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All Journal Epsilon: Jurnal Matematika Murni dan Terapan Square : Journal of Mathematics and Mathematics Education Journal of Applied Data Sciences Jurnal Penelitian Teknologi Informasi dan Sains
Safaat Yulianto
Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics Transportation

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Analisis Pertumbuhan Ekonomi Pra, Saat, dan Pasca Pandemi di Provinsi Nusa Tenggara Timur Menggunakan Analisis Regresi Data Panel Safaat Yulianto; Elisabeth Jeany Juanita Boisala; Zakaria Bani Ikhtiyar
Square : Journal of Mathematics and Mathematics Education Vol. 7 No. 2 (2025)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Economic growth serves as a key factor in analyzing the economic conditions of a country or region. The secondary panel data used in this study consists of a combination of cross-sectional data from five regencies/cities in East Nusa Tenggara (NTT) that were most affected by the COVID-19 pandemic. This time-series data spans from 2018 to 2023 and is split into three phases: the period before the pandemic, the pandemic period, and the post-pandemic period.This study aims to analyze the factors influencing economic growth in NTT Province during the pre-, during-, and the period after the pandemic. The research employs the Fixed Effect Model (FEM) with Gross Regional Domestic Product (GRDP) as the dependent variable. The independent variables include the Regional Minimum Wage (UMR), Per Capita Expenditure, Labor Force Participation Rate (TPAK), and Domestic Investment (PMDN). The research findings indicate that the variables that significantly influence economic growth in NTT Province are the Regional Minimum Wage and Per Capita Expenditure. The Regional Minimum Wage has a negative impact on economic growth, whereas Per Capita Expenditure has a positive impact. This means that an increase in the Regional Minimum Wage will decrease economic growth, while an increase in Per Capita Expenditure will enhance economic growth.
VARIABEL YANG MEMPENGARUHI KRIMINALITAS DI WILAYAH SULAWESI UTARA MENGGUNAKAN REGRESI DATA PANEL Dwi Hartiana Rahmasari; Safaat Yulianto
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 19, No 2 (2025)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v19i2.15377

Abstract

North Sulawesi Province has high economic and tourism potential due to its rich agricultural and mining resources and natural beauty, attracting investors and tourists. However, the high crime rate in North Sulawesi Province raises concerns about economic sustainability and social stability. Investors will tend to choose safe and stable places to invest, while tourists will also choose safe places to vacation. This study aims to analyze the variables that influence crime rates in North Sulawesi Province. Previous studies have shown that crime rates are influenced by several factors, including education, unemployment, poverty, and inequality. These variables will be analyzed using panel data regression. Panel data regression is used because crime rates vary across regions and over time. Thus, panel data can reveal the effects of inter-regional and inter-temporal factors. The study covers 11 districts and 4 cities in North Sulawesi Province, spanning the period from 2021 to 2023. The results of the panel data regression indicate that the most appropriate model is the Fixed Effect Model (FEM). Based on the results of the Fixed Effect Model (FEM), it is found that poverty and inequality significantly influence crime, while education and unemployment do not significantly influence crime. The Adjusted R Square value is 0.8132, meaning that 81.32% of crime in the North Sulawesi region can be explained by education, unemployment, poverty, and inequality, while 18.68% is explained by other factors.
A Bi-LSTM Prediction Model Integrated with GIS for Spatiotemporal Malaria Endemicity Mapping and Early Warning in Indonesia Wellie Sulistijanti; Safaat Yulianto; Abdul Syukur; Ngatimin Ngatimin
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1483

Abstract

Malaria continues to be a major public health challenge in Indonesia, particularly in eastern provinces where transmission patterns are influenced by climatic variability, geographical heterogeneity, and historical incidence trends. This study proposes an integrated spatio-temporal malaria forecasting and early warning framework by combining Bidirectional Long Short-Term Memory (Bi-LSTM), Geographic Information Systems (GIS), and SHapley Additive exPlanations (SHAP). Monthly malaria incidence, climate variables, population data, and provincial spatial data from 12 endemic provinces in Indonesia during 2014–2025 were used. The data were preprocessed through incidence-rate conversion, outlier handling, log transformation, Min-Max normalization, and six-month sliding window segmentation. The proposed Bi-LSTM model was assesed using RMSE, sMAPE, and R², and compared againts Naive Forecasting, SARIMA, and Simple LSTM baselines. The model attained optimol global performance, with an RMSE of 0.0522, sMAPE of 18.39%, and R² of 0.9553. The provincial analysis shows good performance throughout most regions, including high-burden areas like Papua and West Papua, however a decline in relative accuracy was observed in West Nusa Tenggara due to near-zero incidence rates. SHAP analysis revealed that historical malaria incidence was the primary predictor, whereas rainfall emerged as the most significant climatic variable. GIS-based forecasting showed spatial patterns aligned with malaria epidemiology in Indonesia, with Papua exhibiting the gratest predicted incidence in December 2025. These findings demonstrate that the Bi-LSTM–GIS–SHAP framework can support malaria endemicity mapping, interpretable forecasting, and province-level early warning for targeted public health interventions.
Pemodelan Spasial Data Panel pada Analisis Kemiskinan Kabupaten/Kota di Provinsi Daerah Istimewa Yogyakarta Rahma Suryaningtyas Wibowo; Safaat Yulianto
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 3 (2026): September: JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i3.4444

Abstract

Poverty remains one of the main development problems in the Special Region of Yogyakarta Province, which still records a relatively high poverty rate on Java Island. This study aims to describe the factors influencing the percentage of poor population, to examine the existence of spatial effects, and to determine the best model for analyzing poverty in the Special Region of Yogyakarta Province. This study used secondary panel data published by Statistics Indonesia (BPS), covering four regencies and one city during the 2021–2024 period employing the Spatial Panel method. The dependent variable is the percentage of poor population, while the independent variables consist of education indicators (mean years of schooling and expected years of schooling) and health indicators (life expectancy rate and life expectancy at birth). The results of Moran's I test (p-value = 0.007988) and the Lagrange Multiplier test proved a significant spatial dependence in the distribution of poverty. The best model is the Spatial Autoregressive (SAR) Fixed Effect model with an Adjusted R-Square of 0.9554794, in which mean years of schooling has a significant negative effect on poverty, while life expectancy rate and life expectancy at birth have significant positive effects. These findings imply that education-focused policy interventions are effective in reducing poverty in the Special Region of Yogyakarta Province.
Co-Authors Abdul Syukur Dwi Hartiana Rahmasari Elisabeth Jeany Juanita Boisala Ngatimin Ngatimin Rahma Suryaningtyas Wibowo Wellie Sulistijanti Zakaria Bani Ikhtiyar