Implementasi Etika Profesi Pelaksanaan Pemetaan Cerdas Lokasi Industri Rumahan Berbasis WEB-SIG di Dinas P3ACSKB Provinsi Kepulauan Bangka Belitung
DOI:
https://doi.org/10.23960/jpi.v6n1.149Abstract View: 12
Keywords:
Etika profesi, Pemetaan cerdas, Industri Rumahan, Crisp-DM, Web-SigAbstract
Pemerintah Daerah Provinsi Kepulauan Bangka Belitung menghadapi kendala dalam mengklasifikasikan data industri rumahan berdasarkan Peraturan Menteri PPPA No. 2 Tahun 2016. Untuk mengatasi hal tersebut, diusulkan penerapan algoritma K-means guna mengelompokkan data industri rumahan secara lebih optimal. Proses ini diimplementasikan melalui aplikasi cerdas berbasis web dengan pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM), yang meliputi enam tahapan: pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan penerapan. Sebanyak 4560 rumah tangga dijadikan sampel dalam kegiatan ini. Evaluasi kinerja algoritma menggunakan Davies Bouldin Index (DBI) menunjukkan hasil optimal pada iterasi kelima, menghasilkan tiga klaster: pemula (C1) sebanyak 4308, berkembang (C2) sebanyak 167, dan maju (C3) sebanyak 85. Nilai DBI sebesar 0,184 mengindikasikan validitas klaster yang baik. Implementasi algoritma ini dalam sistem informasi geografis berbasis web di Dinas P3ACSKB juga memperhatikan prinsip etika profesi, termasuk tanggung jawab, transparansi, dan akurasi data, sebagai dasar pemanfaatan teknologi informasi secara profesional dan berkelanjutan untuk mendukung pengembangan ekonomi lokal.
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References
Alifiana, M. A., & Susanti, N. (2018). Analisis dan perancangan sistem informasi pemetaan umkm berdasar potensi risiko berbasis gis 1. SENDI_U, 289–294.
Amade, N., Painho, M., & Oliveira, T. (2018). Geo-spatial Information Science Geographic information technology usage in developing countries – A case study in Mozambique. Geo-Spatial Information Science, 21(4), 331–345. https://doi.org/10.1080/10095020.2018.1523995
Anupama, K., Dhodapkar, R., & Ramesh, A. A. (2024). “Women Entrepreneurs in Micro, Small and Medium Enterprises” Associate Professor of English, Badruka College of Commerce and Arts Assistant Professor of Management, Badruka College of Commerce and Arts. In Journal of Informatics Education and Research (Vol. 4). http://jier.org
Ayele, W. Y. (2020). Adapting CRISP-DM for Idea Mining. International Journal of Advanced Computer Science and Applications, 11(6), 20–32. https://doi.org/10.14569/ijacsa.2020.0110603
Binabar, S. W., Siregar, D. J. S. H., & Pratama, W. (2019). Geographic Information System for Mapping the Potency of Batik Industry Centre. Journal of Information System Engineering and Business Intelligent, 5(1), 40–47.
Elkabalawy, M., Al-Sakkaf, A., Mohammed Abdelkader, E., & Alfalah, G. (2024). CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors. Sustainability, 16(17), 7249. https://doi.org/10.3390/su16177249
Gallina, V., Steinwender, A., Zudor, E., Preuveneers, D., & Schlund, S. (2024). Business model development concept for SMEs in the era of twin transition. Procedia Computer Science, 232, 523–532. https://doi.org/10.1016/j.procs.2024.01.052
Helmi, A. M., Farhan, M. S., & Nasr, M. M. (2018). A framework for integrating geospatial information systems and hybrid cloud computing ☆. Computers and Electrical Engineering, 67(March), 145–158. https://doi.org/10.1016/j.compeleceng.2018.03.027
Jafarzadeh, P., Vähämäki, T., Nevalainen, P., Tuomisto, A., & Heikkonen, J. (2024). Supporting SME companies in mapping out AI potential: a finnish AI development case. Journal of Technology Transfer, 0123456789. https://doi.org/10.1007/s10961-024-10122-5
Keinsinyuran. (2014). UU Nomor 11 Tahun 2014.
Lu, Y., & Cao, K. (2019). Spatial Analysis of Big Data Industrial Agglomeration and Development in China. Sustainability, September 2017, 1–22. https://doi.org/10.3390/su11061783
Nhat, N. M. (2024). Applied Density-Based Clustering Techniques for Classifying High-Risk Customers : A Case Study of Commercial Banks in Vietnam. 5(4), 1639–1653.
Permen PPA. (2016). Peraturan Meteri Pemberdayaan Perempuan dan Perlindungan Anak Republik Indonesia Nomor 2 Tahun 2016.
Sutramiani, N. P., Arthana, I. M. T., Lampung, P. F., Aurelia, S., Fauzi, M., & Darma, I. W. A. S. (2024). The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover. Journal of Information Systems Engineering and Business Intelligence, 10(1), 13–24. https://doi.org/10.20473/jisebi.10.1.13-24
Terttiaavini, T. (2024). A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City. Journal of Intelligent System and Computation, 6(1), 46–53. https://doi.org/10.52985/insyst.v6i1.392

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