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Budi Mulyati, Budi
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Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 Chitraningrum, Nidya; Banowati, Lies; Herdiana, Dina; Mulyati, Budi; Sakti, Indra; Fudholi, Ahmad; Saputra, Huzair; Farishi, Salman; Muchtar, Kahlil; Andria, Agus
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.5

Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.
The Analysis of Public Service Management in Kota Serang. The Study of the online Application “RABEG” (Reaksi atas Berita Warga) Nizar, Indra; Hasanah, Budi; Annisarizki, Annisarizki; Mulyati, Budi
Indonesian Journal of Social Science Research Vol. 4 No. 2 (2023): Indonesian Journal of Social Science Research (IJSSR)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijssr.04.02.16

Abstract

The application of RABEG, is one of the media used to improve the services for people in Kota Serang. Howerver, its implementation has many problems, as reflected in the responses and assessments from users on the playstore platform, regarding the Rabeg application. The assessment given to this application was low, only 1.6 on a scale of 5. This research aimed to analyse the public service management in Kota Serang, focusing on the online Rabeg application. The research used descriptive qualitative approach, referred to the theoretical framework from Indrajit. It identified six important indicators; content development, competency building, connectivity, cyber law, citizen interface and capital. The data source were literature from books, mass media, official government documents, official website, the play store platform, and related journals. The findings of the research showed that the management of public services of Rabeg application was face with several obstacles and shortcomings. So the optimizing of public services from this application had not fully achieved.
Co-Authors Ahmad Fudholi Albert Kurniawan Purnomo Andria, Agus Annisarizki, Annisarizki Asep R. Rukmana Banowati, Lies Chitraningrum, Nidya Endang Susilawati Endang Susilawati Farishi, Salman Hasanah, Budi Herdiana, Dina Herlina Herlina Herlina Indra Sakti Karlina Angga Pradhita Lies Banowati Muchtar, Kahlil Nizar, Indra Panjaitan, Riong Seulina Rifka Agustianti Saputra, Huzair Sary Shandy Seulina Panjaitan, Riong Sri Sutjiningtyas Yoana Nurul Asri