Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Algoritme Jurnal Mahasiswa Teknik Informatika JOINCS (Journal of Informatics, Network, and Computer Science)
Kartika, Kurnia Paranita
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Implementasi Algoritma C4.5 Untuk Memprediksi Capaian Pembelajaran Daring (Studi Kasus Siswa MAN 3 Blitar) Hidayatuloh, Muchamad Azis; Kartika, Kurnia Paranita; Permadi, Dimas Fanny Hebrasianto
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i1.3292

Abstract

Learning outcomes are focused on what students want to be able to do during or at the end of a learning process that includes the learning and teaching process. The problem when learning online is that there are network constraints and the lack of teaching hours for teachers and the limited amount of material that can be delivered. So a solution is needed to determine the level of student satisfaction in the process of achieving online learning in schools. This study applies the C4.5 algorithm for the classification of online learning outcomes at MAN 3 Blitar. This study aims to predict the achievement of online learning with the C4.5 algorithm. There are 10 attributes that affect the achievement of online learning, namely: remembering, attendance, assignments, assessment, material, conditions, application, creating, discussion, analysis. With research, it can make it easier for teachers to determine online learning methods with minimal constraints. The results of the research using the C4.5 algorithm are 27 decision trees and rules, the highest gain value is the Create attribute, which is 0.383923. The results of the validation test using the confusion matrix level of accuracy in predicting online learning achievement on average are 88%, precision is 83%, recall is 86%. With the results of the accuracy value obtained, it can be said to be included in the Good Classification.
Implementation of the Triple Exponential Smoothing Method for Predicting Helmet Sales: Implementasi Metode Triple Exponential Smoothing untuk Prediksi Penjualan Helm Saputro, Ronggo Bayu; Kartika, Kurnia Paranita; Puspitasari, Wahyu Dwi
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 5 No. 2 (2022): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v5i2.1607

Abstract

Helmet stock at the Trend Helmet store is very important to prepare, because it is for the readiness of goods to be sold in the future. Therefore, it is necessary to do forecasting to find out the estimated stock in the future. The method that can be used to predict the stock of goods for several periods at once is Triple Exponential Smoothing. Helmet stock forecasting results with Cargloss helmet sales data obtained a forecast for 2022. Alpha, beta and gamma used in this study were 0.3. Cargloss helmet sales forecast results in January as many as 379, February as many as 449, March as many as 431, April as many as 500, May as many as 483, June as many as 552, July as many as 534, August as many as 604, September as many as 586, October as many as 656, November as many as 638 , December as many as 707. The results of the calculation of accuracy with Mean Absolute Percentage Error with Alpha, Beta and Gamma values of 0.3 are 44.4%. Based on the value of Mean Absolute Percentage Error helmet sales forecasting with Triple Exponential Smoothing method is feasible to use.
Co-Authors Dimas Fanny Hebrasianto Permadi Hidayatuloh, Muchamad Azis Puspitasari, Wahyu Dwi Saputro, Ronggo Bayu