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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Eduvest - Journal of Universal Studies Jurnal Sistem Informasi
Aris Budi Santoso
Universitas Indonesia
Aerospace Engineering Computer Science & IT Education Engineering Health Professions Neuroscience Social Sciences

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MASTER DATA MANAGEMENT IMPLEMENTATION IN DISTRIBUTED INFORMATION SYSTEM CASE STUDY DIRECTORATE GENERAL OF TAX, MINISTRY OF FINANCE OF REPUBLIC OF INDONESIA Aris Budi Santoso; Yoga Pamungkas; Yova Ruldeviyani
Jurnal Sistem Informasi Vol. 15 No. 1 (2019): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.734 KB) | DOI: 10.21609/jsi.v15i1.779

Abstract

Information system architecture of Directorate General of Tax (DGT) is centralized with distributed data. The main problem are replication of master and reference data which spread among applications which vary on data structure and the synchronization jobs that spread in many locations and not well managed. Therefore, Master Data Management (MDM) needs to be implemented with accordance to characteristic of centralized distributed information system. Master data management maturity evaluation is conducted using MDM maturity model (MD3M) Spruit dan Pietzka, the result is Data Protection, Data Quality and Maintenance topic have maturity level 3 or defined process stage, while Data Model, Usage and Ownership topic have maturity level 2 or repeatable stage. Solutions to solve MDM issues and to enhance the master data management maturity level are proposed based on Data Management Body of Knowledge (DMBOK). DGT’s MDM issues are related to insufficiency of policy and architecture of MDM system. Policy and architectural approach of centralized MDM system is required to solve that issues. Proposed solution involves the use of data virtualization to enable implementation of centralized system of MDM without consolidate all master and reference data into new database.
Stance Analysis of Policies Related to Emission Test Obligations using Twitter Social Media Data Dwi Retnoningrum; Dea Annisayanti Putri; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.69004

Abstract

Social media is currently widely used to disseminate various kinds of information, whether expressing feelings, or opinions. Public opinion is no exception regarding government policies and the implementation of emission tests, which describe the conditions that exist in society. Information on public opinion data obtained through social media in real time can assist the government in evaluating policies and improving the quality of currently implemented policies, particularly evaluating the implementation of emission tests on motorized vehicles. In this research, the application of stance analysis is used to evaluate emission test policies based on public opinion.In addition, this research aims to combine several machine learning methods and feature extraction methods to find the best combination based on accuracy, training time, and prediction time based on emission test policies. The best model based on the level of accuracy is a combination of Decision Tree and BERT, which reaches a value of 66%. Meanwhile, based on training time, the model that has the advantage is the Ridge Classifier with fasttext text representation. Based on prediction time, there are 3 combination models, namely Decision Tree with word2vec, SVM with Word2Vec, and Logistic Regression with fasttext text representation.
SENTIMENT ANALYSIS OF PUBLIC HEALTH APP REVIEWS USING INDOBERT AND XLM-ROBERTA: A STUDY ON SATUSEHAT MOBILE APP Dimas Ananda; Indra Budi; Aris Budi Santoso; Ali Adil Qureshi
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10083

Abstract

Sentiment analysis is a key method for deriving insights from user-generated content, particularly in evaluating public satisfaction with digital health services. This study conducts a comparative analysis of sentiment polarity classification models on 34,178 Indonesian-language reviews from SATUSEHAT Mobile, a national health application by the Indonesian Ministry of Health. The dataset was manually annotated into positive, neutral, and negative classes. Three model categories were evaluated: classical machine learning (Support Vector Machine, XGBoost), baseline neural networks (Multilayer Perceptron, Convolutional Neural Network), and pretrained transformer-based models (IndoBERT, XLM-RoBERTa). All models were trained using stratified 5-fold cross-validation and tested on a held-out set. Results show that transformer-based models significantly outperform others in all metrics. IndoBERT achieved the highest weighted F1-score (0.8555), followed closely by XLM-RoBERTa (0.8552). Despite the similar average performance, XLM-RoBERTa exhibited the lowest performance variance across folds, making it the most stable and effective model overall. Statistical validation using Friedman and Nemenyi tests confirmed these differences as significant. However, all models struggled with neutral sentiment detection due to data imbalance. Although computationally more expensive than IndoBERT, XLM-RoBERTa offers superior robustness for sentiment classification in Indonesian health-related text. These findings support the integration of transformer-based sentiment monitoring into public health dashboards to enable timely, data-driven service improvements
The Role of Social Media in Shaping Social Movements: A Case Study of #Daruratreformasi In Indonesia Using Text Mining and Network Analytics Alifdaffa Nurfahmi Dekatama; Devin Prayogo; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Eduvest - Journal of Universal Studies Vol. 5 No. 7 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i7.51376

