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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Informatics and Data Science
Parinduri, Syawaluddin Kadafi
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Penerapan Metode ORESTE dalam Menentukan Paket Internet yang Ideal di Kalangan Mahasiswa (Kasus Siantar-Simalungun) Khadafi, Farhan; Wiratama, Firman Dwi; Aryaputra, Safa; Parinduri, Syawaluddin Kadafi
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.7607

Abstract

Choosing the ideal internet package is a challenge for students in the Siantar-Simalungun region. Stable and affordable internet access is essential to support various academic activities, ranging from literature searches and assignment collection to online learning activities. When choosing an internet package, students generally consider several key factors such as price, network speed, signal strength, bonus quotas, and user satisfaction levels. This study uses the ORESTE (Organization, Rangement Et Synthèse De Données Relationnelles Extérieures) method as a multi-criteria decision-making approach to determine the most ideal internet package for students' needs. The research data was collected through a questionnaire distributed to more than 1,217 students from various educational institutions in the region. Through the calculation and analysis process using the ORESTE method, it was found that Telkomsel was the most ideal internet package choice with the lowest preference value of 1.774, which indicates the highest level of preference compared to other alternatives. The results of this study are expected to serve as a reference in helping students choose the internet package that best suits their needs, thereby increasing the efficiency of internet spending while supporting productivity and smooth academic activities with optimal internet access.
Classification Model Optimization using Grid Search and Random Search in Machine Learning Algorithms Parinduri, Syawaluddin Kadafi; Alkhairi, Putrama; Irawan, Irawan; Qurniawan, Hendry
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.136

Abstract

The performance of a machine learning model is highly dependent on the selection and tuning of appropriate hyperparameters. The main problem in this study is how to improve the accuracy and stability of a classification model without sacrificing computational time efficiency, especially in the case of kidney disease classification that requires accurate and fast prediction results. This study aims to optimize the classification model by applying two hyperparameter search methods, namely Grid Search and Random Search, to the Random Forest algorithm. The kidney disease dataset is used as a case study with preprocessing processes including data cleaning, missing value imputation, categorical variable encoding, and normalization. Each model is tested using accuracy, precision, recall, and F1-Score metrics. The results show that the Grid Search_RF model produces the highest performance with perfect accuracy, precision, recall, and F1-Score values (1.0000), while Random Search_RF provides results close to (accuracy 0.9875 and F1-Score 0.9900) with more efficient training time. Meanwhile, the standard Random Forest without tuning still shows competitive performance (accuracy 0.9917 and F1-Score 0.9930). Based on these results, it can be concluded that hyperparameter optimization, using both Grid Search and Random Search, can significantly improve the performance of the classification model, with Random Search being the most efficient method for practical implementation in machine learning-based disease detection systems.
Co-Authors Alkhairi, Putrama Aryaputra, Safa Irawan Irawan Khadafi, Farhan Qurniawan, Hendry Wiratama, Firman Dwi