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All Journal Jurnal Ilmu Dasar Journal of Electrical Engineering and Computer (JEECOM) Jurnal Multidisiplin West Science
Maulana, Bima Wahyu
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Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Mathematics

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Design and Construction of Electrical Energy Source Panel Based on Thermoelectric Generators on Mild Steel Galvalume Maulana, Bima Wahyu; Misto, M.; Arkundato, Artoto; Mulyono, Tri
Jurnal ILMU DASAR Vol 24 No 2 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v24i2.26255

Abstract

A thermoelectric generator (TEG) is a device that converts heat energy into electrical energy. The working principle of this device is based on Seebeck's law, namely this device will produce electrical energy if the cold side and hot side of this device have a temperature difference value of . This device can be used for generator panels whose heat source comes from the sun. The cold side of the TEG is conditioned by utilizing water fluid which is passed over the heatsink. The temperature difference between the hot and cold sides of the TEG generates an electric voltage through the Seebeck effect. The parameters observed in the research on electricity generation using this TEG are voltage, current, electric power, and the temperature difference between the hot and cold sides. The resulting parameter values are as follows; average voltage (0.5495 volts), average electric current strength of 0.04 A, average electric power (0.022 watts). mean temperature difference (16.006 oC). The largest average Seebeck coefficient is 0.0413 V/oC.
Automatic Pill Detection Using Faster R-CNN with an AlexNet Backbone Elvira, Ade Irma; Kurniasar, Arvita Agus; Maulana, Bima Wahyu; Nurul Qomariah, Dinial Utami
Jurnal Multidisiplin West Science Vol 4 No 12 (2025): Jurnal Multidisiplin West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jmws.v4i12.3068

Abstract

Object detection is a crucial component in the development of automated systems in the healthcare domain, particularly in pharmaceutical applications such as pill identification and management. One of the main challenges in image-based pill detection systems is achieving high accuracy and robust generalization under variations in pill shape, color, and illumination conditions. This study applies the Faster R-CNN framework with an AlexNet backbone to detect and classify pill objects in digital images. The model is trained using multiple epoch configurations to analyze the effect of training duration on detection performance. Experimental results show that the proposed approach achieves an accuracy of up to 98%, demonstrating strong detection capability. Increasing the number of training epochs improves the stability and consistency of pill recognition. These results indicate that AlexNet-based Faster R-CNN is effective for pharmaceutical applications, particularly in drug distribution, packaging, and pill counting systems that require high precision and reliability.
Evaluating Microclimate Forecasting and Predictive Control in IoT-Based Greenhouses Surateno, Surateno; Dewanto, Wahyu Kurnia; Yufit, Hendra Riskiawan; Widiawan, Beni; Albaab, Mochammad Rifki Ulil; Firmansyah, Muhammad Hafidh; Muliawan, Agung; Maulana, Bima Wahyu; Pratama, Afis Asryullah; Pradana, Reza Putra; Koh, Seok-Joo
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 4 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i4.17490

Abstract

Greenhouse microclimate conditions can change rapidly, making reactive control less effective in maintaining stable environmental conditions. Short-term forecasting can help anticipate these changes and support timely control decisions. This study presents a secondary analysis of previously reported experimental results from an Internet of Things (IoT)-based melon greenhouse to examine the relationship between forecasting accuracy, forecasting horizon, and operational control performance. The analysis compares persistence, standard Long Short-Term Memory (LSTM), and modified LSTM forecasting approaches and examines environmental and resource-use indicators under predictive control. At a 10-minute forecasting horizon, the modified LSTM achieved a temperature root mean square error (RMSE) of 0.50 °C and a relative humidity RMSE of 2.07%, representing reductions of 21.88% and 20.69%, respectively, compared with the standard LSTM. Temperature RMSE increased to 1.12 °C at a 30-minute horizon, indicating reduced prediction accuracy over the longer horizon. The reported predictive-control configuration was associated with a 45.61% reduction in daytime periods exceeding the 35 °C operational threshold and a 44.74% reduction in daily fan switching events. Reductions in water, electrical energy, nutrient stock, and labour consumption were also reported. These operational outcomes reflect the overall control configuration and cannot be attributed exclusively to the forecasting model. The analysis is limited by the unavailability of raw time-series data for independent validation and statistical significance testing. The findings highlight the importance of evaluating forecasting accuracy and horizon alongside operational control requirements in greenhouse management.
Co-Authors Agung Muliawan Albaab, Mochammad Rifki Ulil Artoto Arkundato Beni Widiawan, Beni Elvira, Ade Irma Firmansyah, Muhammad Hafidh Koh, Seok-Joo Kurniasar, Arvita Agus Misto Nurul Qomariah, Dinial Utami Pradana, Reza Putra Pratama, Afis Asryullah Surateno, Surateno Tri Mulyono Wahyu Kurnia Dewanto Yufit, Hendra Riskiawan