microwave-driven resonance mechanism. We analyze the roles of magnetic anisotropy and polarized microwaves using micromagnetic simulation. Our results reveal two distinct skyrmion phases, designated as SkX type-I and SkX type-II, that emerge at low (Kz < 0.1 meV) and high (Kz > 0.1 meV) magnetic anisotropy, respectively. These two phases exhibit fundamentally different spin dynamics. In SkX type-I, the resonant frequency of the breathing mode lies between the clockwise and counterclockwise gyration modes of Bloch-type skyrmions at very low anisotropy and crosses over the counterclockwise mode at Kz ~ 0.04 meV. In contrast, SkX type-II exhibits unique excitation characteristics, notably the absence of the clockwise mode, while counterclockwise modes persist at both low and high frequencies. This highlights the important roles of magnetic anisotropy on spin dynamics. Moreover, the induced spin excitations generate spin currents with unconventional characteristics under polarized microwave excitation. Specifically, low-energy in-plane excitation produces strongly enhanced spin currents under left-handed circularly polarized microwaves, but these currents are suppressed when right-handed circular polarization is applied, regardless of the sign of the Dzyaloshinskii-Moriya interaction. These findings may provide new insights into the complex interplay between magnetic anisotropy and microwave polarization in resonantly driven spin-current generation. Keywords: Skrymion- Spin dynamics- Spin-current- Micromagnetic simulation Share Link | Plain Format | Corresponding Author (Seno Aji)

110	Instrumentation and Computational Physics	ABS-221
Computational Implementation of Linear Reference Elbow (LRE) Method for Optimal Cluster Determination in k-Means Algorithm Akhmad Yusuf Department of Mathematics, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University Abstract The selection of the optimal number of clusters (k) in the K-Means algorithm still relies on the subjective visual elbow method or computationally intensive techniques like the Gap Statistic. LRE was developed to address this issue through an objective and efficient geometric approach. The aim of this research is to provide an automated Python-based solution for determining the optimal k quickly and reproducibly, particularly for industrial applications. The LRE method calculates the orthogonal distance between the points of the WCSS curve and the reference line connecting the first and last points, then selects the k with the maximum distance. LRE successfully processed a sample dataset at a speed 110 times faster than the Gap Statistic method for the same dataset, while the Elbow method could not be timed due to its subjective nature. This significant difference is particularly evident in algorithmic complexity, where LRE maintains linear time complexity (O(n)), while bootstrap-based methods like the Gap Statistic experience exponential time increases. Testing on two benchmark datasets showed that LRE produced consistent outputs. The deterministic nature of this algorithm eliminates the subjective variability that is the main drawback of manual approaches. Keywords: automated clustering, elbow method, K-Means, computational efficiency Share Link | Plain Format | Corresponding Author (Akhmad Yusuf)

111	Instrumentation and Computational Physics	ABS-229
Steel Shearing Acoustic Emission Signal Identification Using Wavelet Scattering On Micro Forming Irwan Setyanto, Gandjar Kiswanto, Sugeng Supriadi, Siska Titik Dwiyati University Of Indonesia Abstract Real-time defect detection is crucial for advanced in-process quality inspection systems, particularly in addressing intricate and challenging cracks within small-scale products through automation. Acoustic emission (AE), a potent non-destructive technique, offers the capability for rapid crack initiation detection. To advance this detection capability in micro-scale manufacturing, our study investigated acoustic emissions generated during the micro-blanking process and try to recognized it. Using a proprietary 5 kN micro-forming machine, experiments were conducted on 0.1 mm SK-5. Comparison of various signal processing methods is done using Fast Fourier Transform (FFT) , Short-Time Fourier Transform (STFT) , and the wavelet scattering transform (WST). With similar result from STFT and WST, the latter offers a richer feature set that is invariant and stable against small shifts and noise, making it well-suited for AE signal classification tasks where the differences can be nuanced and involve complex time-frequency patterns. Keywords: Acoustics Emission- micro forming- crack monitoring- energy saving Share Link | Plain Format | Corresponding Author (Irwan Setyanto)

