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INDONESIA
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Published by Politeknik Statistika STIS
ISSN : 28099842     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Conference on Data Science and Official Statistics International Conference on Data Science and Official Statistics (ICDSOS) 2023 is organized by Politeknik Statistika STIS and Statistics Indonesia (BPS). This international conference in collaboration with Forum Pendidikan Tinggi Statistika (FORSTAT), Ikatan Statistisi Indonesia (ISI), United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), and United Nations Statistics Division (UNSD). The ICDSOS will bring together statisticians and data scientists from academia, official statistics, health sector and business, junior and senior professionals, in an inviting hybrid environment on November 24th - 25th, 2023. Dealing with the theme of this conference is Harnessing Innovation in Data Science and Official Statistics to Address Global Challenges towards the Sustainable Development Goals. DATA SCIENCE Machine Learning and Deep Learning Data Science and Artificial Intelligence (AI) Data Mining and Big Data Statistical Software Information System Development for Official Statistics Remote Sensing to Strengthen Official Statistics Other data science relevant topic APPLIED STATISTICS Applied Multivariate Analysis Applied Time Series Analysis Applied Spatial Statistics Applied Bayesian Statistics Microeconomics Modelling and Applications Macroeconomics Modelling and Applications Econometrics Modelling and Applications Quantitative Public Policy and Statistical Analysis Applied Statistics on Demography Applied Statistics on Population Studies Applied Statistics on Biostatistics and Public health Other applied statistics relevant topic OFFICIAL STATISTICS Official Statistics Survey Methodology Developments Data Collection Improvements Sustainable Development Goals (SDGs) Indicators Estimation Small Area Estimation (SAE) Non Response and Imputation Methods Sampling Error and Non Sampling Error Evaluation Benchmarking Regional Official Statistics Other official statistics relevant topic
Arjuna Subject : Umum - Umum
Articles 251 Documents
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GLMM and GLMMTree for Modelling Poverty in Indonesia Suseno Bayu; Khairil Anwar Notodiputro; Bagus Sartono
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.333

Abstract

GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMMTree for the actual data applications of poverty in Indonesia and confirms that the GLMMTree algorithm method has better precision than GLMM. The significant predictors that affect poverty in Indonesia are the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding subgroups of provinces with electricity lighting access and clean drinking water sources variables.
Identification of factors affecting the cases of under-age female marriage using geographically weighted panel regression approach in south kalimantan province. ABDULLAH RIFQI
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.337

Abstract

The topic of this study was chosen because the percentage of underage female marriages in South Kalimantan Province was the highest in Indonesia over the last five years, from 2018 to 2022. This signifies that there are social issues in the local community that the government must address. One possible answer is to identify the factors that contribute to the creation of these conditions in each region. Using the Geographically Weighted Panel Regression (GWPR) method, this study attempts to determine the factors that influence the rise of underage female marriage instances in South Kalimantan Province. The number of poor individuals, population density, average duration of schooling, adjusted per capita expenditure, and total population were chosen as independent variables. Data acquired from South Kalimantan Province's Central Bureau of Statistics' periodic releases. Because there was high spatial heterogeneity between each location in this study, it was quite practical to employ the GWPR approach in developing a conjectural model. The results of evaluating the GWPR model with adaptive Gaussian kernel weights provide significant results and the model can explain the variance of data by 55 percent. Testing the parameters of the GWPR model reveals two (two) regional groupings with distinct influencing variables. The first group consists of ten (ten) regions that are considerably impacted by both the number of impoverished people and the average length of schooling, whereas the second group consists of three (three) regions that are impacted solely by the average length of schooling.
A Land cover change analysis of buffer areas in New Capital City of Nusantara, Indonesia: A cellular automata approach on satellite imageries data Maria Shawna Cinnamon Claire; Salwa Rizqina Putri; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.338

