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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 151 Documents
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Deep Learning Approaches for Predicting Intraday Price Movements: An Evaluation of RNN Variants on High-Frequency Stock Data Mochamad Ridwan; Kusman Sadik; Farit Mochamad Afendi
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.v2023i1.278

Abstract

This study discusses the comparison of four recurrent neural networks (RNN) models: Simple RNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional RNN (BiRNN), in forecasting minute-level stock price time series data. The performance of these four models is evaluated using the Mean Absolute Percentage Error (MAPE) on a stock dataset from Bank Central Asia (BBCA.JK). The experimental results reveal that the GRU model exhibits the best performance with an average MAPE of 0.0255%, followed by the LSTM model with an average MAPE of 0.0377%. The BiRNN model also demonstrates good performance with an average MAPE of 0.0668%, while the Simple RNN has the highest average MAPE at 0.5118%. This suggests that more complex recurrent architectures like GRU and LSTM have better capabilities in capturing patterns in high-frequency time series data. This study can be expanded by exploring other models such as CNN, conducting tests on diverse datasets, and experimenting with a wider range of hyperparameter variations. Additional variables such as economic indicators, global market data, and social data can also offer a more comprehensive understanding of factors influencing stock prices.
Exploration of Resnet Variants in High Spatial Resolution Domain Adaptation: From air-to-space imagery Sulisetyo Puji Widodo; Nur Rachmawati
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.v2023i1.280

Abstract

Land cover is nowadays mapped mostly from airborne and space-borne data. Because of the difference in sensors, large spectral differences and inconsistent spatial resolution may arise between these two data sources. Consequently, the same object may exhibit completely different features. In this case, models trained from annotated airborne and ineffective when applied to space-borne data. Cross-Sensor Land-COVER (LoveCS) shows good results in overcoming this problem. LoveCS leverages small-scale aerial image annotations to promote land cover mapping on large-scale spacecraft. LoveCS uses ResNet50 as its encoder. In recent years, many studies have tried to develop other variants of ResNet, such as ResNeXt, ResNeSt, Res2Net, and Res2NeXt. These variants turned out to give better results in a variety of tasks compared to ResNet. Therefore, in this study we modified the LoveCS encoder by replacing ResNet50 with ResNet variants such as ResNeXt, ResNeSt, Res2Net, and Res2NeXt in an effort to improve LoveCS accuracy. Our evaluation shows that Res2Net50 as an encoder improves the performance of LoveCS. The average F1 increases by 1.38%, OA by 1.96%, and Kappa by 2.75% from the baseline method.
Pojok Statistik Virtual Improvement: Development of Online Consultation and Scientific Articles Modules Fahmi Muhammad Sahal; Nori Wilantika
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.v2023i1.284

Abstract

With a commitment to improving statistics literacy in Indonesia, BPS Statistics Indonesia built a Pojok Statistik. Pojok Statistik is a collaborative service between BPS Statistics Indonesia and Universities initiated to answer the needs of academics and students for statistics. Due to the effects of the pandemic and the increasing interest from students, a virtual version of Pojok Statistik (Pojok Statistik Virtual) was built to meet all the needs of the offline version. However, the features are still limited and do not represent the criteria of the Pojok Statistik Offline. From the results of interviews with the Pojok Statistik Team, several plans exist to improve the Pojok Statistik Virtual, including adding online consultation and scientific articles modules. Therefore, this study aims to add online consulting service features and scientific articles modules to the Pojok Statistik Virtual. The system development process uses the Prototyping Methodology. System evaluation is conducted by black-box testing and usability testing using the USE Questionnaire. The evaluation results show that all functions in both modules are running and functioning properly. The usability test results using the USE Questionnaire reveal that these two features are feasible or usable to support the statistical needs of academics and students.
Analysis of the Effect of Technology on the Growth of the Information and Communication Sector in the Bali Province 2016-2021 Milan Puji; Krismanti Tri Wahyuni
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.v2023i1.291

