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All Journal Journal of Consumer Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab G-Tech : Jurnal Teknologi Terapan TEPIAN Journal of Applied Data Sciences Journal of Applied Computer Science and Technology (JACOST) Journal of Applied Research In Computer Science and Information Systems Journal of E-business and Management Science Proletarian : Community Service Development Journal Green Engineering: Journal of Engineering and Applied Science
Erick Fernando
Department Of Information Systems, Faculty Of Engineering And Informatics, Universitas Multimedia Nusantara, Tangerang, Indonesia
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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A Validated Blockchain Model and Architecture for Public Health System for Data Security Erick Fernando; Yulius Denny Prabowo; Winanti Winanti; David Tjahjana; Monika Evelin Johan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6934

Abstract

Blockchain has been identified as a technology that can be used in healthcare systems, especially in terms of data security, privacy, and interoperability. This article explores the application of blockchain in various medical processes ranging from patient registration, visit registration, initial examination, patient examination, diagnostic input, and drug preparation. This study was conducted at a healthcare facility that utilizes blockchain as a technology to manage patient data, medical records, and drug prescriptions. This study uses a combined approach between literature studies and qualitative methods using the Focus Group Discussion (FGD) technique for evaluates the impact of model blockchain. Model validation with domain experts in the health sector (doctors, nurses, administrators, IT experts, and pharmacists). The results research is a validated Model blockchain public health sector improves the security and privacy of patient data but also accelerates operational processes in the healthcare chain. This study concludes that blockchain can be a transformational solution for addressing major problems in the healthcare sector, although cross-sector collaboration is needed to ensure successful implementation.
Comparative Evaluation of LSTM, BiLSTM, and CNN Algorithm for Sentiment Analysis of Tinder Application Galih Isa Yudhistira; Erick Fernando
Journal of Applied Data Sciences Vol 7, No 4: December 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i4.1378

Abstract

This study investigates the performance of three deep learning architectures Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN) for sentiment classification of user reviews from the Tinder application in Indonesia. A dataset of 5,000 recent reviews collected from the Google Play Store was utilized. The research adopts the Knowledge Discovery in Databases (KDD) framework, encompassing data selection, preprocessing, transformation, data mining, and evaluation stages. Experimental results show that, in baseline evaluation, the CNN model achieved the highest accuracy of 98%. After hyperparameter optimization, LSTM improved its performance to 98.2%, while BiLSTM outperformed all models with an accuracy of 98.7%. The application of class weighting significantly enhanced the models’ sensitivity to minority classes, resulting in more balanced and reliable predictions. These findings demonstrate that deep learning models, when combined with hyperparameter tuning and imbalance handling techniques, can achieve highly accurate and robust sentiment classification. Furthermore, this study highlights the effectiveness of BiLSTM in capturing contextual dependencies in user-generated text, making it particularly suitable for sentiment analysis in online dating application reviews. The results provide valuable insights for developers and stakeholders to better understand user satisfaction and improve service quality.
Lightweight Facial Feature-Based Drowsiness Detection Using Haar Cascade, EAR, MAR, and KNN Asri Mulyani; Erick Fernando; Dede Kurniadi; Adinda Jaida Fauziyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7190

Abstract

Vehicle accidents globally are heavily caused by driver fatigue, highlighting the need for automated fatigue detection systems. This research introduces an efficient method to detect driver fatigue using facial geometric features. The proposed method integrates Haar Cascade to locate faces, followed by the extraction of Eye Aspect Ratio (EAR) together with Mouth Aspect Ratio (MAR) metrics, which are then classified through the K-Nearest Neighbor (KNN) algorithm. The experiment utilized a total collection of 2,468 facial samples structured across Open, Closed, Yawn, and No Yawn categories, which were subsequently grouped into “Drowsy” and “Not Drowsy” classes. Model evaluation was performed via Stratified 10-Fold Cross-Validation. The results show that the classifier obtained a mean accuracy rate of 88.2% ± 2.6% with an AUC of 0.93, confirming robust classification effectiveness. These findings position the integrated geometric-feature approach as a systematically validated classical baseline for driver monitoring research. While this study does not empirically measure hardware latency, the stable classification performance suggests that EAR and MAR features provide a reliable foundation for evaluating the performance-to-complexity trade-offs of more advanced models in resource-constrained environments.
Co-Authors Adinda Jaida Fauziyah Asri Mulyani Baihaqi, Kiki Ahmad Beby Tiara David Tjahjana Dede Kurniadi Desman Hidayat Dwi Purwaningrum Edy Gozali Ervana Ervana Francisca Sestri Goestjahjanti Galih Isa Yudhistira Hamuda, Hayadi Ikhsan, Ridho Bramulya Jainuri Jainuri Jeanne Carine Julianto, Ribut Juliater Simarmata Jullend Jullend Gatc Krisna Widi Nugraha Lukman Medriavin Silalahi Miyv Fayzhall Monika Evelin Johan Nana Supiana Nelson Silitonga Nuraini Sari Nurasiah Nurasiah Nurasiah Nuri Wiyono Oktabrianto Oktabrianto Rian Ardianto Riyanto Riyanto Riyanto Riza Phahlevi Marwanto Rosyid Ridlo Al-Hakim Safrizal Safrizal Siti Maesaroh Sri Lestari Sucipto Basuki Sucipto Basuki Sukriyah Winanti Winanti Winanti Winanti Winanti Winanti Yuanita Sinatrya Yuhefizar Yuhefizar Yulius Denny Yulius Denny Prabowo