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All Journal Jurnal Aplikasi Statistika & Komputasi Statistik Prosiding Seminar Nasional Official Statistics
Rindang Bangun Prasetyo
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences

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Analisis Sentimen dan Emosi Publik pada Awal Pandemi COVID-19 Berdasarkan Data Twitter dengan Pendekatan Berbasis Leksikon Yasinta Amalia Nur Jannah; Rindang Bangun Prasetyo
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (351.968 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1483

Abstract

Coronavirus Disease 2019 (COVID-19) is infectious disease caused by Severe Acute Respiratory Illness Coronavirus. The spread of this virus is rapid and hard to control so in March 2020, World Health Organization declared COVID-19 as pandemic. COVID-19 pandemic has led negative impact on almost aspects of life. Start from social, economy, to public’s psychological state. During pandemic, outdoor activities are restricted. The number of unemployed increase. These thought to be background why people experiencing psychological disorders. Therefore, it’s necessary to monitor psychological state so public's mental health is maintained. The purpose of this study is to examine public sentiment and psychic as seen through public's emotional state during COVID-19 pandemic from March to July 2020. Twitter data is used as research data and then analyzed using sentiment and emotion analysis with lexicon-based approach. The results showed that negative sentiments were more widely expressed and fear was the emotion most felt by public. These can be used as recommendation for government to pay more attention public’s emotional state.
Kajian Penerapan Machine Learning untuk Sistem Rekomendasi Mitra Statistik BPS Septianugraha, Damar; Wilantika, Nori; Suadaa, Lya Hulliyyatus; Prasetyo, Rindang Bangun; Huraira, Sabit
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2211

Abstract

BPS routinely conducts censuses and surveys involving BPS partners in data collection and processing. Ensuring these partners exhibit good performance is crucial to minimize the risk of moral hazard, which can negatively impact stakeholders. This research aims to implement machine learning into an information system to recommend statistical partners based on classification results. The best model identified is XGBoost, which is integrated into the system for generating recommendations. System testing using black-box methods confirmed compliance in 41 scenarios. Additionally, the System Usability Scale (SUS) questionnaire yielded an average score of 65.5, indicating the system's potential and suitability for further development. The findings offer insights into utilizing partner characteristics data and evaluation in BPS's censuses and surveys, particularly for selecting assigned partners.
Estimation of Gross Regional Domestic Product per Capita at the Sub-District Level in Bali, NTB, and NTT Provinces Using Machine Learning Approaches and Geospatial Data Putra, I Made Satria Ambara; Prasetyo, Rindang Bangun; Wiguna, Candra Adi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.803

Abstract

Introduction/Main Objectives: This study aims to estimate Gross Regional Domestic Product (GRDP) per capita at the sub-district level. Background Problems: Currently, GRDP per capita is calculated only at the district level by BPS. Novelty: This study estimates GRDP per capita at the sub-district level using a model developed at the district level, applying machine learning and linear regression methods. Research Methods: The model was constructed using geospatial data sourced from satellite imagery, OpenStreetMap, (Village Potential Statistics) PODES, directories of large mining companies, and directories of the manufacturing industry at the district level. Linear regression and machine learning methods, including neural networks, random forest regression, and support vector regression, were used to develop the model. The research focuses on three provinces: Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Findings/Results: The best-performing model was support vector regression, with MAE and MAPE evaluations of 10.33 million and 26.11%, respectively. The results indicate that sub-districts with high GRDP per capita are typically urban areas that serve as economic hubs. The Williamson Index results show that districts in the eastern region have higher inequality levels compared to those in the western region.
Co-Authors Huraira, Sabit Putra, I Made Satria Ambara Septianugraha, Damar Suadaa, Lya Hulliyyatus Wiguna, Candra Adi Wilantika, Nori Yasinta Amalia Nur Jannah