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Jurnal Komputer dan Teknologi (JUKOMTEK)
Published by Yayasan Pendidikan Cahaya Budaya Indonesia
ISSN : 29631289     EISSN : 29619009     DOI : https://doi.org/10.58290/jukomtek
Core Subject : Science,
Computer Science & IT Library & Information Science
Jurnal Komputer dan Teknologi (JUKOMTEK) e-ISSN 2961-9009 dan p-ISSN 2963-1289 merupakan jurnal ilmiah. Jurnal ini berisi tentang karya ilmiah bersifat open access, dan jurnal ilmiah nasional yang mempublikasikan artikel ilmiah hasil penelitian dalam ruang lingkup bidang ilmu komputer serta aplikasi informatika untuk pengembangan TIK. Frekuensi Terbit: 2 kali setahun (bulan Januari dan Juli).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 93 Documents
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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.
PERANCANGAN SISTEM INFORMASI TRACKING PENJUALAN PRODUK ATK BERBASIS WEB Lela Nurlaela; Tuhfatul Habibah Hasibuan; Nanda Andriani
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.v5i2.735

Abstract

The rapid development of information technology has encouraged businesses to utilize information systems to improve the effectiveness and efficiency of data management. Toko Meta, a stationery (ATK) store, still encounters challenges in managing product data, sales transactions, sales tracking, and report generation. These conditions result in inefficient data retrieval and limit the effectiveness of sales monitoring. This study aims to design a web-based Sales Tracking Information System for Toko Meta. The research employed a qualitative method with data collected through observation, interviews, and literature review. System requirements were analyzed using the PIECES method, while the system was developed using the System Development Life Cycle (SDLC). The application was developed using the Laravel framework, PHP programming language, and MySQL database. The results of this study are a web-based information system that provides features for user authentication, product management, sales transactions, sales tracking, and sales reporting. The developed system improves the effectiveness of data management, accelerates information retrieval, facilitates sales monitoring, and produces more accurate and integrated sales reports. Keywords: Information System, Sales Tracking, Laravel, MySQL, Web.
PERANCANGAN SISTEM INFORMASI PENGELOLAAN DATA PENINGKATAN KAPASITAS SDM APARATUR SIPIL NEGARA BERBASIS DATA MINING MENGGUNAKAN ALGORITMA NAIVE BAYES Antonius Ivan Dwiarta Putra; Lela Nurlaela; Tuhfatul Habibah Hasibuan
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.v5i2.736

Abstract

Data management regarding capacity building for State Civil Apparatus (ASN) human resources at the Directorate General of Regional Development, Ministry of Home Affairs, currently faces challenges in data processing, information presentation, and decision-making due to the suboptimal use of information technology. This study aims to develop a web-based information system for managing apparatus capacity-building data by employing the Naïve Bayes algorithm as a classification method to support decision-making. The research adopts a quantitative approach utilizing data mining methods, encompassing data collection, preprocessing, splitting data into training and testing sets, building a classification model using the Naïve Bayes algorithm, and evaluating the model via a Confusion Matrix. The system was developed using the Python programming language and the Streamlit framework, and was implemented using apparatus capacity-building data from the 2024–2026 period. The results demonstrate that the system can integrate data management processes, classify capacity-building levels into Low, Medium, and High categories, and automatically generate analytical dashboards and reports. Model evaluation yielded an accuracy rate of 66.67%, indicating that the Naïve Bayes algorithm delivers satisfactory classification performance to support decision-making in managing apparatus capacity building. Consequently, the developed system can enhance the effectiveness, efficiency, and accuracy of data management while supporting the monitoring and evaluation of competency development programs for the apparatus at the Directorate General of Regional Development, Ministry of Home Affairs.

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