EMPLOYEE CREDIT CLASSIFICATION ANALYSIS USING DECISION TREE BASED CRISP-DM MODEL (CASE STUDY OF SAMSUNG INDONESIA COMPANY)
DOI:
https://doi.org/10.36595/jire.v7i2.1278Keywords:
CRISP-DM, Data Classification, Data Mining, Decision Tree, Employee CreditAbstract
Employee credit program is a form of employee retention as an effort to retain potential employees from the company. In its implementation, providing employee credit is not without risks that may occur. These risks include the inability or failure to pay credit installments when due. To minimize the risks that may occur, a survey and analysis with the right method is needed for cooperative members before providing employee credit. Researchers will use a Decision Tree-based algorithm as a tool for decision making in providing employee credit to Cooperatives at Samsung Indonesia Company. Researchers also use the CRoss-Industry Standard Process for Data Mining (CRISP-DM) model on the data mining development life cycle as a research step taken. This CRISP-DM model is very appropriate to use because it is a neutral model or method and can be used in various industries and combined with various tools. From the measurements that have been carried out using a sample data of 10 records from a total of 584 records, a classification model of 2 employees with non-performing employee credit status and 8 employees with performing employee credit status was obtained. The classification model was obtained based on the Gini Index Value of the Employee ID, Division, and Position attributes are 0.7, 0.3428571, and 0.2714286, respectively. So, the decision to grant credit to employees depends on the Position, after that the Division, and the last is the ID of the employee.
References
Samsung, “Inspiring and Empowering Women at Samsung: How Diversity, Equity and Inclusion Drive Innovation,” https://news.samsung.com/global/inspiring-and-empowering-women-at-samsung-how-diversity-and-inclusion-drive-innovation. Accessed: Apr. 22, 2024. [Online]. Available: https://news.samsung.com/global/inspiring-and-empowering-women-at-samsung-how-diversity-and-inclusion-drive-innovation
E. Retnowati, U. P. Lestari, Jahroni, D. Darmawan, and A. R. Putra, “Retensi Karyawan yang Ditinjau dari Kepercayaan dan Motivasi Kerja,” Jurnal Manajemen, Bisnis, dan Kewirausahaan, vol. 1, no. 1, pp. 65–76, 2021, Accessed: Dec. 13, 2022. [Online]. Available: https://mada.indonesianjournals.com/index.php/mada/article/view/11
N. Nurajijah and D. Riana, “Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 2, pp. 77–82, Apr. 2019, doi: 10.14710/jtsiskom.7.2.2019.77-82.
N. Handayani, H. Wahyono, J. Trianto, and D. S. Permana, “Prediksi Tingkat Risiko Kredit dengan Data Mining Menggunakan Algoritma Decision Tree C.45,” Jurnal Riset Komputer), vol. 8, no. 6, pp. 2407–389, 2021, doi: 10.30865/jurikom.v8i6.3643.
A. Faiz and Benyamin, “Penerapan Data Mining untuk Mengklasifikasi Pola Nasabah Menggunakan Algoritma C4.5 pada Bank Bri Unit Anduonohu Kendari,” Router Research Jurnal Sistem Komputer dan Sistem Informasi, vol. 1, no. 1, pp. 22–28, 2019, doi: https://doi.org/10.29239/j.router.2019.313.
R. Kandayu, W. Silaban, M. Maulana, and E. Sastra Ompusunggu, “KLASIFIKASI KELANCARAN PEMBAYARAN PINJAMAN KOPERASI DENGAN METODE DECISION TREE,” Jurnal Sistem Informasi dan Ilmu Komputer Prima), vol. 5, no. 1, pp. 19–23, 2021.
I. Riswanto and R. H. Laluma, “KLASIFIKASI KELAYAKAN PINJAMAN PADA KOPERASI KARYAWAN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER BERBASIS WEB,” Jurnal Infotronik, vol. 5, no. 1, pp. 11–16, 2020, doi: 10.32897/infotronik.2020.5.1.2.
A. P. Wibawa, M. Guntur, A. Purnama, M. Fathony Akbar, and F. A. Dwiyanto, “Metode-metode Klasifikasi,” in Prosiding Seminar Ilmu Komputer dan Teknologi Informasi, 2018, pp. 134–138.
