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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) 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 Indonesian Journal of Electrical Engineering and Computer Science Khatulistiwa Nursing Journal Jurnal Impresi Indonesia 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
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Religion Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary

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PENGALAMAN PASIEN GAGAL JANTUNG DI RSJPD HARAPAN KITA TERHADAP PERAWATAN DIRINYA DI RUMAH: STUDI FENOMENOLOGI Widiastuti, Ani; Nurachmah, Elly; Sekarsari, Rita; Budi, Indra
Jurnal Keperawatan Widya Gantari Indonesia Vol 7 No 2 (2023): JURNAL KEPERAWATAN WIDYA GANTARI INDONESIA (JKWGI)
Publisher : Nursing Department, Faculty of Health, Universitas Pembangunan Nasional "Veteran" Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52020/jkwgi.v7i2.5789

Abstract

Gagal jantung menjadi masalah kesehatan yang progresif dengan angka mortalitas dan morbiditas yang tinggi di negara maju maupun negara berkembang. Tingginya angka readmission juga menyebabkan tingginya biaya perawatan yang harus dikeluarkan, oleh karena itu diperlukan pendekatan penanganan yang baik dengan meningkatkan efektifitas perawatan diri di rumah. Penelitian ini bertujuan untuk mengeksplorasi pemahaman yang mendalam tentang pengalaman, kebutuhan dan harapan pasien gagal jantung dalam melaksanakan perawatan dirinya (self care) di rumah. Penelitian ini menggunakan desain penelitian deskriptif kualitatif dengan pendekatan fenomenologi. Pemilihan partisipan diambil dengan cara purposive sampling sebanyak delapan orang. Pengumpulan data dilakukan dengan wawancara mendalam dengan membuat pertanyaan berdasarkan tujuan yang ingin dicapai. Data yang diperoleh dianalisis dengan menggunakan langkah-langkah Colaizzi sehingga dapat disimpulkan tema-tema sesuai pengalaman partisipan. Dari hasil analisa data ditemukan dua belas tema utama yaitu : (1) pengetahuan gagal jantung (2) Tanda dan gejala yang dialami (3) respon terhadap penyakit (4) mengatur pola makan (5) mengkonsumsi obat (6) olah raga dan aktifitas (7) kontrol ke dokter (8) hambatan yang dihadapi (9) dukungan keluarga (10) dukungan informasi (11) sumber informasi (12) harapan pasien. Melalui penelitian ini, kebutuhan pasien, kesulitan yang dihadapi serta harapan terhadap perawatan dirinya dapat teridentifikasi dengan jelas. Pasien gagal jantung yang melakukan perawatan diri di rumah membutuhkan dukungan keluarga serta dukungan informasi untuk dapat menjalankan program pengobatan dengan baik. Melalui penelitian ini dapat direkomendasikan untuk disusun media edukasi dan informasi yang dapat memudahkan pasien gagal jantung dalam melakukan perawatan dirinya di rumah sehingga harapan pasien untuk dapat ditangani dengan baik dapat terlaksana.
Query keyword extraction in discriminative marginalized probabilistic neural method for multi-document summarization Subeno, Bambang; Budi, Indra; Yulianti, Evi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp907-915

Abstract

The large number of textual documents in the medical field makes it very difficult for readers to obtain comprehensive information. Users usually use a query approach to get the desired information. Using the correct query will produce relevant information. In the existing discriminative marginalized probabilistic neural method, referred to as DAMEN, used for multi-document summarization, a background sentence query is used to retrieve the top-K relevant documents and then generate a summary of these documents. However, the background sentence query used to retrieve the top-K documents did not provide accurate summary results. The author improved the DAMEN model by adding a keyword extraction process to the query background sentence. We call this model Q-DAMEN. Our model shows significant improvement over the original DAMEN method, with the best results achieved by the variation of using a keyword query entered into the discriminator component and a background sentence query entered into the generator component. The multipartieRank keyword extraction method shows the best results with a Rouge-1 value of 29.12, Rouge-2 of 0.79, and Rouge-L of 15.53. The results demonstrate that the more accurate the keywords extracted from the sentence background query, the more accurate the multi-document summaries generated.
Exploring the influence of soft information from economic news on exchange rate and gold price movements Prastowo, Rahardito Dio; Budi, Indra; Ramadiah, Amanah; Santoso, Aris Budi; Putra, Prabu Kresna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5231-5239

Abstract

Information on business conditions is an important concern for market players and regulators. Hard information relates to easily validated characteristics such as production levels and employment conditions. In contrast, soft information such as consumer and public perceptions—is subjective and difficult to verify. Although previous studies on hard and soft information mainly focus on microeconomics and banking, current developments in big data and machine learning enable broader applications in financial market analysis. This study combined VADER sentiment analysis and support vector machine (SVM) classification (accuracy=85%) to analyze economic news, followed by Granger causality and multiple linear regression to examine causal effects and predictive relationships. The findings reveal that negative news sentiment and the Indonesian Rupiah (IDR) exchange rate influence each other, while positive sentiment has no causal impact on the exchange rate. Both negative and positive sentiments affect gold prices, whereas gold price movements do not influence sentiment. Regression analysis shows that negative sentiment has a stronger effect in decreasing the IDR exchange rate than positive sentiment, with the model explaining approximately 20% of the variance. Integrating sentiment and exchange rate data enhances the predictive model for gold price forecasting and highlights the asymmetric roles of positive and negative news in financial dynamics.
Aspect-Based Sentiment Analysis In The Asean Tourism Sector: A Systematic Literature Review Arnandy, Jovi; Budi, Indra; Ramadiah, Amanah; Putra, Prabu Kresna; Santoso, Aris Budi
Jurnal Impresi Indonesia Vol. 4 No. 11 (2025): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i11.7228

