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All Journal JUITA : Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal CoreIT Techno Nusa Mandiri : Journal of Computing and Information Technology Jiko (Jurnal Informatika dan komputer) Jurnal Tekno Kompak Jurnal Ilmiah Ilmu dan Teknologi Rekayasa Jurnal Teknologi Informasi dan Komunikasi Bulletin of Information Technology (BIT) Melek IT: Information Technology Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Tedc Jurnal Ilmiah Pengabdian Kepada Masyarakat SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Jurnal Komputer dan Teknologi (JUKOMTEK) Journal of Elektronik Sistem InformasI Inventor: Jurnal Inovasi dan Tren Pendidikan Teknologi Informasi Jurnal Sistem Informasi dan Aplikasi
Tri Herdiawan Apandi
Politeknik Negeri Subang
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EVALUASI KINERJA MODEL YOLOv11 UNTUK KLASIFIKASI TINGKAT KEMANISAN BUAH NANAS Willy Muhammad Fauzi; Dwi Vernanda; Tri Herdiawan Apandi
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.734

Abstract

Sweetness is one of the main determinants of pineapple quality, yet its conventional measurement through the TSS/TA ratio requires cutting the fruit open, making it unsuitable for non-destructive, large-scale sorting. Prior work by our group has explored non-destructive classification approaches, including a multi-view attention-based fusion model. As part of the iterative model development process toward that solution, this paper reports and analyzes the performance of a simpler baseline: a single-view YOLO object detection model trained directly to localize and classify pineapples into three sweetness categories-Asam (sour), Manis Ideal (ideal), and Sangat Manis (very sweet)-from a single RGB image per fruit. The model was trained for 50 epochs and evaluated using standard object detection metrics. The baseline achieved an overall mAP@0.5 of 0.555 and mAP@0.5:0.95 of 0.460, with the best F1-score of 0.58 reached at a confidence threshold of 0.183. Per-class analysis shows that the Asam category was the easiest to detect (mAP@0.5 = 0.695), while Manis Ideal (0.505) and Sangat Manis (0.465) were considerably weaker. Confusion matrix analysis at the default confidence threshold reveals that only 32-64% of ground-truth instances per class were correctly classified, notably lower than the recall trend suggested during training, and that the Sangat Manis class-the smallest in the dataset-was most frequently confused with its visual neighbor, Manis Ideal. These findings indicate that a single viewpoint, without any imbalance handling, is not yet sufficient to reliably separate boundary categories, providing empirical grounds for the multi-view and attention-based refinements explored in the continuation of this research.
Klasifikasi Jenis Beras Menggunakan Deep Learning Berbasis Computer Vision dengan Platform Roboflow Imron Hefni F; Muhamad Soleh Sulaeman; Tri Herdiawan Apandi; Willy Muhammad Fauzi
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 4 No 1 (2026): Maret: Sistem Informasi
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v4i1.13175

Abstract

Rice is a fundamental food commodity globally, where accurate variety classification is crucial for pricing, quality control, and food security. Manual classification methods are labor-intensive, time-consuming, and prone to human subjectivity. This research proposes an automated classification system for five rice varieties (Arborio, Basmati, Ipsala, Jasmine, and Karacadag) using a Computer Vision approach with Vision Transformer (ViT) architecture. Unlike Convolutional Neural Networks (CNN) which focus on local features, ViT utilizes selfattention mechanisms to capture global contextual relationships within images. The model was developed using the public "Rice Image Dataset" containing 75,000 images. The methodology includes image preprocessing (resizing and normalization) and training of the ViT Classification model. Model performance was evaluated using standard metrics on a separate test set. The results show that the proposed ViT model achieved an outstanding accuracy of 99.9%. Thesefindings demonstrate that the Transformer-based approach is highly effective and efficient for automating rice variety identification,offering a more robust solution compared to conventional methods.
Penerapan Arsitektur Vision Transformer (ViT) Berbasis Low-Code untuk Klasifikasi Penyakit Padi Lucky Jayadinata; Giga Lukman Maulana; Tri Herdiawan Apandi; Willy Muhammad Fauzi
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 4 No 1 (2026): Maret: Sistem Informasi
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v4i1.13176

Abstract

Rice plant diseases can significantly reduce productivity and threaten food security, making early and accurate detection essential. Traditional manual inspection methods are slow, subjective, and difficult to scale for field conditions. This study aims to develop a rice disease classification system using computer vision and the low-code Roboflow platform with the Vision Transformer (ViT) architecture. The Rice Diseases Image Dataset, consisting of four classes—BrownSpot, Healthy, Hispa, and LeafBlast—underwent preprocessing, data augmentation, and splitting before being trained using the ViT model. The best-performing model (Version 6) achieved an accuracy of 92,2% on the test set. The results demonstrate that the proposed low-code workflow effectively streamlines the deployment pipeline of rice disease classification models, achieving competitive performance suitable for rapid prototyping in precision agriculture. These findings provide an initial foundation for the application of computer vision technologies in supporting precision agriculture practices.
Design and Development of the Sidajaya Tourism Village Information and Promotion Media Slamet Rahayu; Tri Herdiawan Apandi; Taufan Abdurrachman; Nurfitria Khoirunisa; Adi Firmansyah; Asep Saepuloh
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.753

Abstract

The potential of Sidajaya Tourism Village, which is not yet known to many people, has hampered the development of this tourist village. Information about the Sidajaya tourist village is still difficult to access, making people reluctant to look for the information they need, so knowledge about it is limited. Therefore, a system is needed to help the public access information about the Sidajaya tourist village. This research aims to build a website-based Sidajaya Tourism Village Information System as a medium for information and promotion. This system was built using the PHP programming language and MySQL database, with data collection methods through observation and interviews. The system development stage includes analysis, design, implementation, and testing. The result of developing this system is a website that allows the public to search for information about the Sidajaya tourist village easily and supports more efficient promotion and management to increase tourist visits.
Co-Authors Adi Firmansyah Aisyah Suhailla Binti Jili Asep Saepuloh Castaka Agus Sugianto Daniel Happy Putra Desy Triastuti Dwi Fernanda Dwi Vernanda Erick Febriyanto Giar Rahma Yunita Giga Lukman Maulana Gumelar, Mikal Raya H, Haryati Haryati Haryati Haryati Helfira Lustiana Imron Hefni F Kurniawan, Bagas Lani Nurlani Lucky Jayadinata Masesa Angga Wijaya Mohammad Iqbal Muhamad Soleh Sulaeman Muhammad Julmawansa Abdullah Muhammad Soleh Sulaeman Mustikasari Putri, Dewi Nida Hanifah Niqotaini, Zatin Nunu Nugraha Purnawan NurAfifah, Amirah Nurfitria Khoirunisa Nurfitria Khoirunnisa pohan, Nurul Fauziah Rian Hermawan Rian Piarna Risky Prabowo Roby Bayu Maulana Santika, Nisa Slamet Rahayu Slamet Rahayu Subagja, Fikri Hari Taufan Abdurrachman Tri Rezqy Maudy Willy Muhammad Fauzi Wulan Siti Nurul Yogi Saputra, Yogi Yohanes Sinung Nugroho