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INDONESIA
Journal of Mathematics and Scientific Computing With Applications
Published by Yayasan Pena Cendekia Insani
ISSN : 27985512     EISSN : 27985776     DOI : -
Core Subject : Education,
Mathematics
Journal of Mathematics and Scientific Computing With Applications is a broad-based journal covering all branches of computational or applied mathematics with special encouragement to researchers in theoretical computer science and mathematical computing. It covers all major areas, such as numerical analysis, discrete optimization, linear and nonlinear programming, theory of computation, control theory, theory of algorithms, computational logic, applied combinatorics, coding theory, cryptographic, fuzzy theory with applications, differential equations with applications. Journal features research papers in all branches of mathematics that have some bearing on the application to scientific problems, including areas of actuarial science, mathematical biology, mathematical economics, and finance.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 109 Documents
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Analysis of the Accuracy Level of Using the Monte Carlo Method in Predicting the Number of Dengue Fever Sufferers Nurhabidah, Fauziah
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 2 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i2.984

Abstract

Dengue fever is an infectious disease that continues to be a threat to public health in Indonesia. The increasing number of sufferers every year requires accurate prediction strategies to support effective prevention and control policies. This study aims to analyze the level of accuracy of the Monte Carlo method in predicting the number of dengue fever sufferers in Indonesia, especially in North Sumatra Province. The data used in this research is data on dengue fever cases from 2021 to 2023 obtained from the Central Statistics Agency (BPS) and the Health Service. The prediction process is carried out using Monte Carlo simulation which involves a probability-based random calculation process. The research results show that the Monte Carlo method has a fairly high level of accuracy in predicting patterns of dengue fever cases, with an accuracy value of 3440 (99.41%) compared to other conventional prediction methods. In addition, this method is proven to be flexible in dealing with variations in fluctuating data patterns. It is hoped that this research can contribute to strategic decision making, especially in mitigating dengue fever through more accurate predictions.
ANALYSIS OF RAW MATERIAL INVENTORY MANAGEMENT USING THE ECONOMIC ORDER QUANTITY (EOQ) METHOD TO DETERMINE PERIODIC ORDER QUANTITY (POQ) AT EMI BAMBANG’S OPAK FACTORY Hanifa Lubis, Nurul
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 2 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i2.985

Abstract

The study aims to analyze the management of raw material inventory at Emi Bambang Opak Factory through the application of an effective method, namely Economic Order Quantity (EOQ) in determining Periodic Order Quantity (POQ). To determine the optimal order quantity, EOQ is used, while POQ is implemented to determine the efficient ordering interval. The results of this study are the determination of the POQ method at the emi bambang opak factory with a cost difference of Rp 30,717,480 for the cost of managing raw materials and ordering raw materials, this can provide solutions in more economical inventory management, to minimize the cost of storing and ordering raw materials without disrupting production continuity.
TOFU PRODUCTION OPTIMIZATION USING INTEGER LINEAR PROGRAMMING THROUGH BRANCH AND BOUND APPROACH Ani Putri, Khairunnisa
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 2 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i2.986

Abstract

This research optimizes tofu production at Sumedang Mas Ponimin Tofu Factory, Medan, which produces white tofu, yellow tofu, and pong tofu. Facing the challenges of production efficiency and profit improvement due to manual methods, an optimization model was developed using simplex and branch and bound methods. Primary and secondary data were used to evaluate raw material availability, production capacity, and profitability. The results showed an increase in daily production from 118 to 142 boards and an increase in profit from Rp1,888,500 to Rp2,263,500. This research confirms the effectiveness of applying mathematical optimization methods in improving operational efficiency and decision-making in the food industry.
IMPLEMENTATION OF THE CUTTING PLANE METHOD TO OPTIMIZE THE PRODUCTION PROFIT OF PAK SUNAR'S TEMPE FACTORY Aini, Sofiah
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 2 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i2.987

Abstract

This research optimizes tofu production at Sumedang Mas Ponimin Tofu Factory, Medan, which produces white tofu, yellow tofu, and pong tofu. Facing the challenges of production efficiency and profit improvement due to manual methods, an optimization model was developed using simplex and branch and bound methods. Primary and secondary data were used to evaluate raw material availability, production capacity, and profitability. The results showed an increase in daily production from 118 to 142 boards and an increase in profit from Rp1,888,500 to Rp2,263,500. This research confirms the effectiveness of applying mathematical optimization methods in improving operational efficiency and decision-making in the food industry.
PERFORMANCE ASSESMENT EVALUATION EMPLOYEES USING METHOD SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE (SMART) AT THE MEDAN FINANCIAL TRAINING CENTER Wira Afandi , Fikry; Deasy; Alifiah, Nasya; Citra Amanda
Journal of Mathematics and Scientific Computing With Applications Vol. 6 No. 2 (2025)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v6i2.1126

