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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) UNP Journal of Statistics and Data Science
Tessy Octavia Mukhti
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Social Sciences

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Pemetaan Intensitas Gempa Bumi di Wilayah Sumatera Barat Menggunakan Model Epidemic Type Aftershock Sequence Spatio-Temporal Hidayatul Fikra; Dina Fitria; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/171

Abstract

The random spatial and temporal occurrence of earthquakes means that this are still being researched from a seismological and stochastic perspective. Point processes are examples of stochastic processes which explain seismic activity, one of them is Epidemic Type Aftershock Sequence (ETAS) model. It lackness ignores the location or spatial component of. Consequently, the components of time, location, and magnitude will be taken into consideration when discussing the ETAS model in this study. The spatio-temporal model is the name given to this concept. Therefore, in this research,mapping of earthquake intensity will be carried out in the West Sumatra region using the spatio-temporal ETAS model stated in conditional intensity function with eight parameters. The data used are earthquake events in the West Sumatra region with a magnitude threshold of 4 SR and a depth of ≤ 70 km for the period January 2000 to January 2024. Parameter model estimated using the maximum likelihood method and solved using the Davidon Fletcher Powell algorithm. The result shows area of West Sumatra with high earthquake intensity is coastal area, namely West Pasaman, Padang, Mentawai Islands and the South Pesisir. This makes the area vulnerable to seismic disasters
Robust Spatial Autoregressive (Robust SAR) Modeling in the Case of Poverty Percentage in West Java Novi, Yoli Marda; Tessy Octavia Mukhti; Zamahsary Martha
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.61818

Abstract

Poverty is a complex problem influenced by various economic and social factors, such as the open unemployment rate, the minimum wage, population density, and the school participation rate. This study aims to model the poverty rate in West Java Province by considering spatial effects and the existence of outliers through the application of Spatial Autoregressive (SAR) and Robust Spatial Autoregressive (Robust SAR) models. Based on the Lagrange Multiplier test, the SAR model is declared suitable for use. However, the presence of outliers in the data necessitated the use of a robust approach to obtain more accurate results. The analysis showed that the Robust SAR model had a coefficient of determination of 81.53%, higher than that of the SAR model at 77.48%, making it a better model for explaining variations in poverty levels. Of the four independent variables, only School Participation Rate had a significant effect in both models, where an increase in School Participation Rate contributed to a decrease in the poverty rate. This finding confirms the importance of investment in education as a strategic effort to reduce welfare inequality between regions in West Java.
Clustering of Regencies/Cities in NTT Province Based on Poverty Using the Ward and Fuzzy C-Means Methods Ervi Dayana Putri; Tessy Octavia Mukhti
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 14 No 1 (2026): VOLUME 14 No 1, 2026
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v14i1.62887

Abstract

Poverty remains a major problem in Indonesia's economic development. In Indonesia, East Nusa Tenggara Province has the fifth highest percentage of poor people in Indonesia as of September 2024. However, East Nusa Tenggara Province has also shown a decline in its poverty rate. According to a report from the Statistics Indonesia of Nusa Tenggara Timur Province, 18.60 percent of the province's population will be impoverished in 2024. This number is still significantly higher than Indonesia's 8.57 percent poverty rate. Additionally, East Nusa Tenggara Province's districts and cities have not benefited equally from the reduction in poverty because of notable regional differences. This study uses poverty data for East Nusa Tenggara Province in 2024 obtained from the Statistics Indonesia of Nusa Tenggara Timur Province and applies cluster analysis by comparing the Ward hierarchical cluster method and the non-hierarchical Fuzzy C-Means method to group regions based on poverty characteristics. The results of the study concluded that the Ward method with 3 clusters provided better results than the Fuzzy C-Means method. It is anticipated that the government will use these results as a foundation to concentrate more on creating more focused development plans for regions with the highest rates of poverty.
Classification of Determining Factors for Eligibility of Extreme Poverty Social Assistance Recipients in Dumai City for 2024 Using CHAID Nurul Hasni Pajrini; Dina Fitria; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/354

Abstract

Poverty is one of the goals of the Sustainable Development Goals (SDGs). Poverty is a condition in which an individual falls below the standard minimum value of basic needs, both food and non-food. One of the efforts by the Indonesian government to alleviate poverty is through fulfilling needs in various sectors. Although the distribution of social assistance has been successfully implemented, there are still issues in determining beneficiaries who are not properly targeted. Therefore, it is necessary to identify the significant factors influencing the eligibility of social assistance recipients. The application of the CHAID method in classifying the determining factors for eligibility of extreme poverty social assistance recipients in Dumai City for 2024 shows that the significant factors influencing the eligibility status of extreme poverty social assistance recipients in Dumai City for 2024 are house size and neighbors' testimonies. The classification model's accuracy in determining the eligibility factors for extreme poverty social assistance recipients in Dumai City for 2024 is 87.70%.
Co-Authors AL Rezki Ivansyah Arssita Nur Muharromah Atus Amadi Putra defal aditya defran Dewi Febiyanti Dina Fitria Dodi Vionanda Dony Permana Ervi Dayana Putri Fadhilah Fitri Fadhilah Fitri Hariati Ainun Nisa Hidayatul Fikra Ihsan Dermawan Inna Auliya Martia Rosada Muhammad Fadhil Aditya Aditya Nonong Amalita Novi, Yoli Marda Nurul Hasni Pajrini Putri Rivani Salma, Admi Syafriandi Syafriandi Yenni Kurniawati Zamahsary Martha Zilrahmi, Zilrahmi