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All Journal e-Jurnal Arus Elektro Indonesia DEDIKASI PKM International Journal of Healthcare and Information Technology
Phoa, Victor
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Public Health Social Sciences

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Testing and Comparison of SN754410N and L293D Driver IC Variants Phoa, Victor; Rakhmad, Hariyono
Jurnal Arus Elektro Indonesia Vol 10 No 1 (2024)
Publisher : Fakultas Teknik, Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jaei.v10i1.39120

Abstract

The popular transistor-based driver L293D has been produced by several different manufacturers and some have increased functions such as the SN754410N IC. However, replacing driver ICs from the same or different manufacturers sometimes found compatibility problems such as driver unresponsiveness in controlling the motor. To find out the scientific reasons that cause this problem, this study tested the current-carrying ability and switching delay for ICs from ST, HLF, and SN754410N fabrications. From the test results, it was found that there were differences in the ability to conduct current, and differences in the duration of the switching delay, and it was also found that the IC was damaged as much as 10 percent of the samples from the ST fabrication.
Pengembangan Strategi Pemasaran Berbasis Ekonomi Digital Pada Kios UMKM Desa Kembangkuning, Kecamatan Cepogo, Kabupaten Boyolali Wulandari, Eudia Christina; Prawesti, Yuniar; Phoa, Victor
DEDIKASI PKM Vol. 5 No. 2 (2024): DEDIKASI PKM UNPAM
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/dkp.v5i2.39090

Abstract

Desa Kembangkuning mempunyai potensi Usaha Menengah Kecil Mikro (UMKM) yang besar dengan hasil dari pengrajin alumunium dan tembaga sebagai produk utama. Sentuhan teknologi diperlukan untuk branding suatu produk untuk meningkatkan pemasaran yang lebih luas lagi, salah satunya dengan melakukan kegiatan pendampingan pada karyawati kios UMKM Desa Kembangkuning untuk mempelajari strategi marketing dengan pemanfaatan teknologi. Metode yang digunakan adalah survei, observasi, perencanaan, pelaksanaan kegiatan dan evaluasi. Hasil yang didapatkan dari kegiatan ini adalah telah menjadi kios UMKM dengan memanfaatkan teknologi seperti sentuhan sosial media dan marketplace, dengan begitu maka jangkauan pemasaran produk lebih luas dan bisa diakses siapapun dan dimanapun.
Web Platform for Automated Detection of Abnormal Red Blood Cells Using Computer Vision Hasanah, Qonitatul; Fitri, Zilvanhisna Emka; Phoa, Victor; Sari, Dian Kartika
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6718

Abstract

Accurate identification of red blood cell (RBC) morphological abnormalities is essential for anemia screening and hematological assessment; however, manual microscopic examination remains time-consuming, subjective, and highly dependent on expert availability. While recent deep learning studies have demonstrated promising accuracy in RBC classification, many focus primarily on model performance without addressing practical deployment constraints or system-level integration for routine laboratory use. In this study, a web-based prototype system for automated RBC abnormality classification is proposed using a lightweight MobileNetV2 architecture. The dataset consisted of 1,320 microscopic blood smear images collected from Klinik & Laboratorium Parahita in Jember and Surabaya, covering six RBC categories with balanced class distribution. All images were anonymized and verified by a certified clinical pathologist prior to use. The model was trained using transfer learning and evaluated on a held-out test set to assess generalization performance. The proposed model achieved a test accuracy of 89.77%, with consistent precision, recall, and F1-score across classes, indicating reliable multi-class classification performance. Analysis of misclassified samples revealed uncertainty primarily between morphologically similar RBC types, reflected by lower confidence scores. These results demonstrate that lightweight deep learning models can provide effective and efficient support for RBC morphology analysis when integrated into an accessible web-based system. The proposed approach contributes a deployment-oriented diagnostic support tool that has the potential to assist laboratory professionals by improving screening efficiency and consistency while preserving clinical oversight.
Co-Authors Dian Kartika Sari, Dian Kartika Eudia Christina Wulandari Fitri, Zilvanhisna Emka Hariyono Rakhmad Prawesti, Yuniar Qonitatul Hasanah