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All Journal Jurnal Gaussian Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Statistika dan Matematika (Statmat) Journal of International Conference Proceedings KOLONI STATISTIKA
Allo, Caecilia Bintang Girik
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : KOLONI

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PENENTUAN RUTE di APLIKASI GOOGLE MAPS DENGAN MENGGUNAKAN GRAF DAN ALGORITMA PRIM B, Winda Ade Fitriya; Sumardi, Sitti Rosnafi’an; Paranoan, Nicea Roona; Allo, Caecilia Bintang Girik
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 Allo, Caecilia Bintang Girik; Paranoan, Nicea Roona; B, Winda Ade Fitriya; Sumardi, Sitti Rosnafi’an
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
Co-Authors Bobi Frans Kuddi FEBY SERU Gusthvi, Wickly Jonathan k. Wororomi Kurniawan, Rezza Debby Nicea Roona Paranoan Rumaropen, Kartini Wika Sitti Rosnafi’an Sumardi Sukarman, Dian Natasia Dewanti Winda Ade Fitriya B