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All Journal Sistemasi: Jurnal Sistem Informasi HEALTH FRONTIERS (MULTIDISCIPLINARY JOURNAL FOR HEALTH PROFESSIONALS) Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK) Jurnal Abdimas Kesehatan Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Cendra Devayana Putra
Universitas Negeri Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health Veterinary

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Knowledge and Awareness of Radiation Protection Among Healthcare Workers: A Cross-Sectional Study Berliana Devianti Putri; Anisa Dewi Setiawati; Winda Kusumawardani; Cendra Devayana Putra; Gabriel Loi
Health Frontiers: Multidisciplinary Journal for Health Professionals Vol. 4 No. 1 (2026): Health Frontiers
Publisher : Tarqabin Nusantara Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62255/mjhp.v4i1.253

Abstract

Ionising radiation from diagnostic procedures poses significant occupational risks to healthcare workers (HCWs), yet awareness remains suboptimal in many settings, particularly in low- and middle-income countries. This cross-sectional study assessed radiation protection knowledge and awareness among 140 HCWs from inpatient, intensive care, and emergency units in Indonesia, identifying independent predictors of awareness. Knowledge was evaluated using a validated 15-item instrument (categorized as poor, acceptable, or good), while awareness was measured as a binary outcome. Data were analyzed using Pearson’s chi-square test and multivariable binary logistic regression, adhering to STROBE guidelines. The sample was predominantly female (69.3%) with bachelor’s degrees (57.1%). Overall, 46.4% demonstrated good knowledge, 48.6% acceptable, and 5.0% poor, while 68.6% were classified as aware. Multivariable analysis revealed that knowledge level was the sole independent predictor of awareness: compared to poor knowledge, acceptable knowledge significantly increased awareness odds (aOR = 3.48; 95% CI: 1.12–10.80; p = 0.031), as did good knowledge (aOR = 8.65; 95% CI: 2.10–35.60; p = 0.003). These findings confirm that radiation protection knowledge strongly and independently drives awareness among clinical staff. Consequently, healthcare institutions must prioritize continuous, evidence-based radiation safety education—particularly for personnel in high-exposure units—as the foundational strategy to effectively bridge the knowledge–awareness gap and mitigate occupational radiation risks.
Public Opinion on MyTelkomsel Using DeLone and McLean Model on X Bagas Setya Wicaksono; Cendra Devayana Putra; I Kadek Dwi Nuryana; Monica Cinthya
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78043

Abstract

The MyTelkomsel application is a digital service used by Telkomsel customers to access telecommunications information and services. The high number of users is accompanied by the emergence of various user opinions and complaints expressed through social media. This study aims to analyze user satisfaction with the MyTelkomsel application based on public opinions on the X (Twitter) platform using the DeLone and McLean Information Systems Success Model. The research data consist of 1,500 Indonesian-language tweets collected through a crawling process. The data then underwent a text preprocessing stage to improve analysis quality. Sentiment analysis was conducted using the RoBERTa model to classify user opinions into positive, neutral, and negative sentiments. Subsequently, each tweet was labeled into six dimensions of the DeLone and McLean model, namely System Quality, Information Quality, Service Quality, Use, User Satisfaction, and Net Benefits. Sentiment scores were used as quantitative values for each dimension. The relationships among variables were analyzed using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method. The results indicate that System Quality and Information Quality significantly influence User Satisfaction, while Service Quality shows a lower level of influence. This study is expected to provide academic contributions to the application of the DeLone and McLean model based on social media data and offer practical insights for the development of the MyTelkomsel application in improving service quality and user experience. Keywords : MyTelkomsel, Sentiment Analysis, Social Media, DeLone and McLean, User Satisfaction, SEM-PLS
Sentiment Analysis And UTAUT2 Classification On Maxim Application User Reviews Using IndoBERT And Zero-Shot Hilal Hindi Saputra; Cendra Devayana Putra; I Kadek Dwi Nuryana; Monica Cinthya
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78304