112	Instrumentation and Computational Physics	ABS-235
Analisis Kualitas Data Medan Magnet Bumi Dengan Metode Delta F Pada Observatorium Magnet Bumi Serang 2024 Najwa Kaila Nur Alif (a*), Fachriza Fathan, S.Si., M.Si. (b), Wahyudi Nasrul Pratama, S.Tr. M.DM (b) Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta, Jl. Rawamangun Muka Raya No. 11, Jakarta Timur, Daerah Khusus ibukota Jakarta, 13220, Indonesia *najwakaila12[at]gmail.com Abstract Indonesia yang terletak di Ring of Fire memiliki aktivitas geofisika tinggi, sehingga rentan terhadap dinamika medan magnet bumi yang bisa mempengaruhi teknologi dan keselamatan. Data medan magnet kini tak hanya dipakai untuk memantau perubahan geomagnetik, tetapi juga sebagai prekursor gempa dan eksplorasi migas. Oleh karena itu, kualitas data sangat penting agar hasil pengolahan data tetap akurat. Observatorium Magnet Bumi Serang dipilih sebagai lokasi studi karena merupakan salah satu stasiun pengamat geomagnetik aktif yang penting dalam jaringan pengamatan nasional. Studi kasus ini bertujuan untuk menganalisis kualitas data medan magnet bumi yang terekam pada tahun 2024 dengan menggunakan metode perhitungan Delta F, yaitu selisih antara nilai total medan magnet hasil kalkulasi komponen vektor (X, Y, Z) dengan pengukuran sensor proton. Fokus studi ini adalah mengidentifikasi gangguan teknis maupun gangguan dari alam yang mempengaruhi kestabilan data Delta F. Data yang diperoleh bersifat data sekunder dari hasil pencatatan otomatis instrumen magnetometer di stasiun serang yang kemudian diolah dan dianalisis. Hasil studi menunjukkan bahwa nilai Delta F secara umum stabil, namun terdapat gangguan anomali pada 11 Mei 2024 yang diidentifikasi sebagai efek dari aktivitas badai geomagnetik global. Selain itu, kualitas data juga dinilai dari aspek rekaman data, variasi sekuler, dan faktor eksternal lainnya. Studi ini menegaskan bahwa metode Delta F efektif dalam mendeteksi anomali dan menjaga kualitas data geomagnetik sebagai fondasi berbagai aplikasi ilmiah dan kebencanaan. Keywords: Medan Magnet Bumi, Delta F, Kualitas Data, Observatorium Magnet Bumi Serang Share Link | Plain Format | Corresponding Author (Najwa Kaila Nur Alif)

113	Instrumentation and Computational Physics	ABS-237
Pembuatan dan Pengembangan Website Inventory Management System untuk Pengelolaan Alat dan Bahan di Balai Pengujian Mutu Barang Putri Aurelia (a*), Agus Setyo Budi (a), Bani Ikhsan (b) a) Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta Jalan Rawamangun Muka Raya No.11, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta 13220, Indonesia *aurelian.putrilia[at]gmail.com b) Balai Pengujian Mutu Barang, Direktorat Standardisasi dan Pengendalian Mutu, Kementerian Perdagangan, Jalan Raya Bogor No.km No. 26, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta 13740, Indonesia Abstract Website Inventory Management System dikembangkan sebagai solusi digital untuk meningkatkan efisiensi pengelolaan alat dan bahan di Balai Pengujian Mutu Barang, Kementerian Perdagangan RI. Sistem ini dirancang untuk mengatasi kendala pencatatan manual yang menyebabkan keterlambatan pelaporan, kesulitan pelacakan, dan ketidakteraturan data. Pengembangan dilakukan melalui observasi lapangan, wawancara, perancangan antarmuka dengan Figma, serta implementasi menggunakan PHP, MySQL, dan JavaScript dalam lingkungan XAMPP. Fitur utama mencakup pencatatan barang, permintaan, pelacakan status, pengarsipan otomatis, kalibrasi alat, serta integrasi dengan Google Drive API dan PHPMailer untuk notifikasi email. Hasil pengujian menunjukkan bahwa sistem meningkatkan kecepatan, akurasi, dan transparansi pengelolaan inventaris. Proyek ini menjadi sarana penerapan nyata keilmuan fisika komputasi dalam pengembangan sistem informasi di instansi pemerintah. Keywords: Inventory Management System, Sistem Informasi, PHP, MySQL, Figma, Google Drive API, PHPMailer Share Link | Plain Format | Corresponding Author (Putri Aurelia)

