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All Journal Jurnal Ilmu Dasar Jurnal Multidisiplin West Science
Maulana, Bima Wahyu
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Control & Systems Engineering Economics, Econometrics & Finance 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.
Co-Authors Artoto Arkundato Elvira, Ade Irma Kurniasar, Arvita Agus Misto, M. Nurul Qomariah, Dinial Utami Tri Mulyono