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Muh. Isbar Pratama
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Journal of Mathematics, Computation and Statistics (JMATHCOS)
Published by Universitas Negeri Makassar
ISSN : 24769487     EISSN : 27210863     DOI : https://doi.org/10.35580/jmathcos
Core Subject : Education,
Mathematics
Fokus yang didasarkan tidak hanya untuk penelitian dan juga teori-teori pengetahuan yang tidak menerbitkan plagiarism. Ruang lingkup jurnal ini adalah teori matematika, matematika terapan, program perhitungan, perhitungan matematika, statistik, dan statistik matematika.
Arjuna Subject : Matematika - Matematika Terapan
Articles 210 Documents
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A Study of Social Dynamics in Early Childhood Education Using the PEARS Model as a Mathematical Approach to the Spread of Cooperative and Independent Behaviors Pratama, Muhammad Isbar; Lismayani, Angri
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4711

Abstract

This study introduces the PEARS (Potential Adopters, Exposed, Active Adopters, Reluctant Adopters, Stable Adopters) model as a mathematical framework to analyze the spread of cooperative and independent behaviors in early childhood education (ECE) settings. Traditional qualitative methods have limitations in capturing the complexity of social dynamics within classrooms, so the PEARS model, adapted from epidemiological models, offers a fresh quantitative approach. The model categorizes children into five behavioral stages, tracking the transition from initial exposure to stable adoption. Through differential equations, the PEARS model quantifies behavioral spread and interactions, allowing the calculation of key metrics, including the basic reproduction number , which indicates the likelihood of behavior propagation within the group. Numerical simulations underscore the model's applicability in predicting behavior spread and evaluating intervention strategies, facilitating data-driven insights into enhancing positive social dynamics among young children. These findings have implications for designing pedagogical interventions aimed at fostering cooperative and independent behaviors in ECE environments.
Ethnomathematics Exploration in Bamboo Tray Weaving (Pattapi) Ja'faruddin, Ja'faruddin; Sitandi , Flora Frisilia; Musyawir, Musyawir; Mutmainnah , Nadia; Pratiwi, Nur Hikmah Kanzha; Syam , Delia Mustika
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4794

Abstract

This study aims to examine the relationship between mathematics and traditional crafts through a case study of bamboo weaving, specifically patappi or tampah. Using a qualitative case study approach, the research identifies numerical patterns and mathematical modeling inherent in the structure of bamboo weaving. Data were collected through direct observation, visual documentation, and literature review, then analyzed using number pattern concepts and mathematical functions to understand the complex geometric structure within the weaving patterns. The results indicate that the bamboo weaving pattern contains organized geometric structures, such as arithmetic sequences and Pascal’s triangle, reflecting mathematical regularity. Additionally, this study measures the tampah's capacity for filtering rice based on its size and rice density. These findings enrich understanding of the interaction between culture and science and highlight the importance of ethnomathematics in preserving local culture. Keywords: ethnomathematics; arithmetic; number patter; mathematical modeling; culture.
Robust Method with Cross-Validation in Partial Least Square Regression Sibuea, Nuraini; Syamsudhuha, Syamsudhuha; Adnan, Arisman; Silalahi, Divo Dharma
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.4766

Abstract

Partial Least Squares Regression (PLSR) is a multivariate analysis technique used to handle data with highly correlated predictor variables or when the number of predictor variables exceeds the number of samples. PLSR is not robust to outliers, which can disrupt the stability and accuracy of the model. Cross-validation is an important approach to improve model reliability, particularly in data that contains outliers. This study aims to evaluate the effectiveness of K-fold cross-validation and nested cross-validation in a PLSR model using NIRS data from oil palm plantation soil that contains outliers. The methods used in this study include outlier identification using RBF kernel PCA, followed by the application of K-fold cross-validation and nested cross-validation in the PLSR model. The evaluation is based on the Root Mean Square Error (RMSE) and the Coefficient of Determination (R²). The results show that nested cross-validation performs better than K-fold cross-validation. Nested cross-validation results in lower RMSE and higher R², both with and without outliers. K-fold cross-validation is more susceptible to overfitting, whereas nested cross-validation is more effective in mitigating the impact of outliers and improving model accuracy. The conclusion of this study is that nested cross-validation outperforms K-fold cross-validation in improving prediction accuracy and the stability of the PLSR model, especially in data containing outliers. It is recommended to use nested cross-
Classification of Money Market Mutual Fund Products in Indonesia By Using Mahalanobis Distance and Manhattan Distance Indrawan; Azka, Muhammad; Kamila, Isti; Rauf, Nurul Maqfirah; Santoso, Eka Krisna
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6143

