Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal J-Intech (Journal of Information and Technology)
Eka Purwiantono, Febry
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Penerapan Metode Single Exponential Smoothing Pada Peramalan Penjualan Di UD. Kaya Rasa Berbasis Web Prasetyo, Hafedo Rakhmad; Eka Purwiantono, Febry
J-INTECH (Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.832

Abstract

This research was conducted to help forecast the sales of cakes and foods at UD. Kaya Rasa, which fluctuates and experiences varying sales volumes in each sales period. Based on the aforementioned problem, a system is needed to forecast the sales of cakes and foods, namely Single Exponential Smoothing (SES). This research uses the SDLC Waterfall model for system development, while the application will be web-based using the CodeIgniter framework. Black box testing is conducted for system testing. The result of this research is a web-based forecasting application that can predict the sales of cakes and food in the next period based on actual sales data from the previous period. As a result, UD. Kaya Rasa can accurately estimate sales and sales profits for the next period using the SES formula.
Sistem Rekomendasi Jurusan Menggunakan Algoritma Naïve Bayes Gaussian Berbasis Web Perkasa, Ken Bagus Panuluh Yudha; Eka Purwiantono, Febry
J-INTECH (Journal of Information and Technology) Vol 11 No 2 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i2.1090

Abstract

This study aims to develop a web-based department recommendation system using the Gaussian Naive Bayes algorithm to address the issue of student confusion in selecting majors at STIKI Malang. Limited career guidance and information pose challenges for high school graduates in making informed decisions about their suitable majors based on interests and potentials. In this research, training data from 107 active students and graduates are utilized to provide recommendations based on various attributes such as gender, current major, skills, hobbies, reasons for pursuing higher education, program selection motives, interest in mathematics, and interest in English. The Gaussian Naive Bayes method successfully classifies continuous data with an accuracy of 87,85%, effectively dealing with the uncertainty in major selection. It is hoped that this system will assist high school graduates in choosing appropriate majors, reducing major selection errors, and optimizing potential.
Co-Authors Perkasa, Ken Bagus Panuluh Yudha Prasetyo, Hafedo Rakhmad