114	Instrumentation and Computational Physics	ABS-238
Kalibrasi Alat Ukur Intensitas Cahaya (Lux Meter) Dengan Metode Perbandingan Fadhli Abdillah Abiputra, Ahmad Zatnika Purwalaksana, Hary Prayitno 1) Program Studi Fisika, Fakultas MIPA, Universitas Negeri Jakarta 2) PT. Unilab Perdana Abstract Calibration of light intensity measuring instruments (lux meters) is essential to ensure accurate lighting measurements in various work environments. In this study, a lux meter calibration was performed using the comparison method with a standard lux meter, the Gossen Mavolux 5032B, following the PK.F-02:2021 standard from the Indonesian National Standardization Agency (BSN). Two units were tested: the Gossen Mavolux 5032B and the KYORITSU 5202. The results showed that the Gossen lux meter remained within the acceptable deviation tolerance across the entire calibration range, indicating that the device is still accurate and fit for use. In contrast, the KYORITSU lux meter showed significant deviation at low light intensity levels, indicating lower accuracy in that range. The calibration was conducted in a controlled room with temperature and humidity maintained according to standards, and the data was analyzed using Microsoft Excel. The measurement results emphasize the importance of regular calibration to ensure measuring instruments remain accurate and reliable. Keywords: Lux Meter, Calibration, Light Intensity, Comparison Method, Deviation Share Link | Plain Format | Corresponding Author (Fadhli Abdillah Abiputra)

115	Instrumentation and Computational Physics	ABS-239
Kalibrasi Anak Timbangan Kelas F1 Menggunakan Standar Anak Timbangan Kelas E2 Dengan Metode OIML R-111,2004 Annisa Dian Maharatri, Mutia Delina, Ramy Rahmita Universitas Negeri Jakarta Abstract Weighing scale is a mass measuring instrument that plays an important role in ensuring the accuracy of the weighing process in laboratories and industries. To ensure the accuracy and traceability of measurement results, the scales need to be calibrated regularly according to international standards. This calibration activity aims to determine the conventional mass and uncertainty value of F1 class scales using E2 class scales as a reference standard, based on the direct comparison method according to OIML R-111:2004 guidelines. The calibration process is carried out using high-precision electronic scales with the STTS (Standard-Test Test-Standard) method, and takes into account environmental conditions according to ISO / IEC 17025:2017 standards. The calibration results show that all test scales are within the maximum permissible error (MPE) and have measurement uncertainties that are still in accordance with the tolerance requirements. The scales are declared suitable for use as class F1 measuring instruments because they meet the requirements according to OIML R-111:2004. Keywords: calibration, weights, uncertainty, conventional mass, OIML Share Link | Plain Format | Corresponding Author (Annisa Dian Maharatri)

