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All Journal Sistemasi: Jurnal Sistem Informasi JURNAL PENDIDIKAN TAMBUSAI Darmabakti Cendekia: Journal of Community Service and Engagements Journal of Innovation and Future Technology (IFTECH) HEALTH FRONTIERS (MULTIDISCIPLINARY JOURNAL FOR HEALTH PROFESSIONALS) SISFOTENIKA Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK) Jurnal Abdimas Kesehatan ILKOMEDIA: Jurnal Ilmu Komputer dan Multimedia Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Cendra Devayana Putra
Information Systems Study Program, Universitas Negeri Surabaya - Indonesia
Religion 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 Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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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.
EVALUATING PARTICIPANTS’ UNDERSTANDING OF TEXT-TO-DESIGN FOR LOCAL-BASED WISDOM: EVALUASI PEMAHAMAN PESERTA PADA APLIKASI TEXT-TO-DESIGN BERBASIS KEARIFAN LOKAL Cendra Devayana Putra; Daniel Evan Rusli; I Kadek Dwi Nuryana; Rahadian Bisma
Darmabakti Cendekia: Journal of Community Service and Engagements Vol. 8 No. 1 (2026): JUNE 2026
Publisher : Faculty of Vocational Studies, Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/dc.V8.I1.2026.44-49

Abstract

Background: This study presents an evaluation of the dissemination of the Text-to-Design (TTD) application for developing design materials based on local wisdom. Objective: With increased accessibility to AI-supported technology, educators can apply TTD tools to produce learning materials that are consistent, flexible, and culturally contextual. Method: Using survey data and participant feedback, the study analyzes levels of understanding, clarity of presentation, the usefulness of practical sessions, and development recommendations. Results: The findings indicate a high level of understanding (88.9%), strong engagement through hands-on activities, and interest in integrating local content into digital listening materials. Conclusion: These results align with recent research on digital literacy and culturally relevant learning.
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).
Implementasi Modul ESP32 Sebagai Pengendali Kipas Angin Otomatis Berbasis Jaringan WiFi Nadia Alfi Ni’amah; Husna Lathifunisa Arif; Aryawangi Rahmawanto; Edwyn Wahyu Prasetya; Riza Akhsani Setyo Prayoga; Cendra Devayana Putra
Jurnal Ilmu Komputer dan Multimedia Vol. 3 No. 1 (2026): ILKOMEDIA Edisi Juni 2026
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/ilkomedia.v3i1.90

Abstract

Kipas angin konvensional masih menjadi perangkat yang banyak digunakan masyarakat, terutama di wilayah tropis seperti Indonesia. Di sisi lain kipas angin konvensional masih bergantung pada kontrol manual, kipas sering dibiarkan menyala terus-menerus tanpa memperhatikan kebutuhan pengguna, sehingga penggunaan energi menjadi kurang efisien dan kenyamanan tidak selalu terjamin. Penelitian ini bertujuan untuk merancang dan mengimplementasikan SmartFan, yaitu sistem pengendali kipas angin berbasis Internet of Things (IoT) menggunakan ESP32, MQTT, dan Flutter mobile application. Sistem ini dikembangkan untuk memberikan kendali kipas yang lebih presisi, responsif, dan mudah digunakan dibandingkan metode konvensional maupun aplikasi pihak ketiga seperti MQTT Dash. Aplikasi Flutter memungkinkan pengguna mengatur kecepatan kipas secara bertahap (0–100%), melakukan kontrol daya, serta memantau status koneksi perangkat secara real-time. Pada sisi perangkat keras, ESP32 dilengkapi dengan WiFi Manager sehingga dapat dikonfigurasi pada berbagai jaringan tanpa proses pemrograman ulang, serta menghasilkan sinyal PWM yang stabil untuk mengatur putaran motor melalui driver L298N. Pemisahan sumber daya antara ESP32 dan motor melalui powerbank dan adaptor 12V terbukti meningkatkan stabilitas sistem dengan mencegah brownout yang biasanya terjadi akibat lonjakan arus motor. Hasil pengujian menunjukkan bahwa komunikasi MQTT berjalan stabil, respons kendali cepat, dan integrasi antara perangkat keras serta aplikasi berfungsi sesuai tujuan. Secara keseluruhan, SmartFan berhasil dikembangkan sebagai sistem IoT yang efektif, fleksibel, dan mudah digunakan. Penelitian ini juga membuka peluang pengembangan lanjutan melalui penambahan sensor lingkungan, otomatisasi berbasis kondisi dengan kendali jarak jauh berbasis mobile, serta integrasi keamanan MQTT Secure dan platform smart home.
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 Abdillah, Rifqi Aisyah Widayani Akbar, Muhammad Sonhaji Alif Majid Firdaus Andyka Salom Anisa Dewi Setiawati Anistyasari, Yeni Ardhini Warih Utami, Ardhini Warih Aryawangi Rahmawanto Bagas Setya Wicaksono BAMBANG SUJATMIKO Bartolomeus Priya Perkasa Utama Widada Berliana Devianti Putri Cinthya, Monica Clara Lavita Angelina Daniel Evan Rusli Dwi, I Kadek Edwyn Wahyu Prasetya Elfaiz, Ersha Aisyah Endah Sekar Palupi Gabriel Loi Hilal Hindi Saputra Husna Lathifunisa Arif I Kadek Dwi Nuryana I Kadek Dwi Nuryana, I Kadek Dwi Medawati, Riris Mohammad Faiz Rakhman Mohammad Wildan Habibi, Mohammad Wildan Monica Cinthya Monica Cinthya Muhamad Aris Burhanudin Muhammad Sonhaji Akbar Muhammad Sonhaji Akbar Nadia Alfi Ni’amah Nevitya Elmaira Nurjannah Nisa Dwi Septiyanti Pandu Dwi Luhur Pambudi‬‬‬‬‬‬ Rahadian Bisma Rahman, Erik Retno Aulia Vinarti Rifqi Abdillah Riza Akhsani Setyo Prayoga Rizky Basatha Rizqiyatul Khoiriyah Tesa Eranti Putri Wibawa, Ramadhan Cakra Winda Kusumawardani Winda Kusumawardani