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JR : Jurnal Responsive Teknik Informatika
Published by Universitas Ibnu Sina
ISSN : -     EISSN : 26147602     DOI : 10.36352
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering
JR: Jurnal Responsive Teknik Informatika is a scientific journal that highlights the latest advancements in the field of information technology. This journal provides a platform for researchers, practitioners, and academics to share their latest knowledge and findings regarding programming, software development, information security, artificial intelligence, and cloud computing. Through a rigorous peer-review process, JR ensures that each published article has undergone careful evaluation, thus contributing to the advancement of knowledge and technology in informatics. Consequently, JR serves as a vital source for those interested in keeping up with current trends and understanding the latest developments in the field of informatics engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 142 Documents
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Pengembangan Otomasi Inventaris Farmasi Rumah Sakit Gigi dan Mulut Berbasis YOLO Siti Salmiah; Khairul Abdi
Jurnal Responsive Teknik Informatika Vol 9 No 02 (2025): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v9i02.1481

Abstract

Abstrak Pengelolaan inventaris farmasi pada Rumah Sakit Gigi dan Mulut (RSGM) masih banyak dilakukan secara manual melalui pencatatan dan penghitungan stok satu per satu. Proses tersebut rentan terhadap kesalahan manusia, memerlukan waktu yang lama, serta menyulitkan deteksi dini terhadap kekurangan dan kelebihan stok obat maupun alat kesehatan. Penelitian ini bertujuan mengembangkan sistem otomasi inventaris farmasi berbasis algoritma You Only Look Once (YOLO) yang mampu mendeteksi, mengklasifikasikan, dan menghitung item farmasi secara otomatis dari citra rak penyimpanan. Dataset dibentuk dari 2.400 citra enam kelas item (tablet, sirup, ampul, kapsul, salep, dan alat kesehatan) yang telah dianotasi dan diaugmentasi, kemudian dibagi ke dalam data latih, validasi, dan uji dengan rasio 70:15:15. Model dilatih menggunakan pendekatan transfer learning dengan arsitektur YOLOv8 dan diintegrasikan ke dalam aplikasi web berbasis PHP dan MySQL. Hasil pengujian menunjukkan model mencapai mean Average Precision (mAP@0.5) sebesar 0,943, presisi 0,921, recall 0,914, dan skor F1 0,917, dengan kecepatan inferensi rata-rata 38 frame per detik. Penerapan sistem mampu menekan waktu penghitungan stok hingga 82% dibandingkan metode manual. Hasil ini membuktikan bahwa pendekatan berbasis YOLO layak diterapkan sebagai solusi otomasi inventaris farmasi yang akurat dan efisien di lingkungan RSGM.
Comparative Simulation of EfficientNetB0, ResNet50, and MobileNet for Cocoa Pod Disease Detection Okta Veza; Nofri Yudi Arifin; Sherly Agustini; Albertus Laurensius Setyabudhi
Jurnal Responsive Teknik Informatika Vol 9 No 01 (2025): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v9i01.1515

Abstract

The selection of a convolutional neural network (CNN) architecture for cocoa (Theobroma cacao) pod disease detection involves a trade off between classification accuracy and computational efficiency that is decisive for eventual deployment on the mobile hardware available to smallholder farmers. This study presents a controlled comparative simulation of three widely used architectures, EfficientNetB0, ResNet50, and MobileNetV2, under identical, literature-grounded conditions. Rather than reporting field-validated results, a balanced synthetic dataset of 3,000 images spanning four classes (healthy, black pod, pod borer, frosty pod) was generated with class-conditional feature statistics parameterized from published references. All three models were initialized with ImageNet weights, fine-tuned with an identical training protocol and shared data splits, and evaluated on the same held-out test set. In simulation, EfficientNetB0 achieved the highest accuracy (93.8%) and macro F1 (0.938), followed by ResNet50 (92.7%, 0.926) and MobileNetV2 (91.1%, 0.909). When efficiency is considered, the ranking shifts: MobileNetV2 offered the smallest footprint and lowest latency, EfficientNetB0 delivered the best accuracy-per-parameter, and ResNet50 was the most resource-intensive without a commensurate accuracy gain. The dominant error mode across all models was confusion between pod borer and frosty pod. The results indicate that EfficientNetB0 offers the most favorable accuracy efficiency balance for this task, while MobileNetV2 is preferable under strict on-device constraints. All figures are framed explicitly as simulation outputs and discussed in light of the synthetic-to-real domain gap

Page 15 of 15 | Total Record : 142

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