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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 496 Documents
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Classification Of Factors Influencing Diabetes Mellitus Type II By Using Multivariate Adaptive Regression Spline At Rantau Prapat Regional Hospital widya panjaitan; Hendra Cipta
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.34293

Abstract

Diabetes Mellitus is a metabolic disease caused by increased levels of glucose or blood sugar. Diabetes Mellitus is divided into three different types: type I diabetes, type II diabetes, and gestational diabetes or diabetes during pregnancy.  Type 2 diabetes mellitus affects 90–95% of diabetics.  The aim of this research is to identify related factors that influence Type II Diabetes Mellitus by applying the Multivariate Adaptive Regression Spline (MARS) Method. The model with the lowest Generalized Cross-Validation (GCV) score among the models constructed is considered the best model. The research findings show that BF=10, MI=3, and MO=0 are the optimal parameter combinations for the MARS model with a GCV value of 0,09582998 .  According to research using MARS, the predictor variables with an 89.33% classification accuracy that affect the blood glucose levels of Type II Diabetes Mellitus patients include Age (X1), Gender (X2), Blood Pressure (X3), and Comorbidities (X5).
Spatial Weighting Selection in GSTAR and S-GSTAR Models for Temperature Prediction Riani Utami; Utriweni Mukhaiyar; Nabila Mardiyah; Yalela Sa’adah; Erni Widyawati
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.34305

Abstract

Recent research in time series analysis indicates that events at a particular location are not only influenced by events at previous times but also by proximity between locations. Events influenced by both space and time can be modeled using a space-time model. GSTAR model is one such space-time model. In its development, time series data exhibiting seasonal patterns are modeled using Seasonal GSTAR (S-GSTAR). The GSTAR and S-GSTAR models are used to model temperature in the Banjar, Cilacap, and Sleman Districts. The purpose of employing both methods is to compare the best model for modeling temperature at these three locations. Spatial weights used include inverse distance weighting using the Euclidean distance formula, uniform weighting, and cross-correlation normalization weighting. Ordinary Least Squares (OLS) is the estimation method used in this study. The best model obtained is S-GSTAR  with inverse distance weighting, as this model has the smallest RMSE value.
Penentuan Harga Opsi Saham dengan Menggunakan Binomial Trees dengan Penyertaan Implied Volatility Aimmatul Ummah Alfajriyah; Endah R.M Putri; Daryono Budi Utomo; Moch. Taufik Hakiki
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.34476

Abstract

The Black-Scholes model provides an analytical solution in option pricing and has been widely used in finance. This model assumes constant volatility. Pricing option incorporating implied volatility is conducted using implied binomial tree. This study aims to simulate the prices of put options and call options using implied binomial trees, binomial trees and the Black-Scholes model and determine the factors that influence option prices. The simulation was conducted using Matlab. The option price resulted from implied binomial tree and binomial tree are compared with the option prices of the Black-Scholes model to determine the difference of option prices with constant volatility and option prices  incorporating implied volatility. The implied binomial tree method provides better option prices than the binomial tree based on small relative error value to the Black-Scholes model. This is caused by the transition probability value of stock price movements in the implied binomial tree at each point is different, whereas in the binomial tree the value of transition probability is same. Furthermore, increasing the time step causes the option prices obtained from the implied binomial tree converge to the Black-Scholes. It is concluded that these three methods can be used in option pricing. Factors that influence the option price are stock price, strike price, interest rate and maturity date, are also obtained
The Robust Negative Binomial Regression Model on Under-five Mortality due to Pneumonia in the Province of East Java Anggun Qur'ani; Chandra Sari Widyaningrum; Sa’adatur Rohimiyah
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.34512

Abstract

Robust Negative Binomial regression model (RNBR) is a modelling method to overcome a problem if there are outliers and overdispersion in the data. Outliers are data points that are significantly different from other data. Outliers have a significant effect on modelling to the resulting model. Furthermore, overdispersion is indicated by the presence of too large values of Pearson statistics. In this study, the RNBR model was used to determine the factors of the toddler immune variable at post neonatal age that significantly influenced the number of under-five deaths caused by pneumonia in East Java Province. Based on the modelling obtained, it shows that the RNBR model provides more robust results in handling outlier and overdispersion problems. This can be seen from the AIC value of the RNBR model is smaller than the AIC of the Poisson regression model. In addition, and which are measures of the influence of outliers on the model, decreased from 1 for the Poisson regression model to around 0.42 for the RNBR model.
Factors Affecting The Number Of Domestic Flights In Indonesia During Covid-19 Pandemic Using SARIMAX Method Zahrah Zeinawaqi; Abdullah Ahmad Dzikrullah
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.34557