M. F. Arifin and D. Fitrianah, “Penerapan Algoritma Klasifikasi C4.5 dalam Rekomendasi Penerimaan Mitra Penjualan Studi Kasus?: PT Atria Artha Persada,” IncomTech, Jurnal Telekomunikasi dan Komputer, vol. 8, no. 2, pp. 87–102, 2018, doi: 10.22441/incomtech.v8i1.2198.
N. Wahyuningsih and Hendry, “PERBANDINGAN METODE KLASIFIKASI DALAM ANALISIS SENTIMEN MASYARAKAT TERHADAP IDENTITAS KEPENDUDUKAN DIGITAL (IKD),” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 8, no. 4, pp. 1218–1227, 2023, doi: 10.29100/jipi.v8i4.4155.
R. Fatmasari, V. M. Ayu, H. Anto, W. Gata, and L. D. Yulianto, “Analisis Sentimen Dalam Pengkategorian Komentar Youtube Terhadap Layanan Akademik dan Non-Akademik Universitas Terbuka Untuk Prediksi Kepuasan,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 2, pp. 395–404, Sep. 2022, doi: 10.47065/bits.v4i2.1738.
R. Sahara, S. Abdullah, and M. I. Saputra, “Integration Model of Academic Information Systems and Learning Management Systems with REST Web Services Using External Databases,” Journal of Information and Organizational Sciences, vol. 47, no. 2, pp. 373–384, Dec. 2023, doi: 10.31341/jios.47.2.7.
S. N. Luqman et al., “Komparasi Algoritma Klasifikasi Genre Musik pada Spotify Menggunakan CRISP-DM,” Jurnal Sistem Cerdas, vol. 04, no. 02, pp. 114–125, 2021, Accessed: Dec. 07, 2023. [Online]. Available: https://apic.id/jurnal/index.php/jsc/article/view/162/86
C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.
F. N. Dhewayani et al., “Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM,” Jurnal Teknologi dan Informasi, vol. 12, no. 1, pp. 64–77, 2022, doi: 10.34010/jati.v12i1.
S. Huber, H. Wiemer, D. Schneider, and S. Ihlenfeldt, “DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model,” in Procedia CIRP, Elsevier B.V., 2019, pp. 403–408. doi: 10.1016/j.procir.2019.02.106.
I. Rahmianti, “ANALISIS KELAYAKAN PEMBERIAN KREDIT KOPERASI DENGAN METODE DATA MINING DECISION TREE,” Jurnal Informatika & Rekayasa Elektronika), vol. 5, no. 2, pp. 153–161, 2022, doi: https://doi.org/10.36595/jire.v5i2.663.
H. Hasugian, H. Mursyidin, and M. D. Handayani, “SISTEM PENUNJANG KEPUTUSAN PEMBERIAN KREDIT DENGAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) STUDI KASUS: KOPERASI KARYAWAN GATERA PT PLN (PERSERO) AREA KEBAYORAN,” in Prosiding SINTAK, 2018, pp. 465–471.
A. Alibasyah, A. Ajiz, G. Dwilestari, and E. Wahyudin, “Penerapan Algoritma Decision Tree dalam Penentuan Karyawan Kontrak.,” MEANS (Media Informasi Analisa dan Sistem), vol. 7, no. 1, pp. 123–129, 2022, [Online]. Available: http://ejournal.ust.ac.id/index.php/Jurnal_Means/
A. Setiawan, R. Febrio Waleska, M. Adji Purnama, Rahmaddeni, and L. Efrizoni, “KOMPARASIALGORITMAK-NEAREST NEIGHBOR(K-NN), SUPPORT VECTOR MACHINE(SVM), DAN DECISION TREEDALAM KLASIFIKASI PENYAKIT STROKE,” Jurnal Informatika & Rekayasa Elektronika), vol. 7, no. 1, pp. 107–114, 2024, doi: https://doi.org/10.36595/jire.v7i1.1161.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” May 01, 2021, Springer. doi: 10.1007/s42979-021-00592-x.
B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.
B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.
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