Model Data-Driven untuk Prediksi Digitalisasi UMKM Menggunakan GMM dan XGBoost
DOI:
https://doi.org/10.55382/jurnalpustakaai.v5i2.984Kata Kunci:
UMKM, Digitalisasi, Data-Driven, Gaussian Mixture Model, XGBoost, Segmentasi, Klasifikasi, Machine Learning, Prediksi, Transformasi DigitalAbstrak
[1] “?Refleksi 2022 dan Outlook 2023, Kemenkop UKM Ungkap Pencapaian dan Rencana Untuk Pelaku UMKM .” Accessed: May 29, 2025. [Online]. Available: https://ukmindonesia.id/baca-deskripsi-program/refleksi-2022-dan-outlook-2023-kemenkop-ukm-ungkap-pencapaian-dan-rencana-untuk-pelaku-umkm
[2] F. Baderi, “UMKM Pilar Pemulihan dan Pertumbuhan Ekonomi Nasional,” Harian Ekonomi Neraca. Accessed: Dec. 07, 2024. [Online]. Available: https://www.neraca.co.id/article/209137/umkm-pilar-pemulihan-dan-pertumbuhan-ekonomi-nasional
[3] G. Godwin, S. R. P. Junaedi, M. Hardini, and S. Purnama, “Inovasi Bisnis Digital untuk Mendorong Pertumbuhan UMKM melalui Teknologi dan Adaptasi Digital,” ADI Bisnis Digital Interdisiplin Jurnal, vol. 5, no. 2, pp. 41–47, Dec. 2024, doi: 10.34306/abdi.V5I2.1172.
[4] Eliza, F. Hadi, Zefriyenni, and K. Kunci, “Pengembangan E-Commerce di Era Digitalisasi pada UMKM Produk Kale Kota Padang Panjang,” Jurnal Pengabdian kepada Masyarakat Nusantara, vol. 5, no. 2, pp. 2732–2743, Jun. 2024, doi: 10.55338/jpkmn.v5i2.3342.
[5] R. Mardiana, Y. Fahdillah, M. Kadar, I. Hassandi, and R. Mandasari, “Implementasi Transformasi Digital dan Kecerdasan Buatan Sebagai Inovasi Untuk UMKM pada Era Revolusi Industri 4.0,” Jurnal Ilmiah Manajemen dan Kewirausahaan (JUMANAGE), vol. 3, no. 1, Jan. 2024, doi: 10.33998/jumanage.2024.3.1.1552.
[6] S. Baulkani, P. S. Nifasath, and M. M. Priyanga, “Machine Learning Technologies for Agricultural Prediction to Enhance Economic Growth,” Smart Technologies for Sustainable Development Goals, pp. 178–195, 2024, doi: 10.1201/9781003519010-11.
[7] D. Marcelina, A. Kurnia, and T. Terttiaavini, “Analisis Klaster Kinerja Usaha Kecil dan Menengah Menggunakan Algoritma K-Means Clustering,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 293–301, Nov. 2023, doi: 10.57152/malcom.v3i2.952.
[8] A. Heryati, T. Terttiaavini, S. Cahyani, H. Romli, and I. Zaliman, “Optimasi Strategi Pemasaran E-Commerce Melalui Prediksi Konversi Berbasis Machine Learning,” JSAI: Journal Scientific and Applied Informatics, vol. 8, no. 1, pp. 66–73, 2025, doi: 10.36085.
[9] M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science,” pp. 3–21, 2020, doi: 10.1007/978-3-030-22475-2_1.
[10] T. Terttiaavini, “A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City,” INSYST: Journal of Intelligent System and Computation, vol. 6, no. 1, pp. 46–53, Apr. 2024, doi: 10.52985/insyst.V6I1.392.
[11] H. Ren, B. Khailany, M. Fojtik, and Y. Zhang, “Machine Learning and Algorithms: Let Us Team Up for EDA,” IEEE Des Test, vol. 40, no. 1, pp. 70–76, Feb. 2023, doi: 10.1109/mdat.2022.3143427.
[12] T. Milo and A. Somech, “Automating Exploratory Data Analysis via Machine Learning: An Overview,” in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, New York, NY, USA: ACM, Jun. 2020, pp. 2617–2622. doi: 10.1145/3318464.3383126.
[13] V. Çetin and O. Y?ld?z, “A comprehensive review on data preprocessing techniques in data analysis,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 2, pp. 299–312, Apr. 2022, doi: 10.5505/pajes.2021.62687.
[14] J. Rashid and K. Waheed, “Missing Values and Outliers in Research Data,” Pakistan Postgraduate Medical Journal, vol. 31, no. 04, pp. 167–167, Jun. 2020, doi: 10.51642/ppmj.v31i04.404.
