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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Metakognisi : Jurnal Kajian Pendidikan
Muhammad Al Husaini
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Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Integrating Interdisciplinary Islamic Studies With Al-Qur'an Based Project Based Learning: Exploring Concepts And Theories In The Context Of Integrated Islamic Schools Muhammad Al Husaini; Tarmizi Tahir; Zulkarnaen Ali Siddik; Jumadi; M. Nasor
Metakognisi Vol 5 No 2 (2023): Metakognisi
Publisher : Sekolah Tinggi Keguruan dan Ilmu Pendidikan Babunnajah Pandeglang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57121/meta.v3i1.21

Abstract

This study aims to explore the integration of Interdisciplinary Islamic Studies with Al-Qur'an-based Project Based Learning (PBL) in education and examine the relationship between relevant concepts and theories. Through an in-depth literature review, this study investigates the core concepts of this approach and how it can be integrated into educational practices within Integrated Islamic Schools.
Price Trend Prediction and Product Recommendation Using Machine Learning Approach Fedro Rizkyana Padila; Siti Yuliyanti; Muhammad Al Husaini
Telematika Vol 23 No 1 (2026): Edisi Februari 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v23i1.14704

Abstract

Purpose: This study aims to develop an accurate and relevant machine learning-based model for price trend prediction and product recommendation using retail sales data, in order to support operational efficiency and marketing strategies. Design/methodology/approach: The study utilizes sales data from two supermarkets spanning the years 2021– 2024. Five machine learning algorithms are compared, namely Random Forest, XGBoost, Gradient Boosting, Decision Tree, and Linear Regression, with evaluation metrics including R², MAPE, and RMSE. The best- performing model is then optimized using feature importance and feature selection methods. For the recommendation system, a Content-Based Filtering approach is employed based on similarity in product names and categories. Findings/resultWith an R2 of 80%, MAPE of 25%, and RMSE of 1.07 at the start, the Random Forest model performed the best. Following feature optimization, it greatly improved, reaching R2 of 92%, MAPE of 13%, andRMSE of 0.84. The Content-Based Filtering recommendation system successfully provided relevant suggestions, with the highest cosine similarity reaching 0.4268. Originality/value/state of the art: This study is grounded in and extends the findings of previous research.
Co-Authors Fedro Rizkyana Padila Jumadi M. Nasor Siti Yuliyanti, Siti Tarmizi Tahir Zulkarnaen Ali Siddik