Analisis Perbandingan Algoritma Naive   Bayes, J48,dan Random Forest Tree Dalam Peningkatan Loyalitas Pelanggan Umkm Dengan Voucher Belanja

Cendana, Maya; Permana, Silvester Dian Handy

doi:10.30871/ji.v11i2.1157

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…(Nugroho & Emiliyawati, 2017) melakukan penelitian menggunakan algoritma Random Forest dengan melakukan klasifikasi faktor yang mempengaruhi tingkat penerimaan konsumen pada mobil. (Cendana, Dian, & Permana, 2019) melakukan prediksi bisnis pada data yang ada dengan cara menganalisa dan menguji data untuk proyeksi pelanggan mana yang dapat melakukan transaksi kembali pada bisnis UMKM menggunakan algoritma Naïve Bayes, Random Forest Tree, dan J48. (Derisman, 2020) membandingkan hasil algoritma terbaik dari penelitian sebelumnya yaitu Algoritma Naive Bayes, algoritma random forest, Neural Network, untuk mendapatkan algoritma paling baik dalam memperdiksi penyakit jantung.…”

Section: Pendahuluanunclassified

Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Nalatissifa

Gata

Diantika

et al. 2021

JIUP

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Absence is a problem for the company. Absenteeism is defined as a task that is assigned to an individual, but the individual cannot complete the task when he is not present. Absence from work is influenced by many factors, including mismatched working hours, job demand and other factors such as serious accidents / illness, low morale, poor working conditions, boredom, lack of supervision, personal problems, insufficient nutrition, transportation problems, stress, workload, and dissatisfaction. The purpose of this study is to predict absenteeism at work based on the Absenteeism at work dataset obtained from the UCI Machine Learning repository site using the Weka 3.8 application and the Naïve Bayes algorithm, Support Vector Machine (SVM), and Random Forest. In the results of the study, the Random Forest algorithm obtained the highest accuracy, precision, and recall values compared to the Naïve Bayes and SVM algorithms, which resulted in an accuracy value of 99.38%, 99.42% precision and a recall of 99.39%.

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Section: Pendahuluanunclassified

Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Nalatissifa

Gata

Diantika

et al. 2021

JIUP

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“…Then Cendana (2019) conducted a comparison of the Naïve Bayes, J48, and Random Forest Tree algorithms to increase MSME customer loyalty. This study aims to help decision making in giving shopping vouchers so that MSMEs can run to get optimal benefits.…”

Section: Introductionmentioning

confidence: 99%

Application of J48 and Naïve Bayes Algorithms to Predict Ream Bookings at PT. Nippon Presisi Teknik

Bakti,

Eliyani

2023

Eduvest

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In the field of goods production, demand prediction is important. By doing sales predictions, companies can make calculations and forecasts for what raw materials are mostly ordered. J48 and Naïve Bayes algorithm are two popular machine learning technique. By using these two algorithms, this study aims to develop an accurate and more reliable predictive model that help the company to make data driven decision. This study focuses on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm. This research conducted 4 times testing on each algorithm. This study produces high accuracy values with the Naïve Bayes and J48 algorithms. Both algorithm results have a fairly high accuracy value of 94% for Naïve Bayes and 98% for J48. The findings of this study implicate that by using J48 and Naïve Bayes algorithm, company can make informed decisions lead to improved operational efficiency, cost-effective, and resource utilization.

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Analisis Perbandingan Algoritma Naive Bayes, J48,dan Random Forest Tree Dalam Peningkatan Loyalitas Pelanggan Umkm Dengan Voucher Belanja

Cited by 2 publications

References 1 publication

Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Application of J48 and Naïve Bayes Algorithms to Predict Ream Bookings at PT. Nippon Presisi Teknik

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