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All Journal JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Global Health research
Wardhiana, I Nyoman Gde Artadana Mahaputra
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Computer Science & IT Education Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Public Health

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Optimizing Segmentation and Purchase Forecasting in Credit Card Transactions: A PSO-enhanced k-means and ANN Approach Hidayati, S.Kom., M.Sc., Ph.D., Shintami Chusnul; Raharja, Putu Bagus Gede Prasetyo; Wardhiana, I Nyoman Gde Artadana Mahaputra; Klemm, Sebastian
JIEET (Journal of Information Engineering and Educational Technology) Vol. 7 No. 2 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v7n2.p59-65

Abstract

In the rapidly evolving landscape of data-driven marketing, machine learning has emerged as a pivotal tool for analyzing complex consumer behaviors and enhancing strategic decision-making. This paper introduces a novel approach to optimize customer segmentation and purchase forecasting in credit card transactions through the synergistic integration of Particle Swarm Optimization (PSO)-enhanced k-means clustering and Artificial Neural Networks (ANN). The proposed methodology refines customer segmentation by leveraging PSO, resulting in more defined clusters. In the predictive modeling phase, an ANN outperforms conventional methods, providing superior accuracy in purchase forecasting. The study demonstrates the effectiveness of advanced algorithms in enhancing insights from credit card transaction data, offering valuable implications for improved decision-making in the financial domain.
Early Diagnosis of Eye Disease Using an Expert System-Based Chatbot Gama, Adie Wahyudi Oktavia; Dharma, I Kadek Dwi Yudiarsana; Wardhiana, I Nyoman Gde Artadana Mahaputra; Widnyani, Ni Made
Indonesian Journal of Global Health Research Vol 6 No S6 (2024): Indonesian Journal of Global Health Research
Publisher : GLOBAL HEALTH SCIENCE GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/ijghr.v6iS6.5390

Abstract

Chatbots have emerged as popular tools across various domains, including expert systems for disease diagnosis. This research aims to develop a chatbot leveraging the Naive Bayes method within an expert system for diagnosing eye diseases. The Naive Bayes method was chosen for its efficiency in handling data classification and its ability to provide the necessary class probabilities in diagnosis. The resulting chatbot is designed to simplify the diagnosis process for users by providing a user-friendly and easily understandable interface. Evaluation of the system demonstrated an 87% accuracy rate in initial diagnoses when compared against specialist evaluations. Additionally, the User Acceptance Test revealed a high acceptance rate, with an average score of 84.75%, indicating strong user satisfaction with the system’s performance and ease of use. These findings suggest that deploying a chatbot with the Naive Bayes method in an expert system for diagnosing eye diseases has the potential to serve as a valuable platform in supporting medical practitioners in diagnosing eye diseases more efficiently and accurately.
Co-Authors Adie Wahyudi Oktavia Gama Dharma, I Kadek Dwi Yudiarsana Hidayati, S.Kom., M.Sc., Ph.D., Shintami Chusnul Klemm, Sebastian Raharja, Putu Bagus Gede Prasetyo Widnyani, Ni Made