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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JSiI (Jurnal Sistem Informasi) Jurnal Telematika Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika Generation Journal Abdimasku : Jurnal Pengabdian Masyarakat Jurnal UNITEK Jurnal Algoritma Jurnal Masyarakat Madani Indonesia Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) AL-UMRON: Jurnal Pengabdian kepada Masyarakat The Indonesian Journal of Computer Science
Sanjaya, Ucta Pradema
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Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Binary Classification for Predicting the Investment Trends of The Younger Generation Based on Machine Learning Oktana, Weka Brilliant Jaya; Sanjaya, Ucta Pradema
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.v9i6.11549

Abstract

This computational study examines investment behavior patterns among a specialized cohort of 115 final year and thesis writing university students, implementing sophisticated feature engineering to transform categorical survey responses into quantifiable financial metrics. The research methodology leverages this unique dataset where respondents' advanced academic standing provides particularly relevant insights into near-term investment decisions. Experimental outcomes reveal distinct algorithmic performance patterns: Random Forest achieved 69.6% accuracy in multi-class classification with weighted averages of 0.662 precision, 0.696 recall, and 0.678 F1-score, while Logistic Regression demonstrated superior binary classification capability with 82.6% accuracy, supported by 0.818 precision, 0.826 recall, and 0.814 F1-score (weighted averages). The hybrid architecture integrating machine learning with business rules achieved peak performance of 85.2% accuracy, successfully balancing predictive power with operational interpretability. These findings underscore how strategically engineered features combined with a carefully selected respondent pool can effectively decode complex financial behaviors, providing financial institutions with actionable frameworks for developing targeted investment solutions for the graduate student demographic while advancing methodological approaches for specialized survey data in fintech applications.
Opinion Mining of Pedometer Application Reviews on Google Play Store Using Fine-Tuned IndoBERT-Base Primono, Anggi; Sanjaya, Ucta Pradema
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.v10i1.12184

Abstract

User reviews on the Google Play Store provide valuable insights into user satisfaction and application performance. However, manual analysis of these reviews is inefficient due to large data volume and the informal characteristics of the Indonesian language. This study proposes an opinion mining approach using a fine-tuned IndoBERT-Base model to classify user sentiments into three classes: positive, neutral, and negative. A total of 1,665 reviews of a Pedometer application were collected, with 1,636 reviews retained after preprocessing. The dataset was divided into training, validation, and test sets using stratified sampling to preserve class distribution. Experimental results show that the proposed model achieves an accuracy of 94.51% and a weighted F1-score of 0.93 on the test set. Despite strong overall performance, the results indicate that class imbalance significantly affects the classification of neutral and negative sentiments. Error analysis reveals that ambiguous expressions and limited samples in minority classes remain challenging for the model. This study demonstrates that fine-tuned IndoBERT-Base is effective for sentiment analysis of Indonesian mobile application reviews while highlighting the importance of addressing imbalanced data in opinion mining tasks.
Co-Authors Abdul Rohman Ade Pratama Ade Pratama Ahmad Ali Ais, Salma Rihadatul Buulolo, Ginna Anggriani Damayanti, Hevi Alvina Diah Anggraini, Diah Dirgantoro, Guruh Putro Ela Suryani Fauziyah, Dewi Nur Fetrika Anggraini, Fetrika Heni Setyowati Hesti Yunitiara Rizqi Hevi Alvina Damayanti Ilhamy, Rifqi Syahrul Indraningtyas, Yeni Irsyada, Rahmat Ita Aristia Sa'ida Iwan Setiawan Wibisono Kustiyono Kustiyono Nita Cahyani Noor Laila Ramadhani Oktana, Weka Brilliant Jaya Prastya, Ifnu Wisma Dwi Pribadi , Teguh Primono, Anggi Priyanto Priyanto Saputra, Agus Bima Satria Avianda Nurcahyo Sri Mujiyono Sundari, Pipit Wibisono, Bagas Widagdo, Teguh Harso Zakki Alawi Zayn, Afta Ramadhan