Article Per Year (5 Year)
p-Index From 2021 - 2026
1.048
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Faktor Exacta Jurnal Riset Informatika Sains, Aplikasi, Komputasi dan Teknologi Informasi
Devi Fitrianah
Universitas Mercu Buana
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Faktor Exacta

 1 
Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes Devi Fitrianah; Saruni Dwiasnati; Hanny Hikmayanti H; Kiki Ahmad Baihaqi
Faktor Exacta Vol 14, No 2 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i2.9297

Abstract

Customers are people who trust the management of their money in a bank or other financial service party to be used in banking business operations, thereby expecting a return in the form of money for their savings. To reach information to increase company profits, a method is needed to be able to provide knowledge in supporting the data that the company has. The model can be obtained by using predictive data processing of customer data that is categorized as potential or not potential. Data processing can be done using Machine Learning, namely classification techniques. This technique will produce a churn prediction model for determining the category of customers who fall into the Potential or Not Potential category and find out what accuracy value will be generated by applying the classification technique using the Naïve Bayes Algorithm. The parameters used in this study are Gender, Age, Marital Status, Dependent, Occupation, Region, Information. The data used are 150 data from customers who have participated in the savings program to find out whether the customer is in the Potential or Non-Potential category. The accuracy results generated using this data are 86.17% of the tools used by Rapidminner.
Penerapan Fuzzy Sugeno Orde Satu dalam Prediksi Pembelian Devi Fitrianah; Wawan Gunawan; Anggi Puspita Sari
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.11268

Abstract

Given the rapid advancement of information technology has a great influence in the fields of industry and services. This brings changes in competition between companies, so that company players must always create various techniques to survive. This study aims to assist SMEs in making purchases of the products they sell so that there is no excess stock. This research is calculated using the Fuzzy Sugeno algorithm with a system inference method that can be applied to determine the prediction of the number of purchases of goods. The prediction generated for the test data at week 30 is 60 pcs and this is less when compared to the real data, namely 70 pcs so that it can avoid overstock. Furthermore, the prediction results from the test data at week 21 to week 30 are tested to determine the error rate using the MAPE method, so that the result is 31.67%, and that means that the test is considered reasonable (reasonable).
Co-Authors Anggi Puspitasari, Anggi Ayumi, Vina Baihaqi, Kiki Ahmad Difa Lazuardi Aditya Hanny Hikmayanti Handayani Hong Wei Ida Nurhaida Noprisson, Handrie Novelinda Permata Wulandari Raihan Nugroho Jauhari Remmy A. M. Zen Saruni Dwiasnati Wawan Gunawan