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All Journal bit-Tech JURNAL ILMIAH PENELITIAN MAHASISWA
M. Muharrom Al Haromainy
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology

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Harmony Search Algorithm Optimization of Fuzzy C-Means for a Hybrid Filtering Movie Recommendation System Muftah Hi M Naser; Eka Prakarsa Mandyartha; M. Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3258

Abstract

In the era of increasingly abundant digital content, personalized recommendation systems play a critical role in helping users efficiently identify relevant movies. However, traditional approaches such as Collaborative Filtering (CF) and Content-Based Filtering (CBF) continue to suffer from data sparsity, cold-start limitations, and unstable clustering performance. To address these constraints, this study proposes a novel hybrid recommendation framework that integrates Harmony Search (HS) optimization with Fuzzy C-Means (FCM) clustering inside a Hybrid Filtering (HF) architecture. Using a subset of the MovieLens dataset consisting of 560 users who rated the same 37 movies, 60% of the rating values were randomly removed to simulate sparse conditions. HS is employed to optimize the initialization of FCM centroids, improving clustering stability and reducing susceptibility to local minima. The resulting clusters are then leveraged in a hybrid combination of CF and CBF to generate final predictions. Experimental results indicate that the optimal configuration (num_cluster = 4, m = 1.5, α = 0.7) achieves RMSE = 0.8974, MAE = 0.7011, Precision = 0.7515, and Recall = 0.4628. Compared to baseline models, the proposed HS–FCM–HF framework improves RMSE by 37.3% over CBF-only and maintains 7.4% better Precision than CF-only, demonstrating stronger robustness and balanced performance under high sparsity. These findings highlight the theoretical and practical value of integrating metaheuristic optimization with hybrid filtering to enhance both accuracy and generalization. Future work may incorporate multimodal features or real-time adaptive mechanisms to further strengthen personalization capability.
Implementation of the WASPAS Method for Selecting an Optimal Project Leader Erwin Erdiyanto; M. Muharrom Al Haromainy; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3373

Abstract

Selecting an optimal project leader is a critical organizational process that strongly influences project performance, coordination efficiency, and overall operational outcomes. Poor selection decisions may increase delays, inefficiencies, and reduced team productivity. To address these challenges, this study applies the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate eight project leader candidates using five leadership-related criteria: leadership ability, communication skills, professional experience, technical expertise, and problem-solving capability. All candidate scores were compiled into a decision matrix and normalized to ensure comparability across criteria. WASPAS was implemented through its dual-component structure, combining the additive Weighted Sum Model (WSM) and the multiplicative Weighted Product Model (WPM) to generate comprehensive preference values (Qi). This hybrid mechanism enables the method to capture both absolute and proportional differences in candidate competencies. The results show that WASPAS successfully ranked all candidates and identified the strongest performer, with the highest Qi value recorded at 3.00 and the lowest at 2.09, demonstrating a clear distinction in overall competency levels. The top-ranked candidate, Sintya Dwi Rachmawati, consistently scored high across all criteria, confirming the method’s capability to differentiate performance profiles effectively. These findings highlight the methodological precision of WASPAS in supporting structured leadership selection and underscore its potential to enhance fairness and analytical rigor in organizational decision-making. Overall, the study concludes that WASPAS is a reliable and practical multi-criteria decision-making technique suitable for leadership-oriented evaluations within diverse organizational contexts.
Perancangan Data Warehouse untuk Analisis Penjualan Ritel Elektronik Berbasis Business Intelligence Reno Naufal Maulidyan; M. Muharrom Al Haromainy
JURNAL ILMIAH PENELITIAN MAHASISWA Vol 4 No 3 (2026): JUNI
Publisher : Kampus Akademik Publiser

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jipm.v4i3.2604

Abstract

This study proposes a data warehouse design based on a star schema to support electronic retail sales analysis using PostgreSQL, Pentaho Data Integration, and Tableau. The objective is to transform heterogeneous transactional data into a structured analytical environment that can improve business intelligence capability. The research method includes data ingestion into a relational database, staging area construction, ETL-based cleansing and standardization, dimensional modeling, datamart development, and interactive dashboard visualization. The resulting architecture enables multidimensional analysis across time, product, region, sales channel, payment method, and customer type. The findings indicate that the proposed system improves data consistency, analytical efficiency, and decision-support capability, making it suitable as a foundation for retail performance monitoring and strategic planning.
Co-Authors Afina Lina Nurlaili Erwin Erdiyanto Mandyartha, Eka Prakarsa Muftah Hi M Naser Reno Naufal Maulidyan