Abstract

Online reviews are becoming increasingly important for assessing tourist satisfaction, and although aspect-based sentiment analysis (ABSA) can offer in-depth insights, its use in the ASEAN tourism sector is still largely unexamined. Problem: This gap highlights a significant research issue, as there is insufficient knowledge re-garding the present condition, obstacles, and particular possibilities for applying advanced sentiment analysis methods in this distinctive, multicultural area . Objectives: The main aim of this study is to methodically identify worldwide research trends, highlight gaps, and describe the challenges of utilizing ABSA in the tour-ism industry, concentrating on their effects on ASEAN . Methods Used: To accomplish this, a systematic literature review (SLR) was performed on 61 chosen articles following the PRISMA methodology. Re-sults: The findings indicate a notable research gap marked by a narrow emphasis on the ASEAN region and the application of less sophisticated methods in current studies; the main challenges recognized include the varie-ty of languages, cultural differences, and a lack of datasets in local languages . Conclusion & Solution Offered: This organized review of literature concludes by providing suggestions for a plan-based approach that allows the region to use online reviews to improve its tourism competitiveness, emphasizing the necessity of creating local data resources, encouraging cooperation among different fields, and using data-driven tools to support small businesses .
Klasifikasi Stance dan Pemodelan Topik Komentar YouTube Terhadap Narasi Risiko Penyakit Menular di Indonesia Dwika Sandya, Yudha; Budi, Indra
Jurnal Impresi Indonesia Vol. 5 No. 1 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i1.7421

Abstract

asca berakhirnya status darurat kesehatan global COVID-19, komunikasi risiko tetap penting karena potensi kemunculan varian baru dan penyakit menular lain masih terus terjadi. Namun efektivitas komunikasi risiko menghadapi tantangan akibat kelelahan pandemi dan infodemi, sehingga diperlukan pemetaan sikap yang terukur untuk penyesuaian strategi komunikasi di masa pasca pandemi. Penelitian ini memetakan respons publik terhadap narasi risiko penyakit menular COVID-19 dan human metapneumovirus (HMPV) pada komentar YouTube melalui stance detection dan pemodelan topik LDA. Data dikumpulkan dari komentar video bertopik COVID-19/HMPV pada kanal media resmi. Dataset berisi 19.172 komentar dan 9.586 sampel (50%) dianotasi. Penelitian menerapkan klasifikasi dua tahap, yaitu klasifikasi relevansi, diikuti klasifikasi stance pada komentar relevan. Eksperimen membandingkan IndoBERT dan IndoBERTweet dengan Stratified 5-Fold Cross Validation pada skenario tanpa oversampling dan Random Oversampling (ROS). Hasil menunjukkan IndoBERTweet memberikan performa terbaik dengan skor F1-macro 0.8541 pada klasifikasi relevansi dan 0.8474 pada klasifikasi stance. Hasil LDA menunjukkan bahwa respons masyarakat pada COVID-19 dan HMPV memperlihatkan pola serupa yang didominasi penolakan. Studi ini menunjukkan analisis stance dan pemodelan topik dengan LDA pada komentar YouTube dapat mendukung formulasi strategi komunikasi risiko, serta mengindikasikan IndoBERTweet cenderung sesuai dengan  karakteristik teks komentar YouTube yang cenderung informal dan bervariasi.
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
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.
Comparative Topic Modelling of Mobile Banking User Reviews Using LDA and BERTopic: A Case Study of wondr by BNI Halim, Dicky; Budi, Indra; Mubina, Basma Fathan; Budi Santoso, Aris; Kresna Putra, Prabu
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5118

Abstract

This study explores user reviews of the Wondr mobile banking application to identify factors that influence user experience and service quality. The dataset, obtained from the Google Play Store, was processed through several preprocessing steps, including normalization, stopword removal, and stemming. Two topic modelling methods were applied: Latent Dirichlet Allocation (LDA) as a probabilistic baseline and BERTopic as an embedding-based approach. The LDA model was evaluated using coherence scores to determine the most suitable number of topics, while BERTopic was assessed based on topic distribution, interpretability, and additional coherence analysis. The results show that BERTopic produces more semantically meaningful and contextually rich topics, particularly in capturing short-text user reviews. Although BERTopic achieves lower overall coherence compared to LDA, certain topics demonstrate high semantic consistency, especially for well-defined issues such as login verification problems. The analysis reveals that most user feedback is concentrated on positive user experience, while critical issues related to login verification and system errors remain significant concerns. These findings provide actionable insights for improving mobile banking services and demonstrate the effectiveness of embedding-based topic modeling in financial text analytics. These findings highlight a trade-off between statistical consistency and semantic richness in topic modeling approaches. The results provide actionable insights for improving mobile banking services and demonstrate the effectiveness of combining probabilistic and embedding-based methods in financial text analytics.
Co-Authors Abdul Gofur Adila Alfa Krisnadhi Agustina, Mely Angelo, Michael David Anggraini, Veny Ani Widiastuti, Ani Ardin Arianto, Dian Arnandy, Jovi Asmadiannor, Asmadiannor Azarya, Yosia Bagus Rahmat Santoso, Bagus Rahmat Bambang Subeno Budi Santoso, Aris Cloudia, Lorenza Dekatama, Alifdaffa Nurfahmi Dewi, Ni Gusti Agung Ayu Sri Dwika Sandya, Yudha Elly Nurachmah Evi Yulianti 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 Kresna Putra, Prabu 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 Prastowo, Rahardito Dio 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