Abstract

The rapid growth of ride-hailing services has intensified competition, making user feedback on digital platforms a critical asset for service improvement. This study addresses the challenge of managing and extracting actionable insights from large volumes of unstructured user reviews on the Google Play Store for the Maxim application. To overcome this, a comprehensive text-mining framework is proposed, integrating sentiment analysis and technology acceptance modeling. A dataset of 2.000 Indonesian-language user reviews from July to September 2025 was retrieved via web scraping. Data preprocessing was executed using case folding, filtering, and normalization. Subsequently, sentiment classification was performed using the IndoBERT model, while the mapping of user text to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework was automated using a Zero-Shot Classification approach. Finally, Structural Equation Modeling–Partial Least Squares (SEM-PLS) via SmartPLS 4.0 was utilized to test the structural hypotheses. The analytical findings reveal that negative sentiments slightly dominate the dataset (48.05%), heavily driven by system stability and sudden fare adjustments. Furthermore, the structural model proves that behavioral intention, effort expectancy, facilitating conditions, habit, performance expectancy, price value, and social influence exert positive and significant effects on adoption, whereas hedonic motivation exhibits no significant influence.
Web-based Information System for Ornamental Fish Business in Surabaya Cendra Devayana Putra; Muhammad Sonhaji Akbar; Muhamad Aris Burhanudin; Bartolomeus Priya Perkasa Utama Widada; Rizqiyatul Khoiriyah; Pandu Dwi Luhur Pambudi‬‬‬‬‬‬
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6358

Abstract

Digital transformation has emerged as a strategic imperative for small and medium enterprises (SMEs) in emerging economies, yet the ornamental fish retail sector in Indonesia remains predominantly offline, constrained by limited digital infrastructure and high customer knowledge barriers. No prior identified study has implemented artificial intelligence (AI)-assisted consultation within a domain-specific ornamental fish e-commerce platform, and comprehensive security implementation combined with multi-layer testing has been largely absent in comparable SME web systems. This study presents the design, implementation, and evaluation of a web-based information system for Toko Oasis, an ornamental fish and aquascape SME in Surabaya, Indonesia, developed within a Design Science Research (DSR) paradigm following a structured Software Development Life Cycle (SDLC). The system integrates a configurable large language model (LLM) consultation module—supporting OpenAI GPT-4 and Google Gemini—that delivers domain-specific advisory on ornamental fish species selection, aquarium parameters, and aquascape design through natural language interaction. System development produced twelve Unified Modeling Language (UML) artifacts and was evaluated through a tri-layer testing protocol operationalized against the ISO/IEC 25010:2011 software quality model. Functional testing achieved a 100% pass rate across 24 use cases. Performance testing recorded a mean response time of 4.2 seconds under 25 concurrent users, within the defined threshold of 5 seconds. Usability evaluation yielded a mean System Usability Scale (SUS) score of 80.0, classified as Good. Security validation confirmed full compliance across HTTPS/SSL-TLS transport and AES-256 at-rest encryption domains. Comparative analysis against prior literature and analogous commercial platforms confirms that this system constitutes the first identified deployment of AI-assisted consultation in ornamental fish retail, contributing a replicable digitalization architecture for niche-market SMEs in developing economies.
Edukasi Interaktif Tuberculosis dan Keamanan Foto Rontgen untuk Percepatan Program TOSS-TB di Puskesmas Kowel, Kab. Pamekasan Berliana Devianti Putri; Winda Kusumawardani; Tesa Eranti Putri; Riris Medawati; Endah Sekar Palupi; Cendra Devayana Putra; Aisyah Widayani; Alif Majid Firdaus; Andyka Salom
Jurnal Abdimas Kesehatan (JAK) Vol. 8 No. 1 (2026): Januari
Publisher : Universitas Baiturrahim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36565/jak.v8i1.1046

Abstract

Tuberculosis (TB) can affect people of all ages, from young to old. It also impacts the quality of life of human resources and can become an obstacle to national development. TB can be prevented by optimizing the TOSS-TB (Find, Treat, and Cure) program initiated by the Ministry of Health of the Republic of Indonesia to achieve the TB-Free 2030 target. Based on the 2023 East Java Provincial Health Profile Report, the case detection and treatment success TB rate in Pamekasan is still low, at 80,2%, which is below the national target of 90%. This activity employs an interactive educational approach to raise awareness about TB and the safety of X-ray examinations through digital gamification, then promotes public understanding of TB screenings and recommendations for visiting community health centers. This information and interactive games were designed in two languages, namely Indonesian and Madurese. The community service activity began with the creation of the application that provides information and interactive games. Socialization and hands-on application were conducted in September 2025. Sixty-five participants, including community health center heads, TB program managers, health cadres, TB patients, and community members living near TB patients, participated in this activity. Results of the Wilcoxon signed-rank test (α=0.05) showed a significant increase in participants' knowledge regarding TB (p=0.000) and knowledge regarding X-ray safety (p=0.000). Participants also experienced improved skills in operating the application as an educational tool for health cadres in the Kowel Community Health Center, Pamekasan. This activity supports the Sustainable Development Goals, specifically SDGs No. 3 (Good Health and Well-being) and SDGs No. 4 (Quality Education).
Analisis Perbandingan Kualitas Jawaban Pada Qa Berbasis Rag : Kombinasi Zero-Shot Instruction Prompting Dan Self-Verification Nevitya Elmaira Nurjannah; Cendra Devayana Putra
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 5 (2026): IDENTIK - September
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i5.1962

