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All Journal Jurnal Teknik Informatika (JUTIF) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Arifky, Reza
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Computer Science & IT

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Klasifikasi Tingkat Kualitas Terung dengan Algoritma Backpropagation Berbasis Fitur Warna dan Tekstur R, Muh Raflyawan; Arifky, Reza; Tenriajeng, Andi Afrah; Kaswar, Andi Baso; Andayani, Dyah Darma; Azis, Putri Alysia
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.v11i2.10655

Abstract

Manual quality assessment of eggplant is often inconsistent, takes a long time, and is prone to errors due to worker fatigue. This research aims to develop an automated system based on digital image processing to assess eggplant quality efficiently and accurately. The stages begin with image capture using a mobile phone device designed to ensure stable lighting and uniform background. The acquired image is then processed through segmentation using the Otsu thresholding method as well as morphological operations to separate the main object from the background. Color and texture features are extracted through Gray-Level Co-occurrence Matrix (GLCM) analysis and RGB, HSV, and LAB color spaces. Training data amounting to 90% of the total dataset was used to train an artificial neural network-based classification model with a backpropagation algorithm, while the remaining 10% was used for testing. Experimental results showed that the combination of LAB, RGB, HSV, and texture features gave the best results, with a testing accuracy of 86%, recall of 85%, and precision of 92%. This model is very effective in detecting poor quality eggplants with 100% accuracy. This system can support the application of technology in the horticultural sector.
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 Aksa, Muhammad Andi Baso Kaswar Andi Baso Kaswar Awaluddin, Muhammad Ghazali Azis, Putri Alysia Bakri, Muhammad Fajar Dewi Fatmarani Surianto Edy, Marwan Ramdhany R, Muh Raflyawan Tenriajeng, Andi Afrah