Segmentasi Nasabah Kartu Kredit Berdasarkan Perilaku Penggunaan Kartu Kreditnya Menggunakan Algoritma K-Means

Ichwanul Muslim Karo Karo

Abstract


The intensity of credit card customers in transacting has increased in the last 10 years in Indonesia. This is a challenge as well as an opportunity for the Bank. Customer segmentation information is very useful for reducing bad credit or increasing customer credit card limit capacity. This panel aims to segment credit card customers based on their credit card usage behavior with a clustering approach using the K-means algorithm. Meanwhile, the evaluation process of segmentation results uses a sillhoette index. Based on the experimental results, the best number of clusters is six groups. The six groups are shopping hobbies, payment processing when due, payments in installments, withdrawing cash, buying expensive goods, and types that rarely use credit cards.


Keywords


K-Means; sillhoette index.

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References


S. Johan and C.E. Dewi, “Credit Limit of Unsecured Consumer Lending: Evidence from Micro Data,” Economics and Finance in Indonesia, 67(1), 51–62,2021.

N. Anastasia and S. Santoso, “Effects of Subjective Norms, Perceived Behavioral Control, Perceived Risk, and Perceived Usefulness towards Intention to Use Credit Cards in Surabaya, Indonesia,” SHS Web of Conferences, 76, 01032, 2020.

F. Cuandra and Kelvin, “Analysis of influence of materialism on impulsive buying and compulsive buying with credit card use as mediation variable,” 13(1), 7–16,2021.

S. Kumar and L. Karlina, “Intention to Use Credit Card among College Students in Greater Jakarta,” JAAF (Journal of Applied Accounting and Finance, 4(1), 49–59,2020.

K. Jung and M. Y. Kang, “Understanding credit card usage behavior of elderly korean consumers for sustainable growth: Implications for Korean credit card companies,” Sustainability (Switzerland), 13(7),2021.

R. Khandelwal, A. Kolte, N. Veer and P. Sharma, ”Compulsive Buying Behavior of Credit Card Users and Affecting Factors Such as Financial Knowledge, Prestige and Retention Time: A Cross-sectional Research,” Vision,2021.

M. K. Yaseen, M. Raheem and V. Sivakumar, “Credit Card Business in Malaysia: A Data Analytics Approach,” International Journal of Advanced Computer Science and Applications, 11(12), 383–390,2020.

L. Hassani and E. Taati, “Studying product quality by exploring credit card customers behavior via data mining techniques,” International Journal for Quality Research, 14(1), 163–182,2020.

Umuhoza, E., Ntirushwamaboko, D., Awuah, J., & Birir, B. (2020). Using unsupervised machine learning techniques for behavioral-based credit card users segmentation in africa. SAIEE Africa Research Journal, 111(3), 95-101, 2020.

M. Ala’raj, M. F. Abbod and M. Majdalawieh, “Modelling customers credit card behaviour using bidirectional LSTM neural networks,” Journal of Big Data, 8(1), 2021.

Karo, I. M. K., & Huda, A. F.( 2016). Spatial clustering for determining rescue shelter of flood disaster in South Bandung using CLARANS Algorithm with Polygon Dissimilarity Function. In 2016 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA) (pp. 70-75). IEEE,

Yadav, M. L., & Roychoudhury, B. (2018). Handling missing values: A study of popular imputation packages in R. Knowledge-Based Systems, 160, 104-118.

Rahmasari, A., & Noeryanti, N. (2021). PREDIKSI DATA SPASIAL YANG TIDAK TERSAMPEL DAN MENGANDUNG PENCILAN MENGGUNAKAN METODE ROBUST KRIGING. Jurnal Statistika Industri dan Komputasi, 6(2), 132-140.

Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018, April). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP Conference Series: Materials Science and Engineering (Vol. 336, No. 1, p. 012017). IOP Publishing.

Karo, I. M. K., MaulanaAdhinugraha, K., & Huda, A. F. (2017, November). A cluster validity for spatial clustering based on davies bouldin index and Polygon Dissimilarity function. In 2017 Second International Conference on Informatics and Computing (ICIC) (pp. 1-6). IEEE.

Abdulhafedh, A. (2021). Incorporating K-means, Hierarchical Clustering and PCA in Customer Segmentation. Journal of City and Development, 3(1), 12-30.




DOI: https://doi.org/10.17509/seict.v2i2.40220

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