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All Journal Jurnal Ilmu Lingkungan Journal The Winners Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Statistika dan Aplikasinya International Journal of Business, Law, and Education Jurnal Ekonomi dan Pembangunan Indonesia Parameter: Jurnal Matematika, Statistika dan Terapannya
Bagus Sumargo, Bagus
Staf pengajar FMIPA, UBiNus, Jakarta dan Peneliti BPS
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS Sumargo, Bagus; Kadir, Kadir; Safariza, Dena; Asikin, Munawar; Siregar, Dania; Sari, Nilam Novita; Umbara, Danu; Hilmianto, Rizky; Kurniawan, Robert; Firmansyah, Irman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1779-1790

Abstract

Stunting, a condition where children are malnourished for a long period, causes growth failure in children. West Java, Central Java, and East Java are the 3 provinces with the highest prevalence of stunting in 2021. This study aims to group districts/cities in these provinces based on factors that influence stunting using the DBSCAN method (there has been no previous research using this method for this case), so the typology of stunting prevalence is implied. The group results can be valuable input for policy priorities in overcoming stunting. The study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method, which can also detect noises (outliers). The determination of eps and MinPts is based on the average value of the distance from each data to its closest neighbor. The distance obtained then was used in the KNN algorithm to determine eps and MinPts parameters. Clustering is done using standardized data and DBSCAN parameters obtained from the k-dist plot, eps is 1.92, and MinPts is 2. The validation test used is the silhouette coefficient to determine the goodness of the cluster results. The clustering results show that there are 2 clusters and 1 noise that have special characteristics related to factors that influence the prevalence of stunting. Cluster 1 consisted of 97 districts/cities and was characterized by a high percentage of infants under 6 months receiving exclusive breastfeeding and the lowest average per capita household expenditure. Cluster 2 (Bekasi City and Depok City) was characterized by the lowest percentage of households with proper health facilities and infants aged 0-59 months receiving complete immunization. The noise (high stunting prevalence) in Bandung City is characterized by the lowest percentage of households having proper sanitation.
POVERTY MACRO SYSTEM DYNAMICS MODELING BASED ON SIMULTANEOUS EQUATIONS MODELS Sumargo, Bagus; Firmansyah, Irman; Nugraha, Asep Anwar; Mulyono, Mulyono; Siregar, Dania; Nuriza, Felia Aidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0255-0268

Abstract

Poverty factors are multidimensional and complex. Currently, to predict the number of people living below the poverty line using the concept of linear thinking. It is necessary to study the causal relationships among poverty factors in form of a system dynamics model. This study aims to predict the poverty rate people in “The Golden Indonesia” 2030 using poverty macro models. The data used are time series data from 2009 to 2018 at the national level (Indonesia), and data sources from the BPS Statistics-Indonesia, and the Ministry of Environment and Forestry of the Republic of Indonesia. The research method uses a system dynamics model, where the system of thinking is created based on the two-stage least square (2SLS) simultaneous equation model. The 2SLS simultaneous equation model testing results show that there are three significant simultaneous equations, including poverty, economic growth, and human development index. Furthermore, the three simultaneous equations show a causal loop diagram (CLD) in a system dynamics model. The mean absolute percentage error (MAPE) is 2.34%, meaning that the macro poverty model is valid. The scenario formats for prediction include “optimistic” for economic growth and the “moderate” for human development index (HDI), total population, unemployment, and environmental quality index variables. The predicted percentage number of poor people in 2030 is 4.12%, a positive deviation of 0.12% from the government’s target of 4%. All parties need to work hard and together for the “optimistic” scenario to be implemented, which is to raise Indonesia’s economic growth to 7.4%. This study assumes that there is no Covid-19 problem and only predicts 10 years due to limited data used in 2010-2018. The novelty of this study is the alignment of the prediction results between the system dynamics and the simultaneous equation models. In general, the system dynamics model is valid and could answer the complexity of a phenomenon to predict poverty.
Indonesian Students Reading Literacy Score in Framework Hierarchical Data Structure Using Multilevel Regression Maya Santi, Vera; Rahayuningsih, Yuliana; Sumargo, Bagus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp353-368

Abstract

Education is essential for improving the quality of Indonesian society. Indonesia participated in the Programme International Students Assessment (PISA) survey to improve the quality of education. Based on the 2018 PISA survey data, Indonesia's reading literacy score has a hierarchical data structure, which means students at level 1 are nested by schools at level 2. The multilevel model is an appropriate approach to analyze such hierarchical structures. However, quantitative analysis of PISA data is still rarely carried out. This study aims to analyze the explanatory variables that significantly affect Indonesian students' reading literacy from the PISA survey using multilevel regression. This study examined student-level and school-level explanatory variables obtained from the Organization for Economic Cooperation and Development (OECD). Significant parameter tests revealed that, at the student level, factors such as socioeconomic status, teacher support in language learning, teacher-directed instruction, enjoyment of reading, perceived difficulty, competitiveness, mastery goal orientation, disciplined classroom climate in reading, general fear of failure, attitudes toward school, and perceived feedback significantly influence reading literacy. At the school level, school size was found to be a significant factor affecting reading literacy scores. Furthermore, the Intraclass Correlation Coefficient (ICC) indicated that schools accounted for 49% of the total variance.
Co-Authors Agung Purwanto Asikin, Munawar budi Cahyono Dian Handayani Erdien, Fareka Firmasyah, Irman Haida, Rahadita Nur Hilmianto, Rizky Irman Firmansyah Kadir Kadir Kirana, Siti Julpia Lubis, Alvi Pauziah Mulyono Mulyono Mulyono Nazhiifah, Nisriina Nilam Novita Sari Nugraha, Asep Anwar Nugroho, Yoga Dwi Nuriza, Felia Aidah Pratomo, Dody Rahayuningsih, Yuliana Robert Kurniawan, Robert Safariza, Dena Simanjuntak, Naomi Miduk M. Siregar, Dania Sitorus, Agnes Vera Yanti Syaifudin Syaifudin, Syaifudin Umbara, Danu Vera Maya Santi Wahyuni, Krismanti Tri Widyanti Rahayu Wulansari, Ika Yuni