Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Official Statistics
Rahman, Dimas Haafizh
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Sentimen dan Pemodelan Topik Opini Publik Terkait Data Badan Pusat Statistik Tahun 2024 Rahman, Dimas Haafizh; Alistin, Zharifah Dhiya Ayu; Pramana, Setia
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2365

Abstract

The role of BPS has become increasingly crucial with the rising demand and sources of data over time. The quality of BPS data is evaluated through the Data Needs Survey (SKD). The 2024 SKD indicates that 98.16% of consumers are satisfied with the quality of BPS data. However, this evaluation only involved data consumers from BPS PST, and there remains a time gap between the implementation and dissemination of the survey results. Social media platform X, which is popular in Indonesia, allows its users to express their opinions through tweets. This research is conducted to understand public sentiment, identify the best classification model, and discover topics discussed by the public regarding BPS data based on tweets from the X platform in 2024. The tweets were taken through labeling and preprocessing before applying Machine Learning methods to classify public sentiment. The Support Vector Machine (SVM) method, with a weighted average of 0.68, performed best compared to Naïve Bayes, Rocchio Classification, and K-NN in modeling public opinion sentiment. The implementation of LSA and LDA discovered topics consisting of public opinions and issues related to BPS data such as poverty rate manipulation and BPS data as a credible source.
Pembangunan Dataset Sintetis Klasifikasi Baku Lapangan Usaha Indonesia 2020 dengan Generative Artificial Intelligence Silmi Kaffah, M. Ihsan; Rahman, Dimas Haafizh; Amnur, Muh. Alfian; Montolalu, Cloudya Qashwah; Siregar, Amir Mumtaz; Sinulingga, Geraldo Benedictus; Ayu Alistin, Zharifah Dhiya; Raihannur, Cut Indah; Putri Arivia, Anggi Marya; Rahmawati, Arih; Nauli Sihombing, Fiona Audia; Salsabiela, Rahmadika Kemala; Bahy, Sabastian Alfons; Suadaa, Lya Hulliyyatus; Choir, Achmad Syahrul
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2581

Abstract

The limited quality datasets is a fundamental challenge in developing automatic classification of business description into the Indonesia Standard Industrial Classification (KBLI) using machine learning models. This research aims to develop a synthetic KBLI dataset using Generative AI via ChatGPT chatbot with a one-shot prompting technique. This technique is employed to generate business descriptions based on five-digit KBLI codes in order to address the limitations of labeled data and the variability of existing business descriptions. The dataset generated through prompt engineering and manual validation shows that 93,25% of the business descriptions align with the established KBLI standards. The average number of business descriptions per category demonstrates a fairly uniform distribution, ensuring sufficient representation for each five-digit code. This research makes a significant contribution in providing a dataset for training machine learning models in the automatic classification of business descriptions into the five-digit KBLI categories.
Co-Authors Alistin, Zharifah Dhiya Ayu Amnur, Muh. Alfian Ayu Alistin, Zharifah Dhiya Bahy, Sabastian Alfons Choir, Achmad Syahrul Montolalu, Cloudya Qashwah Nauli Sihombing, Fiona Audia Pramana, Setia Putri Arivia, Anggi Marya Rahmawati, Arih Raihannur, Cut Indah Salsabiela, Rahmadika Kemala Silmi Kaffah, M. Ihsan Sinulingga, Geraldo Benedictus Siregar, Amir Mumtaz Suadaa, Lya Hulliyyatus