Abstract

The proposed plan to move Indonesia's capital city to the New Capital City of Nusantara in East Kalimantan Province undoubtedly requires careful efforts to ensure food supply for the population. Population migration to the new capital may pose a food security challenge. To address this fundamental issue, one of the most crucial approaches is to establish buffer areas that can support the food needs of the new capital. The currently existing official Area Sampling Frame survey conducted by the government to assess food vulnerability faced several limitations, including weather conditions, field terrain variations, and high cost. In this study, we propose the utilization of remote sensing satellite imagery data in buffer areas to analyze changes and predict future land cover, which can provide valuable data for assessing food availability. We investigate the integration of a Cellular Automata method with the two most popular analytical methods of classical Logistic Regression and data-driven Artificial Neural Networks, known as CA-LR and CA-ANN, to identify and map land cover changes in the new capital buffer zones. Our findings reveal that both combined methods, CA-LR and CA-ANN, yield fairly promising results, with correctness and kappa statistic values exceeding 80%. Prediction results indicate that buffer areas are predominantly covered by trees, while built-up areas are still limited. The flooded vegetation cover, including rice fields, is predicted to decrease by 2024. This should be a matter of concern for stakeholders, considering the construction of the new capital city is still ongoing and the number of migrants is expected to keep rising.
Construction of Green City Index in Indonesian Metropolitan Districts/Cities Vina Astriani; Risni Julaeni Yuhan; Bony Parulian Josaphat
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.342

Abstract

Urbanization in Indonesia resulted in population density in urban areas, which has the potential for economic growth, marked by increased population income followed by changes in consumption patterns that will cause environmental problems in urban areas. Seeing environmental issues that occur in urban areas, it is necessary to have a green city concept city planning as a sustainable city planning solution without damaging the environment. The measurement of green city achievement has yet to be carried out in Indonesia. This study aims to measure the Green City Index (GCI) in metropolitan districts/cities in Indonesia using Partial Least Squares-Structural Equation Modeling (PLS-SEM). It examines the GCI achievements in Indonesian metropolitan districts/cities. The GCI is formed by a socioeconomic dimension of two indicators and an environmental dimension of eleven indicators. Generally, the highest GCI achievements are in the Bogor District, with a score of 74.3 percent. Bangkalan District achieved the highest socioeconomic dimension index, and Bogor District completed the highest environmental dimension index. In addition, there is a significant and negative relationship between GCI and the Human Development Index (HDI) and economic growth. It is hoped that the government and the community can pay attention to the balance of the environment in their activities.
hyper-Poisson Model for Overdispersed and Underdispersed Count Data Venda Damianus Situmorang; Siti Nurrohmah; Ida Fithriani
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.344

Abstract

The Poisson model is commonly used for modelling count data. However, it has a limitation, namely the equality between the mean and variance (equidispersion) of the data to be modeled. Unfortunately, overdispersion (variance greater than the mean) and underdispersion (variance smaller than the mean) are more often to be found in real cases. Therefore, different models need to be used to handle data with these cases. The hyper-Poisson model is one model that can be used to handle overdispersion or underdispersion cases flexibly. This paper describes the hyper-Poisson model and its application on overdispersed and underdispersed count data.
Vine Copula Model: Application to Chemical Elements in Water Samples Salsabila Zahra Aminullah; Mila Novita; Ida Fithriani
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.346

Abstract

Copula can link the bivariate distribution function with marginal distribution functions without requiring specific information about the interdependence among random variables. There are several types of copulas, such as elliptical copulas, Archimedean copulas, and extreme value copulas. However, in multivariate modeling, each type of copula has limitations in modeling complex dependence structures in terms of symmetry and tail dependence properties. The class of vine copulas overcomes these limitations by constructing multivariate models using bivariate copulas in a tree-like structure. The bivariate copulas used in this study include the Clayton, Gumbel, Frank, Gaussian, and Student's t copula families. This study discusses the construction of vine copula models, parameter estimation, and their applications. The construction of vine copulas is done through the decomposition of conditional probability density functions and substituting bivariate copula density functions into the decomposition results. The data used in the study is the logarithm of the concentration of chemical elements in water samples in Colorado. The parameter estimation method used is pseudo-maximum likelihood with sequential estimation. Model selection is then performed using the Akaike information criterion (AIC) to determine the most suitable model. The results indicate that Caesium and Titanium have a dependency relationship with Scandium. Moreover, Scandium and Titanium exhibit the strongest dependence compared to other variable pairs.
Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach HARUN AL AZIES
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.348