Abstract

This study aims to classify money market fund products listed and supervised by the Financial Services Authority (OJK) with minimal classification error. Mahalanobis distance and Manhattan distance were employed to classify these products. Data was sourced from the Indo Premier Online Technology (IPOT) application. Variables utilized in this research include percentage return, Sharpe ratio, unit growth, and Asset Under Management (AUM) . Additionally, Principal Component Analysis (PCA) was employed to reduce data dimensionality by linearly combining correlated original variables into new variables (principal components). PCA was used to visualize data with more than three dimensions. Based on the principal component analysis, the first two principal components captured 74.43% of the original data information, while the first three principal components captured 98.94%. Classification results using three principal components and standardized data showed the same error rates: 13.33% for Mahalanobis distance and 6.67% for Manhattan distance. For the two principal components, both Mahalanobis and Manhattan distances resulted in an error rate of 13.33%. Therefore, Manhattan distance is the most effective method for classification. Forecasting results indicate that mutual fund A is a good investment choice, while mutual fund B is a poor one. Keywords: Mahalanobis distance; Manhattan distance; Principal Component Analysis
Tail-Value-at-Risk Estimation in Optimal Stock Portfolio Formation in Indonesia Using GFGM Type II-GARCH Copula Abubakar, Rahmah; Apriyanto; Ekawati, Darma
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6332

Abstract

Banking disintermediation encourages people to switch to investing in the capital market. Investors always hope to get capital gains and dividends from traded stocks. LQ45 stocks tend to be in demand because they promise good growth prospects. Stock investment has a high risk with a high-profit offer. One way to deal with risk is to determine the optimal portfolio composition by looking at the TVaR value. This study focuses on estimating the Tail-Value-at-Risk (TVaR) in forming an optimal portfolio of LQ45 stocks using the Copula GFGM Type II-GARCH model. The objectives of this study are (1) to estimate the TVaR of the LQ45 stock portfolio using the Copula GFGM Type II-GARCH model; (2) to apply the copula concept in measuring the dependence of the marginal distribution of LQ45 stocks; and (3) to determine the optimal portfolio of LQ45 stocks. The steps in this research method are to start by determining the return of each LQ45 stock and then testing its stationarity using ADF. Furthermore, heteroscedasticity testing is carried out to determine the best GARCH model. Next, the Copula GFGM-Type II function is used to measure the dependency of each stock. In the end, the TVaR of the portfolio formed from the Copula-GARCH model generation data is calculated. This study uses daily closing price data of stocks listed in the LQ45 index for the period 2017 to 2023. From the available data, there are 14 companies with complete data. based on the results of the data analysis obtained only. After data analysis, only 8 stocks met the assumptions. The best ARMA-GARCH model was obtained from these stocks. The results of the TVaR calculation were obtained from portfolios with different weight compositions. The best model for BBRI.JK stocks is the ARMA(2,2)- GARCH(1,1) model. The best model for BBTN.JK stocks is the ARMA(2,2)- GARCH(1,2) model. The best model for KLBF.JK stocks is the ARMA(1,1)- GARCH(1,2) model. The best model for AMRT.JK stocks is the MA(1)- GARCH(1,1) model. The best model for BRPT.JK stock is the AR(1)- GARCH(1,2) model. The best model for EXCL.JK stock is the ARMA(1,2)- GARCH(1,1) model. The best model for INDF.JK stock is the ARMA(1,1)- GARCH(1,2) model. The correlation value does not provide a significant difference to the TVaR in this portfolio.
Comparison of Word2vec and CountVectorizer with Mutual Information in Support Vector Machine (SVM) for Public Sentiment Analysis Doholio, Nadya Pratiwi; Hasan, Isran K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6640

Abstract

Social media is widely used today. Along with the development of social media, it makes it not only a means of communication but also a means of exchanging opinions. One of the social media that is widely used to exchange opinions is X (Twitter). X is widely used to express opinions, particularly on controversial issues, such as the relocation of IKN. Therefore, sentiment analysis is needed to analyse public opinion regarding this national issue. SVM is widely used to classify sentiment based on several required categories, such as positive or negative. However, SVM will work even more effectively if the features used have good quality. Therefore, feature extraction and selection are necessary to enhance SVM classification accuracy. The selection of appropriate feature extraction is very important for classification. Therefore, this study aims to compare two feature extractions, namely Word2Vec and CountVectorizer by adding Mutual Information feature selection to SVM in classifying public sentiment from X. The results show that SVM with Word2Vec and CountVectorizer is more effective than SVM with Mutual Information feature selection. The results show that SVM with Word2Vec feature extraction and Mutual Information feature selection is more effective overall with 84% accuracy, 90% precision, 90% recall, and 90% f1-score, compared to SVM with CountVectorizer feature extraction and Mutual Information feature selection which has 80% accuracy, 83% precision, 92% recall, and 87% f1-score.
Application of Statistical Quality Control and Failure Mode and Effect Analysis to Improve Panada Tore Quality at PT. Cita Rasa Pagimana Kudo, Syafri Yandi; Isa, Dewi Rahmawaty; Payu, Muhammad Rezky Friesta
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6708

