Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Aulia Nabila Putri; Neva Satyahadewi; Siti Aprizkiyandari

doi:10.37905/jjom.v6i1.21921

Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Aulia Nabila Putri, Neva Satyahadewi, Siti Aprizkiyandari

Abstract

Indonesia's export activities are dominated by non-oil and gas exports consisting of four sectors, namely the processing industry, agriculture, mining, and others. The government must pay attention to non-oil and gas exports for each province because exports can play an essential role in a country's economic growth. This study was conducted to cluster provinces in Indonesia using time series clustering in the non-oil and gas export sector based on data patterns concerning Dynamic Time Warping (DTW) distance. The sectors used in this study are the manufacturing industry sector and the agricultural sector in 34 Indonesian provinces in the period 2017 - 2021. Time series clustering analysis uses the average linkage method with DTW distance and the selection of the optimum number of clusters using the silhouette coefficient method. The results of the analysis in the processing industry sector resulted in 3 optimum clusters, namely cluster 1 consisting of 1 province that has high processing industry exports, cluster 2 consisting of 8 provinces that have medium processing industry exports, and cluster 3 consisting of 25 provinces that have low processing industry exports. As for the agricultural sector, it produces 2 optimum clusters, namely cluster 1 consisting of 5 provinces that have high agricultural industry exports, and cluster 2 consisting of 29 provinces that have low agricultural industry exports. The clustering results in the processing industry sector and the agricultural sectors have a silhouette coefficient value of 0.778 and 0.798, so it is said to have a strong cluster structure.

Keywords

Time Series clustering; Dynamic Time Warping; Non-oil and Gas

Full Text:

PDF

References

Badan Pusat Statistik, Ekspor Menurut Provinsi Asal Barang Tahun 2019. Jakarta: BPS RI, 2020.

I. A. Syaputra and L. T. Laut, ``Determinasi Ekspor Indonesia Tahun 1990-2021,'' Growth: Jurnal Ilmiah Ekonomi Pembangunan, vol. 1, no. 2, pp. 22--42, 2022.

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 6th ed. Upper Saddle River, N.J: Pearson Prentice Hall, 2007.

C. P. Siwi and Y. Nurfirdaus, ``K-Means Cluster Analysis of Sub-Districts In Sidoarjo Based On Long-Term Contraceptive Method,'' JBK, vol. 9, no. 2, p. 161, Oct. 2020, doi: 10.20473/jbk.v9i2.2020.161-170.

D. A. N. Sirodj, I. M. Sumertajaya, and A. Kurnia, ``Analisis Clustering Time Series untuk Pengelompokan Provinsi di Indonesia Berdasarkan Indeks Pembangunan Manusia Jenis Kelamin Perempuan,'' Statistika, vol. 23, no. 1, pp. 29--37, doi: https://doi.org/10.29313/statistika.v23i1.2181.

L. P. W. Adnyani and P. R. Sihombing, ``Analisis Cluster Time Series dalam Pengelompokan Provinsi di Indonesia Berdasarkan Nilai PDRB,'' Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika, vol. 1, no. 1, pp. 47--54, 2021, doi: https://doi.org/10.46306/bay.v1i1.5.

A. D. Munthe, ``Penerapan Clustering Time Series Untuk Menggerombolkan Provinsi di Indonesia Berdasarkan Nilai Produksi Padi,'' Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan, vol. 2, no. 2, pp. 1--11, 2019, doi: https://doi.org/10.32630/sukowati.v2i2.61.

F. Inayah, S. Martha, and N. Imro'ah, ``Pengelompokan Data Time Series Pada Distribusi Listrik Menurut Provinsi di Indonesia,'' Buletin Ilmiah Math. Stat. dan Terapannya (Bimaster), vol. 11, no. 5, pp. 735--742, 2022, doi: http://dx.doi.org/10.26418/bbimst.v11i5.58281.

L. Gubu, D. Rosadi, and Abdurakhman, ``Pembentukan Portofolio Saham Menggunakan Klastering Time Series K-Medoid dengan Ukuran Jarak Dynamic Time Warping,'' Jurnal Aplikasi Statistika & Komputasi Statistik, vol. 13, no. 2, pp. 35--46, 2021, doi: https://doi.org/10.34123/jurnalasks.v13i2.295.

A. T. R. Dani, S. Wahyuningsih, and N. A. Rizki, ``Pengelompokkan Data Runtun Waktu menggunakan Analisis Cluster (Studi Kasus: Nilai Ekspor Komoditi Migas dan Nonmigas Provinsi Kalimantan Timur Periode Januari 2000-Desember 2016),'' Jurnal EKSPONENSIAL, vol. 11, no. 1, pp. 29--37, 2020.

M. I. Rizki, T. A. Taqqiyuddin, and J. J. Cerelia, ``K-Medoids Clustering dengan Jarak Dynamic Time Warping dalam Mengelompokkan Provinsi di Indonesia Berdasarkan Kasus Aktif Covid-19,'' Prosiding Seminar Nasional Matematika, vol. 5, pp. 685--692, 2022.

