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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 Jiko (Jurnal Informatika dan komputer) 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
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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

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User Review Analysis of the BNI Wondr Mobile Banking Application: Systematic Literature Review Mubina, Basma Fathan; Halim, Dicky; Budi, Indra; Ramadiah, Amanah; Putra, Prabu Kresna; santoso, Aris budi
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4541

Abstract

In the digital age, mobile banking has become essential for facilitating efficient financial transactions, with the Wondr mobile banking application from Bank Negara Indonesia (BNI) emerging as a significant innovation in this sector. Designed to provide a secure and user-friendly experience, Wondr aims to meet the diverse needs of its customers. However, to enhance its service and ensure user satisfaction, BNI must actively engage with customer feedback. This study leverages user reviews from platforms like Google Play Store to gain insights into the strengths and weaknesses of the Wondr application. Employing text analysis techniques, we utilise topic modeling through Latent Dirichlet Allocation (LDA) to extract relevant themes from these reviews to identify key areas for improvement and generate targeted recommendations. The findings of this research are intended to inform the ongoing development of the Wondr application, ultimately enhancing user experience and reinforcing BNI’s position within the digital banking landscape.
Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review Permana, Yandi Dwi; Gofur, Abdul; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3679

Abstract

The need for a healthy and pollution-free environment is the basis of the problem that this study examines. Social media has become an integral aspect of daily existence for the majority engaged in the digital realm. It enables individuals from various backgrounds to utilize these platforms to stay updated on the latest information, such as the current state of pollution in Jakarta. This research explores the attitudes of social media users regarding their perspectives on air pollution in Jakarta. The method used includes conducting a Systematic Literature Review of academic papers released from 2020 to 2023. The results of this research can unveil the types of social media platforms utilized, the quantity of datasets, the procedures for data collection, data preprocessing techniques, and the commonly employed methods in sentiment analysis studies concerning the subject of air pollution.
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.
SENTIMENT ANALYSIS OF PUBLIC HEALTH APP REVIEWS USING INDOBERT AND XLM-ROBERTA: A STUDY ON SATUSEHAT MOBILE APP Ananda, Dimas; Budi, Indra; Santoso, Aris Budi; Qureshi, Ali Adil
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : 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
Co-Authors Abdul Gofur Agustina, Mely Ananda, Dimas Angelo, Michael David Anggraini, Veny Ani Widiastuti, Ani Ardin Arianto, Dian Asmadiannor, Asmadiannor Azarya, Yosia Bagus Rahmat Santoso, Bagus Rahmat Bambang Subeno Cloudia, Lorenza Dekatama, Alifdaffa Nurfahmi Dewi, Ni Gusti Agung Ayu Sri 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 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 Prastowo, Rahardito Dio Prayogo, Devin Putri, Irzanes Qureshi, Ali Adil 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