Implementasi Metode CRISP DM dan Algoritma Decision Tree Untuk Strategi Produksi Kerajinan Tangan pada UMKM A
DOI:
https://doi.org/10.30865/mib.v8i1.7050Keywords:
CRISP DM, Decision Tree, Handicrafts, MSMEsAbstract
In industry 4.0, the utilization of information technology and data analysis is very important in helping business decision making. The use of data mining and the Decision Tree algorithm in analyzing data can help to classify products that best suit customer preferences for Micro, Small and Medium Enterprises (MSMEs) handicraft products. In this research we use the Cross Industry Standard Process for Data Mining methodology to analyze data in classifying the types of craft products produced by MSME A to determine production strategy that suits market demand after the Covid-19 pandemic. Covid-19 influenced MSME A as a handicraft producer to temporarily stop production due to decreased demand and produce a special order only. Changes in consumer behavior due to the Covid-19 pandemic mean that MSME A must determine the right production strategy so that products are sold and can reuse the capital. We succeeded in building a fairly effective model with CRISP-DM and the Decision Tree algorithm which has an accuracy rate of 74.2%. This model found as many as 21 types of products that were still selling well during the pandemic, making it useful for MSME A for making production decisions based on market conditions during the Covid-19 pandemic.
References
B. Marr, “How To Understand Your Customers And Their Needs With The Right Data,” Forbes. Accessed: Nov. 15, 2023. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2022/02/03/how-to-understand-your-customers-and-their-needs-with-the-right-data/?sh=4a2d9bed2f68
A. Phillips, “How the creative manufacturing and handmade sector can craft a post-COVID-19 future,” World Economic Forum. Accessed: Nov. 15, 2023. [Online]. Available: https://www.weforum.org/agenda/2021/06/creative-manufacturing-handmade-sector-growth/
C. P. A. Rabbi, “Pandemi Covid-19 Memicu Empat Perubahan Besar Perilaku Konsumen ,” katadata. Accessed: Oct. 14, 2023. [Online]. Available: https://katadata.co.id/happyfajrian/brand/605a31cf8e81f/pandemi-covid-19-memicu-empat-perubahan-besar-perilaku-konsumen
Y. Pusparisa, “Perbedaan Strategi Bertahan UMKM Saat Diterpa Krisis,” Databoks Katadata. Accessed: Oct. 16, 2023. [Online]. Available: https://databoks.katadata.co.id/datapublish/2020/09/26/perbedaan-strategi-bertahan-umkm-saat-diterpa-krisis
Y. A. Singgalen, “Penerapan Metode CRISP-DM dalam Klasifikasi Data Ulasan Pengunjung Destinasi Danau Toba Menggunakan Algoritma Naïve Bayes Classifier (NBC) dan Decision Tree (DT),” 2023, doi: 10.30865/mib.v7i3.6461.
Y. Ansori and C. Wulandari, “CRISP-DM Method On Indonesian Micro Industries (UMKM) Using K-Means Clustering Algorithm,” MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), vol. 14, no. 2, pp. 35–40, Oct. 2022, doi: 10.18860/mat.v14i2.13760.
G. Gunawan, “DATA MINING USING CRISP-DM PROCESS FRAMEWORK ON OFFICIAL STATISTICS: A CASE STUDY OF EAST JAVA PROVINCE,” Jurnal Ekonomi dan Pembangunan, vol. 29, no. 2, pp. 183–198, Dec. 2021, doi: 10.14203/jep.29.2.2021.183-198.
V. Plotnikova, M. Dumas, and F. Milani, “Adapting the CRISP-DM data mining process: A case study in the financial services domain,” in International Conference on Research Challenges in Information Science, 2021, pp. 55–71.
Y. A. Singgalen, “Penerapan Metode CRISP-DM untuk Optimalisasi Strategi Pemasaran STP (Segmenting, Targeting, Positioning) Layanan Akomodasi Hotel, Homestay, dan Resort,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, pp. 1980–1993, 2023, doi: 10.30865/mib.v7i4.6896.
A. Winarni, “Dampak Pandemi Covid-19, Aktivitas Produksi Menurun,” Kumparan. Accessed: Nov. 15, 2023. [Online]. Available: https://kumparan.com/ayu-winarni/dampak-pandemi-covid-19-aktivitas-produksi-menurun-1urqmt7yGGC/full
H. Dhika, H. Veris, J. Raya Tengah No, K. Gedong, P. Rebo, and J. Timur, “PENERAPAN ALGORITMA DECISION TREE DALAM PENJUALAN HANDPHONE,” 2021.