Abstract

Indonesia, which consists of thousands of large and small islands, relies heavily on-air transportation to support mobility between regions. As many as 80% of Indonesia's total air transportation passengers are domestic flight passengers. This shows how vital domestic flights are in Indonesia's air transportation system. However, in 2020, the COVID-19 pandemic had an impact that resulted in a decrease in the number of domestic flights in Indonesia. Therefore, an analysis is needed to determine the factors that affect the number of domestic flights in Indonesia. This study uses the SARIMAX method, a time series regression with the addition of seasonal factors and other variables or exogenous factors that significantly affect the model to improve the model's accuracy. Several exogenous variables are considered, including the number of operating civil aviation airports, positive daily cases of COVID-19, calendar effects during Eid al-Fitr and New Year's Day, and social restriction policies. The results showed that the number of operating airports one week before Eid al-Fitr, one week during Eid al-Fitr, one week before New Year, and Emergency PPKM significantly influenced the number of domestic flights. These variables offer pivotal insights into the influence of external factors on domestic flight patterns, exerting significant impacts on passenger travel behavior and subsequently influencing domestic flight volume. The integration of these variables in the SARIMAX model allows for a comprehensive analysis of the complex dynamics influencing domestic air travel in Indonesia. The best SARIMAX model obtained is SARIMAX (1,1,1)(4,1,1)7 with a MAPE value of 5.35% and a coefficient of determination is 97%.          
Perbandingan Fuzzy Time Series Lee, Chen, dan Singh pada Peramalan Kunjungan Wisatawan Mancanegara ke Indonesia tahun 2023 Ade Setyani Nurmara Sari; Ezra Putranda Setiawan
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.34914

Abstract

Tourism in Indonesia is one of the most reliable sectors because it can increase economic growth. Foreign tourist visits to Indonesia fluctuate every month, so forecasting needs to be done in order to help the Indonesian government in making decisions regarding the development process of the tourism industry to be right on target, efficient, and effective. The purpose of this research is to compare the Lee, Chen, and Singh fuzzy time series methods in forecasting foreign tourist visits to Indonesia.The data used in this study are monthly data on the number of foreign tourist visits to Indonesia from July 2014 to December 2023. The methods used for forecasting are Lee's fuzzy time series method, Chen's fuzzy time series, and Singh's fuzzy time series. The results of this study obtained MAPE values for in-sample data of foreign tourist visits to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 9.81%, 10.35%, and 2.77%, respectively. The MAPE values for out-sample data of foreign tourist arrivals to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 12.99%, 13.35%, 0.80%, respectively. From the MAPE value of in-sample data and out-sample data, it can be concluded that Singh's fuzzy time series has the smallest error value, so Singh's fuzzy time series is better and more accurate in forecasting foreign tourist visits to Indonesia.
STUDI TENTANG IDENTIFIKASI JAMUR BERACUN DAN TIDAK BERACUN DENGAN ALGORITMA CART-LOGITBOOST moch anjas aprihartha; Zulhandi Putrawan; Dicky Zulhan; Fatma Ahardika Nurfaizal
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.35072

