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All Journal Media of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Aksa, Muhammad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Deteksi Tingkat Kematangan Buah Mangga Berdasarkan Fitur Warna Menggunakan Pengolahan Citra Digital Aksa, Muhammad; Ranggareksa, Andi; Aras, Muh Riski Farukhi; Kaswar, Andi Baso; Andayani, Dyah Darma; Intam, Reski Nurul Jariah S
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10578

Abstract

The classification of mango Golek ripeness is crucial for ensuring product quality and its economic value, especially in industrial applications. Manual and subjective ripeness determination often leads to inconsistency, resulting in decreased harvest quality and market value. This study aims to classify the ripeness of Golek mangoes into three categories: unripe, semi-ripe, and ripe, using digital image processing based on HSV and LAB color features combined with the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 300 images, split into 80% training data and 20% testing data. The proposed method includes image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification. The results show that the combination of HSV and LAB color features is effective in distinguishing ripeness levels, with an accuracy of 81.67% on the testing data and an average precision, recall, and F1-Score of 82%. Consistent color patterns in the unripe and semi-ripe categories enhance accuracy, while fluctuations in color intensity in the ripe category pose challenges. This approach shows potential for implementation in automatic sorting systems in industry.
A Decision Support Model for Scholarship Recipient Selection Based on Tsukamoto Fuzzy Logic Muhtadi; Aksa, Muhammad; Naoval, Ahmad; Adiba, Fhatiah; Nasurllah, Asmaul Husna
Media of Computer Science Vol. 2 No. 1 (2025): June 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i1.225

Abstract

This study proposes a decision support model for scholarship recipient selection based on the Tsukamoto fuzzy logic method to overcome the inefficiencies and subjectivity inherent in manual selection processes. The model incorporates three key criteria: Grade Point Average (GPA), parents’ income, and number of dependents. Experiments were conducted using a dataset of 25 students obtained from a public Kaggle repository. The model employs fuzzification, rule formulation, and defuzzification to compute a final decision score for each applicant. The experimental results demonstrate that the proposed model achieves an accuracy rate of 92%, indicating its effectiveness in supporting objective and efficient scholarship selection decisions.
Co-Authors Adiba, Fhatiah Andi Baso Kaswar Andi Baso Kaswar Aras, Muh Riski Farukhi Intam, Reski Nurul Jariah S Muhtadi Naoval, Ahmad Nasurllah, Asmaul Husna Ranggareksa, Andi