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All Journal Jurnal Teknik Informatika (JUTIF) Media of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Aksa, Muhammad
Unknown Affiliation
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.
Article Retrieval And Automatic Summarization System Using BERT-Based Neural Network Model On Chatbot Awaluddin, Muhammad Ghazali; Aksa, Muhammad; Arifky, Reza; Bakri, Muhammad Fajar; Surianto, Dewi Fatmarani; Edy, Marwan Ramdhany; Zain, Satria Gunawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4463

Abstract

The rapid growth of online scientific publications presents challenges in efficiently filtering relevant information. Many search systems still rely on keyword matching, which is often ineffective in understanding the context of user queries. This study develops a chatbot system based on BERT (Bidirectional Encoder Representations from Transformers) for scientific article retrieval and automatic summarization. The system is designed to comprehend user intent and generate summaries of relevant articles. The evaluation was conducted on a dataset of 506 scientific articles, assessing search accuracy based on topic, abstract, author name, and time range. Results show 100% accuracy in searches by author and abstract, with varying performance in topic-based and time-based searches. This system is expected to enhance the efficiency and relevance of scientific literature retrieval and support the productivity of researchers across various fields.
Co-Authors Adiba, Fhatiah Andi Baso Kaswar Andi Baso Kaswar Aras, Muh Riski Farukhi Arifky, Reza Awaluddin, Muhammad Ghazali Bakri, Muhammad Fajar Dewi Fatmarani Surianto Edy, Marwan Ramdhany Intam, Reski Nurul Jariah S Muhtadi Naoval, Ahmad Nasurllah, Asmaul Husna Ranggareksa, Andi