Abstract

Employee performance assessment is one of the important aspects to achieve the goals of an institution. With the application of information technology, a decision support system can be used to provide assessments. One method that can be used to provide employee performance assessments is the SMART (Simple Multi Attribute Rating Technique) method. This method was chosen because of its superiority in being able to solve problems using multicriteria so it is suitable for determining employee performance assessments at the Medan Financial Training Center. The criteria used in determining employee performance assessments at the Medan Financial Training Center are Discipline, Responsibility, Leadership, Innovation, Competence. Data collection was carried out using questionnaires distributed to employees at the Medan Financial Training Center. The results of the study from 20 data samples using the SMART method obtained ranking results from employee performance assessments ranging from very good assessments with a final value of 1 to very poor with a final value of 0.025.
EFFICIENCY ANALYSIS OF PALM OIL INVENTORY CONTROL AT PTPN IV USING MONTE CARLO SIMULATION Devitasari; Yani Sitompul, Apri; Cintya Hasmi Pohan, Dian; Indriyani Ningsih, Fani; Filia Sari, Rina
Journal of Mathematics and Scientific Computing With Applications Vol. 6 No. 2 (2025)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v6i2.1146

Abstract

Uncontrolled palm oil inventory can increase operational costs and disrupt production flow. This study aims to validate the effectiveness of the Monte Carlo simulation method in predicting the total palm oil inventory costs at PT. Perkebunan Nusantara IV. Unlike conventional approaches that ignore uncertainty, this study uses Monte Carlo simulation to model cost variability based on historical data from January to December 2024. The simulation process is performed by generating random numbers using the Linear Congruential Generator (LCG) method and determining the probability distribution from historical data. The simulation is run once using the probability distribution obtained from 12 months of historical data. The simulation results show a predicted total inventory cost of Rp 918.117.054.635.00, lower than the actual cost of Rp 919.958.281.123.00, resulting in a potential savings of Rp 1.841.226.488.00. To measure the reliability of the Monte Carlo simulation results, a MAPE calculation was performed by comparing the simulation results with actual data. The calculation results show that the MAPE value is 0.2%. These findings prove that Monte Carlo simulation not only improves forecasting accuracy but also empirically supports more optimal decision-making in inventory management and efficient stock level determination.
APPLICATION OF WEIGHT PRODUCT AND TOPSIS METHODS IN SELECTING THE BEST ONLINE TRANSPORTATION SERVICE Nur Fitriani; Hamidah Nasution
Journal of Mathematics and Scientific Computing With Applications Vol. 6 No. 2 (2025)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v6i2.1206

Abstract

This study emphasises the application of mathematical and computational modelling to support multi-criteria decision-making in the selection of online transportation services. Using Microsoft Excel, the research employs the Weighted Product (WP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to assess alternatives based on six quantitative criteria: price, promotion, service variety, payment method, convenience, and punctuality. The integrated application of WP and TOPSIS provides a systematic process of normalisation, weighting, and ranking to determine the optimal alternative. The findings indicate that GOJEK achieves the highest preference value (0.6107), followed by GRAB (0.5533), IN-DRIVE (0.5000), and MAXIM (0.3893). The methodological contribution of this research lies in demonstrating how the integration of WP and TOPSIS within computational tools establishes an effective mathematical framework for optimising decision-making in service evaluation.
IMPLEMENTATION OF PRINCIPAL COMPONENT ANALYSIS (PCA) IN DIMENSION REDUCTION BASED ON INDONESIAN HEALTH DATA Rangkuti, Siti Rafiah; Fadhillah, Nurul; Sari, Rita Novita; Faigle, Ulrich
Journal of Mathematics and Scientific Computing With Applications Vol. 6 No. 2 (2025)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v6i2.1313

Abstract

Indonesian health data for 2024 has multidimensional characteristics with a large number of interconnected variables, leading to high complexity in the analysis and visualization process. This complexity poses a challenge in generating information that is easy to understand and can support data-driven decision-making. This research aims to implement the Principal Component Analysis (PCA) method as a technique for dimension reduction and visualization of Indonesian health data. The research method used is a quantitative approach with descriptive-exploratory secondary data analysis. The research stages include data pre-processing, PCA implementation, principal component determination, variable contribution analysis, and data visualization using scatter plots and biplots. The research results show that PCA is able to significantly reduce the number of variables while still retaining most of the main information contained in the data. Principal component analysis-based visualization produces clearer and more easily interpretable patterns and structures in health data. Thus, PCA has proven effective in simplifying the complexity of national health data and supporting the presentation of more informative and actionable information for decision-making in the health sector.
GRAPH INTERPRETATION OF IRREDUCIBLE, REDUCIBLE, PERIODIC, AND APERIODIC PROPERTIES IN MARKOV CHAINS Suci Rachmadini, Haliza; Muhammad, Faisal; Roder, Klause
Journal of Mathematics and Scientific Computing With Applications Vol. 6 No. 2 (2025)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v6i2.1331

Abstract

Markov chains are widely used stochastic models for describing dynamic systems whose future states depend only on short-term probabilistic transitions. Key structural properties irreducibility, reducibility, periodicity, and aperiodicity are crucial for understanding long-term behavior, particularly the existence and stability of stationary distributions. Traditionally, these characteristics are determined through analysis of the transition probability matrix; however, this approach can be computationally demanding and difficult to interpret for large systems. This study explores an alternative representation using directed graphs, where each state is modeled as a node and each positive transition probability as a directed edge. The approach connects irreducibility with strong graph connectivity, while reducibility corresponds to the presence of separate communication classes. Periodicity and aperiodicity are identified through the structure of cycles and the greatest common divisor of return path lengths. The results demonstrate that graph-based analysis provides clearer and more intuitive framework for examining structural properties of Markov chains.

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