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Journal of Science and Technology: Alpha
Published by Lembaga Publikasi Ilmiah Nusantara
ISSN : -     EISSN : 30894298     DOI : https://doi.org/10.70716/alpha
Core Subject : Economy, Science, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences
ALPHA: Journal of Science and Technology is a peer-review journal that could be access to the public, published by Lembaga Publikasi Ilmiah Nusantara with registered number ISSN 3089-4298. ALPHA provides a platform for researchers, academics, professionals, practitioners and students to embed and share knowledge in the form of empirical and theoretical research papers, case studies, literature reviews and book reviews related to science and technology research. ALPHA welcomes and recognizes high quality theoretical and empirical research papers, case studies, review papers, literature reviews, book reviews, conceptual frameworks, analysis and simulation models, technical notes related to research from researchers, academics, professionals, practitioners, and students.
Arjuna Subject : Umum - Umum
Articles 35 Documents
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Analisis Kinerja Sistem Rekomendasi Berbasis Collaborative Filtering Menggunakan Metode Cosine Similarity pada Platform Pembelajaran Digital Ahmad Rayyan Fikri; Maria Clara Devina; Yusuf Malik Arsyad
Journal of Science and Technology: Alpha Vol. 2 No. 3 (2026): Journal of Science and Technology: Alpha, July 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i3.748

Abstract

This study aims to analyze the performance of a recommendation system based on collaborative filtering using the cosine similarity method in a digital learning platform. The main issue addressed is the low effectiveness of users in discovering relevant learning materials due to the large number of available courses, modules, videos, and learning resources. This study employed a quantitative approach with a computational experimental design. The dataset was organized into a user-item interaction matrix consisting of 1,200 users, 320 learning items, and 18,742 valid interactions. The data were divided into training and testing sets using an 80:20 ratio. The evaluated models included user-based collaborative filtering, item-based collaborative filtering, and a popularity-based baseline model. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, Recall, and F1-score. The results indicate that item-based collaborative filtering using cosine similarity achieved the best performance, with an MAE of 0.612 and an RMSE of 0.824. In the Top-10 recommendation evaluation, the model obtained a Precision of 0.364, a Recall of 0.291, and an F1-score of 0.323. These findings suggest that similarity among learning items is more stable than similarity among users. This study concludes that cosine similarity is a suitable baseline method for developing digital learning recommendation systems. However, further improvements are required to address challenges related to data sparsity, the cold-start problem, and evolving user preferences.
Analisis Sentimen Ulasan Pengguna Aplikasi Pembelajaran Menggunakan Metode TF-IDF dan Support Vector Machine: Sebuah Kajian Literatur Rian Aditia; Onis Alamsyah; Abiel Mulya Cipta
Journal of Science and Technology: Alpha Vol. 2 No. 3 (2026): Journal of Science and Technology: Alpha, July 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i3.753

Abstract

The rapid development of digital learning applications has significantly expanded public access to technology-based educational services. As the number of users continues to grow, user reviews published on application distribution platforms have become an important source of information for evaluating service quality and user satisfaction. Sentiment analysis has emerged as one of the most widely adopted approaches for automatically identifying user opinions through text mining and machine learning techniques. This study aims to review the application of the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Support Vector Machine (SVM) algorithm for sentiment analysis of user reviews on educational applications based on previous studies. The research employed a literature review approach by examining scientific articles published between 2020 and 2025 and indexed in Google Scholar, IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. The analysis focused on comparing the methods, text preprocessing techniques, feature extraction approaches, and classification performance reported in previous studies. The review indicates that the combination of TF-IDF and SVM is among the most widely used approaches for sentiment analysis due to its effective feature representation and stable classification performance in text-based datasets. Furthermore, the effectiveness of the classification process is influenced by text preprocessing, dataset characteristics, and model parameter selection. This study is expected to serve as a valuable reference for researchers and application developers in selecting appropriate sentiment analysis methods for digital learning applications.
Analisis Tingkat Kepuasan Pengguna terhadap Sistem E-Learning Berbasis Mobile dengan Pendekatan Technology Acceptance Model Zaydan Arka Mahendra; Keira Safira Nadhira; Elvano Rafiq Pradipta
Journal of Science and Technology: Alpha Vol. 2 No. 3 (2026): Journal of Science and Technology: Alpha, July 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i3.759

