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All Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) INTI Nusa Mandiri Amalee: Indonesian Journal of Community Research & Engagement Jurnal Sistem Komputer dan Informatika (JSON) International Journal of Engineering, Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Decode: Jurnal Pendidikan Teknologi Informasi Indonesian Community Journal Jurnal Pengabdian, Riset, Kreativitas, Inovasi, dan Teknologi Tepat Guna (Parikesit) Jurnal Teras Pengabdian Masyarakat Scientific: Journal of Computer Science and Informatics Karsa Bhakti : Journal of Community Service Indonesian Applied Research Computing and Informatics
Afril Efan Pajri
Universitas Nahdatul Ulama Sunan Giri
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Journal : indonesian applied research computing and informatics

 1 
Development of Personalized Recommendation System for Online Educational Content Based on Machine Learning Dwi Remawati; Khairunnisa; Afril efan Pajri; Kumaratih Sandradewi; Sri Hariyati Fitriasih
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of online educational platforms has increased the demand for intelligent recommendation systems that can personalize learning content to match individual learner needs. However, traditional methods such as Content-Based Filtering (CBF) and Collaborative Filtering (CF) often struggle with issues like data sparsity, limited adaptability, and cold-start problems. This study aims to develop a personalized recommendation system for online educational content by integrating Singular Value Decomposition (SVD) with an adaptive feedback loop to improve recommendation relevance and learner engagement. The proposed machine learning-based method captures latent user-item interactions and dynamically updates recommendations based on real-time user feedback. Experimental evaluation using a dataset of simulated learner interactions demonstrates that the proposed model significantly outperforms baseline methods, achieving higher scores in Precision (0.57), Recall (0.53), F1-Score (0.55), Mean Reciprocal Rank (MRR: 0.52), and Engagement Rate (72.1%). These results suggest that combining matrix factorization with adaptive learning can substantially enhance the performance of educational recommender systems, leading to more accurate, timely, and engaging content delivery.
Impact of Data Normalization on K-Nearest Neighbor Classification Performance: A Case Study on Date Fruit Dataset Muhammad Jauhar Vikri; Afril Efan Pajri; Putri Liana
Indonesian Applied Research Computing and Informatics Vol. 1 No. 2: December (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64479/iarci.v1i2.61

Abstract

Data normalization is a crucial preprocessing step for distance-based classification algorithms such as K-Nearest Neighbor (KNN), as differences in feature scales can significantly affect distance calculations and classification accuracy. This study investigates the impact of data normalization on KNN classification performance using the Date Fruit Dataset as a case study. Three preprocessing scenarios are evaluated: raw data without normalization, Min–Max normalization, and Z-score standardization. In addition, the performance of standard KNN is compared with distance-weighted KNN to assess the contribution of distance weighting under different preprocessing conditions. The experiments are conducted using stratified 10-fold cross-validation, and model performance is evaluated using accuracy and standard deviation. Statistical significance of performance differences is examined using paired t-test, and sensitivity analysis is performed to analyze the effect of varying the number of nearest neighbors. The results show that data normalization leads to a substantial improvement in classification performance compared to raw data. Z-score standardization achieves the highest and most stable accuracy, followed by Min–Max normalization. Distance-weighted KNN consistently produces slightly higher accuracy than standard KNN; however, the improvement is not statistically significant after normalization. Sensitivity analysis indicates that normalized data results in a wider and more stable range of optimal k values. These findings demonstrate that data normalization plays a more dominant role than distance weighting in improving KNN performance. The study provides empirical evidence that proper preprocessing is essential for reliable KNN-based classification and establishes a robust baseline for further enhancements such as feature weighting and metaheuristic optimization.
Co-Authors Alif Yuanita Kartini Alif Zahwa Naisya Ariyanto Amelia Faza Aprillia Dwi Ardianti Arum , Arum Fatmawati Arum Fatmawati Arum Denny Nurdiansyah Diah ayu Istiqomah Diah Fitriani Dwi Remawati Eka Fadlilatus Saadah Eka Fadlilatus Saadah Fatichatur Rochmaniyah Fatichatur Rochmaniyah Fikram, Muhammad Hilmy Aliriad Ifnu Wisma Dwi Prastya Ita Aristia Sa'ida Jauhar Vikri, Muhammad Jauhara Rana Budiani, Jauhara Rana Khairunnisa Kholifatus Sholihah Kumaratih Sandradewi Lindya Rossita Handoko M. Iqbal Tawakkal MARATUS SHOLIHAH, SITI Maruf, Anang Meliysa Pasa Bagna Aprilia Said Muhammad Sholihin Muhammad Sholihin Muhammad Syaiful Anshori Mula Agung Barata Nanda Sahita Pepbelia Veronyca Putri Liana Sabar, Rohmat Abidin Sahri Sahri Sahri Sahri Sahri Sahri Silviana Agustin Sirozul Huda Siti Azizah Aini Siti Shoimah Sri Hariyati Fitriasih Suci Mulyani Sutriawan Taufik Azhary Tawakkal, M. Iqbal Umar Farid Al Faqihi Wella Vernanda Meilasaroh