Article Per Year (5 Year)
p-Index From 2020 - 2025
3.189
P-Index
This Author published in this journals
All Journal Jurnal Keperawatan Jiwa (JKJ): Persatuan Perawat Nasional Indonesia Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) MULTINETICS Jurnal Keperawatan Widya Gantari Indonesia Dinasti International Journal of Education Management and Social Science Indonesian Journal of Electrical Engineering and Computer Science Khatulistiwa Nursing Journal Jurnal Locus Penelitian dan Pengabdian Eduvest - Journal of Universal Studies Majalah Cendekia Mengabdi Journal Of Engineering Sciences (Improsci) The Indonesian Journal of Computer Science Jurnal Gawat Darurat
Budi, Indra
Unknown Affiliation
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Indonesian sentiment analysis in natural environment topics Octovianto, Christofer; Ibrohim, Muhammad Okky; Budi, Indra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1353-1366

Abstract

Indonesia is one of the countries that is rich in biodiversity and has a high population growth. This condition can cause Indonesia to have problems related to the natural environment that are more complex than other countries. Hence, this has created a lot of discussions regarding natural environmental issues in Indonesia on social media platforms. In this case, stakeholders like the government in general can utilize sentiment analysis (SA) to comprehend the public’s views to allow them to better fit the public’s expectations when formulating a particular policy that related to the environmental sustainability (ES) issues. This paper built the first open dataset of Indonesian SA dataset in ES topics collected from Instagram. As the benchmark of our dataset, we used IndoBERT model variant for constructing the model and the experiment result shows that model based on IndoBERT-large-p2 obtained the best performance with 72.44% of F1-score.
Utilizing Translation to Enhance NLP Models in Offensive Language and Hate Speech Identification Kurniawan, Sandy; Budi, Indra
Jurnal Improsci Vol 1 No 4 (2024): Vol 1 No 4 February 2024
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/improsci.v1i4.187

Abstract

The number of social media users in Indonesia has increased in recent years. The surge in social media users leads to more offensive language on these platforms. The use of offensive language can trigger conflicts between users. Therefore, it is necessary to identify the use of offensive language on social media. This study focused on identifying offensive language, hate speech, and hate speech targets on Twitter. The data used were obtained from previous research on identifying offensive language and hate speech. The amount of data is very influential on the performance of the classification. Therefore, data was added using translation in this study. Classical machine learning (SVM et al.) and deep learning (BiLSTM, CNN, and LSTM) algorithms are used as classification algorithms with word n-gram and word embedding as the features. Three scenarios were done based on the training data used in the classification model development. The result shows that scenario 3, which uses translation for data augmentation, can improve the classification model’s performance by 5%.
Sentiment Analysis and Topic Modeling of Public Opinion on Indonesia New Capital City Development Policies Angelo, Michael David; Harwenda, Reyhan Widyatna; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.51234

Abstract

This study investigates public sentiment dynamics and dominant thematic concerns related to Indonesia’s new capital city development project (Ibu Kota Nusantara–IKN), particularly in the context of the political leadership transition from President Joko Widodo to President-elect Prabowo Subianto. Utilizing a dataset comprising 9,451 tweets collected from 2017 to 2025, sentiment analysis and topic modeling were applied to classify sentiment polarity and identify prevailing public discourse themes. Various traditional machine learning models—including Naïve Bayes, Support Vector Machine (SVM), AdaBoost, XGBoost, and LightGBM—were systematically compared with transformer-based deep learning models, specifically IndoBERT, to determine their effectiveness in sentiment classification. Results demonstrated that the IndoBERT model outperformed all traditional classifiers, achieving the highest accuracy, precision, recall, and F1 score, highlighting its superior capability in capturing nuanced linguistic patterns within informal social media texts. Independent samples t-tests revealed statistically significant sentiment shifts between the two political phases, emphasizing the impact of leadership transitions on public sentiment. Topic modeling further identified critical themes such as environmental sustainability, socio-economic implications, transparency, governance, and infrastructure development as central concerns driving public discussions. These findings provide actionable insights for policymakers and stakeholders, underscoring the importance of strategic communication and responsiveness to public sentiment in large-scale government initiatives.
The Role of Social Media in Shaping Social Movements: A Case Study of #Daruratreformasi In Indonesia Using Text Mining and Network Analytics Dekatama, Alifdaffa Nurfahmi; Prayogo, Devin; Budi, Indra; Putra, Prabu Kresna; Santoso, Aris Budi
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.
Sentiment Analysis on Government Public Policies: A Systematic Literature Review Harwenda, Reyhan Widyatna; Angelo, Michael David; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna
Dinasti International Journal of Education Management And Social Science Vol. 6 No. 5 (2025): Dinasti International Journal of Education Management and Social Science (June
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v6i5.4699

