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All Journal Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Susanto, Erliyan Redi
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Sistem Informasi Geografis Dengan Algoritma Dijkstra Untuk Menentukan Jarak Terdekat Pariwisata Di Kabupaten Pesawaran Pamungkas, Aji; Susanto, Erliyan Redi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.844

Abstract

Tourism plays an important role in the development of a region. Apart from introducing the area to a wider audience, tourism is also a major source of creating jobs and encouraging local economic growth. Pesawaran Regency, which was previously part of South Lampung Regency, is now a newly formed region. Its location close to Bandar Lampung City, the capital of Lampung Province, provides strategic value that can accelerate the development of the region. Distance problems and uncertainty in finding routes often become obstacles for many people when visiting various locations. Pesawaran Regency has a number of interesting tourist destinations, one of which is Pahawang Island, which is one of the leading tourist destinations. To make it easier for tourists, both local and international, the fastest route to these tourist destinations is needed. This research uses the Dijkstra algorithm to calculate the shortest distance from one point to the selected tourist destination in Pesawaran Regency. This article will review the application of the Dijkstra algorithm in finding the most efficient route for searching tourist locations in Pesawaran Regency. The research results show that this system can help tourists reach tourist locations more quickly.
Optimasi Hyperparameter Gaussian Naive Bayes Untuk Prediksi Risiko Stroke Pada Data Tidak Seimbang Nida, Khoirun; Mahenra, Ridwan; Susanto, Erliyan Redi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8497

Abstract

Stroke is a serious disease with global impact that requires high-accuracy early detection. Significant difficulties in designing machine learning-based predictive models arise due to disproportionate data conditions (imbalanced datasets). This occurs because the number of stroke cases (minority class) is very small compared to non-stroke cases. This imbalanced data situation often causes models to become biased and potentially produce high false negative rates, which is very risky in a clinical setting. This study focuses on improving the sensitivity of the Gaussian Naive Bayes (GNB) model through hyperparameter optimization and classification threshold adjustment. The research process included data preprocessing, stratified dataset division (70% training and 30% testing), feature scaling, var_smoothing parameter optimization using GridSearchCV, and threshold adjustment to maximize the Recall value. The results showed that the standard GNB model only achieved a Recall value of 0.4400. However, after var_smoothing optimization (1.00×10⁻¹⁰) and threshold adjustment to 0.0100, the Recall value increased significantly to 0.8000. This increase was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). This improvement was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). The high Recall (0.8000) indicates that the model is better for mass screening (early detection phase), although it must be balanced with further diagnostic processes due to low precision. This high Recall value confirms the model's success in minimizing False Negatives, which is a top priority in stroke risk prediction cases.
Co-Authors Aji Pamungkas Mahenra, Ridwan Nida, Khoirun