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INDONESIA
Jurnal Matematika Sains dan Teknologi
Published by Universitas Terbuka
ISSN : 14111934     EISSN : 24429147     DOI : -
Core Subject : Education, Agriculture,
Agriculture, Biological Sciences & Forestry Mathematics Other
Merupakan media informasi dan komunikasi para praktisi, peneliti, dan akademisi yang berkecimpung dan menaruh minat serta perhatian pada pengembangan Matematika, ilmu pengetahuan dan teknologi. Diterbitkan oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Terbuka.
Arjuna Subject : -
Articles 418 Documents
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Application of Synthetic Minority Over-Sampling Technique (SMOTE) to Outlier Data for Probabilistic Neural Network (PNN) Ramdan Hayati; Isran Hasan; Novianita Achmad
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.7209.2024

Abstract

One common model of Artificial Neural Network (ANN) used in classification tasks is the Probabilistic Neural Network (PNN). PNN is an algorithm that utilizes probability functions, eliminating the necessity for a large dataset during its development process. In this research, the best model parameters were initially determined using the sigma parameter and Kernel Density Estimation (KDE) function on a randomly sampled dataset employing the Stratified Random Sampling (SRS) method. The optimal sigma parameter obtained from this process is 0.075, with a Gaussian KDE function. The data used in this study is related to direct marketing campaigns (phone calls) from Portuguese banking institutions collected by S ́ergio. Subsequently, PNN is applied to this dataset to determine its Accuracy and F1-Score values. The results indicate an accuracy rate of 87.117% and an F1-Score of 92.755%. Following this, Synthetic Minority Over-Sampling Technique (SMOTE) is applied to the dataset to balance the data. PNN is then implemented on the oversampled data, and in this phase, an evaluation of the Accuracy and F1-Score values is conducted, resulting in respective figures of 93.437% and 93.511%.
Identifying Factors Influencing the Number of Diarrhea Cases in Children Under Five in West Java Using Negative Binomial Regression Akbar Rizki; Utami Dyah Syafitri; Christin Halim
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.7582.2024

Abstract

The WHO states that diarrhea is the leading killer of children under five worldwide, and Indonesia is no exception, where 10.3% of under-five deaths are caused by diarrhea. West Java Province, with the largest population in Indonesia, has the highest diarrhea cases under five. The potential for diarrhea to become an extraordinary event, which is often accompanied by death, is very likely to occur because diarrhea is an endemic disease in West Java. Therefore, analyzing the factors influencing the children under five diarrhea cases in West Java is essential. Negative binomial regression was used in this study because the response was to count data on the incidence of diarrhea in children under five in West Java. The analysis results show that an increase in the percentage of public premises (PPP) meeting health requirements and population density per km2 will increase the number of diarrhea cases under five in West Java. However, an increase in the percentage of Community-Based Total Sanitation (CBTS), percentage of the population living in poverty, and percentage of households practicing Clean and Healthy Behavior (CHB) will decrease the number of diarrhea cases in West Java.
Application of teh Hybrid Singular Spectrum Analysis – ARIMA Model for Indonesia's Inflation Rate (2018-2023) Sri Rahayu; Aswi Aswi; Muhammad Fahmuddin Sudding
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.7982.2024

Abstract

This research aims to determine the results and accuracy of forecasting inflation rates in Indonesia using Hybrid Singular Spectrum Analysis (SSA) – Autoregressive Integrated Moving Average (ARIMA). Hybrid SSA-ARIMA combines two time series methods to increase forecasting accuracy, especially for economic data that contains trend and seasonal components. The data used is data on the national consumer price inflation rate (Y-on-Y) for the period January 2018 to December 2023. The forecast accuracy obtained by the MAPE value for Singular Spectrum Analysis was 56.26797%, and Hybrid SSA-ARIMA was 18.88851%. This shows that Hybrid SSA-ARIMA has better forecasting capabilities than Singular Spectrum Analysis in predicting the inflation rate in Indonesia.
Application of The Fuzzy Ruey Chyn Tsaur Method to The Time Series Data: (Case Study: Total Exports in East Kalimantan from January 2018 to May 2021) Indah Ayu Kartika Putri; Ika Purnamasari; Suyitno
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.6505.2024