[15] V. Safak, “Min-Mid-Max Scaling, Limits of Agreement, and Agreement Score,” ArXiv, Jun. 2020, Accessed: May 20, 2025. [Online]. Available: https://arxiv.org/pdf/2006.12904
[16] R. Addanki, A. McGregor, A. Meliou, and Z. Moumoulidou, “Improved Approximation and Scalability for Fair Max-Min Diversification,” Jan. 2022, Accessed: May 20, 2025. [Online]. Available: https://arxiv.org/pdf/2201.06678
[17] K. P. Sinaga and M.-S. Yang, “Unsupervised K-Means Clustering Algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/access.2020.2988796.
[18] L. Trento Oliveira, M. Kuffer, N. Schwarz, and J. C. Pedrassoli, “Capturing deprived areas using unsupervised machine learning and open data: a case study in São Paulo, Brazil,” Eur J Remote Sens, vol. 56, no. 1, Dec. 2023, doi: 10.1080/22797254.2023.2214690.
[19] T. Terttiaavini et al., “Clustering Analysis of Premier Research Fields,” International Journal of Engineering & Technology, vol. 7, no. 4.44, 2018, doi: 10.14419/ijet.v7i4.44.26860.
[20] A. Avram, O. Matei, C.-M. Pintea, P. C. Pop, and C. A. Anton, “Comparative Analysis of Clustering Techniques for a Hybrid Model Implementation,” in 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), Á. Herrero, C. Cambra, D. Urda, J. JSedano, H. Quintián, and E. Corchado, Eds., Springer, Cham, 2021, pp. 22–32. doi: 10.1007/978-3-030-57802-2_3.
[21] E. Y. Boateng, J. Otoo, and D. A. Abaye, “Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review,” Journal of Data Analysis and Information Processing, vol. 08, no. 04, pp. 341–357, 2020, doi: 10.4236/jdaip.2020.84020.
[22] O. A. Montesinos López, A. Montesinos López, and J. Crossa, Overfitting, Model Tuning, and Evaluation of Prediction Performance. Springer International Publishing, 2022. doi: 10.1007/978-3-030-89010-0.
[23] S. Khodabandehlou and M. Zivari Rahman, “Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior,” Journal of Systems and Information Technology, vol. 19, no. 1/2, pp. 65–93, Jan. 2017, doi: 10.1108/JSIT-10-2016-0061.
[24] R. Susmaga, “Confusion Matrix Visualization,” Intelligent Information Processing and Web Mining, pp. 107–116, 2004, doi: 10.1007/978-3-540-39985-8_12.
[25] M. Kuhn and K. Johnson, “Feature Engineering and Selection: A Practical Approach for Predictive Models,” Feature Engineering and Selection: A Practical Approach for Predictive Models, pp. 1–297, Jan. 2019, doi: 10.1201/9781315108230
Unduhan
Referensi
“Refleksi 2022 dan Outlook 2023, Kemenkop UKM Ungkap Pencapaian dan Rencana Untuk Pelaku UMKM .” Accessed: May 29, 2025. [Online]. Available: https://ukmindonesia.id/baca-deskripsi-program/refleksi-2022-dan-outlook-2023-kemenkop-ukm-ungkap-pencapaian-dan-rencana-untuk-pelaku-umkm
F. Baderi, “UMKM Pilar Pemulihan dan Pertumbuhan Ekonomi Nasional,” Harian Ekonomi Neraca. Accessed: Dec. 07, 2024. [Online]. Available: https://www.neraca.co.id/article/209137/umkm-pilar-pemulihan-dan-pertumbuhan-ekonomi-nasional
G. Godwin, S. R. P. Junaedi, M. Hardini, and S. Purnama, “Inovasi Bisnis Digital untuk Mendorong Pertumbuhan UMKM melalui Teknologi dan Adaptasi Digital,” ADI Bisnis Digital Interdisiplin Jurnal, vol. 5, no. 2, pp. 41–47, Dec. 2024, doi: 10.34306/abdi.V5I2.1172.
Eliza, F. Hadi, Zefriyenni, and K. Kunci, “Pengembangan E-Commerce di Era Digitalisasi pada UMKM Produk Kale Kota Padang Panjang,” Jurnal Pengabdian kepada Masyarakat Nusantara, vol. 5, no. 2, pp. 2732–2743, Jun. 2024, doi: 10.55338/jpkmn.v5i2.3342.
R. Mardiana, Y. Fahdillah, M. Kadar, I. Hassandi, and R. Mandasari, “Implementasi Transformasi Digital dan Kecerdasan Buatan Sebagai Inovasi Untuk UMKM pada Era Revolusi Industri 4.0,” Jurnal Ilmiah Manajemen dan Kewirausahaan (JUMANAGE), vol. 3, no. 1, Jan. 2024, doi: 10.33998/jumanage.2024.3.1.1552.
