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All Journal Journal of Information Systems and Informatics Prosiding National Conference for Community Service Project International Journal Software Engineering and Computer Science (IJSECS)
Andik Yulianto
Universitas International Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education

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Perancangan dan Implementasi Front End Sistem Daily Output Production di PT. Dynacast Indonesia Hendi Sama; Tasya Selvia Ulfa; Andik Yulianto
National Conference for Community Service Project (NaCosPro) Vol. 7 No. 01 (2025): The 7th National Conference for Community Service Project 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v7i01.10741

Abstract

Perkembangan dalam bidang teknologi informasi telah mendorong banyak bisnis untuk beralih ke sistem digital demi meningkatkan efisiensi dan akurasi dalam operasional mereka. PT Dynacast Indonesia masih mengandalkan pencatatan secara manual selama proses produksi, yang mengakibatkan kesalahan data dan keterlambatan dalam pelaporan. Untuk mengatasi hal ini, dilakukan perancangan dan penerapan sistem Daily Output yang berbasis web. Metode yang digunakan untuk merancang dan menerapkan sistem ini adalah kualitatif, melalui langkah-langkah studi pustaka, wawancara, dan observasi langsung. Proyek ini terdiri dari empat tahap penting: persiapan, pelaksanaan, evaluasi, dan pelaporan. Sistem yang dikembangkan memudahkan pencatatan hasil produksi secara langsung, meningkatkan keakuratan data, serta mempermudah proses pemantauan oleh pihak manajemen.
Developing an Indonesian Fake News Detection System Using IndoBERT and ProtoNet for Few-Shot Learning Yefta Christian; Wilson Wilson; Andik Yulianto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.8156

Abstract

Fake news dissemination through digital media has become a critical concern because inaccurate information may spread swiftly and influence public comprehension, social behavior, and decision-making. This study developed an Indonesian fake news detection web application employing fine-tuned IndoBERT and an IndoBERT-based Prototypical Network (ProtoNet) under few-shot learning scenarios. The research followed the Cross Industry Standard Process for Data Mining framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset was obtained from the publicly available Deteksi Berita Hoaks Indo Dataset on Kaggle and contained 23,944 Indonesian news records, consisting of 11,200 factual and 12,744 hoax articles. Three few-shot scenarios were evaluated: 50-shot, 100-shot, and 300-shot per class. IndoBERT was fine-tuned as a supervised classification baseline, while ProtoNet used IndoBERT embeddings to construct class prototypes and classify texts based on Euclidean distance. The best performance was achieved by fine-tuned IndoBERT in the 300-shot scenario, with 97.12% accuracy and a 97.10% macro F1-score. ProtoNet achieved 92.65% accuracy and a 92.63% macro F1-score in the same scenario. Even in the most constrained 50-shot setting, ProtoNet achieved an F1-score of 90.82%, closely trailing fine-tuned IndoBERT at 91.17% and demonstrating strong metric stability. The web application supports manual text input, URL-based article extraction, model selection, confidence scores, and word-level occlusion explanations. These results demonstrate the effectiveness of IndoBERT for Indonesian fake news detection and the feasibility of prototype-based classification under limited-data conditions. Overall, this work bridges the gap between deep contextual representation, data-efficient learning, and practical, interpretable deployment for public media verification.
Evaluating YOLOv5 and YOLOv8: Advancements in Human Detection Nimatul Ma Muriyah; Joel Hamim Sim; Andik Yulianto
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.944

Abstract

The YOLO (You Only Look Once) method is a state-of-the-art approach in real- time object detection, known for its high-speed image processing capabilities. Recently YOLO versions have differed in performance, particularly in terms of detection accuracy and computational efficiency. The objective of this study is to assess the effectiveness and performance of YOLOv5 and YOLOv8 in real-time human detection applications using the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology also. The dataset was processed through the Roboflow platform, which facilitated both the dataset management and the labeling process. Roboflow's tools streamlined the annotation of images, ensuring consistent labeling for deep learning model training and evaluation. F1 score, recall score, and precision score are compared both YOLOv5 and YOLOv8 to evaluate the performance of these architectures. The result of the evaluations shows that the performance of the YOLOv8 is better than the YOLOv5 which, YOLOv5 achieved F1-score equal 0.5865 (58%), recall score equal 0.83 (83%), and precision score of 0.4535 (45%). Meanwhile, YOLOv8 demonstrated better performance, with F1-score of 0.7921 (79%), recall score of 0.8289 (82%), and precision score of 0.7585 (75%). Base on the evaluations, we concluded that the performance of the YOLOv8 model is greater than the YOLOv5 model for Precision, and F1-Score, while YOLOv5 has slightly better score on recall. The contribution of this study is going to implemented into Audio guidance for the blind’s prototype that have been developing in previous study.
Co-Authors Hendi Sama Joel Hamim Sim Nimatul Ma Muriyah Tasya Selvia Ulfa Wilson Wilson Yefta Christian