Determining Sister City Regency/City Non-Sample Cost of Living Survey (SBH) and Clustering Analysis of Consumption Patterns in West Java using the Machine Learning Method

Raditya Novidianto(1*), Erwin Tanur(2), Andrea Tri Rian Dani(3), Fachrian Bimantoro Putra(4)


(1) Badan Pusat Statistik (BPS) Kuningan Regency
(2) BPS Education and Training Center
(3) Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Mulawarman
(4) Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Mulawarman
(*) Corresponding Author

Abstract


Inflation is a significant data source in policy making. However, not all Regency/cities have inflation figures. As a result, Regency/cities must borrow inflation figures from dietary characteristics, GDP per capita, population, and distance between Regency and cities; this is called a sister city. With the help of machine learning, the similarity level method using distance measures, namely Euclidean distance, CID distance, and ACF distance, can help Regency/cities find sister cities. Furthermore, grouping was carried out using a biclustering algorithm to see the characteristic variables in West Java from the same consumption pattern data. The biclustering parameter with tuning parameter 𝛿=0.1 is the best bicluster with a total of 3 biclusters with a value of MSR/V=0.02433 with identical characteristic variables, namely Average Fish Consumption (X3), Average Meat Consumption (X4), Average Consumption of Eggs and Milk (X5), Average Consumption of Vegetables (X6), Average Consumption of Fruit (X8), Average Consumption of Oil and Coconut (X9), Average Consumption of Housing and Household Facilities (X15), Average Consumption of Various Goods and Services and Average Consumption of Taxes (X16), Levies and Insurance (X19).

Keywords


ACF, Biclustering, CID, Euclidean, Similarity

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References


G. Strasser, T. Messner, F. Rumler, and M. Ampudia, “Inflation heterogeneity at the household level,” ECB Occas. Pap., no. 2023/325, 2023.

S. Sarbaini and N. Nazaruddin, “Pengaruh Kenaikan BBM Terhadap Laju Inflasi di Indonesia,” J. Teknol. Dan Manaj. Ind. Terap., vol. 2, no. I, pp. 25–32, 2023.

A. M. Taylor and M. P. Taylor, “The purchasing power parity debate,” J. Econ. Perspect., vol. 18, no. 4, pp. 135–158, 2002.

S. Indonesia, “Encyclopedia of Social and Economic Indicators,” Statistics Indonesia.

A. B. Santosa, “Analisis Inflasi di Indonesia,” 2017.

A. Adji, T. Hidayat, H. Tuhiman, S. Kurniawati, and A. Maulana, “Pengukuran Garis Kemiskinan di Indonesia: Tinjauan Teoretis dan Usulan Perbaikan,” Jakarta Tim Nas. Percepatan Penanggulangan Kemiskin., 2020.

P. D. Pickupana, P. H. P. Jati, and M. Sukin, “Penentuan Sister City Untuk Pembentukan Diagram Timbang Di Nusa Tenggara Timur Dengan Algoritma K-Means,” J. Stat. Terap. (ISSN 2807-6214), vol. 1, no. 2, pp. 14–24, 2021.

A. Fadlurohman and T. W. Utami, “Pemodelan Generalized Space Time Autoregressive With Variable Exogenous (Gstar-X) Pada Inflasi Enam Kota Survei Biaya Hidup Di Jawa Tengah,” in Prosiding Seminar Nasional Indonesian R Summit, 2020.

A. Mahendra, “Analisis Pengaruh Pertumbuhan Ekonomi, Pendapatan Perkapita, Inflasi Dan Pengangguran Terhadap Jumlah Penduduk Miskin Di Provinsi Sumatera Utara,” J. Ris. Akunt. Keuang., pp. 123–148, 2016.

A. DURROTUSSA’ADAH, “Pembangunan Sistem Pakar untuk Manajemen Pengetahuan pada Kegiatan Peninjauan dan Pengeditan Data Survei Biaya Hidup.” Program Studi Komputasi Statistik Program Diploma IV, 2016.

E. Pujiati, D. Yuniarti, and R. Goejantoro, “Peramalan Dengan Menggunakan Metode Double Exponential Smoothing Dari Brown,” J. Eksponensial, vol. 7, no. 1, pp. 33–40, 2017.

