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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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Extracting Consumer Opinion on Indonesian E-Commerce: A Rating Evaluation and Lexicon-Based Sentiment Analysis Arbi Setiyawan; Arie Wahyu Wijayanto; He Youshi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.22

Abstract

E-commerce as a business platform offers abundant advantages in modern life all over the world. Sellers and buyers at online marketplaces may get benefits and advantages from e-commerce. One of the advantages is that e-commerce can be accessed anywhere and anytime. Despite providing advantages, e-commerce also has disadvantages including product quality fraud and data theft. Online marketplaces provide facilities for consumer evaluation, through star rating and consumer reviews. In this paper, we focus on the Business-to-Consumer (B2C) e-commerce type and extract consumer opinion data from a leading online marketplace in Indonesia and use text mining approaches to compare the rating evaluation and sentiment analysis on consumer reviews. With 2,937 records, we investigate the relationship between star rating and lexicon-based sentiment analysis. From the results, we found that most consumers do not hesitantly provide a good evaluation indicated by a 5-star rating and positive sentiment of reviews. A quite polarized rating distribution is found and indicates a straightforward consumer opinion. However, a further examination of the relation between rating and review, we discover inconsistencies in consumer opinion where the good rating may also contain negative reviews. Our result findings provide an insight to build a more integrated consumer opinion indicator in e-commerce and that online marketplace sellers need to look deeper at the detailed reviews rating.
Knowledge Management System in Official Statistics: An Empirical Investigation on Indonesia Population Census Achmad Muchlis Abdi Putra; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.25

Abstract

National statistical offices around the world show a strong interest in producing reliable, objective, and accurate information in compliance with a high level of professional and scientific standards. Such a set of information provided by government agencies is known as the official statistics. To support the potential of knowledge-based business processes and deliver high-quality public services, knowledge management systems (KMS) are undoubtedly required. In this work, we study the impact of embracing KMS in one of the most massive scale statistical census in South East Asia, the 2020 Indonesia Population Census (IPC2020). The regression analysis is utilized in this study where the perceived usefulness is the dependent variable and the perceived ease of use become the independent variable. Our findings reveal that KMS utilization gains a positive influence on the perceived ease of use and usefulness among the stakeholders and organizing personnel. This provides an incentive to enlarge the range of implementation and improve the system and infrastructure capability to better support the knowledge-driven collaboration among stakeholders of the statistical office.
Optimization of Waste Transportation Routes using Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) in the Eastern and Southern Regions of Bandung City, Indonesia Natasya Afira; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.27

Abstract

Ensuring high-quality and effective urban waste management has been an important priority to achieve sustainable and environmental-friendly cities and communities mandated by Sustainable Development Goals (SDGs). The massively growing population in urban regions of developing countries, such as Bandung City, Indonesia, leads to the increasing volume of daily goods consumption and households waste production. The waste transportation route is one of the main determining factors for the cost of waste management. In this paper, we introduce the Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) to solve the waste transportation route optimization problem in the Eastern and Southern Regions of Bandung City, Indonesia. Compared to the existing traditional evolutionary algorithms, MNSGA-II offers three major important benefits: efficient computational complexity, no requirement of sharing parameters, and a non-elitism mechanism. Algorithm parameters include the number of generations, mutation rate, and crossover rate. Our extensive experiments suggest the best solution resulted in 14 routes with a total distance of 152,63 km. Further, our proposed route optimization is potentially beneficial to support the improvement of the sustainable waste management service system at Bandung City.
Preserving Women Public Restroom Privacy using Convolutional Neural Networks-Based Automatic Gender Detection Desi Kristiyani; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.29

Abstract

Personal safety and privacy have been the significant concerns among women to use and access public restrooms/toilets, especially in developing countries such as Indonesia. Privacy-enhancing designs are unquestionably expected to ensure no men entering the rooms neither intentionally nor accidentally without prior notice. In this paper, we propose a facial recognition approach to ensure women's safety and privacy in public restroom areas using Convolutional Neural Networks (CNN) model as a gender classifier. Our main contributions are as follows: (1) a webcam feed automatic gender detection model using CNN which may further be connected to a security alarm (2) a publicly available gender-annotated image dataset that embraces Indonesian facial recognition samples. Supplementary Indonesian facial examples are taken from a government-affiliated college, Politeknik Statistika STIS students' photo datasets. The experimental results show a promising accuracy of our proposed model up to 95.84%. This study could be beneficial and useful for wider implementation in supporting the safety system of public universities, offices, and government buildings.
Enhancing Official Statistics Data Dissemination using Google Firebase Platform on Mobile Application: A User-Centered Design Approach Wisma Eka Nurcahyanti
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.30

Abstract

The dissemination of official statistics as publicly available information has been mandated in the United Nations Fundamental Principles of Official Statistics (UNFPOS) to be highly accessible to all users. Recently, with an increasing volume of data and public demand, National Statistical Offices (NSO) including Statistics Indonesia (BPS) are being challenged to provide accurate, excellent-quality, and user-friendly information. In this paper, we introduce our attempts to enhance the official statistics data dissemination by developing an Android-based mobile application using a User-Centered Design (UCD) approach to meet the requirement of specifically targeted users. Google Firebase platform is utilized to improve the administrator-level usability in updating the disseminated information. The proposed mobile application is launched at BPS-Statistics Madiun Municipality, East Java Province called Batu Cadas (an acronym for BAca TUjuh CAtatan DAta Statistik). Further evaluations using Black-Box functionality testing, System Usability Scale, and specific needs comparison conclude that the proposed mobile application is sufficient to cover the gap between user needs and the currently existing applications.
Study of Handwriting Recognition Implementation in Data Entry of Survei Angkatan Kerja Nasional (SAKERNAS) using CNN Yusron Farid Mustafa; Farid Ridho; Siti Mariyah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.32

