Perbandingan Algoritma Naive Bayes dan C.45 dalam Klasifikasi Data Mining

Kurniawan, Yogiek Indra

doi:10.25126/jtiik.201854803

Cited by 35 publications

(37 citation statements)

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“…Salah satu algoritma yang dapat digunakan untuk melakukan prediksi dengan menggunakan konsep klasifikasi data mining adalah algoritma C4.5 [13], [14]. Algoritma C4.5 terbukti memiliki akurasi lebih baik daripada algoritma Naïve Bayes pada prediksi kelancaran pembiayaan [15].…”

Section: Pendahuluanunclassified

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Kurniawan

Fatikasari

Hidayat

et al. 2021

J. Tek. Inform. (JUTIF)

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BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

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Section: Pendahuluanunclassified

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Kurniawan

Fatikasari

Hidayat

et al. 2021

J. Tek. Inform. (JUTIF)

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“…Menurut Kurniawan (2018), untuk mengetahui metode terbaik dari beberapa pilihan, maka diberikan perbandingan diantara beberapa metode yang ada. Berdasarkan hasil kuesioner yang disebarkan sebelum menggunakan aplikasi serta setelah menggunakan aplikasi, didapatkan hasil perbandingan pembelajaran manual menggunakan buku dan papan tulis dibandingkan dengan pembelajaran menggunakan aplikasi AR.…”

Section: Uji Penerimaanunclassified

Aplikasi Augmented Reality untuk Pembelajaran Salat bagi Siswa Sekolah Dasar

Kurniawan

Kusuma

2021

JTIIK

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Penggunaan media manual berbasis buku dan papan tulis memberikan beberapa permasalahan pada proses pembelajaran di sekolah. Berdasarkan survey yang telah dilakukan, Di Sekolah Dasar Insan Terpadu (SDIT) Muhammadiyah Al Kautsar yang menggunakan media manual untuk pembelajaran salat memiliki masalah mengenai tingkat minat dan tingkat pemahaman siswa yang kurang tinggi. Penelitian ini memiliki tujuan untuk mengembangkan media pembelajaran mengenai materi salat yang dapat membantu pihak sekolah agar mempunyai metode yang baru dan menarik dalam kegiatan pembelajaran di dalam kelas, terutama untuk materi Salat. Teknologi utama yang digunakan dalam aplikasi ini adalah Augmented Reality (AR). Dalam kasus ini, Augmented Reality (AR) digunakan untuk memvisualisasikan materi Salat dalam bentuk 3D yang diproyeksikan pada tablet atau smartphone. Objek 3D juga diberi animasi agar terkesan realistis dan interaktif. Aplikasi ini membantu memvisualisasikan gerakan salat dalam bentuk 3D animasi. Aplikasi terdiri dari 2 menu, yaitu menu “Play AR” dan “Quiz”. Hasil uji blackbox menunjukkan hasil valid yang berarti aplikasi sudah sesuai dengan yang diharapkan. Aplikasi dapat berjalan baik pada smartphone dengan sistem operasi minimal Kitkat. Rata-rata hasil uji validasi ahli sebesar 86% yang berada pada kategori “Sangat Baik”. Hasil dari User Acceptance Test menunjukkan nilai rata-rata sebesar 89.88% dengan kategori “Sangat Baik”. Penggunaan aplikasi AR pembelajaran salat dapat meningkatkan minat belajar siswa serta tingkat pemahaman siswa terhadap materi Salat yang ditunjukkan dengan peningkatan nilai sebelum dan sesudah menggunakan aplikasi.AbstractThe use of manuals media, such as books and whiteboard, provides several problems in the learning process in schools. Based on the survey that has been done At the Sekolah Dasar Insan Terpadu (SDIT) Muhammadiyah Al Kautsar, which uses manuals media for salat learning, there are problems with the level of interest and the level of student understanding that is not quite high. This research aims to develop a learning media about salat that is to help schools to have new and interesting methods in learning activities in the classroom, especially for Salat subject. The main technology used in this application is Augmented Reality (AR). In this case, Augmented Reality (AR) are used for visualizing the Salat material in 3D object that will be projected on smartphone. 3D object are animated to make it more realistic and interactive. Eventually, this application illustrate the motion of salat in 3D animation object. The application consists of 2 menus, namely “Play AR” and “Quiz” menus. Blackbox test results show valid results, which means the application is as expected. The application can run well on smartphones with a minimum Kitkat operating system. The average expert validation test results are 86% in the “Very Good” category. The results of the User Acceptance Test show an average value of 89.88% with the “Very Good” category. The use of the Salat Learning AR application can improve student learning interest and the level of student understanding of the Salat subject which is indicated by an increase in the value before and after using the application.

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“…Asumsi ini biasa disebut dengan class conditional inedependence. Itu dibuat untuk menyederhanakan komputasi yang tekait dan dalam hal ini disebut sebagai "naïve" [10]. Naïve Bayes Classifier merupakan sebuah pengklasifikasian probabilitas sederhana yang mengaplikasikan Teorema Bayes dengan asumsi ketidak tergantungan (independent) yang tinggi [11].…”

Section: Algoritma Naïve Bayesunclassified

Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu

Gunawan¹,

Zarlis

Roslina

2021

mib

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Students are one of the important pillars in the life cycle of a university. In the process of developing, a university can be influenced by how many bachelor degree (S1) graduates from the university are. The number of graduations of a college sometimes has a low ratio when compared to the number of students admitted in the same school year. This low passing rate of students can be caused by several factors, such as the number of student activities that are participated in, economic factors, and several other unexpected factors. This makes a university must have a scheme or a formula that can predict whether the student can graduate on time. Normally, a bachelor (S1) student takes 8 semesters of education. But the existence of several factors that have been mentioned can make the time to take S1 education to be more, or even fail to graduate. This study will try to compare the results of the analysis of the two methods in the classification algorithm to predict student graduation. The algorithm used is the K-Nearest Neighbor and Naïve Bayes Algorithm. This study also aims to identify the best algorithm among the two classification algorithm choices. This research concluded that the Naïve Bayes algorithm has the same level of accuracy as the KNN algorithm in predicting the graduation of students in the Medical Education study program, which is 90%

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Perbandingan Algoritma Naive Bayes dan C.45 dalam Klasifikasi Data Mining

Cited by 35 publications

References 0 publications

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Aplikasi Augmented Reality untuk Pembelajaran Salat bagi Siswa Sekolah Dasar

Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu

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