116	Instrumentation and Computational Physics	ABS-242
Pengaruh Variasi Faktor Daya Induktif Terhadap Kesalahan Pengukuran Energi Pada Meter kWh Elektronik 1-Fasa 2-Kawat Merek Hefftron Wahyu Esa Wulan Ndari (a*) Ahmad Zatnika Purwalaksana, M.Si. (b) 3. Luthfi Fitriah Ningsih, S.Si.(b) Unit Pengelola Metrologi Provinsi DKI Jakarta Alamat: Jl. Perintis Kemerdekaan No.5, RW.5, West Kelapa Gading, Kelapa Gading, North Jakarta City, Jakarta 14240 Abstract Penelitian ini bertujuan untuk mengkaji pengaruh variasi faktor daya induktif terhadap kesalahan pengukuran energi pada meter kWh elektronik 1 fasa 2 kawat merek Hefftron. Pengujian dilakukan di Unit Pengelola Metrologi Provinsi DKI Jakarta dengan menggunakan alat standar ZERA TPZ 308 6 2 (Energy Meter Reader). Variasi faktor daya diatur pada nilai cos phi = 1- 0.9- 0.8- dan 0.7 melalui pemberian beban induktif terhadap 10 unit meter kWh yang diuji. Diperoleh kesalahan pengukuran meter kWh dan energi alat standar. Hasil penelitian menunjukkan bahwa kesalahan pengukuran cenderung meningkat seiring penurunan faktor daya, namun masih berada dalam batas kelas akurasi kurang lebih 1% untuk meter kelas 1. Temuan ini menunjukkan bahwa meskipun faktor daya induktif memengaruhi akurasi, meter kWh Hefftron tetap memenuhi standar teknis yang berlaku. Penelitian ini memberikan kontribusi dalam evaluasi performa alat ukur energi listrik serta mendukung peningkatan keandalan sistem pengukuran dalam bidang metrologi legal. Keywords: meter kWh, faktor daya induktif, kesalahan pengukuran. Share Link | Plain Format | Corresponding Author (Wahyu Esa Wulan Ndari)

117	Instrumentation and Computational Physics	ABS-245
RANCANG SISTEM PROTOTYPE OTOMATIS GUNA MENGEVALUASI HARGA PENAWARAN VENDOR DALAM PENGADAAN PT PERTAMINA GAS Muhammad Rafi Athallah Aziz Universitas Negeri Jakarta Abstract Dalam alur pengadaan barang dan jasa terdapat tahapan yang harus dilalui salah satunya adalah proses evaluasi harga yang ditawarkan dari peserta pengadaan. Proses evaluasi harga dalam pengadaan barang dan jasa sering kali menghadapi kendala efisiensi dan konsisten. Oleh karena itu, dikembangkan sebuah aplikasi berbasis Python yang dapat mempercepat dan menyederhanakan proses evaluasi harga secara otomatis. Aplikasi ini dibangun menggunakan library dan modul tambahan yang mampu menampilkan antarmuka grafis, serta dapat manipulasi data Excel secara dinamis. Aplikasi ini mampu membaca data dari template Excel, melakukan ekstraksi nilai, dan mengkalkulasi total biaya berdasarkan parameter tertentu, kemudian menyajikannya dalam format yang mudah dipahami. Hasil implementasi menunjukkan bahwa aplikasi ini secara signifikan mengurangi potensi kesalahan manual dan mempercepat proses evaluasi. Dengan demikian, aplikasi ini diharapkan dapat memberikan kontribusi nyata dalam peningkatan efisiensi kerja, khususnya di bidang Supply Chain Management. Keywords: User Dashboard, Digital Workspace, Programming Share Link | Plain Format | Corresponding Author (Muhammad Rafi)

118	Instrumentation and Computational Physics	ABS-246
IMPLEMENTASI DASHBOARD BERBASIS WEB INTERAKTIF UNTUK PEMANTAUAN DAN KEBUTUHAN INFORMASI HARIAN BAGI KHALAYAK LUAS (PUBLIK) Safta Sabrina (a*) a) Program Studi Fisika, FMIPA,Universitas Negeri Jakarta, Jl. Rawamangun Muka No. 01, Rawamangun, Jakarta Timur, 13220, Indonesia Abstract Penerapan Dashboard yang dibangun ini menggunakan bahasa pemrograman Python dengan memanfaatkan framework Streamlit dan Plotly, serta mengintegrasikan Application Programming Interface (API) dari penyedia data terbuka (open data) seperti AQICN atau OpenWeather, dsb. Dashboard yang dikembangkan memiliki antarmuka sederhana dan ramah pengguna, sehingga memungkinkan pengguna untuk mengakses informasi tanpa harus memiliki latar belakang teknis. Proses pembangunan meliputi perancangan tampilan antarmuka, pengambilan data dari API, pengolahan data, serta visualisasi dalam bentuk grafik dan indikator yang mudah dipahami. Hasil akhir dari proyek ini berupa dashboard yang dapat diakses melalui web browser dan mampu menyajikan data yang relevan dan informatif secara dinamis. Dengan adanya dashboard ini, diharapkan masyarakat dapat lebih mudah dalam memperoleh informasi harian yang dibutuhkan untuk mendukung keputusan dan aktivitas sehari-hari. Proyek ini juga menunjukkan potensi pemanfaatan teknologi open-source dalam membangun solusi digital yang efisien dan bermanfaat secara luas. Keywords: Dashboard, Python, API, Interface Publik, Kualitas, Visualisasi, Open-Source, Share Link | Plain Format | Corresponding Author (Safta Sabrina)

