Article Per Year (5 Year)
p-Index From 2019 - 2024
1.633
P-Index
This Author published in this journals
All Journal Jurnal TAM (Technology Acceptance Model) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Global AKM: Aksi Kepada Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference Scientific Journal of Informatics
Ricoida, Desy Iba
Universitas Multi Data Palembang
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Pelaksanan Ujian Kompetensi IT dalam meningkatkan kualitas lulusan di SMKN Suak Tapeh Banyuasin John Roni Coyanda; Desy Iba Ricoida; Dwi Asa Verano
AKM Vol 4 No 1 (2023): AKM : Aksi Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat - Juli 2023
Publisher : Sekolah Tinggi Ekonomi dan Bisnis Syariah (STEBIS) Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36908/akm.v4i1.858

Abstract

Vocational High School (SMK) is a formal education unit that organizes vocational education where the orientation produces graduate students who are ready to work. One area of expertise that is in great demand by students is computer and network engineering (TKJ), where this area of expertise teaches how to make students skilled and competent in the field of computer and network engineering. In this era of digital transformation, computer networks are part of human needs so there are many job opportunities in this field. The purpose of this service and socialization is to increase the understanding and knowledge of SMK students in the field of TKJ in conducting education and preparing themselves to enter the world of work. The partners of this service activity are students of SMKN Suak Tapeh Banyuasin where this activity is carried out in the Computer Laboratory Computer and Network Engineering Study Program. While the method of activity used in this service is planning, directing, guiding and teaching network practicum and conducting competency exams that must be followed before completing education at school. Through this service activity occurs increasing understanding from SMK students majoring in TKJ regarding the areas of expertise they take so as to improve the quality of graduates of SMK N Suak Tapeh and also as a provision to face the world of work. In this era of digital transformation, computer networks are part of human needs so there are many job opportunities in this field. The purpose of this service and socialization is to increase the understanding and knowledge of SMK students in the field of TKJ in conducting education and preparing themselves to enter the world of work. These service partners are students of SMKN Suak Tapeh Banyuasin
Analysis of Student Graduation Prediction Using Machine Learning Techniques on an Imbalanced Dataset: An Approach to Address Class Imbalance Dedy; Desy Iba Ricoida; Desi Pibriana; Rusbandi; Muhammad Rizky Pribadi
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.5528

Abstract

Purpose: Machine learning is a key area of artificial intelligence, applicable in various fields, including the prediction of timely graduation. One method within machine learning is supervised learning. However, the results are influenced by the distribution of data, particularly in the case of imbalanced classes, where the minority class is significantly smaller than the majority class, affecting classification performance. Timely graduation from a university is crucial for its sustainability and accreditation. This research aims to identify a suitable method to address the issue of predicting timely graduation by managing class imbalance using SMOTE (Synthetic Minority Oversampling Technique). Methods: This study uses a five-year dataset with 26 attributes and 1328 records, including status labels. The preprocessing stages involve applying five classification algorithms: Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF). Each algorithm is used both with and without SMOTE to handle the class imbalance. The dataset indicates that 60.84% of the cases represent timely graduations. To mitigate the imbalance, over/under-sampling methods are employed to balance the data. The evaluation metric used is the confusion matrix, which assesses the classification performance. Result: Without SMOTE, the accuracies were 89.12% for DT, 79.65% for NB, 89.47% for LR, 87.72% for KNN, and 90.88% for RF. With SMOTE, the accuracies were 88.89% for DT, 81.48% for NB, 91.05% for LR, 92.59% for KNN, and 89.81% for RF. The algorithms NB, LR, and KNN showed improvement with SMOTE, with KNN yielding the best results. Novelty: Based on the comparison results, a comparison of five algorithms with and without SMOTE can reasonably classify several of the algorithms being compared.
Co-Authors Dedy Denny Denny Dwi Asa Verano Gasim Hartati, Ery John Roni Coyanda Lisa Amelia Lulu Lulu Muhammad Hafidh Putra Harti Pibriana, Desi Ricky Budiman Rizky Pribadi, Muhammad Rusbandi Satria A Solihin Santoso Soni Suciadi