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Journal Global Technology Computer
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 28096118     DOI : https://doi.org/10.47065/jogtc.v2i3.3992
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
Journal Global Technology Computer, ini memiliki bidang kajian: 1. Manajemen Informatika, 2. Sistem Informasi, 3. Game Design, 4. Multimedia System, 5. Sistem Pembelajaran Berbasis Multimedia, 6. GIS, 7. Mobile Programming, 8. Database Design, 9. Network Programming, 10. Distributed System, 11. Data Mining, 12. Sistem Pakar, 13. Kriptografi, dan 14. Sistem Pendukung Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
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Search results for , issue "Vol 4 No 3 (2025): Agustus 2025" : 12 Documents clear
Penerapan Algoritma Decision Tree Data Mining untuk Prediksi Pola Pemberian Kredit pada Koperasi Simpan Pinjam Ginting, Winda Widia Br; Sitepu, Harun Rivaldo; Nainggolan, Laksono; Purba, Andrean Saputra; Surbakti, Asprina Br; Utomo, Dito Putro
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8336

Abstract

Savings and Loan Cooperatives (KSP) play a crucial role in providing access to financing for the public, particularly in underbanked areas. However, lending through KSPs often faces challenges related to the accuracy of creditworthiness assessments, which largely rely on subjective assessments and manual procedures, resulting in the risk of non-performing loans. This study aims to develop a creditworthiness prediction model using the Decision Tree algorithm to improve the accuracy and efficiency of the credit decision-making process. The Decision Tree algorithm was chosen for its ability to classify customers based on historical data in a manner that is easy to understand and interpret. In this study, customer data, including attributes such as Borrower Credit History, Financial Status, Income Amount, Employment Status, and Loan Amount, was used to construct a decision tree. The results showed that the Decision Tree model achieved an accuracy of 86.67%, indicating its effectiveness in predicting creditworthiness and its reliability in supporting credit granting decisions in savings and loan cooperatives. This research contributes to reducing the risk of non-performing loans and improving the efficiency of decision-making in savings and loan cooperatives through the application of data mining techniques based on historical customer data analysis.
Penerapan Data Mining untuk Mengukur Prestasi Kinerja Dosen dengan Menggunakan Algoritma C4.5 Sihotang, Ester Ulina; Ginting, Gogor Abiezer; Dahlia, Icha; Amanda, Rizka; Sembiring, David JM; Peranginangin, Sinek Mehuli Br
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8355

Abstract

Lecturer performance is an important factor in improving the quality of higher education, because lecturers not only act as educators, but also as researchers and community service. However, lecturer performance assessment often faces obstacles, such as the lack of uniform evaluation standards, a tendency for subjectivity in assessments, and limited evaluation instruments capable of assessing performance comprehensively. To overcome these problems, a data-driven approach is needed that can provide objective and measurable analysis results. One method that can be used is data mining with the C4.5 algorithm, which is a decision tree-based classification algorithm. This study aims to apply the C4.5 algorithm to measure lecturer performance achievements based on historical data that includes various indicators of the tridharma of higher education. The research stages include problem identification, literature review, data collection, selection of analysis techniques, implementation of the C4.5 algorithm with the help of RapidMiner software, and analysis of test results. The resulting classification model is visualized in the form of a decision tree so that it is easy to understand and can be used as a basis for evaluation. The test results show that the C4.5 algorithm is able to produce a classification model with an accuracy level of 86.67%. This demonstrates that C4.5 is effective in processing lecturer performance data and producing more objective and transparent evaluations, while also reducing the potential for subjectivity in assessments. This research provides a strategic contribution to supporting managerial decision-making in higher education, particularly in formulating policies for improving the quality of education and sustainable professional development of lecturers.

Page 2 of 2 | Total Record : 12

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