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All Journal Jurnal Sains dan Teknologi Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknologi Sistem Informasi dan Aplikasi JURTEKSI Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Infotekmesin Journal of Technology and Informatics (JoTI) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Ikhsan, Ali Nur
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Infotekmesin

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Analisis Sentimen Media Sosial X Terhadap Kenaikkan PPN di Indonesia Menggunakan Algoritme Naïve Bayes dan Support Vector Machine (SVM) Ikhsan, Ali Nur; Pungkas Subarkah; Alifah Dafa Iftinani; Alif Nur Fadilah
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.v16i1.2518

Abstract

One of the ways to increase state revenue is by raising the Value-Added Tax (VAT). However, implementing a VAT hike policy often elicits both positive and negative responses from the public. With the presence of social media, people can voice their opinions about government policies, including through social media platform X. This study aims to analyze public sentiment on social media X using the Naïve Bayes and Support Vector Machine (SVM) algorithms. The research compares the highest accuracy results before and after the balancing process. The dataset comprises 2,852 rows in CSV format. The findings indicate that the SVM algorithm achieves an accuracy of 98% before balancing and 97% after balancing, while Naïve Bayes achieves an accuracy of 97% before balancing and 90% after balancing. Overall, both algorithms demonstrate good and balanced performance.
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.
Analisis Sentimen Media Sosial X Terhadap Kenaikkan PPN di Indonesia Menggunakan Algoritme Naïve Bayes dan Support Vector Machine (SVM) Ikhsan, Ali Nur; Pungkas Subarkah; Alifah Dafa Iftinani; Alif Nur Fadilah
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.v16i1.2518

Abstract

One of the ways to increase state revenue is by raising the Value-Added Tax (VAT). However, implementing a VAT hike policy often elicits both positive and negative responses from the public. With the presence of social media, people can voice their opinions about government policies, including through social media platform X. This study aims to analyze public sentiment on social media X using the Naïve Bayes and Support Vector Machine (SVM) algorithms. The research compares the highest accuracy results before and after the balancing process. The dataset comprises 2,852 rows in CSV format. The findings indicate that the SVM algorithm achieves an accuracy of 98% before balancing and 97% after balancing, while Naïve Bayes achieves an accuracy of 97% before balancing and 90% after balancing. Overall, both algorithms demonstrate good and balanced performance.
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 Adam Prayogo Kuncoro Aeni, Alfina Nur Ajeng Widiawati, Chyntia Raras Alif Nur Fadilah Alifah Dafa Iftinani Alifian , Raditya Sani Anggraeni, Epri Cahyo, Samsul Dwi Dani Arifudin Dias Ayu Budi Utami, Dias Ayu Budi Fadilah, Alif Nur Faizah, Nailatul Fatiha, Muhammad Reza Hidayat, Muslimin Iftinani, Alifah Dafa Indrawan Firdauzi Irma Darmayanti Isnaini, Khairunnisak Nur Maulana Baihaqi, Wiga Naufal Yogi Aptana Pungkas Subarkah Pungkas Subarkah Rakhmawati, Desty Rizki Wahyudi Robbani, Ayat Akras Rofiqoh, Dayana Sabaniyah, Arbangi Puput Setiawan, Ito Shafira, Lulu Siti Alvi Solikhatin Subarkah, Pungkas Suhaman, Jali Wibowo, Adlan Yuliastuti, Lulu Yunita, Ika Romadoni