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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Surya Masyarakat JSiI (Jurnal Sistem Informasi) ScientiCO : Computer Science and Informatics Journal Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurnal Surya Masyarakat Djtechno: Jurnal Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Riptek SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal INFOTEL Tematik Jurnal Informatika: Jurnal Pengembangan IT Signal and Image Processing Letters International Journal of Artificial Intelligence and Science Jurnal DIMASTIK
Aria Hendrawan, Aria
Universitas Semarang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Comparative Sentiment Analysis on Mobile JKN Application Using Logistic Regression with SMOTE Based Statistical Feature Selection Awaliyah, Rafika Farkhul; Hendrawan, Aria
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10520

Abstract

This study investigates public sentiment on the Mobile JKN application using Logistic Regression enhanced with SMOTE-based statistical feature selection. Unlike prior works that relied solely on conventional feature combinations such as TF-IDF or Word2Vec, this research performs a comparative evaluation of three statistical feature selection techniques: Recursive Feature Elimination (RFE), Chi-Square, and Mutual Information, under both TF-IDF and Word2Vec representations in a low-resource Indonesian language setting. The dataset consists of 2,382 user reviews from the Google Play Store, balanced using SMOTE to mitigate class imbalance. The best configuration, TF-IDF combined with Mutual Information, achieved an accuracy of 73.38% and an F1-score of 50%, indicating a moderate yet consistent performance. A confusion matrix-based error analysis revealed that most misclassifications occurred between neutral and negative classes due to semantic overlap. The relatively low F1-score highlights challenges in sentiment separability, while the superior performance of Mutual Information demonstrates its ability to capture discriminative linguistic features. The superior performance of Mutual Information is attributed to its ability to capture non-linear dependencies between features and sentiment labels, yielding richer discriminative information compared to Chi-Square or RFE. This research establishes a comparative methodological framework that integrates feature selection and data balancing techniques, providing interpretable sentiment classification insights for under-resourced language settings.
Analysis of the Best Social Media Platforms for Promotion Using Machine Learning and RFE Feature Selection: A Comparative Study of Gradient Boosting, XGBoost, CNN, and SVR Putri, Maulina; Hendrawan, Aria
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12049

Abstract

This study aims to identify the most effective social media platforms for digital marketing. The use of social media for promotion continues to grow, yet many businesses still struggle to determine which platforms have the greatest impact. Therefore, this study compares the performance of various machine learning platforms to predict the best platform. The algorithms used are Gradient Boosting Regressor, XGBoost Regressor, Convolutional Neural Network (CNN), and Support Vector Regression (SVR) to estimate digital conversion potential based on user reviews, ad reach, and content trend patterns. A Knowledge Discovery in Databases (KDD) workflow is used to identify the most important key factors. This process includes data preprocessing, TF-IDF feature extraction, sentiment analysis, feature engineering, and feature elimination (RFE). The results showed that the CNN algorithm excelled in prediction, with the highest R² score of 0.74 and the lowest RMSE of 14.78. CNN predictions showed YouTube topping the list in terms of conversion potential, followed by Facebook and TikTok. These results highlight the higher promotional effectiveness of video-based platforms and the importance of machine learning in digital marketing decision-making. However, this study is limited by its reliance on static user review and ad reach data, which may not fully capture the dynamic changes of social media platforms.
Co-Authors Adiuntoro, Alwan Agho, Davit Cany Agusta Praba Ristadi Pinem Agusta Praba Ristadi Pinem April Firman Daru Awaliyah, Rafika Farkhul Basworo Ardi Pramono Cholil, Saifur Rohman Christine Dewi Christioko, B.Very Dimas Dwi Budiarjo Febrian Wahyu Christanto Henny Indriyawati Khairunnisa, Hasna Rizqia Khoiriyah, Rofiatul Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Lenny Margaretta Huizen Luqman Hakim Maulana, Charis Maulida, Innayah Nabilah Putri Nur Wakhidah Oky Dwi Nurhayati Pinem, Agusta Praba Ristadi Pradana, Muhamamd Surya Jati Prind Triajeng Pungkasanti, Prind Triajeng Pulung Nurtantio Andono Putra, Abhinaya Putri, Maulina Rahmat Gernowo ramadhani, jovita wayan Rohman Cholil, Saifur Saifur Rohman Cholil Saputri, Anita Nur Widdia Sekar Sari, Mega Setiaji, Aria Siti Asmiatun, Siti Soiful Hadi Susanto Susanto Titin Winarti Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit