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All Journal JOIV : International Journal on Informatics Visualization Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi Jurnal Pendidikan dan Pengabdian Masyarakat
Budi Irmawati
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Other

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Journal : JOIV : International Journal on Informatics Visualization

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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%.
Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping Wedashwara, Wirarama; Irmawati, Budi; Wijayanto, Heri; Arimbawa, I Wayan Agus; Widartha, Vandha Pradwiyasma
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1813

Abstract

Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).
Co-Authors Adi Kurniawan Afwani, Royana Aghita, Nadiyasari Agitha, Nadiyasari Agus Gunawan Aisyah, Yunda Al Qadri, Ramadhani Alamsyah, Noor Albar, Moh. Ali Anjarwani, Sri Endang Apriliana, Candra Yuli Aranta, Arik Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo, Ario Yudo Arrova Dewi, Deshinta Astuti, Yuli Yana Budi Dwi Satoto Bulkis Kanata Cahyani, Armita Dwi Choirunnisa, Valia Adri Cipta Ramadhani Dafa Rozzi Pratama Dwiyansaputra, Ramaditia Fatmawati, Ririn Dwi Fitri Bimantoro Heri Wijayanto I B K Widiartha I Desak Putu Diah Anggiani I Gede Pasek Suta Wijaya I Wayan Agus Arimbawa, I Wayan Agus Jayusman, Dirga Karinda Irianti Kautsar Sophan, Mohammad Kembang Hapsari, Rinci Kokong, Diah Anggreni Ratna Sari Lalu Muhammad Agung Hidayatullah Lestari, Eke Febi Ma'we, Hannatul Maidatun Izzati Maria Arista Ulfa Muhammad Awaluddin, Muhammad Muhammad Yusuf Murpratiwi, Santi Ika Naim, Nadratul Prawesti, Shafira Indah Rachim, Fauziyah Aulia Raihan, Muhammad Rassy, Regania Pasca Rendy Robbani Rosa Anamisa, Devie Rosidi, M. Ahyar Rumbiak, Rudolf Shabrina, Marwati Maryam Sianturi, Reni Suhada, Destia Teti Zubaidah Utami, Alya Hadranita Vandha Pradwiyasma Widartha WAHIDA, Nurul Wayan, Wininda Wedashwara, Wirarama Yuningsih, Upi Zakiyah Rahmiati