Jurnal Matematika, Statistika dan Komputasi

Prediksi Produk Domestik Regional Bruto (PDRB) di Jawa Timur Menggunakan Metode Autoregressive Distributed Lag (ARDL)

Dwi Agustina

Gross Regional Domestic Product (GRDP) is an indicator of the rate of economic development of a region during a certain period. An increase in GDP will affect economic growth. The percentage rate of GRDP in East Java Province has fluctuated from 2011 to 2023. This research aims to predict Gross Regional Domestic Product (GRDP) using the Autoregressive Distributed Lag (ARDL) method. The data analyzed includes Gross Regional Domestic Product (GRDP) as the dependent variable, while household consumption expenditure, Gross Fixed Capital Formation (PMTB), exports, imports and government consumption expenditure as independent variables. Based on the results of data analysis, the prediction results obtained for Quarters 1 to 4 of 2024 are 732655.53 billion rupiah, 759400.90 billion rupiah, 793509.21 billion rupiah, and 667414.81 billion rupiah, with a Mean Absolute Percentage Error (MAPE) value of 3.22%.

Survival Analysis For Duration Of Job Searching In Banten With Multivariate Adaptive Regression Spline (MARS) Approach

Fairuz Zayyan

Unemployment is a condition which a jobless group of people that have tried searching for job but unable to attain it. One regional economic growth success measure at a macro level is the Gross Regional Domestic Product (GRDP). Banten is considered to have one of the lowest GRDP in Java with the highest unemployment rate in Indonesia. This is a problem because the economic growth level in Indonesia is strongly influenced by economic movements that occur on Java. To describe the unemployment problem, it is necessary to analyze the time length of job searching, which is based on the time from searching for job until getting a job. It is expected that this research can be a new paradigm in solving unemployment problems through interaction and non-linear patterns that are formed. The appropriate method for this problem is survival analysis. To identify non-linear and interaction patterns, Martingale and Cox Snell residual modeling are used in survival analysis with Multivariate Adaptive Regression Spline (MARS). The results of this study indicate that the Cox Snell residual produces a smaller MSE value than the Martingale residual, which is 0.036. Based on Cox Snell residual modeling using MARS, the variables that affect the duration of job search are age, gender, marital status, highest education, and certified training. This study shows an interaction between the variables of age with knot in 23 and education, age with knot 20 and certified training, as well as age with knot 19, gender, and certified training. Age, gender, and certified training were the most important variables and contributed the most to the model.

Forecasting Dry Rubber Production in Indonesia for the Year 2022 Using Pegel's Exponential Smoothing Method with Modified Golden Section Optimization

Rahmi Nurul Ainun Fitrah

Pegel’s Exponential Smoothing is a forecasting method that considers separating trend and seasonal aspects, with additive and multiplicative models. Pegel’s Exponential Smoothing has three parameters, α, β, and γ. Many possible parameter combinations may yield an optimal solution, so a modified Golden Section method is used. The principle of this method is to iteratively reduce the boundary area of x that may produce an optimal objective function value, systematically decreasing the number of search steps to minimize the number of trials. Data obtained from the Central Bureau of Statistics regarding the amount of dry rubber production in Indonesian plantations from January 2017 to December 2022 is assumed to contain a multiplicative seasonal effect due to the relatively unstable seasonal pattern heights. This study compares three trend models: no trend, additive trend, and multiplicative trend in the multiplicative seasonal Pegel’s Exponential Smoothing method. This study aims to predict the amount of dry rubber production in Indonesian plantations from January 2022 to December 2022. Forecast validation results show that the multiplicative trend in the multiplicative seasonal Pegel’s Exponential Smoothing method, with a MAPE of 3.389001% and an RMSE of 8,839.965080, has the best forecasting accuracy for this data compared to the other three trend models.

Application of the Spatial K'luster Analysis by Tree Edge Removal (SKATER) Method for Sub-district Grouping Based on Food Crop Commodities in Pati Regency

Nanda Aurelia Salsabila Salsabila

Pati Regency is an important area in the agricultural sector in Central Java. However, the decline in agricultural land and changes in land use pose challenges in managing this sector. This study uses the SKATER method, which considers spatial aspects, to analyze food crop data in 21 sub-districts in Pati Regency. Data were obtained from the publication "Pati Regency in Figures 2024". With the help of R Studio software, grouping was carried out into 3, 4, and 5 clusters, then analyzed using MANOVA by looking at the Pillai Trace value. As a result, the grouping of 4 clusters proved to be optimal with a Pillai trace value of 0.73010 which divided the area into 4 groups: Cluster 1 consists of 9 sub-districts with medium commodities, Cluster 2 consists of 3 sub-districts with low commodities, Cluster 3 consists of 8 sub-districts with very low commodities, and Cluster 4 consists of 1 sub-district with high commodities.

The The Influence of Socio-Demographic Factors on Youth Smoking Behavior in West Nusa Tenggara Province in 2022

Ari Bahagia Sinaga

More than 8 million people have died from tobacco each year, including people who do not smoke but are exposed to secondhand smoke. In 2022, the percentage of youth who smoke tobacco in West Nusa Tenggara Province reached 23.10 percent, making it the province with the highest percentage of youth who smoke tobacco in Indonesia. Youth are people aged 15 to 24 years. Youth, as the next generation of the nation, are important for the progress and development of the nation, so they are expected to have excellent health in the future. This study aims to determine the characteristics of youth who smoke tobacco in West Nusa Tenggara Province in 2022 and to identify the socio-demographic factors that influence it. Descriptive analysis and binary logistic regression analysis were used on data sourced from Susenas Kor 2022. The results showed that age, gender, ever-married status, school status, working status, and household member smoking behavior had a significant effect on youth tobacco smoking behavior. Male youth aged 20–24 years, unschooled, ever-married, working, and having other household members who smoke have a high tendency to smoke. Based on these results, the government is expected to strengthen the Smoke-Free Area policy to prevent smoking behavior among youth.