Abstract

The social movement #DaruratReformasi emerging in Indonesia since July 2024 has attracted widespread attention both nationally and internationally. This study aims to analyze the communication dynamics and interaction patterns within the social media network of the movement using text mining and network analytics approaches. Topic modeling identifies the dominant key issues in public discourse, while social network analysis reveals the main actors and influencers involved in information dissemination and public opinion formation. A modularity approach is employed to detect naturally formed discussion communities within the network, and temporal analysis illustrates the phases of the movement’s development from initiation to its peak in November 2024. The results indicate that social media serves as a strategic platform for social mobilization and political advocacy, with key actors distributed across interconnected communities. Additionally, the involvement of government institutions as central actors highlights the two-way communication dynamics within the digital public sphere. These findings underscore the urgency of understanding social network structures in the context of modern digital social movements and provide implications for public communication management and mass mobilization strategies in the digital era.
Unraveling Insights from User Reviews of Sapawarga – Jabar Super Apps Through Topic Modeling Basrah Nasution; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.12624

Abstract

This study conducted topic modeling on user reviews of the Sapawarga – Jabar Super App application on the Google Play Store. The dataset comprised user reviews submitted since the date application released until January 7, 2026 yielded 5.082 user reviews. Collected data processed through a series of data processing pipeline namely case folding, remove punctuation, normalization, tokenization, stop word removal and stemming. Topic modeling was conducted using Latent Dirichlet Allocation (LDA) algorithm grouped based on year of data. Topic distribution was visualized using the PyLDAvis to facilitate analysis and interpretation. This study found there are 9 topic after analysing year by year data namely Appreciation of the usefulness of the application in helping residents (1), Technical issues in terms of how to log in to the application (2), Application improvement proposal (3), Ease of information and citizen empowerment (4), General expressions of appreciation and criticism from citizens towards the application (5), Technical issues with payment features (6), Application error problem (7), User appreciation for the ease of vehicle tax payments from the application (8) and Criticism of application constraints in vehicle tax payments (9). The interpretation results indicate that the application is widely used by residents for online tax payments, particularly vehicle tax services, compared to other features or uses of the application. The findings of this study are expected to serve as a valuable insight for relevant stakeholders to improve the quality, performance, and overall effectiveness of the application as a public service platform.
Detection of illegal drug activities in Indonesia via social media X Adi Hanif Sedar; Lukman Yudokusumo; Indra Budi; Amanah Ramadiah; Aris Budi Santoso; Prabu Kresna Putra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3376-3388

Abstract

In today's digital world, social media platforms such as X (formerly Twitter) are widely used for communication but also for illegal activities, such as selling illicit drugs. This study examines how posts from X related to illegal drug sales in Indonesia can be analyzed using machine learning techniques, with a focus on incorporating features related to personally identifiable information (PII) and web URLs. The main questions are how to use classification methods to detect these posts and which drugs are most often mentioned. A total of 11,777 posts from X were collected using web scraping. After cleaning and processing the data, five machine learning models were used. The support vector machine (SVM) model showed the best results with a weighted F1-score of 97.08%. The findings show that abortion pills are the most frequently mentioned drugs, followed by sexual enhancement and anesthetic drugs. This study also used visual tools to show the most common hashtags, posts distribution over time, and types of drugs discussed. This study underlines the importance of monitoring social media to protect public health and highlights how incorporating PII features can improve the detection of illegal activities. Future research should look at posts in other languages and from other platforms, considering PII features for enhanced analysis.
Co-Authors Adi Hanif Sedar Ali Adil Qureshi Alifdaffa Nurfahmi Dekatama Amanah Ramadiah Basrah Nasution Dea Annisayanti Putri Devin Prayogo Dimas Ananda Dwi Retnoningrum Indra Budi Lukman Yudokusumo Prabu Kresna Putra Prabu Kresna Putra Prabu Kresna Putra Yoga Pamungkas Yova Ruldeviyani