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All Journal Jurnal Teknologi Informasi dan Pendidikan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Dedikasi : Jurnal Pengabdian Lentera
Siregar, Amril Mutoi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Evaluasi dan Penyempurnaan Sistem Informasi Benih Anggur Berbasis Web dengan Pendekatan Agile Zy, Ahmad Turmudi; Sari, Nita Winda; Effendi, M Makmun; Nugroho, Agung; Siregar, Amril Mutoi
Dedikasi: Jurnal Pengabdian Lentera Vol. 2 No. 06 (2025): Juni 2025
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/djpl.v2i06.929

Abstract

Kegiatan pengabdian ini bertujuan untuk mengevaluasi dan menyempurnakan sistem informasi benih anggur berbasis web yang telah diterapkan pada Komunitas Anggur Cikarang. Sistem ini dikembangkan dengan pendekatan Agile yang memungkinkan pengembangan iteratif dan partisipatif. Permasalahan utama yang diidentifikasi meliputi kurangnya pusat informasi terintegrasi, minimnya pemanfaatan teknologi informasi, dan jangkauan pemasaran yang terbatas. Solusi yang ditawarkan meliputi desain ulang sistem, pengembangan fitur baru berdasarkan umpan balik pengguna, serta pelatihan dan pendampingan komunitas dalam penggunaan sistem. Hasil kegiatan diharapkan dapat meningkatkan efisiensi manajemen bibit, memperluas distribusi pasar, serta mendorong pemberdayaan ekonomi masyarakat berbasis teknologi.
Analisa Perbandingan Algoritma Support Vector Machine dan K-Nearest Neighbors Terhadap Ulasan Aplikasi Vidio Gumilar, Rizki Bintang; Cahyana, Yana; Sukmawati, Cici Emilia; Siregar, Amril Mutoi
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5640

Abstract

Internet usage in Indonesia reached 77% of the total population in January 2023, with Over The Top (OTT) services showing user growth of 25% every year. The Vidio application, one of the popular OTT platforms with downloads exceeding 50 million, has a 3.5 star rating based on 649 thousand reviews on the Google Play Store. Despite its popularity, Vidio faces complaints regarding limited film selection, payment errors, and excessive advertising, which affects user satisfaction. This research aims to analyze the opinions of Vidio application user comments by applying the SVM (Support Vector Machine) method and the KNN (K-Nearest Neighbors) method to determine the model with the best accuracy. 15,000 review data were collected through scraping, then processed using text preprocessing and TF-IDF vectorization techniques. Model evaluation shows that SVM has an accuracy value of 82%, a precision value of 82%, a recall value of 83%, and an F1-score value of 82%, while KNN has an accuracy of 69%, precision 74%, recall 73%, and F1-score 69% . The research results show that SVM is superior to KNN in classifying the sentiment of Vidio application reviews. It is hoped that these findings can be used by application developers in an effort to improve service and satisfaction of Vidio application users.
Lightweight YOLO Models for Robust Facial Expression Detection Aulia, Achmad Indra; Hutapea, Albert Jofrandi; Siregar, Amril Mutoi; Surjandy
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1120

Abstract

Facial expression recognition is a fundamental component of artificial intelligence systems, particularly in human–machine interaction. However, achieving robust detection accuracy remains challenging due to variations in lighting, facial orientation, and limited training data diversity. While recent lightweight YOLO architectures—YOLOv8n, YOLOv10n, and YOLO11n—have demonstrated strong performance in general object detection, comparative studies evaluating these models specifically for facial expression detection remain limited. This study addresses this gap by systematically comparing these three nano-variant models on a dataset of 2,000 labeled facial images across four expression categories: flat face, angry, sad, and smile. The dataset was divided into training (70%), validation (20%), and test (10%) subsets. Experiments were conducted under two scenarios—with and without data augmentation—using identical training configurations. Augmentation techniques included mosaic composition, HSV variation, geometric transformations, and flipping. Results show that augmentation improved the F1 score of YOLOv10n from 0.68 to 0.72 and YOLO11n from 0.65 to 0.72, with the latter achieving the highest overall precision of 0.82. YOLOv8n exhibited stable performance with an F1 score of 0.75 under both conditions. Confidence threshold optimization revealed distinct optimal operating points for each model, ranging from 0.1 to 0.6, confirming that per-model threshold tuning is necessary to maximize detection performance. These findings provide practical guidance for selecting and configuring lightweight YOLO models for facial expression detection in resource-constrained environments.
Co-Authors Aulia, Achmad Indra Effendi, M Makmun Gumilar, Rizki Bintang Hutapea, Albert Jofrandi Nugroho, Agung Sari, Nita Winda Sukmawati, Cici Emilia Surjandy Yana Cahyana Zy, Ahmad Turmudi