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Part Samples.Rmd
Part Samples
Perbandingan K-Nearest Neighbor, Naive Bayes, dan Decision Tree untuk Klasifikasi Perilaku Merokok di Indonesia Menggunakan SMOTE dan Grid Searcht
Penelitian ini membandingkan tiga algoritma klasifikasi, yaitu K-Nearest Neighbor (KNN), Naive Bayes, dan Decision Tree, dalam mengklasifikasikan perilaku merokok di Indonesia menggunakan data Indonesia Demographic and Health Survey (IDHS) tahun 2017 yang terdiri atas 10.009 responden dengan 10 variabel prediktor sosiodemografi. Untuk mengatasi ketidakseimbangan kelas, diterapkan metode SMOTE (Synthetic Minority Over-sampling Technique), sementara optimasi parameter dilakukan melalui Grid Search dengan 5-Fold Cross Validation pada tiga skenario pembagian data (90:10, 80:20, dan 70:30). Kinerja model dievaluasi menggunakan metrik Accuracy, Precision, Recall, F1-Score, Specificity, dan Kappa, dengan tujuan memperoleh model terbaik yang mampu mengklasifikasikan status merokok secara akurat dan seimbang.
EGARCH-t(1,1) model
Timeseries Bayesian
running TukeyHSD]
My Practice on the following Question. A botany student is comparing the three species in the built-in iris dataset on Sepal.Length and Sepal.Width. (a) Fit one-way ANOVA for these two variables. (b) Run Tukey's Honest Significant Differences post-hoc on your model.
Capitales
Tech Layoffs & Hiring Trends 2026
This dataset simulates modern workforce trends in the global tech industry, including layoffs, hiring patterns, AI adoption, employee sentiment, and market conditions. It is designed to reflect realistic relationships between economic conditions, AI disruption, and workforce changes.
mapa_final
perdon por apenas entregalo,los shp no jalaban
HTML-Valle de Bravo
disculpe la tardanza profe