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All Journal Infotekmesin Jurnal Pengabdian Masyarakat (ABDIRA)
Ajeng Widiawati, Chyntia Raras
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Social Sciences Other

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Perbandingan Random Forest dan K-Nearest Neighbors untuk Klasifikasi Body Mass Index Menggunakan SMOTE-ENN untuk Mengatasi Ketidakseimbangan Data pada Analisis Kesehatan Naufal Yogi Aptana; Ikhsan, Ali Nur; Maulana Baihaqi, Wiga; Ajeng Widiawati, Chyntia Raras
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2553

Abstract

This study aims to compare the Random Forest and K-Nearest Neighbors (KNN) algorithms in Body Mass Index (BMI) classification using the SMOTE-ENN method to address data imbalance. The dataset consists of 2111 entries with demographic and health attributes of individuals. Data imbalance poses a significant challenge that may affect the accuracy of machine learning models. The SMOTE-ENN combination was employed to improve data distribution, enabling models to recognize patterns in minority classes better. Key evaluation factors included both algorithms' accuracy, precision, recall, and F1-score. Results indicate that the Random Forest algorithm achieved higher performance with 100% accuracy than KNN with 96% after applying SMOTE-ENN. These findings highlight the unique contribution of SMOTE-ENN in handling imbalanced data, enhancing classification model quality, and significantly impacting machine learning applications in healthcare.
Meningkatkan Literasi dan Numerasi dengan Memadukan Teknologi pada Program Kampus Mengajar 6 di SD N 2 Tamansari Safitri, Arisanti Dwi; Yunita, Ika Romadoni; Ajeng Widiawati, Chyntia Raras
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 4, No 2 (2024): Abdira, April
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v4i2.445

Abstract

The Teaching Campus is an MBKM (Free Learning Campus) program organized by the Ministry of Education and Culture with the aim of providing opportunities for students to learn and develop themselves through activities outside of lectures. This program gives students the opportunity to become teacher partners, encourage learning innovation, and develop literacy, numeracy, and technology adaptation in the 3T area. Focus is also given to efforts to increase people's interest in reading and the importance of literacy and numeracy, including the obstacles faced in its implementation. This research aims to revive the school literacy movement through literacy and numeracy familiarization activities. The research results show that technical training, understanding material concepts, and introducing technology are the main focuses of mentoring. The implementation of the Literacy and Numeracy Minimum Competency Assessment (AKM) with technological devices provides positive results, including increasing understanding of numeracy material, individual development, motivation and self-confidence of students. This research provides a positive picture of the potential for improving the quality of basic education through the Teaching Campus program.
Perbandingan Random Forest dan K-Nearest Neighbors untuk Klasifikasi Body Mass Index Menggunakan SMOTE-ENN untuk Mengatasi Ketidakseimbangan Data pada Analisis Kesehatan Naufal Yogi Aptana; Ikhsan, Ali Nur; Maulana Baihaqi, Wiga; Ajeng Widiawati, Chyntia Raras
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2553

Abstract

This study aims to compare the Random Forest and K-Nearest Neighbors (KNN) algorithms in Body Mass Index (BMI) classification using the SMOTE-ENN method to address data imbalance. The dataset consists of 2111 entries with demographic and health attributes of individuals. Data imbalance poses a significant challenge that may affect the accuracy of machine learning models. The SMOTE-ENN combination was employed to improve data distribution, enabling models to recognize patterns in minority classes better. Key evaluation factors included both algorithms' accuracy, precision, recall, and F1-score. Results indicate that the Random Forest algorithm achieved higher performance with 100% accuracy than KNN with 96% after applying SMOTE-ENN. These findings highlight the unique contribution of SMOTE-ENN in handling imbalanced data, enhancing classification model quality, and significantly impacting machine learning applications in healthcare.
Co-Authors Ikhsan, Ali Nur Maulana Baihaqi, Wiga Naufal Yogi Aptana Safitri, Arisanti Dwi Yunita, Ika Romadoni