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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Engineering, Mathematics and Computer Science Journal (EMACS)
Purnama, Mohammad Dian
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

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An Implementation of Ordinal Probit Regression Model on Factor Affecting East Java Human Development Index Purnama, Mohammad Dian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.12094

Abstract

An instrument for measuring human development, the Human Development Index (HDI) looks at how well human development has been achieved in relation to a few fundamental aspects of quality of life. In 2023, East Java's HDI showed an increase in the last three years with the latest value of 73.38. Despite the increase, East Java still has the lowest HDI in Java and Bali. This situation suggests the need for an in-depth analysis of the factors that influence HDI. This study aims to identify factors that contribute to HDI to formulate more appropriate policies in the future. The data used is the HDI of East Java in 2023 with ordinal categories. To analyze the ordinal data, the ordinal probit regression method was applied. The results show that the percentage of poor people has a significant influence on HDI. In addition, the classification accuracy of the model is obtained with a value of 50.5%, which indicates that the accuracy of the model in predicting HDI into the right category reaches 50.5%.
Relationship Between Temperature and Humidity on Rainfall: A Multiple Linear Regression Analysis Purnama, Mohammad Dian; Mustafidah, Mutia Eva
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11466

Abstract

Indonesia is one of the tropical countries in the world that has two seasons, the dry season and the rainy season. One of the biggest challenges in tropical countries is flooding caused by heavy rainfall. Not only does it cause flooding, rainfall also affects several sectors especially agriculture. Areas that have a lot of rain-fed agricultural land, especially rice fields, depend on rainfall because it determines crop yields. This study uses data from 12 sub-districts in Mojokerto district where agricultural activities are one of the pillars of the economy in the region. There are various factors associated with rainfall such as temperature and humidity. The data used is the year 2022 using multiple linear regression. Based on the results of the study, both predictor variables have a strong and positive relationship with rainfall with a correlation coefficient of 0.760007. With a significance level of 5% or 0.05, in the partial test, only the humidity variable has a significant effect on the amount of rainfall. While in the simultaneous test, both variables have a significant effect. These factors together have a coefficient of determination of 0.57761 or the contribution of the influence of the two predictor variables of 57.761% while the remaining 42.239% by other variables.
STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS Maulana, Dimas Avian; Sofro, A'yunin; Ariyanto, Danang; Romadhonia, Riska Wahyu; Oktaviarina, Affiati; Purnama, Mohammad Dian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp97-106

Abstract

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.
Enhancing Tourism Demand Forecasting Accuracy Through Clustering Time Series: A Comparison MAPE Analysis of Indonesian Provincial Domestic Tourist Flows Purnama, Mohammad Dian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14112

Abstract

The post-pandemic recovery period of the Indonesian tourism sector poses new challenges for accurate tourism demand forecasting across Indonesia's diverse provincial richness. This research aims to enhance the predictive accuracy of domestic tourism demand by comparing conventional single-provincial forecasting methods with clustering-based time series techniques. The Geometric Brownian Motion (GBM) model analyzed data regarding the monthly influx of domestic tourists to 34 provinces from January 2021 to May 2025. This study utilized average linkage agglomerative nesting (AGNES) clustering to discern structural similarities among provinces. Subsequently, silhouette analysis was employed to determine the optimal number of clusters. The findings demonstrate that the cluster-based forecasting approach markedly improved accuracy relative to the non-clustered model. The Mean Absolute Percentage Error (MAPE) for the traditional provincial forecasts was 16.48%. The first cluster-based model had an MAPE of 13.38% and the second cluster-based model had an MAPE of 6.54%. These findings indicate that grouping provinces with analogous temporal patterns enhances the model's ability to identify the underlying dynamics in domestic tourism flows. The work underscores the efficacy of combining stochastic models with hierarchical clustering to enhance evidence-based tourist planning and policy development. This study improves sustainable tourism management by providing an empirical foundation for enhanced forecasting precision, particularly in post-crisis recovery periods.
Co-Authors AFFIATI OKTAVIARINA Danang Ariyanto Maulana, Dimas Avian Mustafidah, Mutia Eva Romadhonia, Riska Wahyu Sofro, A'yunin