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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Munirul Ula
Malikussaleh University
Computer Science & IT Engineering

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PENENTUAN LOKASI KAFE UNTUK MAHASISWA TEKNIK UNIVERSITAS MALIKUSSALEH MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING: DETERMINATION OF CAFE LOCATION FOR ENGINEERING STUDENTS OF MALIKUSSALEH UNIVERSITY USING THE SIMPLE ADDITIVE WEIGHTING METHOD Ahmad Fajrul Amin; Munirul Ula; Fajriana Fajriana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6453

Abstract

Engineering students at Universitas Malikussaleh require a comfortable, affordable, and academically supportive space for gathering, discussion, and study. However, financial limitationsparticularly among recipients of the Kartu Indonesia Pintar (KIP) program highlight the importance of location and pricing in café selection. This study aims to determine the optimal location for a student café using the Simple Additive Weighting (SAW) method as a Decision Support System. SAW is chosen for its effectiveness in processing multi-criteria decisions by applying specific weights to each criterion. The research was conducted within the Faculty of Engineering at Universitas Malikussaleh, using primary data collected through interviews and direct observation of potential café locations. Evaluation criteria include menu prices, comfort, service quality, distance to campus, and supporting facilities. The SAW method was applied to rank the alternatives, with the highest score representing the most suitable location for students' needs. A web-based application was developed using PHP and MySQL to implement the system, and functionality was validated using both black-box and white-box testing methods. This study offers a data-driven solution for optimizing campus facilities and provides a reference model for other institutions seeking to develop inclusive and strategic student service locations.
SISTEM REKOMENDASI MAKANAN DIET DENGAN PENDEKATAN HYBRID CONTENT-BASED DAN COLLABORATIVE FILTERING Muhammad Azhari Desky; Defry Hamdhana; Munirul Ula
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6647

Abstract

Personalizing dietary plans aligned with specific medical restrictions poses a complex challenge in healthy diet planning. This study aims to develop a diet food recommendation system using a Hybrid approach that integrates Content-Based Filtering and Collaborative Filtering. The system is designed to provide recommendations that are not only personalized to user preferences but also safe regarding medical constraints such as diabetes, hypertension, obesity, and allergies. The system architecture applies a weighted average strategy with a priority weighting scheme of 0.6 for Content-Based and 0.4 for Collaborative. Performance evaluation was conducted using Leave-One-Out Cross Validation on a dataset comprising 51 users, 507 food items, and 265 interaction ratings. Test results demonstrate that the Hybrid method yields more robust performance compared to single methods, achieving a Precision of 0.7418 and Recall of 0.7500. Significantly, this approach improved Recall by 8.0% compared to pure Collaborative Filtering, proving its effectiveness in mitigating data sparsity and cold-start problems. It can be concluded that the system successfully provides relevant, promising, adaptive, and clinically safe food recommendations as a diet decision support tool, although further development in data volume and variety is required for optimal results.
KLASIFIKASI PENYAKIT MATA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MODIFIED BALANCED RANDOM FOREST: CLASSIFICATION OF EYE DISEASES USING THE SUPPORT VECTOR MACHINE AND MODIFIED BALANCED RANDOM FOREST METHODS Muhammad Arief; Munirul Ula; Kurniawati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6655

Abstract

Eye diseases such as refractive, implant, cataract, and other disorders can reduce the quality of life of sufferers if not diagnosed and treated properly. Limited access to medical examinations is one of the obstacles in early treatment, so an accurate and efficient technology-based decision support system is needed. This study aims to compare the performance of the Linear Support Vector Machine (SVM) and Modified Balanced Random Forest (MBRF) algorithms applied to handle class imbalance in classifying four categories of eye diseases using medical records of PIM (Prima Inti Medika) Hospital patients consisting of 2,805 data with 30 features. The research process includes preprocessing, model training, and performance evaluation using 5-Fold Cross Validation as well as testing on a test set of 20% of the data. Evaluation metrics used include accuracy, precision, recall, and F1-score. The test results show that MBRF excels in all metrics, with an average F1-Macro cross-validation of 85.67% (3.33% higher than SVM) and a test set accuracy of 87.32% (3.21% higher). Analysis per category shows an average F1 improvement of 3.6%, with the highest improvement in the Refractive class (+5.0%). MBRF also has a 21% faster training time than Linear SVM and provides clinically relevant feature importance information. Based on these results, MBRF is recommended as the main model in a decision support system for diagnosing eye diseases on a similar dataset.  
Co-Authors Ahmad Fajrul Amin Defry Hamdhana Fajriana, Fajriana Kurniawati Muhammad Arief Muhammad Azhari Desky