Abstract

In the digital era, public discourse on government policies has shifted significantly to online platforms. This presents valuable opportunities for governments to assess real-time public sentiment. However, prior studies on sentiment analysis in public policy remain fragmented, often lacking methodological consistency and domain-wide synthesis. This study conducts a Systematic Literature Review (SLR) to consolidate insights on the techniques, datasets, and trends involved in sentiment analysis applied to government development policies. The review identifies SVM, BERT, and Naive Bayes as the most frequently used and effective methods, with SVM excelling in structured data and simpler tasks, and BERT demonstrating superior performance in handling nuanced textual data. Lexicon based tools such as VADER are also used for quick sentiment classification. Social media platforms, particularly Twitter, emerge as the dominant data sources due to their high volume and real-time nature, while evaluation metrics such as precision, recall, F1-score, and confusion matrix are commonly applied to assess model performance. The findings also reveal evolving research interests from early focus on health policies to recent interest in infrastructure, environmental, and technology-related policies. Public sentiment across these areas varies, with health and environmental policies often eliciting negative responses, while technology policies show more neutral to positive sentiment. By synthesizing methods, datasets, evaluation strategies, and policy domains, this review provides a structured foundation to future research and supports policymakers in designing strategies.
Dinamika Opini Publik Indonesia terhadap Krisis Rohingya dalam Perspektif Waktu menggunakan Traditional Machine Learning dan Deep Learning Istiqomah, Relaci Aprilia; Budi, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4069

Abstract

The Rohingya are an ethnic minority who currently still face persecution and discrimination in Myanmar, so they have to flee to neighboring countries, such as Indonesia. However, the polemic regarding the issue of the existence of Rohingya refugees in Indonesia still shows that there are differences of opinion between groups who support and oppose it. For this reason, this research aims to determine the dynamics of Indonesian public opinion regarding the Rohingya from 2015-2023 via Twitter, as well as find out the topics that are often discussed each year using LDA. This research compares classification methods using traditional machine learning algorithms (NB, SVM, LR, and DT) and deep learning algorithms (LSTM, GRU, LSTM-GRU, and GRU-LSTM). The research results show that the traditional machine learning algorithm, LR, has the highest accuracy. There has been a change in sentiment from initially being dominated by positive sentiment to negative sentiment which is more dominant in the last five years. The topics that are often discussed for positive sentiment are the support of the Indonesian people for the Rohingya in providing assistance and shelter, while the negative topics are related to concerns about the social, economic, and security impacts that may be caused by the presence of Rohingya refugees.
Uncovering the Reasons Behind Abstain Voters' Stances in the 2024 Indonesian Presidential Election: Social Media X Study Cases Putri, Irzanes; Insani, Faiz Nur Fitrah; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4126

Abstract

The Indonesian Government expects the participation of all Indonesian people in holding General Elections. However, according to the 2019 Political Statistics by BPS, there were 34.75 million people who did not exercise their right to vote or were abstain voters (golput) in the 2019 Election. This research aims to analyze individual attitudes towards abstaining voters using stance analysis and topic modelling. From 9,045 collected tweets, subsequent manual annotation revealed 2,566 pro stances, 5,264 neutral stances, and 1,215 contra stances. The classification models utilized are Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Gradient Boosting. The classification outcomes will be analyzed by comparing the accuracy, precision, recall, and F1-score results based on their algorithms and n-grams. The results obtained from the stance analysis show that Random Forest achieved the highest accuracy and precision scores, with values of 84% and 83%, respectively. The discussion topic among those supporting golput due to low trust in the presidential and vice-presidential candidates. Other topics mentioned public feels dissatisfied with the pairs of candidates.
Perbandingan Random Search dan Algoritma Genetika dalam Penyetelan Hyperparameter XGBoost pada Retail Sales Forecasting Tiastama, Sheren Afryan; Budi, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4285

Abstract

Sales is part of the important factor that influences a company in determining two things, namely profits and losses on the company. The right strategy to determine the amount of sales can be done through forecasting. Therefore, sales forecasting requires the right technique to produce accurate results. Machine learning has been proven to help overcome sales forecasting, one of which is XGBoost. However, XGBoost has many hyperparameters that affect its performance, it requires a hyperparameter setting method to produce an optimal hyperparameter. Random searches and genetic algorithms are optimized methods to find the optimal hyperparameter on XGBoost. The two methods of optimization were compared in this study with the measurement of RMSE performance in doing retail sales forecasting on the sales data of the retail company Rossmann Store which comes from the Kaggle site. The research obtained random search results superior to the genetic algorithm with RMSE values on the training process and the testing process are 0.123 and 0.122. Meanwhile, the RMSE values generated by genetic algorithms in the training and testing process are 0.333 and 0.332.
Dukungan Pasangan Terhadap Kepatuhan Diet Penderita Diabetes Melitus Tipe 2 Muthmainnah, Miftahul; Tjomiadi, Cynthia Eka Fayuning; Budi, Indra; Rakhmadhani, Irzal
Khatulistiwa Nursing Journal Vol. 4 No. 2 (2022): July 2022
Publisher : STIKes YARSI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53399/knj.v4i0.183