Abstract

This study aims to analyze user satisfaction with a mobile-based e-learning system using the Technology Acceptance Model approach. The research employed a quantitative approach with an explanatory survey design. The respondents were active users of a mobile-based e-learning system in a higher education environment. Data were collected using a closed-ended questionnaire based on a five-point Likert scale measuring six main variables: perceived ease of use, perceived usefulness, attitude toward using, behavioral intention, actual use, and user satisfaction. A total of 186 valid responses were analyzed using descriptive statistics, validity testing, reliability testing, and multiple linear regression. The results show that user satisfaction was in the high category, with a mean score of 4.01. Perceived usefulness obtained the highest mean score of 4.18, followed by perceived ease of use at 4.12, behavioral intention at 4.05, attitude toward using at 3.96, and actual use at 3.88. The regression results indicate that all Technology Acceptance Model variables significantly influenced user satisfaction, with an R-square value of 0.672. Perceived usefulness was identified as the most dominant factor affecting user satisfaction. These findings indicate that the success of a mobile-based e-learning system is not only determined by the availability of the application, but also by perceived benefits, ease of use, positive attitudes, continuance intention, and actual usage experience. This study provides practical implications for educational institutions to improve feature quality, access speed, navigation, learning content quality, and technical support for mobile e-learning systems.
Perbandingan Kinerja SVM dan Naive Bayes dalam Klasifikasi Data Evaluasi Pembelajaran Muhammad Rizwan Hakimi; Nur Aulia Ramadhani; Daniel Pratama Wijaya
Journal of Science and Technology: Alpha Vol. 2 No. 3 (2026): Journal of Science and Technology: Alpha, July 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i3.760

Abstract

The application of machine learning in educational data analysis has become increasingly important in improving learning evaluation systems. Classification methods are widely used to identify student performance patterns and support academic decision-making. Among the most commonly applied algorithms are Support Vector Machine (SVM) and Naive Bayes, both of which have shown competitive performance in various classification tasks. However, their effectiveness in learning evaluation data classification still requires deeper empirical investigation. This study aims to compare the performance of SVM and Naive Bayes in classifying learning evaluation data based on accuracy, precision, recall, and F1-score. The study used a quantitative experimental approach with a dataset of 1,250 student learning evaluation records consisting of attendance, assignments, participation, midterm, and final examination scores. Data preprocessing included cleaning, normalization, and transformation before model training. The dataset was divided into 80% training data and 20% testing data, with 10-fold cross-validation for validation. The results indicate that SVM achieved an accuracy of 89.6%, precision of 88.9%, recall of 90.2%, and F1-score of 89.5%, outperforming Naive Bayes which obtained 84.3%, 83.7%, 85.1%, and 84.4% respectively. The findings confirm that SVM provides better performance for complex educational datasets and can be recommended for data-driven learning evaluation systems.
Prediksi Nilai Akademik Mahasiswa Menggunakan Regresi Linear Berbasis Data Historis Perkuliahan Zhafran Kenzie; Melvina Grace Aurelia; Rayyan Farrel Mahendra
Journal of Science and Technology: Alpha Vol. 2 No. 3 (2026): Journal of Science and Technology: Alpha, July 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i3.761

Abstract

This study aims to predict students’ academic scores using linear regression based on historical lecture data. The main problem addressed in this research is how academic data recorded during the learning process can be utilized to estimate students’ final scores in a more objective and measurable manner. This study employed a quantitative approach with a predictive research design. The analyzed data included assignment scores, quiz scores, midterm examination scores, attendance percentage, Learning Management System access, forum participation, late assignment submissions, and students’ final scores. A total of 180 valid student records were analyzed through data cleaning, descriptive analysis, correlation testing, multiple linear regression modeling, and model evaluation using R-square, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error. The results show that the multiple linear regression model has strong predictive capability, with an R-square value of 0.784. The midterm examination score was identified as the most dominant predictor, followed by assignment scores, quiz scores, late assignment submissions, attendance, and LMS access. Forum participation showed a positive relationship but was not statistically significant. The evaluation results indicate that the model produced a relatively low prediction error, making it applicable as a basis for an academic early warning system. This study confirms that historical lecture data can be strategically used to support academic decision-making, monitor students at academic risk, and improve the quality of learning management in higher education

Page 4 of 4 | Total Record : 35

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