Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal KOMPUTIKA - Jurnal Sistem Komputer
Selvia Lorena Br Ginting
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Hybrid Classifier System: Support Vector Machines Dikombinasikan dengan K-Nearest Neighbors untuk Menentukan Kelayakan Nasabah Bank dalam Pengajuan Kredit Selvia Lorena Br Ginting; Aldi Azhar Permana
Komputika : Jurnal Sistem Komputer Vol 7 No 1 (2018): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (940.575 KB) | DOI: 10.34010/komputika.v7i1.1402

Abstract

This research intends to build an application that can analyze bank data and then determine the feasibility in terms of creditworthiness, to avoid non-performing loans in the future. The method used is a hybrid method that combines two Data Mining classification techniques namely Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). SVM works by finding the optimal hyperplane and support vectors. Furthermore, the KNN will classify bank data based on identifying the support vectors. With 2000 training data and 103 testing data: cost parameter values = 0.1, gamma = 2, 1998 support vectors, then with K value = 16 the system gives 88.35% suitable data (91 data from 103). In conclusion, the application can work in terms of helping the credit analysts to recommend prospective customers who deserve loans. Keywords – application; data mining; hybrid method; SVM-KNN
Algoritma Apriori untuk Menampilkan Korelasi Nilai Akademik dengan Kelulusan Mahasiswa: Data Mining Selvia Lorena Br Ginting
Komputika : Jurnal Sistem Komputer Vol 6 No 2 (2017): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (421.334 KB) | DOI: 10.34010/komputika.v6i2.1706

Abstract

The number of student data that increases every year certainly results in data accumulation in universities. A data processing technique is needed hence the data that accumulates is not difficult to analyze. This research was conducted to analyze the relationship between student academic data and graduation categories. Varied processing techniques need to be adjusted to the needs of data analysis, the method used in this research is the Apriori algorithm, which is the Association algorithm that uses knowledge of the frequency of previously known attributes to process further information. This research is carried out by utilizing academic data and student graduation data, namely by finding the percentage of the relationship between the value of student courses to graduation categories using data mining. Graduation categories are measured from the length of study students and GPA, while the academic data used is the value of student courses. The information displayed is a value of support (Support Value) and confidence (Certainty Value).
Co-Authors Aldi Azhar Permana