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All Journal Statistika Bulletin of Electrical Engineering and Informatics Journal of Research and Advances in Mathematics Education Jurnal Manajemen & Keuangan Jurnal Ilmiah Peuradeun Narra J Proceedings of International Conference on Multidiciplinary Research International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Aceh International Journal of Science and Technology Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Media Penelitian dan Pengembangan Kesehatan Journal on Mathematics Education
Hizir Sofyan
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Journal : Infolitika Journal of Data Science

 1 
A Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia Sasmita, Novi Reandy; Khairul, Moh; Sofyan, Hizir; Kruba, Rumaisa; Mardalena, Selvi; Dahlawy, Arriz; Apriliansyah, Feby; Muliadi, Muliadi; Saputra, Dimas Chaerul Ekty; Noviandy, Teuku Rizky; Watsiq Maula, Ahmad
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.130

Abstract

The clustering, one of statistical analysis, can be used for understanding population patterns and as a basis for more targeted policy making. In this ecological study, we explored the population dynamics across 23 districts/cities in Aceh Province. The study used the Aceh Population Development Profile Year 2022 data, focusing on the total population, in-migrants, out-migrants, fertility, and maternal mortality as variables. The study employed descriptive statistics to ascertain the data distribution, followed by the Shapiro-Wilk test to evaluate normality, which is crucial for selecting the appropriate statistical methods. The Spearman test was used to determine correlations between the total population and the variable as indicators. Probabilistic Fuzzy C-Means (PFCM) method is used for clustering. To optimize clustering, the silhouette coefficient was calculated using the Euclidean Distance and the elbow method, with the results analyzed using R-4.3.2 software. This study's design and methods aim to provide a nuanced understanding of demographic patterns for targeted policy-making and regional development in Aceh, Indonesia. Based on the data normality test results, only fertility (p-value = 0.45), while the other variables are not normally distributed. Spearman test was used, and the results showed that only in-migrants (p-value = 1.78 x 10-6) and out-migrants (p-value = 2.30 x 10-6) correlated to the Aceh Province population. Using the population variable and the two variables associated with it, it was found that 4 is the best optimum number of clusters, where clusters 1, 2, 3, and 4 consist of three districts/city, nine districts/city, four districts/city and seven districts/city respectively.
Unraveling Geospatial Determinants: Robust Geographically Weighted Regression Analysis of Maternal Mortality in Indonesia Rahayu, Latifah; Ulfa, Elvitra Mutia; Sasmita, Novi Reandy; Sofyan, Hizir; Kruba, Rumaisa; Mardalena, Selvi; Saputra, Arif
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.133

Abstract

Maternal Mortality Rate (MMR) in Indonesia has experienced a concerning annual increase, reaching 4,627 deaths in 2020 compared to 4,221 in 2019. This upward trajectory underscores the urgency of investigating the factors contributing to MMR. Recognizing the spatial heterogeneity and outliers in the data, our study employs the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation approach. Using secondary data from the 2020 Indonesian Health Profile publication, the research seeks to establish province-specific models for MMR in 2020 and identify the key influencing factors in each region. Standard regression analyses fall short in addressing the complexities present in the data, making the RGWR approach crucial for understanding the nuanced relationships. The chosen RGWR model utilizes the Least Absolute Deviation method and a fixed kernel exponential weighting function. Notably, this model maintains a consistent bandwidth value across all locations, showcasing its robustness. In evaluating the model variations, the exponential fixed kernel weighting function emerges as the most optimal, boasting the smallest Akaike Information Criterion (AIC) value of 23.990 and the highest coefficient of determination  value of 93.66%. The outcomes of this research yield 24 distinct models, each tailored to the unique characteristics of every province in Indonesia. This nuanced, location-specific approach is vital for developing effective interventions and policies to address the persistently high MMR. By providing insights into the complex interplay of factors influencing maternal mortality in different regions, the study contributes to the groundwork for targeted and impactful public health initiatives across Indonesia.
Developing a Regional Framework for Disaster Risk Reduction Based on Disaster-Related Data from Aceh, Indonesia Yolanda, Yolanda; Oktari, Rina Suryani; Munawar, Munawar; Lola, Muhamad Safiih; Sofyan, Hizir
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.269

Abstract

Aceh Province is highly vulnerable to various hazards, necessitating effective disaster risk reduction strategies. This study aims to develop an instrument to evaluate disaster risk reduction efforts in Aceh Province and to assess progress toward global disaster resilience targets. The data includes secondary disaster-related records from 2005 to 2024 and primary data from the instrument validation process, demonstrating excellent validity results based on the Content Validity Ratio (CVR) and Content Validity Index (CVI). The findings highlight significant improvements in key areas, including reductions in disaster mortality, affected populations, economic losses, damage to critical infrastructure, and strengthened early warning systems. However, challenges persist in implementing local disaster risk reduction strategies and enhancing international cooperation. This study offers practical insights for policymakers and contributes to strengthening disaster resilience and advancing disaster risk management research in sub-national contexts.
Co-Authors A.A. Ketut Agung Cahyawan W Agus Hendra Al Rahmad Ahmad Watsiq Maula Apriliansyah, Feby Arif Saputra, Arif Asep Rusyana Bahrun Bahrun Binti Firdaus, Shausan Dahlawy, Arriz Dimas Chaerul Ekty Saputra Earlia, Nanda Elly Elly Erliza Noor Faisal, Naidi Ghazi Mauer Idroes Hamdani M. Syam, Hamdani Heru Fahlevi Ibrahim, Husaini Idroes, Ghazi M. Irvanizam, Irvanizam Ismail Ismail Istakri, Dedi Jamil, T. M. Kemala, Pati Khairul, Moh Kruba, Rumaisa Linda Wahyuna Mardalena, Selvi Marshanda, Devira Marzuki Marzuki Maulana, Aga Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Mudatsir Mudatsir Muhamad Safiih Lola Muhammad Adam, Muhammad Muhammad Nasir Muhammad Subianto Muliadi Munawar Munawar Mustaqim Muzailin Affan Nasir Usman Noviandy, Teuku R. Nur Azmi, Nur Nurdjannah J. Niode Oktari, Rina S. Oktari, Rina Suryani Oktavia, Rini Pramana, Setia Purwoko, Agus RAHAYU, LATIFAH Rahmadhania Rahmah Johar Rasyid, Jusnaini Rimadeni, Yeni Rina S. Oktari Rinaldi Idroes Rusli Yusuf Safrizal Rahman Safrizal Rahman, Safrizal Salmawaty Arif Sasmita, Novi Reandy Suhendra, Rivansyah Syaridin, Syaridin Syazana, Nurul Syukri, Shara Tatsara, Nidya Teuku Rizky Noviandy TRINA EKAWATI TALLEI Ulfa, Elvitra Mutia Usman, Said Usman, Tarmizi Usnul, Uliyatul Yolanda Yolanda, Yolanda Yunus, Saifuddin