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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Devi, Rahma
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Classification of Corn Leaf Disease Images Using Convolutional Neural Network Algorithm Fitriani, Irma; Devi, Rahma; Sajjana, Ariandra Fokker Chaya; Irfan, Muhammad
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2105

Abstract

Corn leaf diseases can reduce crop yields and cause financial losses, thus requiring accurate and objective classification methods. This study aims to classify four corn leaf conditions, namely Blight, Common Rust, Gray Leaf Spot, and healthy leaves, using a Convolutional Neural Network (CNN) approach based on image processing. A systematic comparative evaluation was conducted on three CNN architectures, namely MobileNetV2, ResNet50V2, and DenseNet201, by examining the effect of architecture-optimizer pairs using Adam and RMSprop to determine the optimal model configuration. The results showed that the proposed approach was effective in classifying corn leaf diseases, with the highest accuracy of 93% achieved by the combination of DenseNet201 and the Adam optimizer. This study contributes by providing a structured comparative analysis of the performance of CNN architectures and optimizers as a reference for the development of more accurate and efficient early detection systems for plant diseases.
Machine Learning Based Prediction of Health Risks in Pregnant Women Devi, Rahma; Inggih Permana; Novita, Rice; Febi Nur Salisah
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.766

Abstract

Pregnancy is an important phase that requires optimal health monitoring to prevent complications that are risky for both mother and fetus. The high maternal mortality rate in Indonesia emphasizes the importance of early detection of pregnancy risks. The use of machine learning offers an effective predictive approach to quickly and accurately identify pregnancy risks. This study aims to compare the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree C4.5, Random Forest, Support Vector Machine, and Naive Bayes, using the Maternal Health Risk Dataset. The hold-out validation method with data sharing of 80% training data and 20% test data was used in this study. Model evaluation is conducted based on accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest had the best performance with an accuracy of 93%, followed by Decision Tree at 93%, SVM at 82%, Logistic Regression at 76%, and Naive Bayes at 72%. Thus, Random Forest is rated as the most optimal algorithm in predicting pregnancy risk and potentially supporting the development of decision support systems for health workers. This research is expected to be the basis for the development of a machine learning-based decision support system to increase the effectiveness of health services for pregnant women.
Co-Authors Febi Nur Salisah Fitriani, Irma Inggih Permana Muhammad Irfan Rice Novita Sajjana, Ariandra Fokker Chaya