Abstract

Markov chains are stochastic processes in which current events depend only on events one step back. The transition probability matrix shows the probability of movement between states in a Markov chain process. The transition likelihood matrix can be used to help forecast future changes. This research has the purpose of forecasting using the fuzzy Ruey Chyn Tsaur method combined with the Markov chain concept. In this study, the determination of interval length was carried out using Sturges and averages based. The results showed that the value of MAPE based on Sturges (5.95%) is lower than the value based on the average (6.02%). In June 2021, forecasting of the total exports was obtained at USD 1,687.17 million for the Sturges method and USD 1,728 million for average based.
Route Optimization in Asymmetric Capacitated Vehicle Routing Problem (ACVRP) Model using Tabu Search Algorithm (Case Study: Car Oil Distribution of PT. Kencana Central Mobil) Thedorus Junjun; Mariatul Kiftiah; Fransiskus Fran
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.6575.2024

Abstract

PT. Kencana Central Mobil is an automotive workshop that distributes oil to nine regular customers spread across Pontianak and its surroundings. The route from the depot to the customer and between customers has a different length, because when making a round trip using different roads. From this information, the author conducted observations in the field to measure the distance between customers with the help of the Avanza application. Thus, the problem in this company is included in the Asymmetric Capacitated Vehicle Routing Problem. The ACVRP model is a problem where the route from location  to  is not the same as the route from location  to . Based on the problems that have been explained, this study uses the Tabu Search algorithm to solve it. The Tabu Search algorithm works by moving from one route to another, so that when related to a problem the company can find a trip by choosing the shortest route. There are six steps in solving this problem, namely determining the initial route, finding alternative routes by swapping two node positions so that the routes that can be formed each iteration are  routes (nine is the number of customers), choosing the best route among alternative routes, determining the new best route, updating the tabu list and checking the stopping criteria. From the calculation results, there is a difference in the distance traveled from the initial route which is 63.16 km long, while when calculated using the Tabu Search Algorithm, it can be seen that the tabu search criteria stops at the 7th iteration with a route length of 50.26 km so that it differs by 12.9 km from the initial route. The length of the route is optimal because it has the shortest route length of all the literature that has been traced.
The Role of Generalized Space Time Autoregressive (GSTAR) Modelling in Understanding Economic Indicators: Farmer Value Food Crops Subsector Riska Yulianti; Achmad Fauzan
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.10260.2025

Abstract

Indonesia, as an agrarian nation, relies heavily on agriculture for rural livelihoods. The Farmer Terms of Trade (FTT) is a key indicator of farmer welfare. However, agriculture is often seen as ineffective in boosting income and reducing poverty. Despite this, the sector remains crucial for national development, especially the crops sub-sector, which sustains the country's food supply. The Generalized Space Time Autoregressive (GSTAR) model is employed to explore data relationships across proximate locations, focusing on geographical or observational locational factors. This analysis incorporates three spatial weights in the GSTAR model: (1) queen contiguity- weights, (2) uniform location weights, and (3) inverse distance spatial weights. Our findings indicate that the GSTAR model (11)I(1) with uniform spatial weight emerges as the optimal model. This model not only satisfies the white noise and normality assumptions but also demonstrates superior performance metrics, including a Mean Squared Error (MSE) of 2.34, Root Mean Squared Error (RMSE) of 1.53, and Mean Absolute Percentage Error (MAPE) of 1.10%. These figures notably surpass those obtained with the GSTAR models employing queen contiguity-based weights and inverse distance spatial weights, thereby highlighting its efficacy in capturing the dynamics within the crops sub-sector.
Estimating The Probability of Depression in Adolescent Based on Internal Family Factors using Binary Logistic Regression and Naïve Bayes Muhammad Hasan Sidiq Kurniawan; Achmad Fauzan
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.10312.2024

