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All Journal Journal of Information Systems and Informatics PROFICIO: Jurnal Pengabdian Masyarakat Jurnal Informatika Ekonomi Bisnis
Johan, Monika Evelin
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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Journal : Journal of Information Systems and Informatics

 1 
Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models Wongso, Bernard Pratama; Johan, Monika Evelin; Fianty, Melissa Indah
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.568

Abstract

This study aims to determine whether there is an increase in accuracy results for predicting pregnancy risk with a classification algorithm that goes through and without going through the clustering stage. After that, compare which classification algorithm gets the best improvement. This study uses the K-Means clustering approach, as well as the SVM, Naive Bayes, and K-Nearest Neighbor (KNN) classification algorithms. The pregnancy risk dataset used comes from the UCI Machine Learning Repository. Evaluation metrics used include accuracy, precision, recall, and F1-score. The results of the study revealed that the K-Means model with KNN provided the highest performance compared to the other two, with an accuracy of 79.53% and an average F1-score of 0.8. The implementation of K-Means resulted in an increase in accuracy of 0.4%, 1.57%, and 2.76% on KNN, SVM, and Naive Bayes respectively, which confirms the impact of clustering in improving classification performance. The resulting model can be used in real-time via a website built using the Flask API, and offers tools that can help health practitioners to plan treatments effectively and minimize the risk of pregnancy.
Application of Clustering-Based Data Mining for the Assessment of Nutritional Status in Toddlers at Community Health Centers Fianty, Melissa Indah; Johan, Monika Evelin; Aulia, Azka; Veronica, Mella Margareta
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.586

Abstract

Nutritional status is a crucial foundation for human health and development. Global facts indicate serious challenges in ensuring adequate nutrition, and the situation is no different in Indonesia. This research collected data from the Kelapa Dua Tangerang community health center and utilized data mining techniques with the k-means clustering algorithm to delve deeper into the nutritional status of toddlers. The research findings revealed that nearly 37.3% of toddlers experience issues with abnormal height or weight, as well as poor nutritional conditions, highlighting the importance of careful and timely intervention. With regular health monitoring by community health centers and active parental involvement, actions can be taken to support the optimal growth and development of these children. The results of this research provide a strong understanding to address malnutrition issues, which will ultimately support the formation of a healthier and more promising future generation in Indonesia.
Development of Web-based Application for Private School Tuition Fee Management with Prototyping Model Wiratama, Jansen; Johan, Monika Evelin; Sobiyanto, Sobiyanto; Wijaya, Matthew Chandra; Sugara, Victor Ilyas
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.588

Abstract

Private schools need help in handling school fees and financial processes. Traditional manual payment systems result in data processing issues, delayed financial reporting, and complications from misplaced records. Late fee payments threaten school income, which is crucial for staff salaries. Modern solutions are imperative. Desktop applications have limitations, requiring installation on specific devices, leading to compatibility concerns. This research opts for a web-based application. It employs prototyping models and predictive abilities using the Naïve Bayes algorithm. The web-based application aims to streamline fee management and predict payment delays, enhancing financial transaction management while prioritizing data security through database encryption. This web-based solution aligns with private schools' operational needs, simplifying payments and increasing late payment prediction accuracy. Extensive black-box testing validated its suitability, satisfying administrative staff needs. Four test cases gained administrative team approval. This innovation empowers private schools to optimize operations and financial management. In summary, the research tackles critical financial challenges private schools face by introducing a web-based application that simplifies payment processes, enhances accuracy in predicting late payments, and aligns seamlessly with administrative needs.
Co-Authors Aulia, Azka Faza, Ahmad Fianty, Melissa Indah Ririn Ikana Desanti Sobiyanto, Sobiyanto Sugara, Victor Ilyas Sulaiman, Agus Suryasari Susanto, Meitio Veronica, Mella Margareta Wella Wijaya, Matthew Chandra Wiratama, Jansen Wongso, Bernard Pratama