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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Media Teknik Elektro dan Komputer
Syaharani, Widya
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Computer Science & IT Electrical & Electronics Engineering

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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.
Application of k-Means Algorithm on Clustering Poor Population Data for Extreme Poverty Elimination Syaharani, Widya; Sriani, Sriani
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4384

Abstract

Poverty is one of the social problems faced by almost every country in the world. One of the factors causing poverty has not been resolved, namely in an implementation of social assistance policies, the government's survey of the community is still carried out manually so that it is not right on target. So, this research aims to identify the criteria possessed by each group of poor people resulting from data grouping using the K-Means clustering algorithm. By applying the K-Means clustering algorithm to the data of the Targeting for the Acceleration of the Elimination of Extreme Poverty (P3KE) of Sei Litur Tasik Village and modeling the data clustering of the poor population of Sei Litur Tasik Village. The results of testing and evaluating the K-Means Clustering model on the data of the Acceleration of the Elimination of Extreme Poverty (P3KE) are determined to be 2 optimal clusters with an interia value of 0.40 using the Silhouette Score testing method where cluster 1 rich category is 366 families and cluster 2 poor category is 60 families. Modeling of the data clustering system design using the K-Means clustering method was carried out on Google Collaboratory and assisted by supporting literature. The results showed the accuracy of K-Means clustering of 85.92% which means that the accuracy of the analyzed data can be correctly grouped into the appropriate cluster category.
Co-Authors Azhar, Joehari