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Journal : Journal of Information Systems and Informatics

Predictive Analytics on Shopee for Optimizing Product Demand Prediction through K-Means Clustering and KNN Algorithm Fusion Febima, Mesi; Magdalena, Lena
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.720

Abstract

This study focuses on predictive analysis in the context of the Shopee market, aiming to optimize product demand forecasting through the combination of K-Means clustering and KNN algorithms. With the exponential growth of e-commerce platforms like Shopee, accurately predicting product demand is becoming increasingly important for inventory management and marketing strategies. In this research, we propose a novel approach that combines the strengths of K-Means clustering and the KNN algorithm to improve demand prediction accuracy. By leveraging K-Means clustering to group similar products into two clusters, namely “Low Interest” with 64 data points and “High Interest” with 25 data points, we then apply the KNN algorithm to predict demand within each cluster. The KNN algorithm produces two classifications: Low Sales and High Sales. Based on tests using the KNN algorithm with k values of 3, 5, and 7, it was demonstrated that the product “Soraya Bedsheet Cotton Gold Motif Dallas Ask Grey Tua” can be predicted to fall under “High Sales.” The sales prediction accuracy rate for Shopee marketplace products is 96%. The implications of these findings indicate that the combination of K-Means and KNN algorithms can improve the accuracy of product demand predictions and optimize inventory and marketing strategies.
Actor-Critic Reinforcement Learning for Personalized STEM Learning Path Optimization Hatta, Muhammad; Magdalena, Lena; Putra, Dwi Pasha Anggara; Runa, Yohanes Michael Fouk; Irfansyah, Ananda; Valentino, Fernando
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1270

Abstract

This study addresses the critical need for adaptive learning in non-formal education settings, particularly Community Learning Centres (PKBM) in Indonesia, where student heterogeneity and limited resources challenge conventional teaching methods. We developed a personalized learning path optimization model using Actor-Critic Reinforcement Learning (RL) to enhance STEM competency development. The novel framework integrates cognitive, affective, and personality features to dynamically adjust material difficulty based on real-time analysis of student cognitive states (quiz performance, completion rate) and affective conditions (emotional level), moving beyond static predictive approaches. Experimental results on a synthetic dataset demonstrate that the Actor-Critic agent achieves statistically significant higher rewards (-2.92 vs -3.01, p<0.05) and greater output stability compared to a random baseline. Although the absolute reward difference is modest, it reflects more consistent adaptive policy performance, despite limited effect size (Cohen's d=0.0317). Feature importance analysis confirms that quiz_score and emotion_level are the dominant factors influencing adaptive recommendations, while personality traits show negligible impact. The framework offers a viable pathway for scalable, personalized learning in resource-constrained environments. Future work should validate the model with real-world student data and refine reward functions to strengthen practical impact.
Co-Authors Agus Sevtiana Ahmad Gunawan Akbari, Safitri Ali Reza Immamifar, Muhammad Ananda, Fikri Anggara Putra, Dwi Pasha Aprillia, Ariesya Asri Aulia, Siti Nur Baihaqi, Muhammad Faqih Christina, Stefanny Chritviona Parera, Shalom Daphne, Gabrielle Apta Eustacia Fahrudin, Rifki Fahrudin, Rifqi Fajriannoor Fanani, Fajriannoor Febima, Mesi Gitama, Gytha Nurhana Dhea Praadha Habib Bahtiar, Usman Ika Kartika Ilyasa, Reza Irfansyah, Ananda Ivanov, Nikita Jati Kusuma, Fathurrochman Jayawarsa, A.A. Ketut Julianingsih, Dwi Kanivia, Aan Kartika, Viar Dwi Kitriawati, Kitriawati Kurniawan, Satria Wahyu Kusnadi Leni Agustin, Leni Lia Dahlia, Lia Loka, Diah Pita Mansyur, Asep Abdul Maratis, Jerry Marsani Asfi Martinez, Daniel Mehta, Silpha Melly Amalia Mita, Shella Muhammad Hatta Muhammad Hatta Muhammad Kahfi, Muhammad Mulyasari, Hany Mulyasari, Hany Nakeisha Wahyudi, Nayla Nas, Chairun Nuche, Asher Nur'Faradila, Dellatia Ayu Nurhajijah, Sitta Nurpatimah, Suci Oktaviani, Adelia Oktaviani, Priti Parameswari Pramuwardhani, Andita Sri Parman, Suhadi Petrus Sokibi, Petrus Putra, Dwi Pasha Anggara Putri, Tiara Eka Rahadi, Dwi Meldiansyah Rahmanhadi, Dimas Ramadhan, Abdan Syakur Ramirez, Santiago Renaldi, Refan Rifqi Khosyi, Muhammad Rinaldy, Rio Runa, Yohanes Michael Fouk Sahputra, Illal Dwi Saiful Hadi, Muhammad Savitri, Agnes Novalita Septiani, Willy Eka Setiawan, Sandy Solihah, Yuni Awalaturrohmah Solikhah, Mar'atus Solikhah, Mar’atus Sri Watini Susanto, Ivan Sutrisna Suwandi Suwandi Tiomas Simorangkir, Naomi Trivena, Stephani Turini, Turini Unang Solihin Valentino, Fernando Wicaksono, Freddy Wijaya, Abi surya Wijaya, Cintya Fransisca Wijaya, Samuel Winurcahyono, Alexander Wirjawan, Tri Wahyu