Abstract

Technology continues to develop and drive economic growth. Bali, as a province that is open to foreign tourists in Indonesia, has a great opportunity to adopt technology more quickly. This study aims to analyze the effect of technology development as well as other variables such as the number of workers in the ICT sector, household consumption for Information and Communication Technology (ICT), and the amount of accommodation for Gross Regional Domestic Product (GRDP) of the ICT Sector in districts/cities of Bali Province. The analysis to be used is descriptive and inferential analysis with panel data regression from regencies/cities in Bali Province period 2016-2021. The model used is the fixed effect model. In general, the GRDP of the ICT Sector continues to increase, but its growth is decreasing every year. Meanwhile, technological developments in Bali Province tend to increase every year. With a significance level of 5 percent, the percentage of e-commerce users, the percentage of ownership of cell phones, and the number of accommodations have a significant positive effect on the GRDP of the ICT sector. Technological progress has not been fully utilized, therefore the GRDP growth of the ICT sector tends to decrease every year.
Study of Economic Vulnerability and Its Influence on the Economy in Sumatera Island Using the Household Consumption Expenditure Approach Adin Nugroho; Prientananda Ghina Salsabila
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.v2023i1.293

Abstract

The readiness of a region to face shocks and spillover effects from the surrounding area needed to be developed early. Each region had different economic structure so that the policy and strategy that was used to deal with current and future global uncertainties should be different as well. This study aimed to analyze economic vulnerability and the characteristics of its grouping, and analyze the effect of inflation, unemployment rate, foreign investment, and economic vulnerability towards the economy of provinces in Sumatera. The method performed in this study was Cluster Analysis for grouping and creating economic vulnerability variable, Panel Regression Analysis to analyze the effect between variables in general, and GWPR (Geographically Weighted Panel Regression) analysis to analyze spatial effect of regions. The result showed that the variable of economic vulnerability had a negative and significant effect on household consumption expenditures, especially in the Province of Lampung and Sumatera Selatan.
Development of Student’s Uniform Compliance Detection System Using Real Time Image Recognition at Politeknik Statistika STIS Ardian Fajri Saputra; Yunarso Anang
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.v2023i1.298

Abstract

Regulations in the Politeknik Statistika STIS (hereinafter called Polstat STIS) aims to produce graduates who are qualified, with integrity and trusted. In enforcing regulations in Polstat STIS, there are a student squad of regulations enforcement, which is called Satuan Penegak Disiplin or SPD in Indonesian, which aims to maintain the order, discipline and student ethics during their activities on and off campus. In upholding the regulations, SPD carries out surprise inspection and during the weekly morning assembly to check completeness and tidiness of student’s uniform as well as his/her look. However, previous research related to the student’s commitment to the campus regulations shows that half of the students have low commitment. This is partly due to the lack of supervision of students. Therefore, it is necessary to monitor the discipline and neatness of students on an ongoing basis. In order to conduct the monitoring and inspection on a more regular basis, the method of image recognition can be used to assist in overseeing student discipline and neatness. In this study, we developed a system which can detect in a real-time manner the completeness of attributes the student wears. The system we developed uses object detection to detect the completeness of student attributes. The system shows and records student(s) whose attributes are incomplete. The system expected to improve the discipline and neatness of the students.
Competitiveness and Factors Influencing Indonesian Clove Exports to Eight Export Destination Countries from 2005-2020 Siti Ainia Hidayati; Ekaria
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.v2023i1.299

Abstract

Indonesia is the largest clove producer and exporter in the world, but from 2005 to 2020 the average clove export was dominated by Madagascar. As the largest clove producer, Indonesia should be able to dominate the export market, especially cloves. Therefore, this study aims to determine the competitiveness position of Indonesian cloves and analyze the economic factors that affect Indonesian cloves exports. In this study, the analysis method use Revealed Comparative Advantage (RCA), Export Product Dynamics (EPD), and a Fixed Effect Model (FEM) for panel data of eight export destination countries from 2005-2020. The results show that the competitiveness of Indonesian cloves is above the world average. The competitive position of Indonesia's clove exports in the Netherlands, Pakistan, Saudi Arabia, United Arab Emirates, United States, and Vietnam is a rising star. At the same time, the other two markets (India and Singapore) are falling stars. In addition, the export prices have a significant effect on the volume of Indonesian clove exports. Indonesian clove production and destination countries' GDP per capita have a positive effect, while economic distance has a negative effect on the volume of Indonesian clove exports.
The Socio-Economic Factors Influencing Sugar-Sweetened Beverages (SSB’s) Consumption in Household of DKI Jakarta Province in 2020 Muhammad Gozali Yahya; Arya Candra Kusuma; Norvan Bagus Ramadhan
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.v2023i1.300