119	Instrumentation and Computational Physics	ABS-247
TALENT MAPPING MINGGUAN UNTUK OPTIMASI PENUGASAN PROYEK BERBASIS EXCEL DAN POWER QUERY Noel Laudikia Christian Bless Universitas Negeri Jakarta Abstract Seiring meningkatnya kompleksitas alokasi sumber daya dalam proyek berskala besar, kebutuhan akan sistem alokasi karyawan yang bersifat real-time dan terotomasi menjadi semakin mendesak. Penelitian ini mengusulkan metode weekly talent mapping berbasis Microsoft Excel dan Power Query untuk mengoptimalkan penugasan proyek. Proses ETL (Extract-Transform-Load) data karyawan, durasi proyek, dan status perencanaan dikembangkan dalam tahapan dari ingestion, transformasi (split, replace null), normalisasi (expand to new rows), hingga merge dengan data perencanaan yang kemudian dimuat ke dalam PivotTable interaktif lengkap dengan slicer dan conditional formatting. Hasil implementasi menunjukkan pengurangan waktu pemrosesan data dibandingkan metode manual, sekaligus meningkatkan akurasi identifikasi beban kerja dan ketersediaan staf per minggu. Workflow otomasi data, data modeling, serta visualisasi interaktif, memperkuat kompetensi teknis dan keterampilan kolaborasi profesional. Solusi ini tidak hanya memberikan kerangka komputasi yang scalable untuk volume data yang terus bertumbuh, tetapi juga mendukung pengambilan keputusan alokasi sumber daya dengan responsivitas tinggi dalam lingkungan bisnis yang dinamis. Keywords: Data Managing, Power Query, ETL Otomasi, PivotTable UI, Data Modeling, Visualisasi Interaktif Share Link | Plain Format | Corresponding Author (Noel Laudikia Christian Bless)

120	Instrumentation and Computational Physics	ABS-249
Pengembangan Sistem Monitoring Konsumsi Daya Alat Laboratorium Berbasis IoT Dengan Integrasi Google Sheets Dan Web Dashboard Real-Time Radhi Athaya Nugraha Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta Abstract Penelitian ini mengembangkan sistem monitoring konsumsi daya listrik peralatan laboratorium berbasis Internet of Things (IoT) menggunakan mikrokontroler ESP32 dan sensor PZEM-004T. Sistem secara otomatis mengukur arus, tegangan, dan daya aktif arus bolak-balik (AC) secara real-time. Data pengukuran dikirim dan disimpan ke Google Sheets dengan pengelompokan berdasarkan tanggal monitoring untuk memudahkan pengelolaan data historis. Selain penyimpanan data, sistem dilengkapi web dashboard interaktif yang menampilkan grafik parameter listrik secara real-time serta fitur pemilihan tanggal monitoring melalui dropdown menu. Hasil pengujian menunjukkan sistem mampu bekerja stabil, akurat, dan responsif terhadap perubahan beban listrik. Visualisasi data yang dihasilkan bersifat informatif dan mudah dipahami. Secara keseluruhan, sistem ini memenuhi spesifikasi perancangan dan memberikan solusi pemantauan konsumsi daya yang efektif, efisien, dan mudah diakses berbasis cloud. Keywords: Monitoring Daya, Internet of Things, Google Sheets, Web Dashboard, Laboratorium. Share Link | Plain Format | Corresponding Author (Radhi Athaya Nugraha)

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