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All Journal Media Statistika PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Aditama, Farhan Satria
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Computer Science & IT Decision Sciences, Operations Research & Management

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MULTICLASS CLASSIFICATION OF MARKETPLACE PRODUCTS WITH MACHINE LEARNING Aditama, Farhan Satria; Krismawati, Dewi; Pramana, Setia
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.25-35

Abstract

The use of marketplace data and machine learning in the collection of commodity data can provide an opportunity for Statistics Indonesia to complete the commodity directories for various surveys. This research adopts machine learning to train a product classification model based on existing datasets to predict whether a new dataset falls into which KBKI category. The dataset contains more than 32,000 products from 26 classes consisting of product data from two biggest marketplaces in Indonesia. Algorithms used for classification include Random Forests (RF), Support Vector Machines (SVM), and Multinomial Naive Bayes (MNB). Results indicate that MNB is the most effective algorithm when considering the trade-off between accuracy and processing time. MNB achieved the highest micro-average F1 scores, with 91.8% for Tokopedia and 95.4% for Shopee, and has the fastest execution time approximately 5 seconds.
Logical Modelling of Statistical Data Using the SDMX Standard: Case Study on the Quarterly Gross Regional Domestic Product Table Amandasari, Kartika; Pratama, Nano Yulian; Aditama, Farhan Satria; Marsisno, Waris
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 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.v2025i1.641

Abstract

Poverty, as a national issue, necessitates data-driven policy planning informed byaccurate and consistent statistics. To ensure the optimal quality and consistency of statistical datareporting across diverse regions, the adoption of an international standard is crucial. TheStatistical Data and Metadata Exchange (SDMX) standard facilitates the structured exchange ofdata and metadata. This study aims to design and implement a statistical indicator data modelusing the SDMX standard to improve table consistency. We utilized Quarterly Provincial GrossRegional Domestic Product (GRDP) data as a case study and applied the Design ScienceResearch Method (DSRM) as the methodology. The results demonstrate that modeling theGRDP data using SDMX yields a uniform and highly consistent table structure, significantlyenhancing the consistency of statistical data reporting across regions.
Co-Authors Amandasari, Kartika Krismawati, Dewi Marsisno, Waris Pramana, Setia Pratama, Nano Yulian