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Decision Support System for Determining Extracurricular Interest Using the Naive Bayes Method Azhar, Joehari; Hasugian, Abdul Halim
Journal La Multiapp Vol. 5 No. 3 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i3.1430

Abstract

The Decision Support System (SPK) is a tool that can help individuals make more effective and efficient decisions. In the context of education, SPK can be used to assist students in determining extracurricular specializations that suit their interests and talents. This research aims to develop an SPK to determine extracurricular specializations for students using the Naive Bayes method. The Naive Bayes method was chosen because of its ability to classify based on probability. The data used in this study include student profiles, academic scores, and student interest in various types of extracurriculars. The results of the study show that the SPK developed can provide recommendations for extracurricular specialization with quite high accuracy. In addition, the system is also easy to use and can help students make more informed decisions.
Prakiraan Cuaca Dengan Menggunakan Metode Naive Bayes Classifier Azhar, Joehari; Syaharani, Widya
Jurnal Media Teknik Elektro dan Komputer Vol 1 No 1 (2024): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v1i1.25

Abstract

Weather forecasting is one of the important areas in human life. Planning, transportation, agriculture, and tourism are just a few of the uses for weather forecasts. Weather forecasting is the process of predicting future weather conditions. Various methods exist for weather forecasting, including manual and computer-based methods. The manual calculation process for weather prediction still lacks accuracy, so researchers conducted a study to develop a simple system that can produce more accurate weather predictions. The method used in this study is a Naïve Bayes classifier by using training data as data for an event from previously known facts or reality. The final test results, conducted on a simple system and compared with manual calculations, demonstrated a higher level of accuracy.