MEMAHAMI NIAT PEMBELIAN KONSUMEN RITEL KECIL INFORMAL: PENDEKATAN DATA MINING DENGAN DECISION TREE
Keywords:
bisnis ritel kecil informal, datamining, decision tree, niat pembelian
bisnis ritel kecil informal, datamining, decision tree, niat pembelian
Abstract
Penelitian ini mengenai analisis niat pembelian konsumen dalam bisnis ritel kecil informal di negara berkembang. Penggunaan metode Decision Tree dalam data mining menjadi fokus penelitian untuk memprediksi niat pembelian berdasarkan Data Subsistence Retail Consumer. Hasil penelitian menunjukkan bahwa model Decision Tree memiliki akurasi di atas 90 persen, yang menandakan hasil yang signifikan dan baik dalam menganalisis niat pembelian konsumen. Item kualitas produk toko sesuai dengan harga yang dibayarkan pada niat yang dirasakan menjadi prediktor paling kuat dalam mempengaruhi niat pembelian. Implikasi teoritis dari penelitian ini memberikan panduan bagi strategi pemasaran dan bisnis untuk lebih memahami faktor-faktor yang memengaruhi perilaku pembelian konsumen secara efektif. Implikasi praktisnya adalah perusahaan dapat menggunakan model ini sebagai alat handal dalam merancang strategi pemasaran yang lebih efektif untuk meningkatkan niat pembelian konsumen dan mencapai kepuasan konsumen yang lebih tinggi.
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References
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Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20 - 28. https://doi.org/10.38094/jastt20165
Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2021). Towards early purchase intention prediction in online session based retailing systems. Electronic Markets, 31(3), 697-715. https://doi.org/10.1007/s12525-020-00448-x
Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2022). An analyses of the effect of using contextual and loyalty features on early purchase prediction of shoppers in e-commerce domain. Journal of Business Research, 147, 420-434. https://doi.org/https://doi.org/10.1016/j.jbusres.2022.04.012
Horvat, T., Havaš, L., & Srpak, D. (2020). The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes. Symmetry, 12(3).
László, K., & Ghous, H. (2020). Efficiency comparison of Python and RapidMiner. Multidiszciplináris Tudományok, 10(3), 212-220.
Malgas, M., & Zondi, W. B. (2020). Challenges facing small business retailers in selected South African townships. The Southern African Journal of Entrepreneurship and Small Business Management, 12(1), 1-9. https://doi.org/10520/ejc-sajesbm_v_i_a202
Matzavela, V., & Alepis, E. (2021). Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments. Computers and Education: Artificial Intelligence, 2, 100035. https://doi.org/https://doi.org/10.1016/j.caeai.2021.100035
Nugroho, A., & Religia, Y. (2021). Analisis optimasi algoritma klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 504 - 510. https://doi.org/10.29207/resti.v5i3.3067
Nyagadza, B., Mazuruse, G., Simango, K., Chikazhe, L., Tsokota, T., & Macheka, L. (2023). Examining the influence of social media eWOM on consumers’ purchase intentions of commercialised indigenous fruits (IFs) products in FMCGs retailers. Sustainable Technology and Entrepreneurship, 2(3), 100040. https://doi.org/https://doi.org/10.1016/j.stae.2023.100040
Prakash, G., Choudhary, S., Kumar, A., Garza-Reyes, J. A., Khan, S. A. R., & Panda, T. K. (2019). Do altruistic and egoistic values influence consumers’ attitudes and purchase intentions towards eco-friendly packaged products? An empirical investigation. Journal of Retailing and Consumer Services, 50, 163-169. https://doi.org/https://doi.org/10.1016/j.jretconser.2019.05.011
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. https://doi.org/10.1007/BF00116251
Religia, Y. (2021). Evaluasi model data mining. In Data mining dan aplikasinya (pp. 193-210). Widina Bhakti Persada Bandung.
Religia, Y. (2023). Theory of reasoned action dalam mempengaruhi niat adopsi tiktok dikalangan UMKM. Journal of Economic, Business and Engineering, 4(2), 199-205. https://doi.org/10.32500/jebe.v4i2.4334
Religia, Y., & Amali. (2021). Perbandingan optimasi feature selection pada Naïve Bayes untuk klasifikasi kepuasan airline passenger. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 527 - 533. https://doi.org/10.29207/resti.v5i3.3086
Religia, Y., Nugroho, A., & Hadikristanto, W. (2021). Analisis perbandingan algoritma optimasi pada Random Forest untuk klasifikasi data bank marketing. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 187 - 192. https://doi.org/10.29207/resti.v5i1.2813
Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural Processes, 148, 56-62. https://doi.org/https://doi.org/10.1016/j.beproc.2018.01.004
Singh Kushwah, J., Kumar, A., Patel, S., Soni, R., Gawande, A., & Gupta, S. (2022). Comparative study of regressor and classifier with decision tree using modern tools. Materials Today: Proceedings, 56, 3571-3576. https://doi.org/10.1016/j.matpr.2021.11.635
Syed Nor, S. H., Ismail, S., & Yap, B. W. (2019). Personal bankruptcy prediction using decision tree model. Journal of Economics, Finance and Administrative Science, 24(47), 157-170. https://doi.org/10.1108/JEFAS-08-2018-0076
Topal, I. (2019). Estimation of Online Purchasing Intention Using Decision Tree. Journal of Management Economics Research, 17(4), 269-280. https://doi.org/10.11611/yead.542249
Topîrceanu, A., & Grosseck, G. (2017). Decision tree learning used for the classification of student archetypes in online courses. Procedia Computer Science, 112, 51-60. https://doi.org/https://doi.org/10.1016/j.procs.2017.08.021
Vaca, C., RiofrÍo, D., Pérez, N., & BenÍtez, D. (2020, 7-8 Aug. 2020). Buy & Sell Trends Analysis Using Decision Trees. 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020),
Yap, B. W., Ong, S. H., & Husain, N. H. M. (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38(10), 13274-13283. https://doi.org/https://doi.org/10.1016/j.eswa.2011.04.147
Zulu, M., & Nkunal, A. (2022). Subsistence Retail Consumer Data Version 1). https://doi.org/10.17632/5z37z85jck.1
Zulu, V. M., & Nkuna, A. M. (2022). Data modelling of subsistence retail consumer purchase behavior in South Africa. Data in Brief, 42, 108094. https://doi.org/10.1016/j.dib.2022.108094
