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All Journal Jurnal EECCIS MATAPPA: Jurnal Pengabdian Kepada Masyarakat KOMPUTEK Jurnal Teknik Elektro dan Komputer TRIAC RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Electrical Engineering and Computer (JEECOM) Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat ELECTRA : Electrical Engineering Articles Insearch: Information System Research Journal Informatics, Electrical and Electronics Engineering Jurnal Fortech International Journal of Multidisciplinary Approach Research and Science Jurnal FORTECH
Bachri, Affan
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Real-Time Monitoring of Solar Battery and Lamp Conditions Using Internet of Things Bachri, Affan; Laksono, Arief Budi; Santoso, Eko Wahyu; Firmansyah, Achmad Rifqi Adi
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.16851

Abstract

Solar photovoltaic battery systems require continuous observation to prevent unnoticed voltage decline, abnormal temperature, and lamp failure. This study developed a compact Internet of Things monitoring prototype using an ESP32 microcontroller, an INA219 sensor for voltage, current, and power, an LM35 sensor for battery temperature, and a light-dependent resistor for lamp-state detection. Sensor data were shown locally on a 16x2 liquid-crystal display and transmitted through Wi-Fi to a Blynk dashboard at a five-second update interval. The study applied experimental and engineering research procedures covering hardware design, software integration, component validation, and integrated tests under off, three load, and charging conditions. The INA219 achieved average accuracies of 99.69% for voltage, 99.32% for current, and 99.49% for power, while the LM35 reached 97.08% accuracy. The light sensor correctly separated the tested off condition at 3 lux from lamp-on conditions between 230 and 2100 lux. Across all operating modes, the local display and Blynk presented consistent measurements, and negative current reliably indicated charging. The prototype therefore provides accessible real-time monitoring of battery electrical behavior, temperature, charging direction, and lamp status, although long-term data logging and field-scale validation remain necessary.
Predicting Daily Goride Orders From Online Hours, Day Type, And Weather Susilo, Purnomo Hadi; Rifani, Mochammad Sihabudin Firdaus; Bachri, Affan; Mustain, Mustain; Sholikhin, Mochammad
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 3 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i3.16937

Abstract

Daily order fluctuations make it difficult for motorcycle ride-hailing drivers to plan online working time. This study develops an interpretable multiple linear regression model to predict the number of daily GoRide orders from online hours, day type, and weather. The dataset contains 100 driver-day observations collected from eight GoRide driver-partners operating in central Tulungagung. To preserve observation order, the first 70 records were used for training and the last 30 for testing. Day type and weather were expressed as binary indicators, yielding the operational equation ŷ = −1.9887 + 1.0036X₁ + 1.4187Dweekend + 2.3968Dclear. The model obtained R² = 0.8440 and MAPE = 15.62% on training data. On the held-out test set, it achieved R² = 0.8341, MAPE = 13.48%, MAE = 0.989 orders, and RMSE = 1.322 orders. Twenty of 30 predictions (66.7%) were within one order of the actual value, and 26 (86.7%) were within two orders. However, predictions were positively biased by 0.784 orders on average, with 23 of 30 observations overpredicted and a maximum absolute error of 4.458 orders. The model was integrated into a Flask–MySQL web application for data management and interactive prediction. The results show that a compact linear model can provide useful and explainable driver-level estimates, but its reliability remains conditional on the small, locally collected sample. Rolling validation, driver-aware evaluation, richer temporal variables, and count-regression benchmarks are required before operational deployment at scale.
Co-Authors Abdul Ghofur Afandi, Faizal Qurnia Agus Setia Budi, Agus Setia Amri, Sholihul Anak Agung Gede Sugianthara Annisa Kesy Garside Arif Darmawan, Aura Darmawan, Aditya Ekasari, Ratna Ezra Syirojudin, Moh Firmansyah, Achmad Rifqi Adi Hanif, Abu Hartantyo, Sugeng Dwi Hidayat, Faruq Dwi Fahrul Ilmi, Ulul Iqbal, Moh Irawan, M Rudi Javanas, Ishmed Kadir Mahamad, Abd Kafabi, Muhammad Aflah Khuluqil Adzim, Moch Ikhwan Laksono, Arief Budi Laksono, Arif Budi M. Ghofar Rohman Mauladi, Kemal Farouq Ni’am, Wafirun Prakoso, Sigit Dwi Prasetyo, M. Romadhoni Eko Purnomo Hadi Susilo, Purnomo Hadi Rifani, Mochammad Sihabudin Firdaus Rifky Aisyatul Faroh Rizky Dwi Saputra, Rizky Dwi Rohman, Ahmad Atho’ur Rohman, Mohammad Ainur Romadlon Ardliyansyah, Moh Soleh Rudianto, andi andi Santoso, Eko Wahyu Saputra, M. Bayu Irwan Sholikhin, Mochammad Susilo Putra, Moch Pradipta Syafi Uddin, Moch Ulul Ilmi Utomo, Heru Prasetio Wahyu Santoso, Eko Wakhid, Abdur Rohman Waliuddin, Aubait Widyanto, Muhamad Irfan Zainal Abidin Zulkifli Lubis