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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal KARISMATIKA BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika Jurnal Abdidas Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Soft Computing Exploration Asian Journal of Community Services (AJCS) JURNAL HANDAYANI PGSD FIP UNIMED Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sinaga, Marlina Setia
Universitas Negeri Medan
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Comparison of supervised machine learning methods in predicting the prevalence of stunting in north sumatra province Saragih, Vinny Ramayani; Arnita, Arnita; Indra, Zulfahmi; Taufik, Insan; Sinaga, Marlina Setia
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.498

Abstract

Stunting is a growth and development disorder in children caused by chronic malnutrition and repeated infections. Stunting has significant short- and long-term impacts and is one of the major health issues currently faced by Indonesia. The prevalence of stunting in North Sumatra Province is 18.9%, and the provincial government aims to reduce this prevalence to 14% by 2024. This study aims to compare the performance of several supervised machine learning methods in predicting stunting prevalence in North Sumatra Province. The data used is secondary data from 2021 to 2023, covering 33 districts/cities in the province. This study evaluates three machine learning models: Support Vector Regression (SVR), Decision Tree, and Random Forest, using evaluation metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The analysis results show that Random Forest provides the most accurate and consistent predictions, with lower MSE, MAE, RMSE, and MAPE values compared to the other models in most areas. Decision Tree yields good results in some regions but tends to produce higher errors in certain cases. SVR exhibits a more varied performance, with some regions showing higher prediction errors. Overall, Random Forest is the superior model for predicting district/city-level data, although model selection should be tailored to the data characteristics and application needs
Application of sugeno's fuzzy inference system (FIS) in determining palm oil production Maristella; Sinaga, Marlina Setia
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 8 No. 1 (2025): Jurnal Absis
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v8i1.2788

Abstract

Palm oil is an important export commodity in Indonesia, and factors such as palm oil prices, production, and palm oil prices influence palm oil production. In this research, the fuzzy logic method is used to overcome uncertainty in predicting palm oil production. Various fuzzy methods, including Mamdani, Tsukamoto, and Sugeno, are used to model production based on certain factors. The type of research carried out in this research is a literature study and this research uses secondary data or data obtained by other parties. Secondary data taken is CPO price data, palm oil prices and palm oil production data. The prediction results for palm oil production using the Sugeno fuzzy method for several months are based on factors such as CPO prices and palm oil prices in the fuzzy system. The final results of MAPE provide information about the level of accuracy of the model in predicting palm oil production, which is 7.09%. FIS Sugeno connects input-output with fuzzy rules. The steps include variable selection, membership functions, rules, inference, defuzzification, evaluation, optimization, and implementation. The predicted MAPE is 7.09%, indicating the accuracy of the model in estimating palm oil production compared to the actual value.
GAME THEORY APPLICATION ON ONLINE TRANSPORTATION COMPANY AND DRIVER INCOME LEVELS DURING THE PANDEMIC Sinaga, Marlina Setia; Arnita, Arnita; Rangkuti, Yulita Molliq; Febrian, Didi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.063 KB) | DOI: 10.30598/barekengvol16iss2pp713-720

Abstract

Online motorcycle taxi drivers are a group of people who are economically affected by the Covid-19 pandemic. This study aimed to provide a balanced choice strategy for drivers and companies. Game theory was applied to conflict of interest situations as a research method. Choices for online transportation companies and drivers are analyzed and arranged in a payoff table until they reach the saddle point. Simulation software as an illustration of a balanced model. This research resulted in driver diligence and incentive strategies as optimal strategies for drivers and companies. If drivers improve performance by choosing a driver diligence strategy, the driver's expectations of getting incentives will be more realistic. Meanwhile, for the company, when the driver's diligence increases, the choice of providing incentives will provide balanced benefits as well.
OPTIMIZATION OF MEDAN CITY WASTE TRANSPORTATION SYSTEM USING MULTIPLE-TRIP VEHICLE ROUTING PROBLEM (MTVRP) MODEL AND SIMULATED ANNEALING Marpaung, Faridawaty; Arnita, Arnita; Dewi, Sri; Sinaga, Marlina Setia; Widyastuti, Eri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp3059-3072

Abstract

Medan generates approximately 2,000 tons of waste daily, yet only 800 tons are successfully transported to landfills, indicating significant inefficiencies in waste transportation. This study addresses the issue by applying the Vehicle Routing Problem with Multiple Trips (VRPMT) combined with the Simulated Annealing (SA) algorithm to optimize waste transport operations. The VRPMT model allows each vehicle to make multiple daily trips, enhancing fleet utilization while ensuring that all service points are visited, vehicle capacities are not exceeded, and vehicles return to the depot after each trip. The study focuses on Tegal Sari Mandala II (TSM II), Medan Denai, a densely populated neighborhood with narrow roads that require bestari pedicabs for flexible waste collection. Data includes waste collection points, vehicle capacities, transport frequencies, and operational costs. The SA algorithm begins with a random route solution, then iteratively evaluates and improves it by minimizing total distance and cost. It also avoids local optima through a controlled temperature reduction process. Results demonstrate significant improvements: total travel distance was reduced from 12,500 meters to 8,646 meters (a 30.8% reduction), and operational costs decreased from IDR 12,000 to IDR 8,946 (a 25.5% reduction). On average, each bestari pedicab completed two daily trips, maximizing capacity utilization and minimizing penalty costs. The system integrates a structured database and Google Maps API for route visualization, enhancing planning and monitoring. Overall, this approach contributes to more efficient, cost-effective, and environmentally friendly waste transportation. It supports climate action goals and provides a scalable, replicable model for sustainable urban waste management in other regions facing similar logistical challenges. However, this study has some limitations. The VRPMT model was applied only in a neighborhood with a limited vehicle type, which may reduce its generalizability to broader urban areas with more complex logistics. Also, the Simulated Annealing algorithm settings were manually tuned and not benchmarked against other metaheuristic methods. Future studies could improve the model by considering dynamic traffic conditions, integrating real-time data, or testing hybrid optimization approaches to enhance its effectiveness and adaptability.
Co-Authors Anitaria . Simanullang Arnita Arnita Arnita Arnita Br Barus, Jesika Casadae Wanda DIdi Febrian Elisabet Desy Yulianawati Hia Eri Widyastuti Erlinawaty Simanjuntak Grace Meisel Hamidah Nasution Hutapea, Brian Insan Taufik July Hartiny . Kairani, Nerli Lasker Pangarapan Sinaga Manullang, Sudianto Maristella Marpaung, Faridawaty Marpaung, Faridawaty Nadya, Fauza Oktariani Purba Restina Silalahi Ria Rahmadita Surbakti Saragih, Vinny Ramayani Silaban, Dewi Fortuna Silalahi, Restina Sri Dewi Tamara, Angga Yulita Molliq Rangkuti Zulfahmi Indra, Zulfahmi