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All Journal Scientific Journal of Informatics
Rudi Kurniawan
Universitas Bina Insan
Computer Science & IT

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Deteksi Dini Terhadap Penyakit Tumor Otak Menggunakan Citra Magnetik Resonance Imaging (MRI) dengan Pendekatan Deep Convolutional Neural Network Muhamad Salman; Rudi Kurniawan; Bunga Intan; Budi Santoso
Jurnal Ilmiah Informatika Vol. 10 No. 1 (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.v10i1.37-41

Abstract

This study aims to develop an early detection system for brain tumors using MRI images with a Deep Convolutional Neural Network (DCNN) based on the ResNet152V2 architecture. Rapid detection of brain tumors is crucial for improving recovery chances; however, manual processes often face challenges due to limitations in technology and medical expertise. Therefore, this research offers an automated solution for analyzing MRI images.The methods used include data collection from public datasets, image preprocessing, and training the DCNN model. The ResNet152V2 model was chosen for its ability to address the vanishing gradient problem and its effectiveness in feature extraction. The results show that the model achieved an accuracy of 92.38% in classifying four types of brain tumors: Meningioma, Glioma, Pituitary, and No Tumor. Evaluation using a confusion matrix and classification report indicates good performance. This research is expected to contribute to the early diagnosis of brain tumors and serve as a reference for future studies in the application of artificial intelligence in the medical field.
Klasifikasi Penyakit pada Buah Jeruk Berdasarkan Citra dengan Pendekatan Transfer Learning Menggunakan Arsitektur Densenet-121 Sheli Agustina; Asep Toyib Hidayat; Satrianansyah; Rudi Kurniawan
Jurnal Ilmiah Informatika Vol. 10 No. 1 (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.v10i1.42-47

Abstract

This study aims to develop a classification system for citrus fruit diseases based on digital images using a machine learning approach. The primary challenge in citrus cultivation is disease attacks that affect both the quality and quantity of production. In this research, image processing techniques were applied to extract color, shape, and texture features from citrus fruit images, which were then used as input for classification algorithms. This study uses the DenseNet-121 architecture for orange fruit image classification. The dataset used consisted of images of healthy citrus fruits and those affected by various diseases, such as blackspot, canker, and greening. The testing results showed that the DenseNet-121 architecture achieved the highest accuracy in classifying citrus diseases, with an accuracy rate of up to 99%. This system is expected to assist farmers and relevant stakeholders in early disease detection and in taking appropriate control measures.
Co-Authors Asep Toyib Hidayat Budi Santoso Bunga Intan Muhamad Salman Satrianansyah Sheli Agustina