A. Harumeka, “Pemanfaatan Data Survei Sosial Ekonomi Nasional Untuk Memilih Sister City Pada Kabupaten/Kota Non-Sampel Survei Biaya Hidup Di Jawa Timur: Utilization of National Socio-Economic Survey Data to Select Sister Cities in Non-Sample Districts/Cities of the Cos,” J. Ilm. Komputasi dan Stat., vol. 2, no. 2, pp. 26–31, 2023.

T. W. Liao, “Clustering of time series data—a survey,” Pattern Recognit., vol. 38, no. 11, pp. 1857–1874, 2005.

A. A. Mattjik, I. Sumertajaya, G. N. A. Wibawa, and A. F. Hadi, “Sidik peubah ganda dengan menggunakan SAS.” 2011.

A. Budi and H. Maulana, “Pengenalan Citra Wajah Sebagai Identifier Menggunakan Metode Principal Component Analysis (PCA),” J. Tek. Inform., vol. 9, no. 2, 2016.

P. Galeano, D. Peña, and R. S. Tsay, “Outlier detection in multivariate time series by projection pursuit,” J. Am. Stat. Assoc., vol. 101, no. 474, pp. 654–669, 2006.

R. Ardalova, “Analisis Harga Cabai Merah Besar di Pasar Eceran Jakarta dengan Menggunakan Analisis Gerombol Deret Waktu”.

R. M. KUSAIRI, “An Improved Biclustering Algorithm With Overlapping Control For Identification Of Informative Genes And Pathways,” 2021.

S. C. Madeira and A. L. Oliveira, “Biclustering algorithms for biological data analysis: a survey,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 1, no. 1, pp. 24–45, 2004.

S. Dutta, M. Hore, F. Ahmad, A. Saba, M. Kumar, and C. Das, “SBi-MSREimpute: A Sequential Biclustering Technique Based on Mean Squared Residue and Euclidean Distance to Predict Missing Values in Microarray Gene Expression Data,” in Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2018, Volume 2, Springer, 2019, pp. 673–685.

L. A. Mina and G. W. Sledge Jr, “Rethinking the metastatic cascade as a therapeutic target,” Nat. Rev. Clin. Oncol., vol. 8, no. 6, pp. 325–332, 2011.

D. J. Dhandio, M. A. Simanjuntak, S. Martha, and S. Supandi, “Peramalan Inflasi Kota Pontianak dengan Metode Double Exponential Smoothing: Pontianak City Inflation Forecasting Using the Double Exponential Smoothing Method,” in Jurnal Forum Analisis Statistik (FORMASI), 2023, pp. 51–66.

Ł. Pawluczuk and M. Iskrzyński, “Food web visualisation: Heat map, interactive graph and animated flow network,” Methods Ecol. Evol., vol. 14, no. 1, pp. 57–64, 2023.

M. Kirişci, “New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach,” Knowl. Inf. Syst., vol. 65, no. 2, pp. 855–868, 2023.

V. A. P. Sangga, “Perbandingan algoritma K-Means dan algoritma K-Medoids dalam pengelompokan komoditas peternakan di provinsi Jawa Tengah tahun 2015,” 2018.

B. P. Statistik, “Data dan Informasi kemiskinan kabupaten/kota tahun 2018,” Jakarta Badan Pus. Stat., 2019.

S. Sudirman, I. G. Indradi, A. Sriyono, and A. Prayitno, “Analisis Determinan Dan Program Pengentasan Kemiskinan Rumahtangga Petani Dalam Rangka Mendukung Arahan Kebijakan Reforma Agraria Dalam Mengatasi Kemiskinan Petani (Studi Di Desa Bogem Kecamatan Bayat, Klaten Jawa Tengah),” 2011.

B. Pudjianto and M. Syawie, “Kemiskinan dan pembangunan manusia,” Sosio Inf. Kaji. Permasalahan Sos. dan Usaha Kesejaht. Sos., vol. 1, no. 3, 2015.

R. F. Pratama, “Menerapkan Algoritma Support Vector Machine (SVM) di Klasifikasi Masyarakat Tanjung Lowland di Lampung Timur,” J. Portal Data, vol. 2, no. 10, 2022.

M. Raymond, “Ilmu Peluang dan statistika untuk insinyur dan ilmuwan,” 2016.


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