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<![CDATA[Pola Pernikahan Asortatif dan Ketimpangan Pengeluaran Penduduk di Jawa Barat ]]>
<![CDATA[Kristy, Nurine]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2310
<![CDATA[West Java is one of the provinces with the largest population in Indonesia. West Java's economic in 2023 was 5.05 percent, but the economic gap increases continuesly every year. Assortative mating, especially education, are indicated to have contributed to widening the gap in society. This study aims to analyze assortative mating, both in terms of age and education in West Java in 2022. This study also analyzes the relationship between assortative mating and per capita expenditure inequality. Graphs, tables, Kendall's Tau correlation, and the Lorenz curve were used in this study. This study use data from Susenas, March 2022. The results of the analysis showed that education assortative mating occurred in West Java. The marriage pattern that occurred was positive assortative mating in all regional categories. The relationship between assortative mating and per capita expenditure inequality is that there is an indication that assortative mating has an association with per capita expenditure inequality that is getting worse (more unequal).]]>
<![CDATA[Kontruksi Graf Pengetahuan pada Direktori Perusahaan menggunakan Basis Data Graf Neo4j ]]>
<![CDATA[Prataba, Guruh Dewa]]>;
<![CDATA[Putra, Yoga Cahya]]>;
<![CDATA[Widodo, Eko Teguh]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2314
<![CDATA[This study addresses the importance of integrating establishment directory data in Indonesia through the knowledge graph approach using Neo4j. Company directories, which contain basic information such as names, addresses, and Standard Classification of Indonesian Business Fields (KBLI) codes, are often dispersed across various formats and sources, making comprehensive data analysis challenging. This research proposes the use of knowledge graphs as a solution to overcome the limitations of data integration based on Relational Database Management Systems (RDBMS), highlighting the flexibility and efficiency of Neo4j in handling complex data and performing intuitive queries. Case studies in this research demonstrate that knowledge graphs can be used to identify significant patterns and relationships between establishment entities, aiding in industry mapping, regional economic planning, and supporting data-driven decision-making. However, the study also identifies challenges in data collection and transformation, which require further research to enhance the application of knowledge graphs in the future.]]>
<![CDATA[Integrasi Citra Satelit Radar dan Data AIS untuk Monitoring Tumpahan Minyak dengan Pendekatan Machine Learning ]]>
<![CDATA[Putri Suhendi, Brigitta Aurelia]]>;
<![CDATA[Marsisno, Waris]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2318
<![CDATA[Oil spill cases are routine occurrences in the Riau Archipelago Province, particularly in the northern part of Bintan Island, as one of the consequences of the island's strategic location near Singapore, a global trade and financial hub. The limitations of resources and data pose obstacles for the government in addressing this issue. Therefore, in this study, the author offers a solution by detecting oil spill areas using Sentinel-1 radar data and wind speed using the adaptive thresholding method, the best classification model from 7 machine learning algorithms and 2 deep learning algorithms. Based on the accuracy, precision, recall, and F1-score values of these 9 algorithms, the best algorithm obtained is XGBoost (Extreme Gradient Boosting). Followed by mapping and estimating the area of the oil spill at 177.54 ha, as well as identifying ships passing through the oil spill area using AIS (Automatic Identification System) data and wind direction, with the result being 2 ships passing through the area.]]>
<![CDATA[Analisis Faktor yang Memengaruhi Kesempatan Kerja di Kota Jayapura ]]>
<![CDATA[Setiawan, Tommy]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2320
<![CDATA[Unemployment is a major issue currently faced by Jayapura Municipality. Population growth, along with labor migration from other regions, has contributed to a labor surplus in the city. This surplus has led companies to adopt specific criteria to screen prospective workers. Moreover, the significant influx of migrant workers into Jayapura has raised concerns about unequal employment opportunities for indigenous Papuans, as employers are feared to be more inclined to hire workers from outside the Papua region. This study aims to identify the factors that influence employment opportunities in Jayapura, using data from the August 2024 National Labor Force Survey (Sakernas) and applying a Binary Logistic Regression method. The results show that the variable 'Place of Birth' has a significant effect on employment opportunities in Jayapura, where workers born in Papua are less likely to be employed compared to those born outside Papua. In addition, variables such as Gender, Age, Disability Status, Marital Status, and Education Level also have a significant influence on employment opportunities. Meanwhile, training experience and the use of technology were found to have no significant effect on employment opportunities in the city.]]>
<![CDATA[Kesenjangan Ekonomi Digital di Indonesia: Penyusunan Indeks Ekonomi Digital dan Klasifikasi Provinsi Tahun 2024 ]]>
<![CDATA[Risky, Risky]]>;
<![CDATA[Natalia, Desa Ayu]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2330
<![CDATA[The digital economy plays a vital role in supporting inclusive and sustainable economic growth. However, accurately measuring the state of the digital economy in Indonesia remains a challenge. This study aims to develop a Digital Economy Index (IED) for 2024 that reflects the level of readiness of supporting infrastructure for the digital economy and the classification of IED at the provincial level. The index is based on data from the 2024 National Socio-Economic Survey (Susenas) and the Village Potential Survey (Podes), utilizing nine indicators grouped into three main dimensions: digital and economic infrastructure, internet accessibility, and access to economic facilities. Factor analysis was used to reduce indicators and assign weights, followed by linear aggregation to form a composite index. The Jenks Natural Breaks classification method was used to categorize provinces into low, medium, and high groups. The findings reveal significant digital disparities across regions. Provinces in Java and parts of Sumatra fall into the high category, while many eastern provinces remain in the medium to low groups. Kepulauan Riau Province scored the highest, while Papua Pegunungan scored the lowest. These results highlight the need for more equitable digital infrastructure development to support a more balanced and inclusive national digital economy transformation.]]>