Abstract

Depression is a common mental health issue affecting approximately 300 million people globally, with severe cases potentially leading to death. Adolescents with depression are reported to have a 30-fold increased risk of suicide compared to other age groups, making early identification and intervention in this age group essential. Internal family factors play a crucial role in influencing adolescent mental health, including variables such as family type, parenting style, residence status, birth order, and parental occupation. This study aims to identify which internal family factors significantly impact the likelihood of depression and to estimate the probability of depressive episodes in adolescents. By understanding these factors, preventive measures can be better tailored to reduce adolescent depression in the future. The data utilized in this study is primary data obtained through a sampling process employing the simple random sampling method applied to first-year university students. A binary logistic regression model was employed to analyze the significance of each family-related factor. Findings indicate that parenting style and parental occupation are among the most significant factors associated with adolescent depression. The novelty of this study lies in the exploration of internal family factors that are rarely examined comprehensively in previous research, such as the effects of family type, birth order, and parental occupation. Additionally, the study adopts a dual-method approach, combining logistic regression and Naive Bayes, to provide a robust and comparative analysis of predictive accuracy. Probability estimates were conducted using both binary logistic regression and Naive Bayes methods. Results from these analyses suggest that a democratic parenting style tends to foster more stable mental health in adolescents, while adolescents with parents employed in the private sector or similar occupations face a higher likelihood of depression. Both methods demonstrated high predictive accuracy, with 96.31% for logistic regression and 96.93% for Naive Bayes.
The Factors that Influence People's Consumption of Moringa in Surabaya City Desy Fajariyah; Pismia Sylvi
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.10361.2025

Abstract

The decrease in the prevalence of nutrition recorded in the Indonesian Nutrition Status Survey (SSGI) in 2021-2022 by 2.8% (Ministry of Health of Indonesia, 2023) indicates an improvement in the nutritional status of the community. One of the factors that influence nutritional status is the consumption pattern of nutritious vegetables. Moringa (Moringa Oleifera), has an important role in improving nutrition due to its high nutritional content. However, the utilisation of Moringa leaves is still limited due to the community's lack of understanding about the benefits and consumption of Moringa leaves. This study aims to identify the factors that influence people's consumption of moringa leaves with a case study in Surabaya City. A total of 70 respondents who had consumed moringa were selected through a two-stage cluster sampling method. Analyses were conducted to evaluate the influence of consumption goal variables, availability, price, and information sources on moringa consumption patterns. The results show that the source of information is a factor that has a significant effect on consumption patterns. These findings indicate the importance of disseminating appropriate information to increase the utilization of moringa in the moringa vegetable diet for people in Surabaya.
Comparative Analysis of Influenza Model Solutions Using Euler, Heun, and RK4 Methods Nabila Asyiqotur Rohmah; Mohamad Nur Fauzi
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.10533.2025

Abstract

This study explores the numerical solutions of an influenza epidemiological model, specifically the SEIR (Susceptible, Exposed, Infected, and Recovered) type, which is represented by a system of nonlinear differential equations. Three numerical methods were applied to solve this model: the Euler method, Heun’s method, and the fourth-order Runge-Kutta (RK4) method. The solutions obtained from these numerical methods were compared to the reference solution from ODE45, as the exact solution of the SEIR model remains unknown. Numerical simulations revealed that using either a very large step size ( ) or a very small step size      led to significant numerical errors. Among the five different step sizes tested,  provided the most accurate results. Based on the average computational time across different step sizes, the Euler method was the fastest, while RK4 was the slowest. However, the Euler method exhibited the largest error margin, whereas Heun’s and RK4 methods produced comparable errors. Although Heun’s method had the same error margin as RK4, it required less computational time, making it the most efficient choice for this case.
The Impact of Data Splitting on ANN Performance in Predicting Foreign Tourist Visits to Inodnesia Akbar Rizki; Muhammad Dzakwan Alifi; Haidar Ramdhani; Lilis Indra Purnama; Shalma Kaisya Candradewi; Farid Yafi Suwandi; Adelia Putri Pangestika
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.11104.2025

Abstract

The data sharing stage is an important step in model building using Artificial Neural Network (ANN) methods to avoid the risk of overfitting and underfitting that can affect model performance. Proper data division aims to ensure that the model can generalize well to data that has never been seen before. Generally, data sharing is done by dividing the dataset into two main parts, namely training and testing data. However, to better address overfitting, there are also those who divide the data into three parts, namely training, testing, and validation. This study aims to evaluate the performance of ANN modelling using these two ways of dividing data. The model is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics to measure prediction error. The data used is data on foreign tourist arrivals to Indonesia, which has a fluctuating pattern and is influenced by calendar effects. The results show that the data division type with two groups generally produces a smaller MAPE value than the data division into three groups. However, the model with two parts of data is not able to capture the seasonal pattern in the data. On the other hand, the model with three parts of data can overcome this problem better. The best model was obtained with the proportion of training data, validation data, and test data of 80%, 10%, and 10%, respectively, which resulted in a MAPE value of 24.45%.

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