N. L. W. S. Telagawathi, N. M. Suci, and K. K. Heryanda, “STRATEGI TRANSFORMASI DIGITAL UMKM KERAJINAN TANGAN PADA MASA PANDEMI COVID-19 DI KABUPATEN GIANYAR, BALI,” Jurnal Ilmu Sosial dan Humaniora, vol. 11, no. 2, pp. 204–212, Aug. 2022, doi: 10.23887/jish.v11i2.39734.
Y. Christian and K. O. Y. R. Qi, “Penerapan K-Means pada Segmentasi Pasar untuk Riset Pemasaran pada Startup Early Stage dengan Menggunakan CRISP-DM,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 4, p. 966, Aug. 2022, doi: 10.30865/jurikom.v9i4.4486.
M. Lewrick, P. Link, and L. Leifer, The DESIGN THINKING PLAYBOOK mindful digital transformation of teams, products, services, ... businesses and ecosystems, 1st ed. JOHN WILEY & Sons, 2018.
T. Frisendal, Design Thinking Business Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. doi: 10.1007/978-3-642-32844-2.
C. Stedman, “DEFINITION data mining,” techtarget network. Accessed: Oct. 27, 2023. [Online]. Available: https://www.techtarget.com/searchbusinessanalytics/definition/data-mining
M. North, Data mining for the masses, vol. 615684378. Global Text Project Athens, 2012.
I. Sutoyo, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik,” Jurnal Pilar Nusa Mandiri, vol. 14, no. 2, pp. 217–224, 2018.
M. Afdhal and V. Ariandi, “MEMPREDIKSI PENJUALAN PADA TOKO HANIFAH METODE C.45,” Jurnal Teknologi Dan Sistem Informasi Bisnis-JTEKSIS, vol. 4, no. 2, p. 248, 2022, doi: 10.47233/jteksis.v4i2.460.
R. Wahyudi, “Aplikasi Pendukung Penetapan Harga Produk Untuk Usaha Mikro, Kecil, dan Menengah (UMKM) Berbasis Web Responsive,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 3, no. 2, p. 105, Apr. 2019, doi: 10.30865/mib.v3i2.1107.
N. HOTZ, “What is CRISP DM?,” Data Science Process Alliance. Accessed: Oct. 27, 2023. [Online]. Available: https://www.datascience-pm.com/crisp-dm-2/
R. Wirth and J. Hipp, “CRISP-DM: Towards a standard process model for data mining,” in Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, 2000, pp. 29–39.
IBM Corporation, “IBM SPSS Modeler CRISP-DM Guide Product Information,” IBM Corporation. IBM Corporation, 2016.
E. Damayanti and S. Kuswayati, “ANALISIS DAN IMPLEMENTASI FRAMEWORK CRISP-DM (CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING) UNTUK CLUSTERING PERGURUAN TINGGI SWASTA,” Jurnal STT Bandung, 2006, Accessed: Dec. 14, 2023. [Online]. Available: https://ejournal.sttbandung.ac.id/assets/file/SRI%20ERINA%20DAMAYANTI.pdf
N. Ratama, “Analisa Dan Perbandingan Sistem Aplikasi Diagnosa Penyakit Asma Dengan Algoritma Certainty Factor Dan Algoritma Decision Tree Berbasis Android,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 3, no. 2, pp. 177–183, May 2018, doi: 10.30591/jpit.v3i2.848.
M. A. Hasanah, S. Soim, and A. S. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir,” 2021. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
E. D. Zamani, A. Griva, and K. Conboy, “Using Business Analytics for SME Business Model Transformation under Pandemic Time Pressure,” Information Systems Frontiers, vol. 24, no. 4, pp. 1145–1166, Aug. 2022, doi: 10.1007/s10796-022-10255-8.
Efraim. Turban, D. DELEN, and R. SHARDA, Business intelligence and analytics : systems for decision support, 3rd ed. Pearson, 2019.
N. Hotz, “What is CRISP DM?,” Data Science Process Alliance. Accessed: Nov. 14, 2023. [Online]. Available: https://www.datascience-pm.com/crisp-dm-2/
N. H. Purnomo, B. Pamungkas, and C. Juliane, “Penerapan Algoritma C4.5 Untuk Klasifikasi Tren Pelanggaran Kendaraan Angkutan Barang dengan Metode CRISP-DM,” JURNAL MEDIA INFORMATIKA BUDIDARMA, 2023, doi: 10.30865/mib.v7i1.5247.
V. J. Richardson, R. A. Teeter, and K. L. Terrell, “Data Analytics for Accounting,” 2022.
F. Gorunescu, Data Mining : Concepts, Models and Techniques. Springer, 2011.
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