Abstract

The air quality in the Jakarta area is examined in this study using artificial intelligence (AI) to assist a semi-supervised learning technique. The clustering approach is used in this article to separate air pollution into three main categories moderate, low, and high levels. This clustering helps identify shared characteristics among measures like particulates (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), even when air quality labels are not always accessible. Using the Random Forest method, the air quality will be categorized in this experiment with an accuracy rate of 93%. Additionally, the results of variable significance analysis are examined on this article to identify the variables with the biggest effects on air quality, notably PM10, SO2, and NO2. This study demonstrates the enormous potential of applying machine learning techniques, particularly semi-supervised learning approaches, to assist sustainable environmental regulations while also monitoring and enhancing Jakarta's air quality. We describe the experimental procedures, the findings, and the implications of our research for comprehending and addressing urban air pollution in this article.
Automatic Detection and Counting of Urban Housing and Settlement in Depok City, Indonesia: An Object-Based Deep Learning Model on Optical Satellite Imageries and Points of Interests Atut Pindarwati; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.349

Abstract

Detecting urban housing and settlements has a substantial position in decision-making problems such as monitoring housing and development, not to mention the widelyrequired urban mapping application. One of the most important goals in the United NationsSustainable Development Goals (SDGs) is to improve urban living conditions globally by2030. We propose an automatic detection of urban housing and settlements on remote sensingsatellite imagery data using object detection-based deep learning using semantic segmentationand the potential availability of remote sensing datasets at high spatial resolutions, Open StreetMap (OSM) geolocation point of interest dataset, and Sentinel-2 optical satellite imagery data.The detection model using Mask Region-based Convolutional Neural Networks (Mask R-CNN) is implemented in Depok City, Indonesia. These regions were chosen because it is thesecond most populous suburb in Indonesia and the tenth most populous globally and, making itchallenging to extract building features from satellite imagery. This model categorizes dense,moderate, and sparse conditions and has a promising result of an average precision of 100%and an F1-score of 67% with evaluation performance metrics only considering pointsassociated with buildings, not building boundaries or the intersection over union (IoU). Themodel performance has been compared to ground check results of field surveys, and itperforms best in sparse conditions. Our findings offer the potential implementation of themodel for fast and accurate monitoring of housing, settlement, and regional planning in urbanareas.
Analysis of Indonesian Domestic Tourist Quality: Case Study: Domestic Tourist Digital Survey 2021 Martha Zalukhu; Neli Agustina
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.350

Abstract

Domestic tourism is being the main focus of the government strategy to revitalize the tourism sector. It is then crucial to consider elements that can raise the quality of tourism, in terms of domestic tourists and increase the added value rather than merely number of trips. The analysis of quality is important in tourism to support the idea of sustainable tourism, which is promoted in the 8th agenda of Sustainable Development Goals (SDGs). Quality analysis must be done in micro modelling that takes into account tourist characteristics and particular travel-related features because this sector depends on tourism demand and tourist expenditure in tourist locations. Thus, the goal of this study is to give a general overview of the qualities and characteristics of domestic tourists and to examine how these attributes affect their quality. The results of descriptive analysis method indicate that Indonesian domestic visitors’ quality remains poor. Age, genders, education level, employment status, transportation mode, accommodation type and travel companion affect the quality of domestic tourists.
Text Analysis Study on Urban farming News Toward Food Security in Indonesia: Sentiment Analysis, Named Entity Recognition, Topic Modelling, and Social Network Analysis Dewi Krismawati; Satria Bagus Panuntun
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.352

Abstract

Urban farming is an increasingly popular trend in agricultural activities. Urban farming is an attempt to achieve urban sustainability from an environmental, social and economic perspective. In order to understand the phenomenon of urban farming in society, one of the media used is a news portal. This research aims to gain an in-depth understanding of community perceptions, social networks and issues related to the urban farming phenomenon. Data was collected using the web-scraping method on three national news portals in Indonesia. Data analysis was carried out using sentiment analysis, NER, topic modelling and social network analysis methods. Sentiment analysis shows that there is a generally positive sentiment towards urban farming. Government officials and environmental activists are frequently mentioned as supporting and promoting urban agriculture. Social network analysis reveals interactions between government agencies, non-governmental organisations and the media. The relationships between these stakeholders form a network that plays a role in building awareness, cooperation and knowledge exchange to strengthen food security through urban agriculture.

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