p-Index From 2021 - 2026
6.986
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Computer Engineering and Applications Journal (ComEngApp) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUITA : Jurnal Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Computer Engineering and Applications Journal (ComEngApp) Jurnal Informatika Upgris Sinkron : Jurnal dan Penelitian Teknik Informatika JIEET (Journal of Information Engineering and Educational Technology) Jurnal Ilmiah Matrik Indonesian Journal of Information System BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informatika Global Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence Indonesian Community Journal International Journal of Advanced Science Computing and Engineering JEECS (Journal of Electrical Engineering and Computer Sciences) AnoaTIK: Jurnal Teknologi Informasi dan Komputer Jurnal INFOTEL Journal of Computer Science Application and Engineering
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Computer Science Application and Engineering

First Step for Vehicle License Plate Identification Using Machine Learning Approach Amirah; Sanmorino, Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 1 (2023): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v1i1.6

Abstract

Automated vehicle license plate identification, critical in modern transportation systems, finds application in traffic monitoring, law enforcement, and transportation optimization. This study explores machine learning's potential to enhance accuracy and efficiency in this domain. Leveraging neural networks and pattern recognition, it aims to build an automated system robust across diverse conditions. Addressing limitations in traditional methods, it focuses on adapting to lighting, angles, and image quality variations. The societal impact includes streamlining law enforcement and optimizing traffic flow, revolutionizing transportation and surveillance. Methodologies cover data collection, ethical considerations, preprocessing, feature extraction, model selection, and iterative refinement. Ethical data handling ensures privacy compliance. Feature extraction methods like HOG, LBP, CNNs, and color histograms capture crucial aspects for identification. Model selection spans SVMs, CNNs, decision trees, and ensemble methods, considering task complexity and dataset characteristics. This study evaluates machine learning's potential for revolutionizing license plate identification systems.
A Data Processing Information System for Oil Palm Harvest Results Febrianza, M; Sanmorino, Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v4i1.91

Abstract

Oil palm plantations require accurate and timely harvest data to support effective operational and managerial decision making. However, many plantations still rely on manual data recording methods that are prone to delays, errors, and data inconsistency. This study develops a data processing information system for oil palm harvest results using the Agile–Scrum approach to improve efficiency and data reliability. The system supports structured harvest data entry, automated processing, and real time reporting. Black-box testing was conducted to evaluate functional correctness, and the results show that all tested system functions operated as expected, with a 100% pass rate across key scenarios, including data entry, validation, and report generation. Performance comparison results indicate that the proposed system reduces data entry time by approximately 70%, decreases error rates by up to 85%, and shortens daily report preparation time from several hours to less than five minutes. These results demonstrate that the developed system effectively enhances accuracy, efficiency, and data accessibility in oil palm harvest management.