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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Systems and Informatics
Winanti Winanti
Universitas Insan Pembangunan
Computer Science & IT Engineering

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Exploring Customer Relationship Management: Trends, Challenges, and Innovations Erick Fernando; Rudi Sutomo; Yulius Denny Prabowo; Jullend Gatc; Winanti Winanti
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.541

Abstract

This study presents a comprehensive exploration of recent advancements in Customer Relationship Management (CRM), acknowledging its pivotal role in fostering crucial connections both within the industry and with customers at large. The study delves deeply into CRM, aiming to enhance overall customer satisfaction. The primary focus of this study centers around critical facets of CRM, encompassing strategies for managing customer relationships, applications of information technology, analysis of customer data, and approaches for customer retention. Employing a literature review methodology, this research rigorously examines the most recent journals germane to the field of CRM. A total of 46 articles sourced from reputable journal databases constitute the foundation of this investigation. The findings of this study yield an enriched comprehension of contemporary developments concerning challenges, factors driving success, relevant domains, and implementation goals within the realm of Customer Relationship Management.
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 Analysis of KNN and Decision Tree Classification Algorithms for Early Stroke Prediction: A Machine Learning Approach Karin Eldora; Erick Fernando; Winanti Winanti
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.664

Abstract

Stroke is the second most deadly disease in the world and the third leading cause of disability. However, most deaths due to stroke can be prevented by recognizing the symptoms of stroke and taking preventive measures using information technology. Therefore, this research utilizes the role of information technology using a machine learning approach to predict stroke in a person using the K-Nearest Neighbor and Decision Tree classification methods. The two algorithms were compared to determine which algorithm was more effective in predicting stroke. Data analysis using the CRISP-DM approach was carried out using a dataset containing 5110 observations with 12 relevant attributes. Implementation of Exploratory Data Analysis (EDA) was also carried out for preprocessing, and oversampling techniques were applied to overcome the problem of unbalanced classes. The research results show that the predictive model with the highest level of accuracy was obtained at around 97.1845% using the K-Nearest Neighbor algorithm. This research makes a significant contribution to stroke prevention efforts through the use of information technology and machine learning algorithms for early identification of stroke risk.
Co-Authors David Tjahjana Erick Fernando Erick Fernando Jullend Gatc Karin Eldora Monika Evelin Johan Sutomo, Rudi Yulius Denny Prabowo