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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer Asia
Andira, Aysza Belia Auly
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Computer Science & IT Decision Sciences, Operations Research & Management

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Perbandiangan VGG16 dan MobileNetV2 untuk Klasifikasi Tingkat Kematangan Buah Apel Ivani, Aryani Rizky Rahmalia; Kurniadi, Ahmad Zulfi; Andira, Aysza Belia Auly; Wahyuni, Ida
JURNAL SISTEM KOMPUTER ASIA Vol 3 No 1 (2025): JISKOMSIA - Volume 3, Nomor 1, Tahun 2025
Publisher : Institut Tekonologi dan Binisi Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jiskomsia.v3i1.136

Abstract

This research discusses deep learning to classify the maturity level of apples, especially in Batu City, which is known as the center of apple production in Indonesia. This study implements the Visual Geometry Group16 (VGG) model as a deep learning-based image classification method to accurately identify the maturity level of apples. The data used was in the form of images of apples with various levels of ripeness, which were categorized into ripe, half-ripe, and raw. The design of the VGG16 model was chosen because of its simple yet powerful architecture in the extraction of visual features. This research process includes collecting apple image data, preprocessing, model training, and performance evaluation based on accuracy, precision, recall, and F1-score metrics. The results of the experiment showed that the accuracy obtained was 98%. So the VGG16 model is able to classify the maturity level of apples, with the potential for application in automation systems in the agricultural sector.
Comparative Analysis of Ahmad-Yusoff and Jaro-Winkler Approaches for Javanese Language Stemming Andira, Aysza Belia Auly; Ahda, Fadhli Almu'iini; Sulistyo, Danang Arbian
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.7879

Abstract

This research presents a performance comparison between two approaches for identifying the base form of affixed Javanese words: the Ahmad Yusoff Sembok (AYS) rule-based stemming algorithm and the Jaro-Winkler (JW) string similarity approach. Javanese was selected as the focus because of its complex morphological structure, encompassing prefixes, suffixes, infixes, and confixes, along with significant speech-level and dialectal variation, which together pose challenges for natural language processing. The dataset comprises 720 manually annotated word lemma pairs. Evaluation was carried out using precision, recall, F1-score, accuracy, and Cohen’s Kappa, complemented by error analysis on over-stemming and under-stemming cases. Results indicate that JW achieves higher overall performance (83.19% accuracy, 83% F1-score) compared to AYS (73.19% accuracy, 73% F1-score), with AYS producing more over-stemming errors (88 cases) and JW showing more under-stemming errors (47 cases). These outcomes suggest that similarity-based approaches are more effective in addressing Javanese morphological complexity, while also contributing a benchmark dataset of manually annotated Javanese word lemma pairs, a comparative evaluation framework between rule-based and similarity-based approaches, and practical insights for the development of stemming tools in regional languages that currently lack NLP resources.
Co-Authors Fadhli Almu’iini Ahda Ida Wahyuni Ivani, Aryani Rizky Rahmalia Kurniadi, Ahmad Zulfi Sulistyo, Danang Arbian