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All Journal Journal of Advanced Technology and Multidiscipline (JATM)
Yutika Amelia Effendi
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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User Preference-Based Movie Recommender System Using Decision Tree Method Yutika Amelia Effendi; Reizo Nararya Alinandito; Muhammad Akmal Rinaldy; Rizka Dwi Nurwicaksanti; Muhammad Daffa Tristan
Journal of Advanced Technology and Multidiscipline Vol. 5 No. 1 (2026): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v5i1.85027

Abstract

This study presents the design and implementation of a movie recommendation system aimed at improving content discovery amidst the rapid growth of the film industry. The system utilizes the Decision Tree Algorithm, a non-parametric supervised learning technique known for its hierarchical structure and interpretability, to provide personalized movie suggestions. Empirical evaluation demonstrates the Decision Tree's effectiveness, achieving a Mean Absolute Error (MAE) of 0.942, which outperforms a Collaborative Filtering baseline (MAE 1.242). This research highlights the practical utility of Decision Trees in enhancing recommendation accuracy and efficiency, contributing to transparent and effective AI systems for personalized content discovery.
Classification of Students Online Education Adaptability: Impact of Undersampling vs SMOTE on Decision Tree Performance Yutika Amelia Effendi; Muhammad Ilham; Dina Taubah
Journal of Advanced Technology and Multidiscipline Vol. 5 No. 1 (2026): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v5i1.85029

Abstract

Online education has gained unprecedented prominence since the COVID-19 pandemic, highlighting the crucial role of student adaptability in ensuring academic success. In this context, accurately predicting adaptability levels categorized (Low, Moderate, and High) has become a vital research focus. This study investigates such classification using a Decision Tree algorithm and addresses a key limitation found in previous research, which suggested the need to implement oversampling techniques to reduce class confusion, particularly between adjacent categories. A public dataset comprising 1,205 student records was used, originally published by Suzan et al. (2021). The dataset was significantly imbalanced (Moderate: 625, Low: 480, High: 100), implementing the application of two resampling strategies: random undersampling and SMOTE (Synthetic Minority Over-sampling Technique). Results show a clear performance gap between the two approaches. The undersampled model achieved an accuracy of 76.67%, while the SMOTE based model reached 95% accuracy. Improvements were particularly notable in minority class performance.
A Comparative Study of Multinomial Naive Bayes and Long Short-Term Memory (LSTM) for Sentiment Classification on the IMDB Movie Review Dataset Yutika Amelia Effendi; Achmad Arif Mahzumi; Violeta Hawariznova Willes; Yahya Bachtiar Ivansyah
Journal of Advanced Technology and Multidiscipline Vol. 5 No. 1 (2026): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v5i1.85031

Abstract

This study presents a comparative analysis of two widely-used sentiment classification models—Multinomial Naive Bayes (MNB) and Long Short-Term Memory (LSTM)—using the IMDB movie review dataset. The research is centered on binary sentiment classification, identifying whether a movie review expresses a positive or negative sentiment. The preprocessing pipeline involves lowercasing, tokenization, removal of stopwords and special characters, and stemming (applied only in the LSTM pipeline). The MNB model employs a Bag-of-Words approach using CountVectorizer, while the LSTM model uses an embedding layer followed by a sequence-based deep learning architecture. Performance is evaluated using accuracy, precision, recall, and F1-score on a test set of 25,000 reviews. The Naive Bayes model achieved an accuracy of 85.93%, while the LSTM model outperformed it with an accuracy of 90%. Further tests on new, handcrafted reviews showed that the LSTM model exhibited higher confidence in predictions, especially in clearly polarized reviews. These findings highlight that while Naive Bayes is computationally efficient and performs adequately, LSTM offers superior accuracy and robustness in understanding semantic patterns in text. This research contributes to the development of more reliable AI-based sentiment analysis systems and offers insights for practitioners deciding between traditional machine learning and deep learning approaches in natural language processing tasks.
Co-Authors Achmad Arif Mahzumi Dina Taubah Muhammad Akmal Rinaldy Muhammad Daffa Tristan Muhammad Ilham Reizo Nararya Alinandito Rizka Dwi Nurwicaksanti Violeta Hawariznova Willes Yahya Bachtiar Ivansyah