DOI: https://doi.org/10.14710/jtsiskom.2021.14247

Pengelompokan wilayah menurut potensi ekonomi menggunakan modifikasi algoritme fuzzy k-prototypes untuk penentuan target pembangunan desa

Regional clustering based on economic potential with a modified fuzzy k-prototypes algorithm for village developing target determination

Badan Pusat Statistik Provinsi Jawa Tengah. Jl. Pahlawan Nomor 6, Kota Semarang, Jawa Tengah 50241, Indonesia

Received: 21 Jun 2021; Revised: 19 Aug 2021; Accepted: 20 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
The clustering algorithm can group regions based on economic potential with mixed attributes data, consisting of numeric and categorical data. This study aims to group villages according to their economic potential in determining village development targets in Demak Regency using the fuzzy k-prototypes algorithm and modified Eskin distance to measure the distance of categorical attributes. The data used are PODES2018 data and the 2019 Wilkerstat Mapping. Village clustering produces three village clusters according to their economic potential, namely low, medium, and high economic clusters. Clusters of high economic potential are located on the main transportation routes of Semarang–Kudus and Semarang–Grobogan. However, villages on the main transportation route are still included in the low economic cluster. Considering the status of the urban/rural village classification, most of these villages are included in the urban village category. The results of this clustering can be used to determine village development targets in increasing the Village Developing Index in Demak Regency.
Fulltext View|Download Email colleagues
Keywords: clustering; mix attributes; fuzzy k-prototypes; village potential; village developing index
Funding: Badan Pusat Statistik;Pusat Pendidikan dan Pelatihan Badan Pusat Statistik

