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All Journal Jurnal Ilmiah Wahana Pendidikan JITCoS : Journal of Information Technology and Computer System
Muhammad Farhan
Universitas Islam Negeri Sumatera Utara
Religion Computer Science & IT Control & Systems Engineering Education Social Sciences Other

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Eksistensi Radio 91,6 UMSU FM Sebagai Media Dakwah Muhammad Rizky Ananda; Muhammad Farhan; Dayana Agustina; Iqbal Syahputra; Luthfi Dzil Ikram
Jurnal Ilmiah Wahana Pendidikan Vol 9 No 17 (2023): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8315822

Abstract

One form of da'wah through mass media is da'wah via radio, as is the case with commercial radio 91.6 UMSU fm. Radio 91.6 UMSU Fm is a business entity owned by Muhammadiyah University North Sumatra (UMSU). 91.6 UMSU FM is commercial radio and not community radio. This makes radio 91.6 Umsu Fm the only campus radio in North Sumatra that is commercial. The type of research used in this research is descriptive analysis which functions to describe or give an overview of the object to be studied through the collected data as it is. In this study the authors describe or describe Radio 91.6 UMSU FM using radio as a medium of propaganda. The author uses data sources related to the existence of Radio 91.6 Umsu Fm as a propaganda medium. Meanwhile, the secondary data used in this study are related radio parties, e-books, journals and the internet. The results showed that since 91.6 UMSU FM radio switched from community radio to commercial radio, UMSU radio experienced very significant progress. Radio 91.6 FM has a YouTube account UMSU FM Medan and also has an application, namely UMSU FM which can be downloaded in the play store and app store. So, people can still listen to 91.6 UMSU FM exist from anywhere and anytime.
Monte Carlo Simulation for Rice Yield Risk Estimation Based on Weather and Soil Quality Factors Nouval Khairi; Muhammad Farhan; Muhammad Zhilali Rahman
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.36

Abstract

This study applies Monte Carlo simulation to estimate rice yield risks in the Medan region during 2024 by incorporating key weather variables (temperature, rainfall, and humidity) and soil quality indicators (pH, water content, salinity, texture, and organic matter). Given the increasing impacts of climate change and land degradation on food security, a probabilistic approach is essential for quantifying uncertainties in crop production. Using 10,000 simulated scenarios based on historical and field-derived parameter distributions, the model estimates an average rice yield of approximately 4.2 tons per hectare with a standard deviation of 0.2 tons per hectare, indicating relatively stable production under normal conditions. However, 20% of the simulations produce yields below 3.9 tons per hectare, reflecting elevated risks of crop failure during adverse environmental situations. Sensitivity analysis identifies rainfall and soil pH as the most influential variables, where extreme deviations may reduce yields by up to 35%. These findings offer critical evidence for policymakers and farmers to develop adaptive management strategies aimed at safeguarding sustainable rice production in the region.
Employee Performance Classification Using K-Nearest Neighbor and Random Forest with Work Behavior Scenario Simulation Hafiz Aryanda; Alwi Syahputra; Muhammad Farhan; Ade Aulia Dharma
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.83

Abstract

Employee performance evaluation is a critical aspect of human resource management, directly influencing productivity, promotion decisions, and career development planning. Traditional approaches often fail to capture hidden patterns in complex employee data, making machine learning (ML) a promising solution for more accurate and objective classification. This study aims to model and compare two ML algorithms, K-Nearest Neighbor (KNN) and Random Forest, for employee performance classification, followed by scenario-based simulation using the best-performing model. The research employed a quantitative computational approach with a dataset of 100,000 employee records and 20 features. Preprocessing steps included feature selection, binarization of performance scores, handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), and feature engineering to enrich data representation. The dataset was split into 80% training and 20% testing. KNN was first built as a baseline model, then optimized through hyperparameter tuning using GridSearchCV with cross-validation. Random Forest was implemented with 100 decision trees and bootstrap sampling to enhance accuracy and stability. Results show that the tuned KNN model achieved an accuracy of 70.88%, improving from 63.85% baseline. However, Random Forest outperformed KNN significantly, reaching 97.17% accuracy with lower error rates. Scenario simulations using Random Forest demonstrated practical applicability in predicting employee performance based on work behavior profiles. In conclusion, Random Forest provides a more robust and reliable model for employee performance classification compared to KNN. The study contributes by integrating algorithm comparison with simulation design, offering actionable insights for human resource decision-making. Future research may explore additional ensemble methods and real-time evaluation frameworks.
Co-Authors Ade Aulia Dharma Alwi Syahputra Dayana Agustina Hafiz Aryanda Iqbal Syahputra Luthfi Dzil Ikram Muhammad Rizky Ananda Muhammad Zhilali Rahman Nouval Khairi