<![CDATA[Identifikasi Daerah dengan Infrastruktur Telekomunikasi Tertinggal di Indonesia Menggunakan Algoritma UPGMA dan WPGMA ]]>
<![CDATA[Raihannabil, Syfriza Davies]]>;
<![CDATA[Wicaksono, Achmad Maulana Andi]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2332
<![CDATA[Telecommunication infrastructure plays a crucial role in regional development in the digital era. Although ICT usage and mastery in Indonesia have improved, challenges remain in the equitable distribution of access and infrastructure. Using secondary data from the National Socio-Economic Survey (Susenas) by BPS, this study aims to identify regions with underdeveloped telecommunications infrastructure and determine the best hierarchical clustering algorithm. UPGMA and WPGMA were applied. The results show that UPGMA outperforms WPGMA, producing four optimal clusters: 1 province is very advanced, 4 are advanced, 28 are underdeveloped, and 1 is very underdeveloped. Therefore, the results of this study are expected to be the basis for formulating more targeted telecommunications infrastructure development policies, especially for areas that are classified as very underdeveloped, so that the digital divide can be reduced and inclusive digital transformation can be realized.]]>
<![CDATA[Menakar Peran Bantuan Sosial untuk Mewujudkan Ketahanan Pangan di Jawa Tengah pada Rumah Tangga Miskin Multidimensi ]]>
<![CDATA[Muthia, Azka]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2342
<![CDATA[Poverty is a multidimensional phenomenon strongly linked to food insecurity. In Central Java Province, there has been a noticeable increase in food consumption inadequacy, coupled with the issue of misdirected aid recipients. The provincial government's policy focus is poverty alleviation and enhancing food security, primarily through social assistance programs. This research utilizes March 2022 Susenas data and employs Propensity Score Matching (PSM) and ordinal logistic regression methods. The analysis reveals that a multidimensional approach to poverty significantly impacts food security in Central Java. Moreover, using Propensity Score Matching (PSM), the results show that the social assistance provided has no positive effect on the food access and food utilization dimensions for multidimensional poor households, except for those receiving both food and non-food assistance. This highlights an ongoing issue of misdirected aid. Additionally, while social assistance increases the quantity of food, it does not lead to improvements in food quality. One key implication of this study is the critical need to ensure that social assistance is accurately targeted to households. To address this, the government should consider developing a program that empowers the integrated data system of Central Java, incorporating a more precise spatial mapping approach.]]>
<![CDATA[Pengaruh Faktor Sosial Ekonomi Terhadap Perkembangan Indeks Pembangunan Manusia di Provinsi Papua Tengah Tahun 2018-2024 ]]>
<![CDATA[Nilawati, Arum]]>;
<![CDATA[Haqi, Alma Ainul]]>;
<![CDATA[Riefky, Muhammad]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2344
<![CDATA[Central Papua Province is the region with the second lowest human development index in Indonesia. The low human development index in Central Papua is due to socio-economic factors. This study aims to examine socioeconomic factors on the Central Papuan human development index in 2018–2024 with panel data regression. The data obtained from the secondary data of the official publication of the BPS of Central Papua Province in 2018-2024 with the variables used including the human development index, poverty rate, open unemployment rate, per capita expenditure, and life expectancy. The results of the analysis explained that the poverty rate, open unemployment rate, and per capita expenditure affect the human development index, while the life expectancy rate has no impact on the human development index. So that the results of this research can be used as a guideline for human development policies in Central Papua.]]>
<![CDATA[Determinan Backlog Perumahan di Indonesia Tahun 2024 dengan Pendekatan Regresi Robust MM-Estimation ]]>
<![CDATA[Safitri, Gita]]>;
<![CDATA[Herwindalita, Erina]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2349
<![CDATA[Home ownership backlog is still a serious problem in Indonesia, especially among low-income people. This study aims to analyze the effect of regional macro variables on the backlog of home ownership in Indonesia in 2024. The method used is robust MM-estimation regression with a unit of analysis of 38 provinces. The data used are secondary data from the BPS Statistics and the Ministry of PUPR. The independent variables include population density, Construction Cost Index (IKK), poverty rate, and unemployment rate (TPT). Based on the research results, population density, IKK and the poverty rate have a significant effect on backlog, while the unemployment rate has no significant effect. Based on the research that has been done, controlling population density and construction cost efficiency need to be prioritized in reducing backlog, as well as expanding housing subsidies for low-income people as a long-term solution.]]>
<![CDATA[The Impact of Economic Digitalization on Regional Economic Convergence in Indonesia ]]>
<![CDATA[Dwipa, I Dewa Made Agus Arsana]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2025i1.2354
<![CDATA[Regional economic convergence refers to the tendency to reduce development disparities across regions. This study examines the impact of economic digitalization on regional convergence in Indonesia amid post-pandemic digital acceleration. Using SAKERNAS data, digitalization is measured to address gaps in understanding its varied effects. Employing β-convergence panel regression framework, the study compares model robustness using Ordinary Least Squares (OLS), Robust Standard Errors, and Generalized Least Squares (GLS). The findings reveal that while economic digitalization significantly contributes to growth, it does not sufficiently drive convergence without the interaction of labor and capital factors. These results underscore the necessity of context-sensitive digitalization strategies aligned with regional economic structures to promote inclusive and equitable development.]]>
