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All Journal Compiler Jurnal Manajemen Informatika Scientific Journal of Informatics KLIK: Kajian Ilmiah Informatika dan Komputer
Agus Suhendar
Universitas Teknologi Yogyakarta
Computer Science & IT

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Journal : Scientific Journal of Informatics

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IMPLEMENTASI MODEL CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK DETEKSI KESEGARAN BUAH PISANG BERDASARKAN CITRA KULIT Alvina Putri Damayani; Agus Suhendar
Jurnal Ilmiah Informatika Vol. 10 No. 2 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i2.73-79

Abstract

The assessment of banana freshness is currently still done manually through visual observation, touch, and smell. This method is subjective and prone to errors in perception between individuals, which can cause losses for farmers, traders, and costumers. Inaccuracies in assessing freshness levels can result in the distribution of substandard fruit, reduced market competitiveness, and waste of resources. To address these issues, this study designed and implemented a banana freshness classification system using a Convolutional Neural Network (CNN) algorithm. The system was develoved in the form of a Python and Flask-based website. Equipped with a Text-to-Speech (TTS) feature to improve accessibility for users with visual impairments. The research stages included problem identification, banana image data collection, image preprocessing (resize, normalization, augmentation), CNN architecture design, model training, implementation, and testing. The dataset consist of 1,664 images classified into two categories: fresh and not fresh. The implementation result show that the system can classify banana freshness in real-time through visual and audio displays. This system has the potentional to improve the efficiency and objectivity of classification, as well as support the digitization of the agricultural sector.
KLASIFIKASI KESEGARAN IKAN TONGKOL BERDASARKAN CITRA MATA BERBASIS CONVOLUTIONAL NEURAL NETWORK (CNN) Fitria Ningsih; Agus Suhendar
Jurnal Ilmiah Informatika Vol. 10 No. 2 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i2.88-94

Abstract

It Fish freshness is a crucial factor in ensuring food quality and safety. However, the conventional assessment process still relies on human observation, which is subjective supporting system, the risk of distributing non-fresh fish to consumers remains high, potentially affecting public health and consumer trust in fishery products. To address this issue, a fish freshness classification system based on eye image analysis using the Convolutional Neural Network (CNN) method was developed. The system development stages include collecting fihs eye image data, labeling, image preprocessing, CNN model training, and implementing the system in an convolution and pooling layers to extract visual features from the images. The initial testing results show that the system can classify fish freshness into two categories, Fresh and Not Fresh, with a high level of accuracy. This system is expected ti assist the public and fishery industry practitioners in evaluating fish quality more accurately ang efficiencly.
DETEKSI PENYAKIT CITRUS VEIN PHLOEM DEGENERATION (CVPD) PADA DAUN JERUK MENGGUNAKAN METODE SEGMENTASI K-MEANS DAN ARSITEKTUR EFFICIENNET Mawar Pratama sari; Agus Suhendar
Jurnal Ilmiah Informatika Vol. 10 No. 2 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i2.80-87

Abstract

Citrus vein phloem degeneration (CVD) is a devastating disease of citrus plants and seriously impacts crop quality. Although manual detection is feasible, this method faces many challenges, such as the similarity of early symptoms between healthy and infected leaves. Therefore, manual detection is time-consuming and inefficient. Therefore, an accurate and efficient automatic detection method is needed. This study aims to combine two methods: the K-Means segmentation method and the EfficientNet architecture to build an automatic detection model for CVD in citrus leaves. This method aims to improve the classification accuracy of citrus leaf images. This study is divided into two stages: the first stage uses the K-Means algorithm for image segmentation, and the second stage uses the EfficientNet model for classification. The K-Means segmentation method is used to separate the leaf surface from the background, focusing only on the parts of the leaf that show disease symptoms. The segmentation results are then processed in the second stage using the EfficientNet model. The EfficientNet model is known for its efficient feature extraction and excellent performance in recognizing complex visual patterns. The results showed that combining the K-Means segmentation method with the EfficientNet architecture significantly improved the accuracy of CVPD detection compared to a traditional CNN model without segmentation. This system is expected to assist farmers in detecting CVPD and support the implementation of smart agriculture technology in automated plant health monitoring.
Co-Authors Alvina Putri Damayani Elisabeth Kurnia Andini Fitria Ningsih Gabriel Nicolas Sumampow Ikhsan Akbar Lintang Ardi Avdillah Mawar Pratama sari Murti Retnowo Visco Adam Bramasta