Abstract

The use of Paper and Pencil Interviewing (PAPI) at BPS requires manual data entry that cannot be separated from the human ability to recognize handwriting. For computers, handwriting recognition is complex work that requires complex algorithms. Convolutional Neural Network (CNN) is an algorithm that can accommodate the complexity of handwriting recognition. This research intends to conduct a study on the implementation of the handwriting recognition model using CNN in recognizing handwriting on the PAPI questionnaire in data entry activities. Handwriting recognition model was built using the EMNIST dataset separately according to its character type and provides 89% accuracy for characters in the form of letters and numbers, 95% for characters in the form of letters, and 99% for characters in the form of numbers. Implementation of the handwriting recognition on the questionnaire image shows good results with 83.33% accuracy. However, there are problems found in the process of character segmentation where characters are not segmented correctly because the line of writing continues on the character that should be separated and disconnected characters when they should be joined. The result obtained in this study is expected to be a consideration regarding the entry method data used by BPS later.
Bayesian Network Model to Distinguish COVID-19 for Illness with Similar Symptoms Emir Luthfi; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.36

Abstract

Numerous diseases and illnesses exhibit similar physical and medical symptoms, such as COVID-19 and its similar disguised illness (common cold, flu, and seasonal allergies). In this study, we construct a Bayesian Network model to distinguish such symptom variables in a classification task. The Bayesian Network model has been widely used as a classifier comparable to machine learning models. We develop the model with a scoring-based method and implement it using a hill-climbing algorithm with the Bayesian information criterion (BIC) score approach. Experimental evaluations using publicly available Mayo Clinic based data using this Bayesian Network model that present Directed Acyclic Graph (DAG) which can show the relationship between the similar symptoms and the type of disease with Conditional Probability Table (CPT). This model shows a promising accuracy performance up to 93.14% which is better than the performance of other machine learning classifiers, including the Support Vector Machine (SVM) and the ensemble approaches such as Random Forest (RF), while slightly smaller than that of the neural networks (NN).
Learning Bayesian Network for Rainfall Prediction Modeling in Urban Area using Remote Sensing Satellite Data (Case Study: Jakarta, Indonesia) Salwa Rizqina Putri; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.37

Abstract

Rainfall modeling is one of the most critical factors in agricultural monitoring and statistics, transportation schedules, and urban flood prevention. Weather anomaly during the dry season in urban coastal areas of tropical countries such as Jakarta, Indonesia has become a challenging issue that causes unexpected changes in rain patterns. In this paper, we propose the Bayesian Network (BN) approach to model the probabilistic nature of rain patterns in urban areas and causal relationships among its predictor variables. Rain occurrences are predicted using temperature, relative humidity, mean-sea level (MSL) pressure, cloud cover, and precipitation variables. Data are obtained from the remote sensing sources of the National Oceanic and Atmospheric Administration (NOAA) satellite in Jakarta 2020-2021. We compare both of the score-based, i.e., Hill Climbing (HC), and hybrid structure learning algorithms of Bayesian Network including the techniques of Max-Min Hill Climbing (MMHC), General 2-Phase Restricted Maximization (RSMAX2), and Hybrid-Hybrid Parents & Children (H2PC). Further, we also compare the performance of score-based model (Hill Climbing) under five different popular scorings: Bayesian Information Criterion (BIC), K2, Log-Likelihood, Bayesian Dirichlet Equivalent (BDE), and Akaike Information Criterion (AIC) methods. The main contributions of this study are as follows: (1) insights that the hybrid structure learning algorithms of Bayesian Network models are either superior in performance or at least comparable to its score-based counterparts (2) our proposed best performed Bayesian Network model that is able to predict the rain occurrences in Jakarta with a promising overall accuracy of more than 81 percent.
The Best K-Exponential Moving Average with Missing Values: Gold Prices in Indonesia, Saudi Arabia, and Turkey during COVID-19 Fadhlul Mubarak; Atilla Aslanargun; Ilyas Siklar
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.42

Abstract

There have been missing values in the gold price data for Indonesia, Saudi Arabia, and Turkey at the weekend so that imputation techniques have been carried out to solve this problem. The imputation method of replacing NAs with the latest non-NA values also known as last observation carried forward (LOCF) made it a solution to overcome the missing values. This study selected the best -exponential moving average based on the smallest mean absolute percentage error (MAPE) from simulations. The 2-exponential moving average analysis was the best analysis for the price of gold which has missing values in Indonesia, Saudi Arabia, and Turkey during COVID-19, while the largest MAPE values are different for each country.
Changing in National Infrastructure Policy: How It Affect Indonesia’s Economy? Case Study of Indonesia 2010 and 2016 IO Table and 2016 IRIO Table Ade Marsinta Arsani; Chaoqing Huang
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.44

Abstract

This research would like to firstly figure out how new infrastructure policy affects national economic structure changes, and secondly figure out does the new policy effect on inter-regional economy linkage. This study uses economic structure, growth decomposition, location quotient, and linkage analysis on Input-output table to indicate national and inter-regional level economic changes between 2010 and 2016 in Indonesia. We find that economic structure generally remains the same, only transportation and real estate sector increased their contribution, this may indicate the beginning of infrastructure development stage. During 2010 to 2016, the growth was led by the expansion of domestic demand in almost all sectors, however in some sectors the technological changes have a negative contribution. Furthermore, the two most linked sectors are manufacturing and electricity sectors. Inter-regional analysis indicated that Java and Sumatera have more power and sensitivity level compared to other regions. The suggestion to booster economy development is to implement technological process and publish policy considering regional characteristics may accelerate economic equity across regions.

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