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All Journal PAWIYATAN Jurnal Transformatika Jurnal Informatika Upgris JOURNAL OF APPLIED INFORMATICS AND COMPUTING Fokus Ekonomi JURNAL TEKNOLOGI DAN OPEN SOURCE Building of Informatics, Technology and Science Indonesian Journal of Community Services JURNAL ILMIAH AGRINECA Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Community Empowerment Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Teknika Buletin Sistem Informasi dan Teknologi Islam Jurnal Publika Pengabdian Masyarakat Jurnal Pengabdian Mandiri J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Tematik Jurnal Ilmiah Informatika dan Komputer Jurnal Sarjana Ilmu Pendidikan Widya Teknik JUSS (Jurnal Sains dan Sistem Informasi)
Setyoningsih Wibowo
UNIVERSITAS PGRI SEMARANG
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Transportation Other

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Journal : journal of applied informatics and computing

 1 
Classification of Melinjo Fruit Ripeness Using a Convolutional Neural Network (CNN) Based on Digital Images Kurniawan, Anggi Ade; Wibowo, Setyoningsih; Mutiara Sari, Nur Latifah Dwi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11744

Abstract

The subjective and ineffective manual sorting of melinjo fruit, a key ingredient in Indonesian cuisine, results in inconsistent quality. This study aims to create and evaluate an automated classification system for judging the ripeness of Gnetum gnemon fruit in order to solve these issues and offer a reliable and objective quality control method. The approach was to create a customized Deep Convolutional Neural Network (Deep-CNN). The model was trained and evaluated using a simple dataset of 5,718 images that were separated into three maturity levels: raw, semi-ripe, and fully ripe. Twenty percent of the dataset was used for testing, and the remaining 80 percent was used for training. Image preparation techniques like contrast enhancement and scaling to 250x250 pixels were applied in order to optimize the model's input data. The evaluation was conducted using a test dataset consisting of 1,144 photos. After eight epochs of training with the Adam optimizer, the generated Deep-CNN model demonstrated remarkable efficacy with a final classification accuracy of 99.91%. The high level of performance that remained throughout the testing phase confirmed the model's strong ability to accurately identify the ripeness levels of melinjo fruit. The previously unresolved issue of automated melinjo classification is addressed in this work with a tailored and remarkably accurate (99.91%) solution. Its primary advantage is that it provides a trustworthy and unbiased technical alternative to subjective hand sorting. This directly meets industry needs by offering a scalable method to improve operational effectiveness, standardize product quality, and increase the commercial value of melinjo fruit of agricultural products.
Comparative Analysis of K-Nearest Neighbors and Support Vector Machine for Student Stress Prediction Lutfi Khoirul Umam; Setyoningsih Wibowo; Agung Handayanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13224

Abstract

Student stress has emerged as an important issue because it can negatively affect both academic achievement and mental well-being. The growing development of machine learning techniques has enabled the creation of predictive models that can classify stress levels using various student-related factors. This research evaluates and compares the effectiveness of the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms in predicting student stress categories. The dataset was sourced from Kaggle and contains questionnaire-based data, including variables such as sleep quality, frequency of headaches, academic achievement, study workload, participation in extracurricular activities, and stress level classifications. The study followed several stages, including data preprocessing, feature normalization, model development, and performance assessment. Model evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix metrics. To validate the practical implementation of the models, both algorithms were incorporated into a web-based application built with the Flask framework and supported by a MySQL database. The experimental results revealed that the KNN algorithm delivered superior classification performance compared to SVM. KNN obtained an accuracy score of 88.46% and a weighted F1-score of 0.89, whereas SVM achieved 46.15% accuracy with a weighted F1-score of 0.41. These findings suggest that KNN is more effective in identifying patterns within the student survey data. In addition, the successful deployment of the prediction system demonstrates that conventional machine learning methods can be utilized to provide real-time assessments of student stress levels and support mental health monitoring efforts.
Co-Authors Agung Handayanto Agung Handayanto Aldiansyah, Edo Putra Aldo Kamadi Ari Tri Jaka Harjanta Aris Tri Joko Harjanto Bagus Priyatno Bambang Agus Herlambang, Bambang Bambang Hadi Kunaryo Baromim Triwijaya Darmaputra, Mochamad Fadjar Febrian Murti Dewanto Fitri Yulianti Hartanti, Lusia Permata Sari Iksan, Helmi Irna Farikhah Khoiriya Latifah, Khoiriya Kurniawan, Anggi Ade Mega Novita Mei Sulistyoningsih Melapa, Yabes Aryanto Mohamad Fajarianditya Nugroho Mohamad Fajarianditya Nugroho Muhammad Khoirul Lutfi Mutiara Fitri Rahmaningtyas Mutiara Sari, Nur Latifah Dwi Ngatmini . Noora Qotrun Nada, Noora Qotrun Nugroho Dwi Saputro Nugroho Dwi Saputro, Nugroho Dwi Nur Khoiri Nur Latifah Dwi Mutiara Sari Nur Latifah Dwi Mutiara Sari Nurul Hidayah Ramadhan Renaldy Reni Rakhmawati Rifki Hermana Rizky Muliani Dwi Ujianti Sigit Ristanto Slamet Budirahardjo Slamet Budirahardjo Slamet Budirahardjo Sri Wahyuni Suyitno Suyitno Tri Widiastuti Waliyansyah, Rahmat Robi Widya Aprilia Yanuar Noor Wicaksono Yusuf Ma'iin Rohmatulloh