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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Abi Fajar Ahmad Fauzi
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique Abi Fajar Ahmad Fauzi; Ahmad Faqih; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.756

Abstract

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Peningkatan Layanan RT Melalui Sistem Informasi Administrasi Berbasis Web Dodi Solihudin; Edi Tohidi; Abi Fajar Ahmad Fauzi; Ade Valentino
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Administrative services at the neighborhood level (Rukun Tetangga/RT) are an essential component in supporting good governance within communities. However, in practice, these services are still frequently managed manually, leading to various issues such as service delays, data entry errors, and inefficiencies in documentation. This Community Service Program (PKM) aims to design and implement a web-based neighborhood administrative information system to assist RT administrators in delivering faster, more accurate, and transparent services to residents. The implementation methods include needs assessment, system design, software development, as well as training and technical assistance for both administrators and residents. The system is developed using web-based technologies (PHP, MySQL, and HTML/CSS), allowing access via computers or smartphones. Key features of the system include resident data management, automated issuance and printing of official letters, archive management, and financial and activity reporting. The implementation results indicate a significant improvement in the efficiency of RT administrative services. RT administrators are no longer burdened with manual record-keeping, and residents can access services independently from their homes. Moreover, the system supports administrative transparency, as all activities are digitally recorded and easily traceable. This program has a positive impact on digital literacy among residents and strengthens the integration of information technology with public services at the micro community level. In the future, the system is expected to be replicated in other RTs as a community-based digital transformation solution.
Co-Authors Ade Valentino Ahmad Faqih Dodi Solihudin Edi Tohidi Kaslani