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Sistem Informasi Geografis Pencarian Lokasi Agen Bus dan Travel Terdekat di Kota Semarang Berbasis Mobile dengan Metode Dijkstra Perancangan Permainan Gelembung Huruf (Tokoh Wayang) Berbasis Sistem Operasi iOS Menggunakan Gamesalad Watermelon ripeness detector using near infrared spectroscopy Aplikasi Visualisasi 3D Pada Struktur Sistem Rangka Manusia Berbasis Android Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise More related articles
Most cited articles
Backward Chaining Analysis Model in Detecting Game Addiction Levels in Children Decision Support System in Recommending Best Unit in PDAM Tirta Lihou Using Promethee Method Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms Pengembangan Teknologi Informasi Mobile Learning Universitas Diponegoro Berbasis Android Design of wireless sensor networks (WSN) to monitor temperature and humidity using nrf24l01 More cited articles
  1. G. Gan, C. Ma, and J. Wu, Data clustering: Theory, algorithms, and applications, second edition. 2020. doi: 10.1137/1.9781611976335
  2. N. Puspitasari, R. Rosmasari, and S. Stefanie, “Penentuan prioritas perbaikan jalan menggunakan fuzzy c-means : studi kasus perbaikan jalan di Kota Samarinda,” Jurnal Teknologi dan Sistem Komputer, vol. 5, no. 1, pp. 7-14, 2017. doi: 10.14710/jtsiskom.5.1.2017.7-14
  3. L. Arsy, O. D. Nurhayati, and K. T. Martono, “Aplikasi pengolahan citra digital meat detection dengan metode segmentasi k-mean clustering berbasis Opencv dan Eclipse,” Jurnal Teknologi dan Sistem Komputer, vol. 4, no. 2, pp. 322-332, 2016. doi: 10.14710/jtsiskom.4.2.2016.322-332
  4. S. Hidayat, R. Rismayati, M. Tajuddin, and N. L. P. Merawati, “Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 2, pp. 133–139, 2020. doi: 10.14710/jtsiskom.8.2.2020.133-139
  5. T. Nabarian, S. Sutoto, N. Gusmawati, D. P. Trimaratus Sholehah, A. Nizar Hidayanto, and A. M. Sari, “Clustering of provinces in Indonesia based on regional investment capacity with density-based spatial clustering of applications with noise method,” in International Conference on Computing Engineering and Design, Singapore, Apr. 2019, pp. 3–8. doi: 10.1109/ICCED46541.2019.9161110
  6. M. Y. Shih, J. W. Jheng, and L. F. Lai, “A two-step method for clustering mixed categroical and numeric data,” Tamkang Journal of Science and Engineering, vol. 13, no. 1, pp. 11–19, 2010. doi: 10.6180/jase.2010.13.1.02
  7. A. Ahmad and L. Dey, “A k-mean clustering algorithm for mixed numeric and categorical data,” Data and Knowledge Engineering., vol. 63, no. 2, pp. 503–527, 2007. doi: 10.1016/j.datak.2007.03.016
  8. Z. Huang, “Clustering large data sets with mixed numeric and categorical values,” in the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore, Feb. 1997, pp. 21–34
  9. N. Chen, A. Chen, and L. X. Zhou, “Fuzzy K-prototypes algorithm for clustering mixed numeric and categorical valued data,” Ruan Jian Xue Bao/Journal of Software, vol. 12, no. 8. pp. 1107–1119, 2001
  10. H. Prasetyo and A. Purwarianti, “Comparison of distance measures for clustering data with mix attribute types for Indonesian potential-based regional grouping,” in International Conference on Information Technology Systems and Innovation, Bandung, Indonesia, Nov. 2014, pp. 13–18. doi: 10.1109/ICITSI.2014.7048230
  11. H. Prasetyo and A. Purwarianti, “Comparison of distance and dissimilarity measures for clustering data with mix attribute types,” in the International Conference on Information Technology, Computer, and Electrical Engineering: Green Technology and Its Applications for a Better Future, Semarang, Indonesia, Nov. 2014, pp. 276–280. doi: 10.1109/ICITACEE.2014.7065756
  12. Badan Pusat Statistik, Peta Tematik Lokasi Pemusatan Kegiatan dan Sarana Sosial Ekonomi di Indonesia. Jakarta: Badan Pusat Statistik, 2012
  13. H. Prasetyo, “Perbaikan algoritma fuzzy k-prototypes untuk pengelompokan data beratribut campuran (studi kasus: pengelompokan desa menurut potensinya di Provinsi Jawa Tengah),” Institut Teknologi Bandung, Bandung, 2015
  14. Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi, Peringkat Status Indek Desa Membangun (IDM) Provinsi Jawa Tengah. Jakarta, 2020
  15. A. Aditya, B. N. Sari, and T. N. Padilah, “Perbandingan pengukuran jarak Euclidean dan Gower pada klaster k-medoids Comparison analysis of Euclidean and Gower distance measures on k-medoids,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 1, pp. 1–7, 2021. doi: 10.14710/jtsiskom.2021.13747
  16. S. Wang and W. Shi, Data mining and knowledge discovery. 2012
  17. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, “A geometric framework for unsupervised anomaly detection,” Applications of Data Mining in Computer Security, pp. 77–101, 2002. doi: 10.1007/978-1-4615-0953-0_4
  18. S. Boriah, V. Chandola, and V. Kumar, “Similarity measures for categorical data: A comparative evaluation,” in Proceedings of the 2008 SIAM International Conference on Data Mining, vol. 1, pp. 243–254, 2008. doi: 10.1137/1.9781611972788.22
  19. K. Zhou and S. Yang, “Fuzzifier selection in fuzzy c-means from cluster size distribution perspective,” Informatica, vol. 30, no. 3, pp. 613–628, 2019. doi: 10.15388/informatica.2019.221
  20. Badan Pusat Statistik, “Klasifikasi desa perkotaan dan perdesaan di Indonesia tahun 2020,” Perka BPS Nomor 120 Tahun 2020, 2020. Available: https://www.bps.go.id/website/fileMenu/Perka-Nomor-120-Tahun-2020.pdf [Accessed: Aug. 18, 2021]
  21. A. Prasetyo, “Efisiensi transportasi untuk pertumbuhan ekonomi di Jawa Tengah melalui pendekatan interaksi spasial,” Jurnal Penelitian Transportasi Darat, vol. 17, no. 3, pp. 157–170, 2015
  22. F. Kartiasih, “Dampak infrastruktur transportasi terhadap pertumbuhan ekonomi di Indonesia menggunakan regresi data panel,” Jurnal Ilmiah Ekonomi dan Bisnis, vol. 16, no. 1, pp. 67–77, 2019. doi: 10.31849/jieb.v16i1.2306
  23. A. Palilu and R. Suripatty, “Pengaruh infrastruktur transportasi terhadap pertumbuhan ekonomi Kota Sorong Provinsi Papua Barat,” Ekuivalensi, Jurnal Ekonomi Bisnis, vol. 4, no. 2, pp. 238–257, 2016

Last update:

No citation recorded.

Last update: 2024-11-21 14:56:20

No citation recorded.