Sistem Prediksi Keuntungan Influencer Pengguna E-Commerce Shopee Affiliates menggunakan Metode Naïve Bayes

Susanti, Susanti; Sari, Aisum Aliyah; Anam, M. Khairul; Jamaris, Muhamad; Hamdani, Hamdani

doi:10.29408/edumatic.v6i2.6787

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Setelah dilakukan pre-processing data, didapatkan dataset yang terdiri dari 9 variabel independen (Suhu Min, Suhu Max, Suhu Rata, Kel_Rata, LPM, Kec_Angin Max, Arah Angin, Kec_Angin Rata, dan Curah Hujan dan 1 variabel dependen (Kondisi Cuaca).Kemudian dataset tersebut dijadikan sebagai data latih dan data uji dengan rasio 90:10, 80:20, 70:30, 60:40, 50:50. Data latih digunakan untuk mendapatkan nilai pada tabel probabilitas dan data uji digunakan untuk menguji tabel probabilitas yang dihasilkan oleh data latih (Susanti et al, 2022). Langkah berikutnya adalah membuat model Gaussian Naïve Bayes dan mengoptimasi menggunakan metode Univariate Features Selection ANOVA-f test.…”

Section: Gambar 2 Proses Pengujian Model Gaussian Naïve Bayesunclassified

Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

Lindawati,

Fadhli,

Wardana

2023

Edumatic

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The importance of conducting weather prediction research is due to the significant influence of weather changes on daily life. The purpose of this study is to apply an optimal machine-learning classification method for weather prediction. The method used is the Gaussian Naïve Bayes model, which has been optimized using Univariate Feature Selection ANOVA-f test and Hyperparameter Tuning GridsearchCV techniques. The data used consists of 6454 daily weather data in Palembang City. There are 5 tests on the Gaussian Naïve Bayes model before and after optimization. The research results show that the optimization of the model successfully improves the performance in weather prediction. The highest accuracy result after optimization reaches 98.33% with 644 test data, an improvement from the pre-optimization accuracy of only 96.95%. Before optimization, the predictions for weather conditions such as sunny, cloudy/rainy, light rain, and heavy rain match the actual data. However, there were 20 prediction errors when dealing with data that should represent very heavy rain conditions. After optimization, the number of prediction errors for the very heavy rain data was reduced to seven. The optimization approach used in this research helps find the most suitable parameter combinations and eliminates irrelevant features, allowing the model to consider only significant features in weather p

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Section: Gambar 2 Proses Pengujian Model Gaussian Naïve Bayesunclassified

Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

Lindawati,

Fadhli,

Wardana

2023

Edumatic

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“…Support Vector Machine mampu menangani dataset dengan banyak atribut dan memisahkan kelas stunting dengan baik (Hovi et al, 2022). Naive Bayes efisien dalam mengatasi fitur yang saling tergantung dalam data stunting (Armansyah & Ramli 2022;Rahayu et al, 2022;Sari et al, 2020;Susanti et al, 2022).…”

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Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita

Masacgi,

Rohman

2023

Edumatic

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Stunting is a serious problem for the health of toddlers, who experience malnutrition during their development. Poor nutritional intake or a lack of knowledge about nutrition can be a factor in stunting. This study aims to optimize the stunting classification algorithm in toddlers using the K-Nearest Neighbor algorithm, Support Vector Machine, and Naïve Bayes by using bagging optimization. This study uses toddler data totaling 10,000 records, 7 attributes, and 2 classes taken from Krobokan Health Center, Semarang City. Based on the findings of this study, the Bagging Method shows excellent performance with 89.77% accuracy, 95.57% precision, 85.45% recall, and a 90.27% F1-score. This shows great potential for improving the model's ability to classify data with a high degree of accuracy, completeness, and balance. The SVM model has a high recall of 80.59%, while the Naïve Bayes model gets an F1-score of 71.84%, indicating that Naïve Bayes has a good balance between precision and recall. Overall, the Bagging Model is the best choice, with far superior performance compared to other models. Based on the results obtained, the Bagging Method can predict stunting data accurately, showing a very good level of truth in stunting prediction efforts.

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Sistem Prediksi Keuntungan Influencer Pengguna E-Commerce Shopee Affiliates menggunakan Metode Naïve Bayes

Cited by 2 publications

References 15 publications

Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita

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