Abstract

Non-communicable diseases (NCDs) are responsible for causing 41 million deaths annually, constituting approximately 74% of all global fatalities. One of the key factors contributing to the elevated risk of NCD’s is the excessive consumption of sugary beverages, which encompass a variety of liquid products containing added sugars. This research endeavor seeks to identify the socioeconomic factors that perform in shaping the consumption patterns of sugary beverages within households residing in Province of DKI Jakarta. This study using data from the 2020 Susenas survey, contain a total of 5,456 sampled households. Binary logistic regression is used for modelling whether households had consumed sugary beverages during the preceding week or not. Variables such as marital status, gender, age, educational attainment, employment status of the household head, as well as internet accessibility, economic status, internet usage motives, and household size, were found to influence the likelihood of consuming sugar-sweetened beverages (SSBs) in DKI Jakarta Province. Based on these findings, it is recommended to enhance the use of the internet for promoting healthy lifestyles.
A Sentiment Analysis and Topic Modelling of The Socio-Economic Registration 2022 Indah Simbolon; Nicholas H Manurung; Sukma Andini; Lya Hulliyyatus Suadaa
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.v2023i1.301

Abstract

Socio-Economic Registration or Regsosek is an activity of Statistics Indonesia (BPS) that aims to collect data related to the profile, social and economic conditions, and welfare levels of all residents in 514 regencies/cities in Indonesia. One indicator of the success of Regsosek 2022 is the response and opinion from the community regarding the activity. The response and opinion can provide an overview of the implementation of Regsosek 2022 so that the picture can be used as a lesson learned to carry out the following population data collection. This study uses several methods to analyze the results of community responses and opinions on Regsosek activities, especially on Twitter social media. The method used in this research is sentiment analysis classification with four techniques: Naïve Bayes, Nearest Centroid, K-Nearest Neighbors, and Support Vector Machine. Then, the performance of the four techniques will be compared. In addition, the topic modeling method will also be used with two techniques, namely Latent Semantic Analysis and Latent Dirichlet Allocation. Data is collected using web scraping techniques. The results obtained from the sentiment analysis classification are that the Nearest Centroid method provides the best results with a relatively high and balanced f1-score value in positive and negative sentiments, which are 59% and 66%, respectively. Moreover, LDA modeling results are better than the LSA method for topic modeling results.
Is The Wealth Index Better than The Proxy Means Test in Poverty Targeting? A Study in Brebes and East Jakarta Nuri Taufiq; I Made Giri Suyasa
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.v2023i1.302

Abstract

The ranking of household welfare status in targeting recipients of social protection programs is important and needs attention. Appropriate welfare status ranking is one of the keys for making the various types of programs designed by the government right on target. The Proxy Means Test method is popular in Indonesia in the 2015 Integrated Database Updating. Based on another popular statistical approach to ranking welfare status, the Wealth Index method is also known. Global surveys, such as Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and World Food Program Surveys, have always used the Wealth Index to rank household welfare. Using Susenas data from March 2017 to March 2022, this study found that the Proxy Means Test method is better than the Wealth Index method in both Brebes Regency and East Jakarta City. The value of the classification error rate in Brebes Regency and East Jakarta City using the Proxy Means Test method is 13.94 percent and 10.37 percent, respectively. In comparison, the Wealth Index method is 25.12 percent and 14.74 percent. This research emphasizes that the results of the ranking of household welfare status are not only influenced by the method used but also by the socioeconomic conditions and characteristics of households data in the areas targeted by the program.

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