Article Per Year (5 Year)
p-Index From 2020 - 2025
1.318
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Statistika dan Aplikasinya Prosiding Seminar Nasional Pengabdian Kepada Masyarakat
Siregar, Dania
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Clustering Municipality of Dengue Hemorrhagic Fever Typologies in Central Java Erdien, Fareka; Sumargo, Bagus; Nazhiifah, Nisriina; Kirana, Siti Julpia; Siregar, Dania; Mulyono
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08108

Abstract

Cases of dengue hemorrhagic fever (DHF) generally occur in areas with high temperatures. Central Java Province, Indonesia is one of the regions that has high temperatures, making it vulnerable to dengue cases. The study aimed at grouping DHF endemic areas in Central Java needs to be done to assist the government in determining policies to control or prevent DHF. The cluster analysis method used in this study is Average Linkage. The results showed that there were 3 clusters formed. Cluster A is a cluster with the characteristics of having the highest average percentage of households that have access to safe drinking water. Cluster B is the cluster with the highest average number of protected springs. While cluster C is dominant in 4 factors with the highest average, namely the percentage of households that behave in a clean and healthy life, the percentage of healthy homes, the number of Polindes (Village Maternity Hut), and the percentage of households that have access to proper sanitation. Clusters A and B are clean water type and Cluster C is a sanitation type, where clean water and sanitation are both indicators of environmental health. Therefore, environmental health is closely related to the presence of dengue fever in a community environment. The determination of three clusters was based on the chosen method and criteria. Other methods or criteria might suggest a different optimal number of clusters. The findings are specific to Central Java Province and may not be generalizable to other regions with different environmental and social contexts. In this case, it is necessary to pay attention to the community for environmental health in order to overcome or prevent the occurrence of DHF. where clean water and sanitation are both indicators of environmental health. Therefore, environmental health is closely related to the presence of dengue fever in a community environment.
Characteristics of Provinces in Indonesia Based on JKN Indicator Outcomes by Gaussian Mixture Model with Expectation-Maximization Algorithm and Biplot Siregar, Dania; Rahayu, Widyanti; Wardana, Bintang Mahesa; Ketrin Natasya Stefany; Bayu Wibisono
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08102

Abstract

Indonesia, an archipelago with a population of 257.77 million in 2022, faces significant challenges in enhancing the quality of life to improve human resource productivity. This study aims to identify provincial characteristics in Indonesia based on the outcomes of the Jaminan Kesehatan Nasional (JKN) program from 2019 to 2021. Using a Gaussian Mixture Model (GMM) with the Expectation Maximization (EM) algorithm, we cluster 34 provinces based on 14 health indicators. The data were obtained from the BPJS website and included variables such as access to health services, program effectiveness, and service quality. Our methodology allows for clustering provinces with similar health outcomes and analyzing the unique indicators for each cluster using biplot analysis.The results indicate significant variation in cluster membership across the years. In 2019, three clusters were identified, with cluster sizes of 16, 12, and 6 provinces. In 2020, the optimum model also had three clusters, but with different member distributions: 24, 7, and 3 provinces. By 2021, four clusters emerged with sizes of 9, 16, 3, and 6 provinces. These findings highlight the dynamic nature of health outcomes across Indonesia's provinces and suggest the need for tailored policy interventions to improve the JKN program's effectiveness.The study's limitations include the reliance on available BPJS data and the assumption that the selected health indicators comprehensively represent the JKN program's impact. This research's novelty lies in its use of advanced clustering techniques to provide a nuanced understanding of regional health disparities in Indonesia, which can inform more targeted and effective health policies.
THE APPLICATION OF THE ARTIFICIAL NEURAL NETWORK (ANN) METHOD FOR FORECASTING THE SOUTHERN OSCILATION INDEX (SOI) Fathia Syahla Az Zahra; Bagus Sumargo; Siregar, Dania; Auria Yusrin Fathya
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08205