Abstract

Latar Belakang: Perubahan yang terjadi selama proses kehamilan menimbulkan kecemasan pada ibu hamil. Apabila tidak ditangani, maka akan berdampak terhadap kondisi ibu dan janin. Kecemasan dapat diatasi apabila ibu hamil memiliki mekanisme koping yang baik dengan cara meningkatkan spiritual self-care. Tujuan: Tujuan penelitian ini adalah untuk mengidentifikasi hubungan spiritual self-care dengan kecemasan ibu hamil trimester III. Metode: Desain penelitian menggunakan studi cross-sectional. Penelitian dilakukan di UPT Puskesmas Kampung Dalam. Sebanyak 40 sampel ibu hamil trimester III dipilih secara purposive dengan kriteria inklusi penelitian ini yaitu ibu hamil trimester III yang berkunjung ke UPT Puskesmas Kampung Dalam Pontianak Timur, ibu hamil trimester III yang mengalami kecemasan. Pengumpulan data menggunakan instrumen penelitian yang terdiri dari kuesioner spiritual self-care practice dan kuesioner Zung self-rating anxiety scale yang telah dialih bahasa oleh peneliti sebelumnya. Hasil: Hasil uji statistik dengan korelasi Kendall’s tau-b menunjukkan bahwa terdapat hubungan yang bermakna antara spiritual self-care dengan kecemasan ibu hamil trimester III dengan nilai p = 0,038 (p Kurang dari 0,05). Kesimpulan: Terdapat hubungan yang bermakna antara spiritual self-care dengan kecemasan ibu hamil trimester III.
Analisis Sentimen Berbasis Aspek dan Pemodelan Topik pada Candi Borobudur dan Candi Prambanan Arianto, Dian; Budi, Indra
MULTINETICS Vol. 8 No. 2 (2022): MULTINETICS Nopember (2022)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v8i2.5056

Abstract

This study focuses on conducting aspect-based sentiment analysis and topic modelling of tourism destinations in Indonesia, which are Borobudur Temple and Prambanan Temple using Google Maps and Tripadvisor user reviews. Aspect-based sentiment analysis was done using five classical machine learning algorithms, which are NaïveBayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Extra Trees (ET) using the unigram+bigram+trigram feature and the application of combination of the use of training and test data, stopwords removal, stemming, emoji processing, and over-sampled training data. The performance of models was evaluated by comparing F1-scores on each experimental result. Topic modelling was carried out using Latent Dirichlet Allocation (LDA) method which evaluated by its coherence score. The results show that LR is a model that can predict data well in almost all scenarios in this study with the highest score on the Attraction aspect with a score of 84.4%, Amenity 84.2%, Accessibility 89.1%, Image 70%, and HR 92.8%. Meanwhile, DT can predict data well on the Price aspect with a score of 91.3%. From the results of topic modelling, we recommend some approaches for the development of tourism in Borobudur Temple and Prambanan Temple, one of which is the government can lower the price of admission to Prambanan Temple and Borobudur Temple for foreign tourists so that they can compete with tourist attractions in neighboring Indonesia because many reviews state that the price of entrance tickets to Prambanan Temple and Borobudur Temple is too expensive for foreigners.
Co-Authors Abdul Gofur Agustina, Mely Angelo, Michael David Anggraini, Veny Ani Widiastuti, Ani Ardin Arianto, Dian Asmadiannor, Asmadiannor Azarya, Yosia Bagus Rahmat Santoso, Bagus Rahmat Cloudia, Lorenza Dekatama, Alifdaffa Nurfahmi Dewi, Ni Gusti Agung Ayu Sri Elly Nurachmah Habibi, Nur Hakidah Halim, Dicky Harwenda, Reyhan Widyatna Heryadi, Abdi Taofan Ibrohim, Muhammad Okky Insani, Faiz Nur Fitrah Irmawan, Oky Ade Istiqomah, Relaci Aprilia Izzuddin, Muhammad Sulthan Krisnadhi, Adila Alfa Kurniawan, Sandy M. Sobirin Mohtar Melda, Melda Mohammad Basit, Mohammad Mubina, Basma Fathan Munawarah Munawarah, Munawarah Muthmainnah, Miftahul Nataly, Onevia Berlian Octovianto, Christofer Permana, Yandi Dwi Prabu Kresna Putra, Prabu Kresna Prayogo, Devin Putri, Irzanes Rakhmadhani, Irzal Ramadiah, Amanah Rizka Windiastuti, Rizka Rosalina, Nadya Sahid, Irfanda Husni Santoso, Aris Budi Sekarsari, Rita Subhannur Rahman, Subhannur Tiastama, Sheren Afryan Tjomiadi, Cynthia Eka Fayuning Umi hanik Fetriyah, Umi hanik Virdasari, Sweetryani Wati, Eka Setya Wijaksono, M. Arief