Combination of SMOTE and Tomek Links Algorithms to Improve Performance on Unbalanced Data Classification with Random Forest Method Approach

Muhammad Rizky

Rapid technological advancements go hand in hand with an increase in the amount and complexity of available data. However, data that has an unbalanced class distribution is often encountered. Classifying unbalanced data causes the resulting classification model to predict the majority class and ignore the minority class. There are several methods to overcozme unbalanced data including oversampling and undersampling. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling method that balances the data by generating synthetic instances for the minority class. Meanwhile, Tomek Links is an undersampling method that removes data from the majority class that has similar characteristics. In this study, a comparison of the performance of the random forest method and the random forest method using the SMOTE-Tomek Links algorithm on the Religious Harmony Index (KUB) data was carried out. The results obtained in the random forest method are accuracy of 96.62%, precision of 98.4%, and recall of 74.6%. While the SMOTE-Tomek Links random forest method obtained an accuracy value of 97.55%, precision of 94.9%, and recall of 90.3%.

Comparison of Extreme Learning Machine (ELM) and Multi-Support Vector Machine (Multi-SVM) Methods in the Identification of Herbal Plants.

Luluk Sarifah

Hereditarily, Indonesian people have utilized herbal plants as ingredients for making traditional medicines, even with technological advances, they have been utilized in the pharmaceutical industry that are efficacious for health. There are around 2,039 species of herbal medicinal plants in Indonesia that sometimes have similarities, making it difficult to identify the types of herbal plants and switch to using more practical chemical drugs. The purpose of this study is to facilitate the identification of herbal plant types using machine learning methods and digital images by comparing the performance of the Extreme Learning Machine (ELM) and Multiclass Support Vector Machine (Multi-SVM) methods, so that the most effective and efficient method for identifying herbal plants can be obtained. The ELM method was created to overcome the weaknesses of feedforward artificial neural networks, especially in terms of learning speed, while the Multi-SVM method is a supervised machine learning algorithm that helps in classification problems and this method is also a further development of the SVM method. Based on the simulations that have been carried out, identification using the ELM method obtained accuracy for 5 types of herbal plant data, training and testing each with an accuracy of 100%. 10 types of herbal plant data, training data obtained 99% accuracy and testing data of 96.667%. 20 types of herbal plant data training data obtained 91% accuracy and testing data of 75%. 30 types of herbal plants training data obtained 80.33% accuracy and testing data of 68%. As for the Multi-SVM method in identifying 5 types of herbal plant data, training data accuracy was obtained. 52% and testing 66.667%. 10 types of herbal plants for training data obtained 39% accuracy and testing 30%. 20 types of herbal plants for training data obtained 23.5% accuracy and testing 23.33%. and 30 types of herbal plants for training data obtained 16.67% accuracy and testing 14.44%.

Optimasi Portofolio Berdasarkan Model Mean-Variance dengan Menggunakan Lagrange Multiplier pada Saham IDX30

Werry Febrianti

This study aims to optimize the portofolio of stocks included in the IDX30 index using the Mean-Variance model developed by Markowitz. The Lagrange Multiplier method is used in this study to determine the optimal fund allocation by minimizing risk and optimizing expected return. The data used is the daily closing price of stocks from 15 companies listed in the IDX30 index over the last five years (2019-2024). The results show that the Mean-Variance and Lagrange Multiplier methods are effective in identifying the optimal portofolio that can minimize investment risk while maximizing returns.

Nonparametric Regression with A Fourier Series Approach on Food Security Index in East Java

Nur Afriani

Nonparametric regression is a regression model approach used when the regression curve is
unknown. One of the estimators that can be used to estimate nonparametric regression
approaches is the Fourier series. The Fourier series is a trigonometric polynomial that has
flexibility so that it can adjust effectively to the local properties of the data. The purpose of the
study is to estimate a nonparametric regression model with a Fourier series approach and
obtain factors that affect the food security index in East Java in 2022. Factors thought to be
influential are the prevalence of stunted toddlers, rice production, the percentage of
households with access to clean water, the percentage of poor people, and life expectancy.
Based on the results of the study, it was obtained that the best model of nonparametric
regression of the Fourier series is a model with 3 oscillations which is shown with a minimum
Generalized Cross-Validation (GCV) value is 7.60 and a determination coefficient value
is 90.89%.

Implementation Of Mathematical Logic Based Artificial Intelligence In Breast Cancer Diagnosis: A Literature Review

Andi Sitti Nur Fatimah Madaeng

Abstract

Introduction: Breast cancer is the most common type of cancer in Indonesia so accurate diagnosis is required. Implementation of mathematical logic based on Artificial Intelligence (AI) can be a solution for more accurate diagnosis. Objective: To find out the AI-based mathematical logic approach in breast cancer diagnosis. Methods: The literature study was conducted using search tools from several databases, namely ScienceDirect, PubMed, Google Scholar with keywords within the study limit of the last 10 years in English. There were 9 literatures reviewed in this literature review. Discussion: AI through Convolutional Neural Networks (CNNs), Naïve Bayes (NB), and Support Vector Machine (SVM) systems. have diagnostic capabilities. CNN DLs has an accuracy value of 0.9, specificity of 0.96, and sensitivity of 0.86 whereas, SVM has an accuracy of 98% and NB has an accuracy value of 97.82%. Conclusion: AI in breast cancer diagnostics improves accuracy, speed, efficiency of diagnosis, and makes treatment more personalized.