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All Journal International Journal of Evaluation and Research in Education (IJERE) Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Systems and Informatics Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Afrig Aminuddin
Universitas AMIKOM Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Transfer Learning-Based Convolutional Neural Network for Accurate Detection of Rice Leaf Disease in Precision Agriculture Sari, Bety Wulan; Prabowo, Donni; Pristyanto, Yoga; Aminuddin, Afrig
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.420-432

Abstract

Background: Traditional approaches to rice disease identification depend mainly upon visual examination, which is quite labor-intensive and generally demands a certain skill level from people engaged in this activity. However, these approaches suffer from high time costs and potential errors and are impractical for large-scale daily monitoring. The recent rise of deep learning has offered opportunities for automated detection process improvement, which needs to be fast-accurate as good farmer-centric.   Objective: This study aims to enhance the accuracy of image rice leaf disease classification via feature extraction for rice leaf disease in four instances of pre-trained CNN models and provide an automated solution for early detection ahead of timely care by obtaining insights into crop production through precision agriculture. Methods: This study combined transfer learning with four pre-trained CNN models - InceptionResNetV2, MobileNetV2, DenseNet121, and VGG16. Results: The outcome of this research enables the identification of the optimal model to relate datasets where DenseNet121 achieved the highest accuracy, i.e. 99.10%, followed by MobileNetV2, having a precision of 97.10%. Conclusion: The new framework results in a highly accurate and high-throughput early disease detection element in precision agriculture, better than state-of-the-art approaches based on traditional techniques. Keywords: Deep Learning, DenseNet121, Image Classification, Rice Leaf Diseases, Transfer Learning
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods Switrayana, I Nyoman; Ashadi, Diki; Hairani, Hairani; Aminuddin, Afrig
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Co-Authors Abdullah, Khairul Hafezad Abdulloh, Ferian Fauzi Acihmah Sidauruk Agastya, I Made Artha Alam Miah, Mohammad Badrul Apitiadi, Satyo Dwi Asadulloh, Bima Pramudya Ashadi, Diki Bety Wulan Sari Bety Wulan Sari, Bety Wulan Dhanar Intan Surya Saputra Diki Ashadi Donni Prabowo, Donni Gunawan, Puguh Hasta Hairani Hairani Hammood, Waleed A. Harianto, Fetrus Jari I Nyoman Switrayana Joang Ipmawati Kusnawi Kusnawi Kusuma, Velizha S Majid Rahardi Mohammad Iskandar Dzulkurnain Mustofa, Dinar Norhikmah, - Wiji Nurastuti, Wiji Wirasto, Anggit Yoga Pristyanto