Abstract

Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding language model (LM) responses in the context of retrieved results, but does not automatically guarantee evidence-based (faithful) responses. This study examines the effect of combining zero-shot instruction prompting and self-verification on the faithfulness of responses to answerable questions, across four RAG pipeline configurations: (1) RAG, (2) RAG + prompting, (3) RAG + self-verification, and (4) RAG + prompting + self-verification, which were tested on three scales of the Qwen3 language model (0.6B, 4B, 8B) using the SQuAD v2.0 dataset. A total of 100 queries were selected via stratified random sampling from the validation split to avoid topic bias. Faithfulness was measured at the claim level using an NLI model (DeBERTa-v3-large). The results show that basic RAG (configuration 1) achieved the highest average faithfulness score, while configurations 2–4 (with prompting and/or self-verification) showed relatively similar and lower scores. In SLM, adding self-verification lowered faithfulness the most, while in LLM the score remained relatively stable across configurations. A retrieval-quality control analysis indicated that this decline was linked to the generator's capacity rather than retrieval quality. These findings suggest that the combination of prompting and self-verification does not automatically improve the quality of evidence-based answers, and its benefits depend on the adequacy of the language model's capacity.  
Perbandingan Kinerja Lstm, Random Forest, Dan Xgboost Dalam Memprediksi Harga Penutupan Indeks Harga Saham Gabungan (Ihsg) Berbasis Data Historis Mohammad Faiz Rakhman; Cendra Devayana Putra
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 5 (2026): IDENTIK - September
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i5.1979

Abstract

This study compares the performance of three machine learning algorithms, Long Short-Term Memory (LSTM), Random Forest, and Extreme Gradient Boosting (XGBoost), in predicting the next-day closing price of Indonesia's Composite Stock Price Index (IHSG) as a baseline before further feature engineering is applied in a broader ongoing study. Daily historical price data (Open, High, Low, Close, Volume) covering January 2015 to early 2026 were collected from Yahoo Finance. Two feature representations were compared: the raw 5-dimensional OHLCV attributes, and a 64-dimensional temporal representation extracted from the same OHLCV data using a Bidirectional LSTM (Bi-LSTM) encoder. Each representation was evaluated using LSTM, Random Forest, and XGBoost, with performance measured by Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) on a chronological 80:20 train-test split. The results show that the raw OHLCV representation combined with LSTM achieved the best performance (RMSE = 196.11, MAE = 164.14, MAPE = 2.18%), outperforming Random Forest and XGBoost on the same representation (MAPE 3.78% and 3.80%). Encoding the OHLCV data into a 64-dimensional Bi-LSTM representation without any external signal reduced accuracy across all three algorithms, most notably for LSTM (MAPE rising to 7.13%), indicating that unsupervised temporal encoding discards useful absolute price information when no additional predictive feature is introduced. These findings establish a validated baseline configuration and evaluation pipeline for IHSG closing-price prediction, intended as the foundation for a subsequent study that integrates external textual features into the same experimental framework.  
Co-Authors Aisyah Widayani Alif Majid Firdaus Andyka Salom Anisa Dewi Setiawati Bagas Setya Wicaksono Bartolomeus Priya Perkasa Utama Widada Berliana Devianti Putri Endah Sekar Palupi Gabriel Loi Hilal Hindi Saputra I Kadek Dwi Nuryana Medawati, Riris Mohammad Faiz Rakhman Monica Cinthya Muhamad Aris Burhanudin Muhammad Sonhaji Akbar Nevitya Elmaira Nurjannah Pandu Dwi Luhur Pambudi‬‬‬‬‬‬ Rizqiyatul Khoiriyah Tesa Eranti Putri Winda Kusumawardani Winda Kusumawardani