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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Eksponensial
Ariyawan, Jovansha
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Pengelompokan Kabupaten dan Kota di Jawa Timur berdasarkan Percepatan Pemulihan Ekonomi Menggunakan Pendekatan Hirearki Mahadesyawardani, Arinda; Zhafirab, Azizah Atsariyyah; Ariyawan, Jovansha; Humaira, Edla Putri; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1273

Abstract

The Covid-19 pandemic's diverse impact on Indonesia's economy, particularly in East Java, spurred the government to formulate a comprehensive work plan targeting three key objectives, one of which is to expedite economic recovery. This plan focuses on three key indicators: economic growth, open unemployment rate (TPT), and the gini ratio. It is known that during the pandemic, East Java initially experienced economic growth that contracted until eventually showing positive growth in the second quarter of 2021, which has been supported by national policies. This study explores district and city classification in East Java based on economic recovery indicators through hierarchical clustering. The analysis identifies Ward's linkage as the most effective model, with a cophenetic correlation coefficient of 0.9311. Internal clustering validation tests reveal two optimal clusters. Cluster 1 is characterized by a notably high average acceleration of economic recovery across all three indicators. The findings suggest that the government should optimize the economic stimulus program for cluster 2 and focus on enhancing income redistribution and job opportunities for cluster 1.
Comparing MARS and Binary Logistic Regression to Modelling Hepatitis C Cases using the SMOTE Balancing Method Chamidah, Nur; Ramadhanti, Aulia; Ramadhani, Azzah Nazhifa Wina; Syahputra, Bimo Okta; Ariyawan, Jovansha; Kurniawan, Ardi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.33196

Abstract

Hepatitis is an inflammatory liver disease caused by viral infection and remains a major global public health concern, responsible for approximately 1.4 million deaths annually. Egypt is among the countries with the highest prevalence of Hepatitis C. To address this issue and support Goal 3 of the Sustainable Development Goals (SDGs), this study applies a quantitative approach using secondary data to analyze factors influencing Hepatitis C infection in Egypt. Two statistical models Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS) were compared, with the SMOTE method implemented to correct class imbalance. The dataset consisted of 608 patient observations, initially imbalanced at a ratio of 86.5:13.5, and were balanced to 52.6:47.4 after SMOTE application. The results revealed that the MARS model demonstrated superior predictive performance compared to binary logistic regression. All independent variables were found statistically significant (p < 0.05), except sex. Additionally, all odds ratios were less than 1, indicating a lower probability of Hepatitis C infection relative to non-infection. These findings highlight the relevance of statistical modeling and data-driven strategies in supporting preventive health measures. 
Co-Authors Ana, Elly Ardi Kurniawan Dita Amelia Humaira, Edla Putri M. Fariz Fadillah Mardianto Mahadesyawardani, Arinda Nur Chamidah Ramadhani, Azzah Nazhifa Wina Ramadhanti, Aulia Syahputra, Bimo Okta Zhafirab, Azizah Atsariyyah