Abstract

The manufacturing industry in Banggai Regency, Indonesia, is growing rapidly and is the main sector supporting PDRB. PT Cita Rasa Pagimana, a producer of Pagimana's signature panada tore, faces product quality problems such as charring and crumbling despite clear production standards, causing losses due to the large number of defective products. This study aims to implement a combination of Statistical Quality Control (SQC) method with Seven Tools and Failure Mode and Effect Analysis (FMEA) to identify the causes and resolve panada tore production defects. The results showed 6.680 defective products out of 189.590 products in December 2024, with charred defects accounting for 64% (4.393 products) and crumbled defects 36% (2.287 products). Based on FMEA, the five main causes with the highest RPN are: inconsistent frying technique, absence of oil thermometer, improper dosage of raw materials, overheating room temperature, and rough handling of dough. Recommendations for improvement include training on frying techniques, providing an oil temperature thermometer, adjusting the dosage of raw materials, adjusting the room temperature, and periodic training to improve employees' understanding of the production process, which is expected to reduce defects and improve product quality.
Analysis of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in Forecasting Antam Gold Prices in Indonesia Sanusi, Wahidah; Syam, Rahmat; Sari, Yulfiana
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6760

Abstract

This research is a type of applied research with a quantitative approach to analyze the results of Antam's gold price forecasting in Indonesia with the best Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model obtained. GARCH is a model used to analyze data volatility over time, especially in forecasting data that frequently experiences fluctuations. The research data used is 234 weekly historical data on Antam gold prices in Indonesia in the period January 2020 to June 2024. The results of this research show that by using the best ARIMA (2,1,2) GARCH (1,2) model, gold price forecasting results are obtained. Antam in Indonesia for the period July 2024 to June 2025, namely IDR 1,376,096 to IDR 1,781,239. This model has a high level of accuracy in forecasting Antam's gold price in Indonesia with a Mean Absolute Percentage Error (MAPE) value of 1.13%.
Evaluation of the Adaptive Fuzzy Neuro Inference System and Fuzzy Model Time Series Markov Chains in Forecasting Crude Oil Prices Hinelo, Ikrar Prasetyo; Nuha, Agusyarif Rezka; Hasan, Isran K; Nasib, Salmun K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6763

Abstract

The development of a country's economy is greatly influenced by global economic conditions, given the increasingly close links between countries through economic relations and international cooperation. One of the main factors in economic growth is international trade, particularly export and import activities. Crude oil is one of the most actively traded commodities. Given the highly volatile crude oil market, accurate price forecasts are crucial in economic and financial decision-making. This study compares the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Time Series Markov Chain (FTSMC) in forecasting the price of West Texas Intermediate (WTI) crude oil using time series data from 2020 to 2024 with saturated sampling technique. The implementation of both methods is carried out through Matlab Online and R-Studio software, with results showing that ANFIS has higher accuracy than FTSMC, as evidenced by the Mean Absolute Percentage Error (MAPE) value of 1,8010% for ANFIS and 3,7567% for FTSMC. Further analysis shows that ANFIS with a triangular membership function as well as significant lags at lag 1, lag 3, lag 4, and lag 7 is able to produce more accurate predictions and match the trend of actual data. Therefore, ANFIS is recommended as a more effective method in forecasting WTI crude oil prices, which can provide valuable insights for policy makers and industry stakeholders.
Mapping the Relative Risk of Tuberculosis in Indonesia Using the Bayesian Spatial Conditional Autoregressive Leroux Model Aswi, Aswi; Nurhikmawati, Nurhikmawati; Shanty, Meyrna Vidya; Herman, Nur Taj Alya’; Sukarna, Sukarna
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6814

Abstract

Tuberculosis (TB) is an infectious disease caused by infection with the Mycobacterium Tuberculosis bacteria. Indonesia ranks second globally in terms of the number of TB cases, after India, followed by China. Modeling is needed to evaluate the relative risk (RR) of TB cases in Indonesia to identify areas that have a high RR of being infected with the bacteria. One approach used to estimate the RR of TB in Indonesia is Bayesian Conditional Autoregressive (CAR). This research aims to identify the RR rate of TB cases in Indonesia using the Bayesian spatial CAR Leroux approach based on TB case data from 2021 to 2022. The best model selection is based on Deviance Information Criteria values, the Watanabe Akaike Information, and residuals from Modified Moran's I. Analysis results shows that in 2021, the Bayesian spatial CAR Leroux Model with Inverse Gamma prior (0.5; 0.5) is the best model. DKI Jakarta Province has the highest while Bali Province has the lowest RR. In 2022, the Bayesian spatial CAR Leroux Model with Inverse Gamma prior (1;0.01) is the best model, with DKI Jakarta Province still having the highest RR, while Bali still has the lowest RR.

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