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All Journal Journal of Information Technology and Computer Science Jurnal Abdi: Media Pengabdian Kepada Masyarakat
Ulfa Siti Nuraini
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Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Pelatihan Pengolahan Dan Visualisasi Data Sebagai Penunjang Peningkatan Pendidikan Menggunakan Microsoft Excel Hasanuddin Al-Habib; Yuliani Puji Astuti; Fadhilah Qalbi Annisa; Riskyana Dewi Intan Puspitasari; Harmon Prayogi; Ulfa Siti Nuraini
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2025): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v10i2.37764

Abstract

The aim of the community service to provide training and mentoring in educational data processing using Microsoft Excel. The community service activity involves teachers as participant from Sekolah Indonesia Bangkok, Thailand. The activities are carried out in phases which include planning, coordination with partner school, obtaining necessary permits, developing training module, conducting the training, and evaluating the outcomes. The training consisted of four stages which are a pre-test, training material presentation, a question-and-answer session, and a post-test. The output of the community service was a Microsoft Excel training module that cover both the technical aspects of the software and simple practical application. The effectiveness of the training was evaluated based on the results of the pre-test and post-test. The result showed a significant improvement regarding to participants’ understanding, as evidenced by the increase in the number of correct answers and the average percentage between the pre-test and post-test. The average percentage of correct answers of the participants before the training was around 34% and after the training about 52%. Meanwhile, the average percentage of the pre-test is 62,27% and post-test is 87,91%.
Comparative Analysis of Random Forest, Support Vector Machine, and K-Nearest Neighbor with Image Feature Extraction for Rice Leaf Disease Detection Nadia Nafista; Wawu Tri Ambodo; Analicia; Ulfa Siti Nuraini
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112856

Abstract

Plant diseases and pest infestations have caused a decline in global food production of up to 40%, including in Indonesia, making an efficient and accurate disease detection system essential to support food security. This study proposes a supervised learning approach to detect rice leaf diseases based on image processing. Leaf images are processed through the stages of image segmentation, normalization, Gaussian blur, Canny edge detection, visualization of diseased areas, and hybrid feature extraction. Supervised learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were trained and compared. Test results show that Random Forest delivers the best performance with an accuracy of 97%, outperforming SVM and KNN. These findings indicate that the proposed approach can serve as an effective and reliable solution for the automatic detection of rice leaf diseases.
Co-Authors Analicia Fadhilah Qalbi Annisa Harmon Prayogi Hasanuddin Al-Habib Nadia Nafista Riskyana Dewi Intan Puspitasari Wawu Tri Ambodo YULIANI PUJI ASTUTI