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All Journal JAMBURA JOURNAL OF PROBABILITY AND STATISTICS KOLONI
Caecilia Bintang Girik Allo
Universitas Cenderawasih
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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PENENTUAN RUTE di APLIKASI GOOGLE MAPS DENGAN MENGGUNAKAN GRAF DAN ALGORITMA PRIM Winda Ade Fitriya B; Sitti Rosnafi’an Sumardi; Nicea Roona Paranoan; Caecilia Bintang Girik Allo
KOLONI Vol. 2 No. 1 (2023): MARET 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/koloni.v2i1.434

Abstract

Along with the times, many technologies and applications were created to meet human needs. Applications that are quite developed at this time is a navigation application. One of the well-known and frequently used navigation applications is Google Maps. By using the Google Maps application, people can find out where they are and know the route to get to their destination very easily. This paper discusses route selection in the Google Maps application using the prim graph and algorithm. Keywords: Graph, Prims’s Algorithm Prim, Route, Application
PERBANDINGAN METODE KLASIFIKASI KEGAGALAN SIMULASI MODEL IKLIM Perbandingan Metode Klasifikasi Kegagalan Simulasi Model Iklim Caecilia Bintang Girik Allo; Nicea Roona Paranoan; Winda Ade Fitriya B; Sitti Rosnafi’an Sumardi
KOLONI Vol. 2 No. 1 (2023): MARET 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/koloni.v2i1.438

Abstract

Simulation of climate model is used to produce climate models used to estimate climate in the future using some software. Simulation of climate model has two probability, they are success or failure. The problem is when the simulation is fail. There are 18 variables that used to predict the simulation. Feature selection is used to reduce the dimension of variables using RFECV method. There are 11 variables that important to simulation of climate. There are 46 from 540 simulations that fail. Furthermore, SMOTE is used to handle imbalance cases. The classification method used in this paper are Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The AUC value were not significantly different for the four methods when using SMOTE. However, the highest AUC was obtained by SVM method, so the simulation of climate model can be predicted by SVM method. Keywords: AUC, SMOTE, RFECV, Logistic Regression, SVM, Random Forest, Naïve Bayes
ANALISIS MATEMATIKA PADA PENYEBARAN VIRUS NIPAH (NiV) DENGAN MENGGUNAKAN KENDALI OPTIMAL METODE Pontryagin Maximum Principle (PMP) Sitti Rosnafi’an Sumardi; Cecilia Bintang Girik Allo; Winda Ade Fitriya B; Nicea Roona Paranoan
KOLONI Vol. 2 No. 1 (2023): MARET 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Nipah virus (NiV) is a virus that can be transmitted. This journal discusses the use of optimal control strategies to minimize populations or individuals infected with the NiV virus. We developed a mathematical model for the spread of Nipah vision (NiV) with two control strategies, namely community awareness and treatment. The aim of this study is to minimize the number of infected individuals and to reduce the costs required to create awareness and treatment at set time intervals. To achieve this goal the authors use the Pontryagin Maximum Principle (PMP) method. To see the effectiveness of using the optimal control strategy, the authors use the Runge Kutta Order 4 method (RK 4: Forward & Backward). The results of the simulation show that using two controls (public awareness and treatment) can optimally reduce the number of individuals infected with Nipah virus (NiV). Keywords: Optimal Control, Infectious Diseases, Nipah Virus, PMP, Runge Kutta Order 4
Comparing Logistic Regression and Support Vector Machine in Breast Cancer Problem Caecilia Bintang Girik Allo; Leonardus Sandy Ade Putra; Nicea Roona Paranoan; Vincentius Abdi Gunawan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.19246

Abstract

There are several methods used for the classification problems. There are many different kinds of fields that can be used. Nowadays, Support Vector Machine (SVM) is a popular classification method that has been proposed by many researchers. Using the same method but different distribution methods for creating training and testing data in the same dataset can yield varying results in terms of prediction accuracy, which is crucial in classification. In this paper, we compare the prediction accuracy between SVM results and Logistic Regression results to determine the better method to  classify the current condition of the patient after undergoing some treatment.  Several treatments are used in this paper, including feature selection, feature extraction, separating the train and testing data using Holdout and K-Fold CV. Stepwise selection is done to reduce the features. Training and testing dataset is obtained using the five stratified and non-stratified holdout and five fold stratified and non-stratified cross validation. The result shows that the best method to classify the cancer dataset is five fold stratified cross validation SVM with radial kernel. The obtained accuracy is 81,816% with variance as much as 0,94%.
Co-Authors Gunawan, Vincentius Abdi Nicea Roona Paranoan Putra, Leonardus Sandy Ade Sitti Rosnafi’an Sumardi Winda Ade Fitriya B