S. Baulkani, P. S. Nifasath, and M. M. Priyanga, “Machine Learning Technologies for Agricultural Prediction to Enhance Economic Growth,” Smart Technologies for Sustainable Development Goals, pp. 178–195, 2024, doi: 10.1201/9781003519010-11.
D. Marcelina, A. Kurnia, and T. Terttiaavini, “Analisis Klaster Kinerja Usaha Kecil dan Menengah Menggunakan Algoritma K-Means Clustering,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 293–301, Nov. 2023, doi: 10.57152/malcom.v3i2.952.
A. Heryati, T. Terttiaavini, S. Cahyani, H. Romli, and I. Zaliman, “Optimasi Strategi Pemasaran E-Commerce Melalui Prediksi Konversi Berbasis Machine Learning,” JSAI: Journal Scientific and Applied Informatics, vol. 8, no. 1, pp. 66–73, 2025, doi: 10.36085.
M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science,” pp. 3–21, 2020, doi: 10.1007/978-3-030-22475-2_1.
T. Terttiaavini, “A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City,” INSYST: Journal of Intelligent System and Computation, vol. 6, no. 1, pp. 46–53, Apr. 2024, doi: 10.52985/insyst.V6I1.392.
H. Ren, B. Khailany, M. Fojtik, and Y. Zhang, “Machine Learning and Algorithms: Let Us Team Up for EDA,” IEEE Des Test, vol. 40, no. 1, pp. 70–76, Feb. 2023, doi: 10.1109/mdat.2022.3143427.
T. Milo and A. Somech, “Automating Exploratory Data Analysis via Machine Learning: An Overview,” in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, New York, NY, USA: ACM, Jun. 2020, pp. 2617–2622. doi: 10.1145/3318464.3383126.
V. Çetin and O. Y?ld?z, “A comprehensive review on data preprocessing techniques in data analysis,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 2, pp. 299–312, Apr. 2022, doi: 10.5505/pajes.2021.62687.
J. Rashid and K. Waheed, “Missing Values and Outliers in Research Data,” Pakistan Postgraduate Medical Journal, vol. 31, no. 04, pp. 167–167, Jun. 2020, doi: 10.51642/ppmj.v31i04.404.
V. Safak, “Min-Mid-Max Scaling, Limits of Agreement, and Agreement Score,” ArXiv, Jun. 2020, Accessed: May 20, 2025. [Online]. Available: https://arxiv.org/pdf/2006.12904
R. Addanki, A. McGregor, A. Meliou, and Z. Moumoulidou, “Improved Approximation and Scalability for Fair Max-Min Diversification,” Jan. 2022, Accessed: May 20, 2025. [Online]. Available: https://arxiv.org/pdf/2201.06678
K. P. Sinaga and M.-S. Yang, “Unsupervised K-Means Clustering Algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/access.2020.2988796.
L. Trento Oliveira, M. Kuffer, N. Schwarz, and J. C. Pedrassoli, “Capturing deprived areas using unsupervised machine learning and open data: a case study in São Paulo, Brazil,” Eur J Remote Sens, vol. 56, no. 1, Dec. 2023, doi: 10.1080/22797254.2023.2214690.
T. Terttiaavini et al., “Clustering Analysis of Premier Research Fields,” International Journal of Engineering & Technology, vol. 7, no. 4.44, 2018, doi: 10.14419/ijet.v7i4.44.26860.
A. Avram, O. Matei, C.-M. Pintea, P. C. Pop, and C. A. Anton, “Comparative Analysis of Clustering Techniques for a Hybrid Model Implementation,” in 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), Á. Herrero, C. Cambra, D. Urda, J. JSedano, H. Quintián, and E. Corchado, Eds., Springer, Cham, 2021, pp. 22–32. doi: 10.1007/978-3-030-57802-2_3.
E. Y. Boateng, J. Otoo, and D. A. Abaye, “Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review,” Journal of Data Analysis and Information Processing, vol. 08, no. 04, pp. 341–357, 2020, doi: 10.4236/jdaip.2020.84020.
O. A. Montesinos López, A. Montesinos López, and J. Crossa, Overfitting, Model Tuning, and Evaluation of Prediction Performance. Springer International Publishing, 2022. doi: 10.1007/978-3-030-89010-0.
S. Khodabandehlou and M. Zivari Rahman, “Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior,” Journal of Systems and Information Technology, vol. 19, no. 1/2, pp. 65–93, Jan. 2017, doi: 10.1108/JSIT-10-2016-0061.
R. Susmaga, “Confusion Matrix Visualization,” Intelligent Information Processing and Web Mining, pp. 107–116, 2004, doi: 10.1007/978-3-540-39985-8_12.
M. Kuhn and K. Johnson, “Feature Engineering and Selection: A Practical Approach for Predictive Models,” Feature Engineering and Selection: A Practical Approach for Predictive Models, pp. 1–297, Jan. 2019, doi: 10.1201/9781315108230.
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