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All Journal Journal of Technology and Informatics (JoTI) Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Naris Kirana
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PALEMBANG CITY TOUR GUIDE APPLICATION WITH SHORTEST ROUTE SEARCH USING A* METHOD Naris Kirana; Aryanti; Sarjana
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 4 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i4.3187

Abstract

In the current digital era, technology has become an essential tool in planning travel experiences. Tourists often face challenges in determining efficient travel routes amidst the numerous available tourist attractions. A map-based application that provides recommendations for the shortest routes can help optimize tourists' time and experience. Palembang, as a tourist destination rich in history and culture, faces challenges in accommodating first-time visitors, particularly in finding efficient travel routes. To address this issue, the development of a tourist guide application based on mapping technology and route-search algorithms, such as the A* method, can serve as an effective solution. This application can offer guidance on the fastest and shortest routes, avoid traffic congestion, and enhance the overall tourist experience. Furthermore, it supports the tourism sector by promoting local attractions and making it easier for tourists to plan their trips. By implementing this technology, the quality of the tourism experience in Palembang can be improved, in line with the global trend of digitalization in the tourism industry.
Segmentasi dan Klasifikasi Risiko Perdarahan dengan Algoritma K-Means dan Naive Bayes Berdasarkan Data Klinis dan Transfusi Aryanti Aryanti; Fadhilah Dwi Wulandari; Muhammad Rafiif; Faris Alghaniyyu; Naris Kirana; M. Nawval Alfazri
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

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Abstract

Bleeding risk assessment is essential in clinical decision-making, especially for patients undergoing frequent blood transfusions. This study presents a machine learning approach combining K-Means clustering and Gaussian Naive Bayes classification to assess bleeding risk based on clinical and transfusion history data. Patients were categorised into K-Means clusters, with the ideal number of clusters established by the Elbow Method and Silhouette Score. PCA visualisation demonstrated distinct distinctions among clusters. Cluster 0 contained patients with higher transfusion volume and frequency, showing significantly higher bleeding risk. Subsequently, the Naive Bayes classifier was trained on clinical features to predict bleeding risk and categorized into two risk levels. The model achieved 85.45 percent accuracy on training data and 86.67 percent on testing data, with the highest predictive accuracy observed in Cluster 0 (95.65 percent). These results highlight the potential of combining unsupervised and supervised learning techniques to enhance bleeding risk stratification and support better transfusion management.
Co-Authors Aryanti aryanti aryanti Fadhilah Dwi Wulandari Faris Alghaniyyu M. Nawval Alfazri Muhammad Rafiif Sarjana