Abstract

Indonesia's seasons are influenced by global phenomena such as ENSO. This phenomenon affects rainfall intensity in Indonesia through its two main phases: El Nino and La Nina. One method to detect these events is by analyzing the Southern Oscillation Index (SOI). A highly accurate SOI forecasting model is critical for both short-term and long-term development planning, particularly in anticipating future extreme seasons. One of the methods used for forecasting is the Artificial Neural Network (ANN). This study aims to develop an ANN model capable of predicting the SOI index. Based on forecasting using training data, the optimal model architecture identified is 12-7-1, which achieved the smallest MSE value of 0.0095 and a MAPE of 17.6851. With an error rate below 20%, the 12-7-1 architecture demonstrates strong forecasting capabilities. The study forecasts the SOI index for the next 12 months, indicating a trend from negative values at the beginning of the year to more positive values toward the year's end.
DETERMINATION OF IMPORTANT VARIABLES IN DIVORCE TYPE CLASSIFICATION USING THE RANDOM FOREST METHOD WITH SMOTE Siregar, Dania; Bintang Mahesa Wardana; Ahmad Syauqi Baihaqy; Liswatun Naimah; Almira Nindya Putri; Qory Meidianingsih; Dini Safitri
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08209

Abstract

Central Jakarta is highly strategic area situated at the heart of the Indonesian capital. It serves as the central hub for the government, history, tourism, and elite shopping sectors with convenient access to various buffer areas surrounding the capital. However, the availability of these facilities does not necessarily ensure the continuity of domestic life within the community. This can be observed from the increasing divorce rate in the region since 2017. Notably, a higher proportion of divorce suits are filed by wives than by husbands. There are various factors that can trigger divorce lawsuits such as continuous disputes and arguments, economic factors, and domestic violence. And these factors certainly cannot be separated from the individual profiles of married couples such as age, occupation, education level, and duration of marriage. The purpose of this study is to determine the level of importance of the variables used in the classification of wife-initiated divorce and husband-initiated divorce of married couples in the Central Jakarta area through the Random Forest method. Random Forest is a development of the CART (Classification and Regression Tree) method obtained through the application of bootstrap aggregating and random feature selection methods to the standard CART method. The number of wife-initiated divorce cases exceeds that of husband-initiated divorce cases, necessitating the use of SMOTE technique to address the imbalance in the data set. The results showed that the most important variable used to classify divorce cases was the plaintiff's age followed by the defendant's occupation, the defendant's age, and the plaintiff's occupation.
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.
MODELING POVERTY IN WEST JAVA PROVINCE USING NEGATIVE BINOMIAL REGRESSION WITH PENALIZED SMOOTHLY CLIPPED ABSOLUTE DEVIATION Santi, Vera Maya; Baihaqi, Aulia; Siregar, Dania
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2557-2570

Abstract

The number of poor people is an example of discrete or count data. One commonly used regression model for count responses is the Negative Binomial regression. Regression modeling with many predictor variables results in the problem of multicollinearity. This condition causes the parameter estimator to become unstable. One method to overcome this problem is to use the penalty function to optimize the selection of predictor variables. This study aims to analyze the factors influencing the number of poor people in West Java Province using Negative Binomial regression with the Smoothly Clipped Absolute Deviation (SCAD) penalty function. The research data was sourced from the Central Bureau of Statistics in 2022, covering 27 districts/cities in West Java Province with 21 predictor variables. The method applied selects variables and estimates parameters simultaneously in the Negative Binomial regression model. Based on the AIC value, it was found that the Negative Binomial penalized SCAD model (AIC = 628.12) had better performance than the Negative Binomial regression model (AIC = 634.34). The Negative Binomial penalized SCAD regression model yielded five significant predictor variables with value of 92.8%. This model is simpler than the Negative Binomial regression model with six predictor variables. The regional minimum wage, number of cooperatives, percentage of the population who have health insurance, the pure college enrollment rate, and non-food expenditure are important variables as factors affecting the number of poor people in West Java Province.
PELATIHAN ANALISIS STATISTIK MENGGUNAKAN WEBSITE INTERAKTIF UNTUK MENDUKUNG PENGAMBILAN KEPUTUSAN BERBASIS DATA PENDIDIKAN BAGI GURU SMA MATEMATIKA DI KABUPATEN SUKABUMI Siregar, Dania; Suyono; Vera Maya Santi; Auria Yusrin Fathya; Sinta Rahmadani; Jaisy Aulia; Maulida Audia Firdaus
Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT - SNPPM2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Negeri Jakarta

