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All Journal Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek REKAYASA Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Network Engineering Research Operation [NERO] JURNAL INSTEK (Informatika Sains dan Teknologi) Berdikari: Jurnal Pengabdian Masyarakat Indonesia Jurnal Sistem Cerdas JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Innovative: Journal Of Social Science Research Journal of Information Technology and Cyber Security
Budi Dwi Satoto
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Physics Public Health Other

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Classification of Rice Disease Using Deep Learning Object Detection Yolov8 Dwi Satoto, Budi; Rosa Anamisa, Devie; Yusuf, Muhammad; Kautsar Sophan, Mohammad; Kembang Hapsari, Rinci; Irmawati, Budi; Arrova Dewi, Deshinta
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3578

Abstract

Rice plant pests and diseases are among the primary threats to agricultural production, particularly in rice-growing regions, which can result in a significant decrease in crop yields and food production. Therefore, technology is essential for accurately detecting and classifying pests and diseases. In this research, the author proposes using deep learning-based object detection for moving objects. This is because observations are made on relatively large land areas. Images are captured by drone cameras as videos, which are then used to create ground-truth markers and identification targets during training. YOLO v8 is the latest object detection model on moving media. This model offers advantages in speed and accuracy, making it well-suited for applications that require precise results on agricultural land. The dataset comprises videos of rice plants infested with pests and diseases. After completing labeling and training, the YOLO v8 model can detect and classify pests and diseases in real time using markers in the form of frames with identification labels. Farmers can identify pest and disease attacks earlier by implementing this system, enabling more effective, timely pest control measures. The study's results showed that the training accuracy was 91.5%. The F1-Confidence measurement value obtained was 0.84, the Precision-Recall Curve was 0.891, and the Recall Confidence Curve was 0.97. The trial results, based on experimental data, achieved confidence accuracy of 80% to 95%.
Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation Budi Dwi Satoto; Rima Tri Wahyuningrum; Bain Khusnul Khotimah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26222

Abstract

Corn is one of the essential commodities in agriculture. All components of corn can be utilized and accommodated for the benefit of humans. One of the supporting components is the quality of corn seeds, where a specific source has the physiological qualities to survive. The problem is how to get information on the quality of corn seeds at agricultural locations and get information through the physical image alone. This research tries to find a solution to obtain high accuracy in classifying corn kernels using a convolutional neural network because there is a profound training process. The problem with convolutional neural networks is the training process takes a long time, depending on the number of layers in the architecture. This research contributes to increasing the computing time with the proposed contribution by adding Region proposals with a convex hull to use on a custom layer. The method's purpose is a region proposal area with a convex hull to increase the focus on the convolution multiplication process. It affected reducing unnecessary objects in background images. A custom layer architecture by maintaining the priority layer is an option to get a shorter computational time in constructing a model. In addition, the architecture that is made still considers the stability of the training process. The results on the classification of corn seeds are obtained by a model with an average accuracy of 99.01%—the Computational training time to get the model is 2 minutes 30 seconds. The average error value for MSE is 0.0125, RMSE is 0.118, and MAE is 0.0108. The experimental data testing process has an accuracy ranging from 77% -99%. In conclusion, using region proposals can increase accuracy by around 0.3% because focused objects assist the convolution process
Improving RoBERTa Performance through Hyperparameter Optimization for Sentiment Analysis of Indonesian Tourism Reviews Imamah, Imamah; Thida, Myo; Rachman, Fika Hastarita; Satoto, Budi Dwi; Herawati, Sri; Kustiyahningsih, Yeni; Rochman, Eka Mala Sari; Zakiyah, Meita Lailatuz
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5672

Abstract

The performance of transformer models such as RoBERTa in sentiment classification is influenced by hyperparameter settings, especially the epoch and batch sizes. However, no previous study has examined the impact of changes in the number of epochs and batch sizes on the performance of each class in classification tasks, especially in Indonesian-language sentiment analysis of tourism reviews. Therefore, this study aims to fill this gap by analyzing the performance of RoBERTa and the impact of various hyperparameter settings on sentiment for each class. The dataset consists of 3,875 reviews from visitors to Lake Sarangan on Google Maps. The batch sizes used in this study are 8 and 16, and the epoch range is 2 to 4. There are three classes of sentiment: negative, neutral, and positive. The results demonstrate that increasing the batch size from 8 to 16 does not linearly improve model performance. The optimal combination of epoch=4 and batch size=8 achieved 91% accuracy, with significant improvements in recall and F1-score across all classes, especially in positive sentiment classification. This research offers valuable insights into fine-tuning RoBERTa for sentiment analysis in Indonesian contexts, providing recommendations for future sentiment analysis tasks in natural language processing.
Co-Authors Abidin, Mochammad Syahrul Achmad Yasid Amillia Kartika Sari Amin Abdillah Angkoso, Cucun Very Ari Kusumaningsih Arrova Dewi, Deshinta Bain Khusnul Khotimah Bain Khusnul Khotimah Bain Khusnul Khotimah Bain Khusnul Khotimah Bain Khusnul Khotimah Bain Khusnul Khotimah Budi Irmawati Efendi, Moch Rizal Eko Budi Khoendori Eza Rahmanita Faisal Dzikri, Mohammad Fika Hastarita Rachman, Fika Hastarita Firli Irhamni Firmansyah, Muhammad Iqbal Hadiri, Machsus Imam Utoyo Imamah Imamah Iswati Iswati Iswati Iswati, Iswati Jannah, Maughfirotul Javier, Najamuddin Kautsar Sophan, Mohammad Kembang Hapsari, Rinci Khotimah, Bain Khusnul Khotimah, Bain Khusnul Laili, Moh Linda Rimala Dewi Moh Laili Muhammad Yusuf Mukhamad Khoironi Novi Prastiti, Novi Putra, Wahyu Zainur Rima Tri Wahyuningrum Riries Rulaningtyas Rochman, Eka Mala Sari Rosa Anamisa, Devie Sensusiati, Anggraini Dwi Soesilo, Budi Sri Herawati Thida, Myo Thrisna, Mochamad Adrian Nuradha Yeni Kustiyahningsih Yudha Dwi Putra Negara, Yudha Zakiyah, Meita Lailatuz