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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Kesehatan Vokasional BAREKENG: Jurnal Ilmu Matematika dan Terapan
Suyono, Billy Christandy
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Health Professions Mathematics Mechanical Engineering Nursing Physics Public Health Transportation

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ANALYSIS OF GLOBAL ECONOMIC UNCERTAINTY IMPACT ON INDONESIA’S FINANCIAL AND TRADE VOLATILITY USING VECTOR ERROR CORRECTION MODEL WITH EXOGENOUS VARIABLES Amelia, Dita; Suliyanto, Suliyanto; Nugraha, Galuh Cahya; Suyono, Billy Christandy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2963-2980

Abstract

Increasing global economic uncertainty due to the influence of geopolitical dynamics and monetary policy adjustments from major countries has significantly impacted financial and trade stability in Indonesia. This research examines the relationship between global economic uncertainty and the volatility of Indonesia's financial and trade indicators using the Vector Error Correction Model with Exogenous Variables (VECM-X) approach. The model incorporates external factors such as the US Dollar Index (DXY), Volatility Index (VIX), and Trade Policy Uncertainty (TPU), using monthly data from January 2019 to December 2024. The results of the analysis show that each variable has different volatility with patterns that tend to fluctuate, and there is a cointegration relationship between the variables of the Rupiah exchange rate (USD/IDR), Jakarta Composite Index (JCI), interest rates, export, and imports. The causality test results show that exports, JCI, and imports affect interest rates without a reverse relationship, while there is a one-way relationship between exports and imports and JCI and the exchange rate. In addition, imports and JCI have a two-way relationship that affects each other. Impulse Response Function (IRF) results indicate dynamic short-term interactions among endogenous variables, which gradually stabilize over the medium to long term. In addition, the variance decomposition results show that most of the variability of each variable is explained by itself in the short term, with contributions from other variables increasing over time. This research contributes to Sustainable Development Goals (SDGs) point 8: Decent Work and Economic Growth, by providing insight to strengthen Indonesia's macroeconomic resilience. Integrating exogenous global indicators into the VECM-X model offers a more comprehensive understanding of how global shocks affect domestic stability. However, this study is limited to a macro-level analysis using secondary data and does not account for microeconomic or sectoral variations.
Modelling Factors Affecting the Middle Income Trap in Indonesia Using Generalized Additive Models (GAM) Amelia, Dita; Suliyanto, Suliyanto; Zhafira, Azizah Atsariyyah; Ramadhanti, Aulia; Suyono, Billy Christandy; Hizbullah, Firqa Aqila
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.35119

Abstract

Indonesia is currently facing a significant challenge known as the Middle Income Trap (MIT), a condition where economic growth stagnates after reaching middle-income status, hindering progress toward becoming a high-income country. This study aims to identify and model the socio-economic factors influencing MIT at the provincial level in Indonesia during the 2020–2023 period. The Generalized Additive Model (GAM) is employed to estimate nonlinear relationships between predictors and the response variable while capturing complex patterns in panel data. GRDP per capita is used as an indicator of MIT, with six predictor variables: life expectancy, poverty rate, informal employment share, secondary education completion rate, food insecurity prevalence, and population density. The results showed that the best model was obtained based on the minimum GCV and AIC values of the Gaussian family with an identity link function and 5 knot points with the highest correlation of 99,9%. Five variables show nonlinear effects, while food insecurity exhibits a significant negative linear impact. The findings provide a valuable reference for designing inclusive and adaptive eco nomic policies based on each region’s socio-economic characteristics to mitigate MIT risks and also supports the achievement of Sustainable Development Goal (SDG) 8, which promotes decent work and sustained economic growth.
Performance Evaluation of Machine Learning Algorithms for AIDS-Infected Patient Classification Kurniawan, Ardi; Marthabakti, Citrawani; Putri, Larisa Mutiara; Suyono, Billy Christandy; Alisiah, Rindiani Ahmada
Jurnal Kesehatan Vokasional Vol 10, No 3 (2025): August
Publisher : Sekolah Vokasi Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkesvo.107716

Abstract

Background: According to UNAIDS (2023), approximately 39.9 million people are living with HIV worldwide, with 1.3 million new cases and 630,000 AIDS-related deaths in 2023. This indicates that HIV/AIDS remains a serious global health threat. Machine learning methods have the potential to improve the accuracy of AIDS infection classification.Objective: This research is aimed to determine the best classification method based on prediction accuracy and to identify the method with the best performance for further analysis.Methods: This research used a quantitative approach by evaluating the performance of machine learning algorithms: Decision Tree, Random Forest, XGBoost, Naive Bayes, and Logistic Regression. Secondary data were obtained from the UCI Machine Learning Repository, comprising 2,000 observations of AIDS patients and 23 variables. Model evaluation used a confusion matrix to calculate accuracy, precision, recall, and F1-score. The best model, logistic regression, was further analyzed with parameter significance tests, odds ratios, and goodness of fit.Results: Logistic regression yielded an accuracy of 88.4%, precision and recall of 90%, and the highest F1-score. Variables significant to AIDS were: time, preanti, symptom, offtrt, and cd420. The model passed the Hosmer and Lemeshow test (p-value = 0.365) with a Nagelkerke R-Square of 0.642.Conclusion: Machine learning approaches, particularly logistic regression, support early detection of AIDS and data-driven medical decision-making.
Co-Authors Alisiah, Rindiani Ahmada Ardi Kurniawan Dita Amelia Dita Amelia, Dita Hizbullah, Firqa Aqila Marthabakti, CitraWani Nugraha, Galuh Cahya Putri, Larisa Mutiara Ramadhanti, Aulia Suliyanto Suliyanto Zhafira, Azizah Atsariyyah