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

Abstract

Tantangan utama dalam pengambilan keputusan pendidikan adalah keterbatasan literasi statistik dan keterampilan guru dalam mengolah data, terutama melalui teknologi interaktif. Hal ini terlihat dari kuesioner pra-pelatihan, di mana sebagian besar guru menyatakan keraguan atau ketidaksetujuan terhadap pengetahuan mereka, dan mayoritas belum pernah menggunakan situs web interaktif untuk analisis statistik. Program layanan masyarakat ini bertujuan untuk meningkatkan literasi statistik guru melalui pelatihan analisis data menggunakan situs web interaktif berbasis R-Shiny. Pelatihan dilaksanakan pada 13 Agustus 2025, dengan peserta terdiri dari guru matematika SMA di Kabupaten Sukabumi, bekerja sama dengan MGMP Matematika SMA Sukabumi sebagai mitra layanan masyarakat. Materi pelatihan mencakup statistik deskriptif, analisis inferensial, pengujian hipotesis, dan regresi. Evaluasi pasca-pelatihan menunjukkan peningkatan yang signifikan: lebih dari 80% peserta setuju atau sangat setuju bahwa materi pelatihan sistematis, mudah dipahami, dan relevan, serta aplikasi tersebut mudah diakses dan ramah pengguna. Selain itu, 75% peserta sangat setuju bahwa mereka memperoleh pengetahuan baru yang berguna untuk pengambilan keputusan berbasis data dalam pendidikan. Kesimpulannya, pelatihan berbasis teknologi interaktif secara efektif meningkatkan kompetensi guru, memperkuat motivasi mereka, dan menumbuhkan budaya pengambilan keputusan berbasis data di sekolah. Translated with DeepL.com (free version) Abstract The main challenge in educational decision-making is the limited statistical literacy and skills among teachers in processing data, particularly through interactive technology. This was evident from the pre-training questionnaire, in which most teachers expressed doubt or disagreement about their knowledge, and the majority had never used an interactive website for statistical analysis. This community service program aimed to enhance teachers’ statistical literacy through training in data analysis using an R-Shiny-based interactive website. The training was conducted on August 13, 2025, with participants consisting of senior high school mathematics teachers in Sukabumi Regency, in collaboration with the Sukabumi Senior High School Mathematics MGMP as the community service partner. The training materials covered descriptive statistics, inferential analysis, hypothesis testing, and regression. Post-training evaluation showed a significant improvement: more than 80% of participants agreed or strongly agreed that the materials were systematic, easy to understand, and relevant, and that the application was accessible and user-friendly. Furthermore, 75% of participants strongly agreed that they gained new knowledge useful for data-driven decision-making in education. In conclusion, interactive technology-based training effectively improved teachers’ competence, strengthened their motivation, and fostered a data-driven decision-making culture in schools.
Co-Authors Ahmad Syauqi Baihaqy Almira Nindya Putri Asikin, Munawar Auria Yusrin Fathya Bagus Sumargo Bagus Sumargo, Bagus Baihaqi, Aulia Bayu Wibisono Bintang Mahesa Wardana Dini Safitri Erdien, Fareka Fathia Syahla Az Zahra Hilmianto, Rizky Irman Firmansyah Jaisy Aulia Kadir Kadir Ketrin Natasya Stefany Kirana, Siti Julpia Liswatun Naimah Maulida Audia Firdaus Mulyono Mulyono Mulyono Nazhiifah, Nisriina Nilam Novita Sari Nugraha, Asep Anwar Nuriza, Felia Aidah Qory Meidianingsih Robert Kurniawan, Robert Safariza, Dena Sinta Rahmadani SUYONO Umbara, Danu Vera Maya Santi Wardana, Bintang Mahesa Widyanti Rahayu