Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen

Authors

  • Anne Mudya Yolanda Universitas Riau, Pekanbaru, Indonesia
  • Arisman Adnan Universitas Riau, Pekanbaru, Indonesia
  • Rustam Efendi Universitas Riau, Pekanbaru, Indonesia
  • Haposan Sirait Universitas Riau, Pekanbaru, Indonesia
  • Irfansyah Irfansyah Universitas Riau, Pekanbaru, Indonesia
  • Okta Bella Syuhada Universitas Riau, Pekanbaru, Indonesia
  • Rahmad Ramadhan Laska Universitas Riau, Pekanbaru, Indonesia
  • Riko Febrian Universitas Riau, Pekanbaru, Indonesia

DOI:

https://doi.org/10.46963/jam.v5i2.766

Keywords:

Inflation, CPI, Principal Component Analysis, Biplot Analysis, Variance

Abstract

Inflation of a region can be measured from the Consumer Price Index (CPI) by spending group. The aim is to look at the factors that influence monthly inflation based on the CPI for 2021. Principal Component Analysis is used to reduce the expenditure group variables in the CPI, followed by biplot analysis to display the visualization of the first two main components of the PCA in a two-dimensional graph. The results of the main component analysis, (1) the primary expenditure component consists of housing, water, electricity and household fuel variables; equipment, tools and household routine maintenance; transportation; information, communication and financial services; recreation, sports and culture, (2) secondary expenditure components include food, drink and tobacco variables; health; education; general, and (3) complementary expenditure components, namely clothing and footwear variables; personal equipment and other services. These three components simultaneously can represent 88.1% of the diversity of the data. Biplot analysis succeeded in describing the similarity and position of the variables with a total variance of 75%

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References

Badan Pusat Statistik. (2022a). Indeks Harga konsumen (IHK). Retrieved from Inflasi website: https://www.bps.go.id/subject/3/inflasi.html

Badan Pusat Statistik. (2022b). Inflasi. Retrieved from Inflasi website: https://www.bps.go.id/subject/3/inflasi.html

Badan Pusat Statistik Kabupaten Indragiri Hilir. (2021). Kabupaten Indragiri Hilir Dalam Angka 2022 (Vol. 23). BPS Kabupaten Indragiri Hilir.

Badan Pusat Statistik Kabupaten Indragiri Hilir. (2022). Berita Resmi Statistik No. 01/01/1403/Th. IX, 3 Januari 2022: Perkembangan Indeks Harga Konsumen Agustus 2022. Tembilahan. Retrieved from https://inhilkab.bps.go.id/pressrelease/2022/01/03/376/perkembangan-indeks-harga-konsumen-tembilahan-desember-2021.html

Badan Pusat Statistik Provinsi Riau. (2021a). Inflasi (Persen), 2021. Retrieved from Inflasi website: https://riau.bps.go.id/indicator/3/1/2/inflasi.html

Badan Pusat Statistik Provinsi Riau. (2021b). Inflasi Triwulanan (Q to Q) Provinsi Riau Triwulan IV 2021. Pekanbaru: Badan Pusat Statistik Provinsi Riau.

Badan Pusat Statistik Provinsi Riau. (2021c). Perkembangan Indeks Harga Konsumen Provinsi Riau Desember 2021. Retrieved from Inflasi website: https://riau.bps.go.id/pressrelease/2022/01/03/830/perkembangan-indeks-harga-konsumen-provinsi-riau-desember-2021.html

Badan Pusat Statistik Provinsi Riau. (2022). Berita Resmi Statistik No. 01/01/14/Th. XXIII, 3 Januari 2022: Inflasi Desember 2021, NTP Desember 2021, Pariwisata November 2021. Pekanbaru.

Gower, J., Lubbe, S., & Roux, N. le. (2011). Principal Component Analysis Biplots. In Understanding Biplots (pp. 67–144). John Wiley & Sons, Ltd. https://doi.org/https://doi.org/10.1002/9780470973196.ch3

Ilmaniati, A., & Putro, B. E. (2019). Analisis komponen utama faktor-faktor pendahulu (antecendents) berbagi pengetahuan pada usaha mikro, kecil, dan menengah (UMKM) di Indonesia. Jurnal Teknologi, 11(1), 67–78.

Johnson, R. A., & Wichern, D. . (2007). Applied Multivariate Statistical Analiysis (6th Editio). New Jersey: Prentice Education, Inc.

Leleury, Z. A., & Wokanubun, A. E. (2015). Analisis Biplot Pada Pemetaan Karakteristik Kemiskinan Di Provinsi Maluku. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 9(1), 21–31. https://doi.org/10.30598/barekengvol9iss1pp21-31

Noya van Delsen, M. S., Wattimena, A. Z., & Saputri, S. (2017). Penggunaan Metode Analisis Komponen Utama Untuk Mereduksi Faktor-Faktor Inflasi Di Kota Ambon. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 11(2), 109–118. https://doi.org/10.30598/barekengvol11iss2pp109-118

Olawale, F., & Garwe, D. (2010). Obstacles to the growth of new SMEs in South Africa: A principal component analysis approach. African Journal of Business Management, 4(5), 729–738.

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Published

2022-12-30

Issue

Vol. 5 No. 2 (2022): Al-Muqayyad

Section

Articles

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Copyright (c) 2022 Anne Mudya Yolanda, Arisman Adnan, Rustam Efendi, Haposan Sirait, Irfansyah Irfansyah, Okta Bella Syuhada, Rahmad Ramadhan Laska, Riko Febrian

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How to Cite

Yolanda, A. M., Adnan, A., Efendi, R., Sirait, H., Irfansyah, I., Syuhada, O. B., Laska, R. R., & Febrian, R. (2022). Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen. AL-Muqayyad, 5(2), 69-79. https://doi.org/10.46963/jam.v5i2.766