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All Journal SMATIKA SENSITEK Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Data Science and Information System INOVTEK Polbeng - Seri Informatika Smatika Jurnal : STIKI Informatika Jurnal
Reflan Nuari
Universitas Teknokrat Indonesia
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Application Of K-Means Algorithm to Cluster Students' Reading Patterns in the Digital Age Yongky Permana Putra; Reflan Nuari
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/j8gz8h32

Abstract

This study aims to group students' reading patterns in the digital era using the K-Means algorithm. This algorithm divides data into clusters, such as reading duration, type of reading, reading frequency, and devices used. Data were obtained through questionnaires distributed to 224 students of SMK Negeri 4 Bandar Lampung, with 214 valid data analysed after the preprocessing stage. The selection of vocational high school students as this study was based on previous journal references that examined reading patterns in PAUD to SMA students, so special attention is paid to vocational high school students, understanding reading patterns that have different needs compared to references with other levels of education. The clustering process produced four clusters with unique characteristics, reflecting differences in reading patterns based on the type of media used, intensity, and digital devices. The results of the study showed that clusters with high digital reading intensity can be directed to utilise e-books and online learning platforms optimally, while clusters with a preference for printed books require strengthening physical reading habits through literacy activities. With a Davies-Bouldin index value of -2.224, the quality produced is proven to be very good. These findings provide guidance for educators to develop technology-based education policies and personal approaches to improving student literacy. Designing learning programs with methods and student reading patterns to support the quality of education in the digital era.
Performance Comparison Of BERT Metrics and Classical Machine Learning Models (SVM,Naive Bayes) for Sentiment Analysis Adib Ulinuha El Majid; Reflan Nuari
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wmh3rg23

Abstract

Sentiment analysis is one of the important methods in understanding public opinion from large amounts of text, such as product reviews or user comments. Many studies have shown that the BERT (BiDirectional Encoder Representations from Transformers) model has advantages over classical machine learning models such as Support Vector Machine (SVM) and Naïve Bayes. However, there are still few studies that systematically compare the performance of the two on datasets from various topics and languages, especially those with imbalanced label distributions. This study compares four BERT variants (bert-base-uncased, distilbert-base-uncased, indobert-base-uncased, and distilbert-base-indonesian) with two classical models using three datasets of IMDb 50K (English), Amazon Food Reviews (English), and Gojek App Review (Indonesian). The classical model uses the TF-IDF vectorisation method, while the BERT model is optimised through a further training process (fine-tuning) with a layer freezing technique. The evaluation is carried out using accuracy, precision, recall, and F1-score. The results show that the BERT model excels on English data, while on imbalanced Indonesian data, SVM and Naïve Bayes produce higher F1-score results. These findings indicate that the selection of the right model must be adjusted to the characteristics of the data used.
Co-Authors Adib Ulinuha El Majid Aflahah Apriliyani Alvi Suhartanto Alvi Suhartanto Hanafi Hanafi Heni Sulistiani Hery Nurmawan Izudin Ismail Juwari Juwari Kusrini Marwan Noor Fauzy Muhammad Fajrian Noor S. Samsugi Yoga Dwi Pambudi Yongky Permana Putra Yudha Chirstianto F Yulita Fatma Andriani