Badan Pusat Statistik, Analisis Komoditas Ekspor 2017 - 2021 Sektor Petanian, Kehutanan, dan Perikanan; Industri Pengolahan; Pertambangan dan Lainnya. BPS RI, 2022.

Kementerian Pertanian, ``Peluang Ekspor Perkebunan Masih Bertahan,'' Dec. 21, 2020. [Online]. Available: https://ditjenbun.pertanian.go.id/peluang-ekspor-perkebunan-masih-bertahan/

R. Sitepu, Irmeilyana, and B. Gultom, ``Analisis Cluster terhadap Tingkat Pencemaran Udara pada Sektor Industri di Sumatera Selatan,'' Jurnal Penelitian Sains, vol. 14, no. 3(A), pp. 11--17, 2011, doi: https://doi.org/10.56064/jps.v14i3.208.

Imasdiani, I. Purnamasari, and F. D. T. Amijaya, ``Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward (Studi Kasus : Kemiskinan Di Provinsi Kalimantan Timur Tahun 2018),'' Jurnal EKSPONENSIAL, vol. 13, no. 1, pp. 9--17, 2022.

C. Cindy, C. Cynthia, V. Vito, D. Sarwinda, B. D. Handari, and G. F. Hertono, ``Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province),'' Journal of Mathematical and Fundamental Sciences, vol. 53, no. 3, pp. 466--486, Jan. 2022, doi: https://doi.org/10.5614/j.math.fund.sci.2021.53.3.9.

R. Novidianto and A. T. R. Dani, ``Analisis Klaster Kasus Aktif COVID-19 Menurut Provinsi di Indonesia Berdasarkan Data Deret Waktu,'' Jurnal Aplikasi Statistika & Komputasi Statistik, vol. 12, no. 2, pp. 15--24, 2020, doi: https://doi.org/10.34123/jurnalasks.v12i2.280.

T. Susilowati, D. Sugiarto, and I. Mardianto, ``Uji Validasi Algoritme Self-Organizing Map (SOM) dan K-Means untuk Pengelompokan Pegawai,'' Jurnal RESTI : Rekayasa Sistem dan Teknologi Informasi, vol. 4, no. 6, pp. 1171--1178, 2020, doi: https://doi.org/10.29207/resti.v4i6.2492.

M. Yohansa, K. A. Notodiputro, and E. Erfiani, ``Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta,'' ComTech: Computer, Mathematics and Engineering Applications, vol. 13, no. 2, pp. 63--73, Nov. 2022, doi: 10.21512/comtech.v13i2.7413.

D. A. I. C. Dewi and D. A. K. Pramita, ``Analisis Perbandingan Metode Elbow dan Sillhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,'' Jurnal MATRIX, vol. 9, no. 3, pp. 102--109, 2019, doi: http://dx.doi.org/10.31940/matrix.v9i3.1662.

D. I. Yunistya, R. Goejantoro, and F. D. T. Amijaya, ``The Application Of K -- Harmonic Means Method In District/City Grouping (Case Study: Poverty in Kalimantan Island in 2020),'' Jurnal Matematika, Statistika, dan Komputasi, vol. 19, no. 1, pp. 51--64, 2022, doi: 10.20956/j.v19i1.21116.

Nicolaus, E. Sulistianingsih, and H. Perdana, ``Penentuan Jumlah Cluster Optimal pada Median Linkage dengan Indeks Validitas Silhouette,'' Buletin Ilmiah Math. Stat. dan Terapannya (Bimaster), vol. 5, no. 2, pp. 97--102, 2016, doi: http://dx.doi.org/10.26418/bbimst.v5i02.15564.

M. Orisa and A. Faisol, ``Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi,'' Jurnal Teknologi Informasi dan Terapan, vol. 8, no. 2, pp. 86--90, 2021, doi: https://doi.org/10.25047/jtit.v8i2.258.

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons Inc, 1990.

Kementerian Perindustrian, ``Alami Surplus Januari-April 2020, Ekspor Industri Pengolahan Naik 7 Persen,'' May 28, 2020. [Online]. Available: https://kemenperin.go.id/artikel/21730/Alami-Surplus-Januari-April-2020,-Ekspor-Industri-Pengolahan-Naik-7-Persen.

Kementerian Koordinator, ``Pengembangan Komoditas Hortikultura untuk Peningkatan Kinerja Ekspor Nasional dan Perekonomian Daerah,'' Jan. 29, 2022. [Online]. Available: https://ekon.go.id/publikasi/detail/3621/pengembangan-komoditas-hortikultura-untuk-peningkatan-kinerja-ekspor-nasional-dan-perekonomian-daerah

DOI: https://doi.org/10.37905/jjom.v6i1.21921