Abstract

Mushrooms are one of the groups of living organisms in the fungal regnum which have umbrella-like body characteristics. The body consists of an upright part that functions as a rod to support the hood as well as a hood that is horizontal and rounded with different color variations. There are types of mushrooms that can be a food source for humans. Some types of mushrooms can be eaten or processed like other foods. Apart from that, some types of mushrooms are dangerous if consumed by humans because they are poisonous. Based on these problems, this study offers a new contribution in identifying types of poisonous and non-toxic mushrooms based on mushroom characteristics using the CART algorithm combined with the LogitBoost boosting algorithm. The aim of this research can be used as material for further studies in making tools that can effectively and accurately differentiate between poisonous and non-toxic types of mushrooms. This can help reduce cases of poisoning due to consumption of poisonous mushrooms. The data used is secondary data from public sources UCI Machine Learning Repository. Evaluation of model performance resulted in an accuracy of 98.79%; recall 98.70%; specificity 98.85%; precision 98.56%; F1-Score 98.63%, and AUC 0.9876. These results show that the model is very effective in detecting poisonous mushrooms and has minimal errors in classification.
Pendugaan Area Kecil Tingkat Kemiskinan Anak di Pulau Maluku dan Papua Tahun 2023 Priatmadani Priatmadani; Putri Puspita Sari; Ervan Nur Rahmat; Puput Budi Aji; Faried Akbar Nafiis; Nofita Istiana
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.35293

Abstract

Child welfare issues such as child poverty pose a challenge for Indonesia. The provinces in Maluku and Papua have the highest rates of child poverty. Data on child poverty at regencies/municipalities level is needed to address this issue through targeted policies. The direct estimations have a Relative Standard Error (RSE) value of more than 25 percent, necessitating the use of an indirect method, Small Area Estimation (SAE). This study aims to compare the results of indirect estimates of the percentage of children aged 0-17 living in poverty at the regencies/municipalities level in Maluku and Papua using SAE Empirical Best Linear Unbiased Prediction (EBLUP) and Hierarchical Bayes Beta (HB Beta) methods. Susenas KOR March 2023 data is used to produce direct estimates, while Podes 2021 data is used to form auxiliary variables. The results indicate that the SAE HB Beta method provides estimates with better RSE compared to SAE EBLUP. All regencies/municipalities in the Maluku and Papua have a fairly good level of accuracy.
Pengaruh Penggunaan Random Undersampling, Oversampling, dan SMOTE terhadap Kinerja Model Prediksi Penyakit Cardiovascular (CVD) Uswatun Hasanah; Agus Mohamad Soleh; Kusman Sadik
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.35552

Abstract

Cardiovascular Disease (CVD) or commonly known as Heart Disease is a leading cause of mortality globally, prompting extensive research into predictive models to assess individual risk and plan preventive measures. Machine learning approaches such as Random Forest, Support Vector Machine (SVM), and LASSO Logistic Regression have showed promise. Recent studies have indicated that traditional resampling methods like Random Oversampling, Random Undersampling, and SMOTE may not significantly improve model discrimination. This study aims to evaluate the impact of these techniques on the performance of Cardiovascular Disease (CVD) prediction models, utilizing data from the UCI Machine Learning Heart Disease database. By employing LASSO Logistic Regression, Random Forest, and Support Vector Machine (SVM) with resampling techniques, including Random Oversampling, Random Undersampling, and SMOTE. This research seeks to enhance understanding of model performance in addressing class imbalances within the dataset and contribute to refining cardiovascular disease (CVD) prediction strategies. This study demonstrates that the use of the SMOTE technique significantly enhances the performance of cardiovascular disease (CVD) prediction models. Specifically, when combined with the Random Forest algorithm, SMOTE achieves the best performance in terms of accuracy, sensitivity, and specificity. This highlights the importance of selecting appropriate resampling techniques to handle class imbalance in datasets. Consequently, this research contributes to refining CVD prediction strategies and provides new insights into improving prediction accuracy in imbalanced medical data.
Bahasa Inggris Dhea Dewanti; Kristuisno Martsuyanto Kapiluka; Febryna Sembiring; Ajeng Bita Alfira; Anang Kurnia
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.35584

Abstract

Multilevel binary logistic regression analysis is a development of logistic regression for hierarchical data structures. Hierarchical data is data from a population that has levels. This research examines the relationship model of Life Expectancy, Mean Years of Schooling, Expected Years of Schooling, Regency/City Minimum Wage as explanatory variables at level 1 (Regency) and Gross Regional Domestic Income (GRDP) as an explanatory variable at level 2 (Provincial) against Unemployment Rate (UR) as a response variable. The research results show that Life Expectancy and Minimum Wage at level 1 and GRDP at level 2 have a significant influence on district/city TPT on Java Island in 2022

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