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Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model

Peramalan Inflasi Provinsi Kalimantan Timur Menggunakan Model Hybrid Singular Spectrum Analysis-Autoregressive Integrated Moving Average

Jurnal Matematika, Statistika dan Komputasi · Vol. 18 No. 1 (2021) · pp. 78–92

DOI
10.20956/j.v18i1.14284
ISSN
2614-8811, 1858-1382
Published
2021-09-02
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%

Indonesian

Metode hybrid Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) merupakan kombinasi metode peramalan yang baik untuk meningkatkan akurasi peramalan dan cocok untuk data ekonomi yang cenderung memiliki pola tren dan musiman, salah satunya adalah data inflasi. Tujuan dari penelitian ini adalah memperoleh hasil peramalan inflasi Provinsi Kalimantan Timur tahun 2021 menggunakan model hybrid SSA-ARIMA. Hasil peramalan inflasi Provinsi Kalimantan Timur tahun 2021 menggunakan model hybrid SSA-ARIMA(1,1,1) secara keseluruhan mengalami peningkatan dan inflasi tertinggi pada tahun 2021 terjadi pada bulan Desember sebesar 0,92% dengan tingkat akurasi peramalan berdasarkan Root Mean Square Error (RMSE) sebesar 0,069399.

Keywords

Collaboration network of the authors

39 authors · 89 co-authorship links

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  • Monash University
  • Affiliation unknown
  • Different organisations
  • Same organisation
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  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Endang NurcahyaniAffiliation unknown6
Bambang IrawanMonash University4
Sumardi SumardiAffiliation unknown2
YuliantyAffiliation unknown2
Novita AndrianiAffiliation unknown2
Adelia RamadhaniAffiliation unknown2
Asrul SaniAffiliation unknown1
Muhammad SudiaAffiliation unknown1
Rochmah AgustrinaAffiliation unknown1
Herta M. ManullangAffiliation unknown1
Darnah DarnahAffiliation unknown1
Selsi SelsiAffiliation unknown1
Risma RasmaniAffiliation unknown1
Tundjung Tripeni HandayaniAffiliation unknown1
Resta TaniaAffiliation unknown1
Wanda AmeliaAffiliation unknown1
vega abresaAffiliation unknown1
Endang Nurcahyani2Affiliation unknown1
Lili ChrisnawatiAffiliation unknown1
Nurshella AphertaAffiliation unknown1
Resya Tamara AgustinAffiliation unknown1
Lutfiah YuniarAffiliation unknown1
Rezza Kusumma DewiyatnoAffiliation unknown1
Kharisma Purnama AgustinAffiliation unknown1
Memi Nor HayatiAffiliation unknown1
Kartika RamadaniAffiliation unknown1
M. FathurahmanAffiliation unknown1
Yusrian PalilingAffiliation unknown1
Suyitno SuyitnoAffiliation unknown1
Ni Made Shantia AnandaAffiliation unknown1
SuyitnoAffiliation unknown1
Ahmad SofwanAffiliation unknown1
Eka HaryaniAffiliation unknown1
Ihsan Nuril HikamAffiliation unknown1
Asep SutarmanAffiliation unknown1
Zaharuddin ZaharuddinAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model</dc:title>
	<dc:title xml:lang="id">Peramalan Inflasi Provinsi Kalimantan Timur Menggunakan Model Hybrid Singular Spectrum Analysis-Autoregressive Integrated Moving Average</dc:title>
	<dc:creator xml:lang="en">Arumsari, Melisa </dc:creator>
	<dc:creator xml:lang="en">Wahyuningsih, Sri </dc:creator>
	<dc:creator xml:lang="en">Siringoringo, Meiliyani </dc:creator>
	<dc:subject xml:lang="en">ARIMA</dc:subject>
	<dc:subject xml:lang="en">Forecasting</dc:subject>
	<dc:subject xml:lang="en">Hybrid</dc:subject>
	<dc:subject xml:lang="en">Inflation</dc:subject>
	<dc:subject xml:lang="en">SSA</dc:subject>
	<dc:subject xml:lang="id">ARIMA</dc:subject>
	<dc:subject xml:lang="id">Hybrid</dc:subject>
	<dc:subject xml:lang="id">Inflasi</dc:subject>
	<dc:subject xml:lang="id">Peramalan</dc:subject>
	<dc:subject xml:lang="id">SSA</dc:subject>
	<dc:description xml:lang="en">The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%
 </dc:description>
	<dc:description xml:lang="id">Metode hybrid Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) merupakan kombinasi metode peramalan yang baik untuk meningkatkan akurasi peramalan dan cocok untuk data ekonomi yang cenderung memiliki pola tren dan musiman, salah satunya adalah data inflasi. Tujuan dari penelitian ini adalah memperoleh hasil peramalan inflasi Provinsi Kalimantan Timur tahun 2021 menggunakan model hybrid SSA-ARIMA. Hasil peramalan inflasi Provinsi Kalimantan Timur tahun 2021 menggunakan model hybrid SSA-ARIMA(1,1,1) secara keseluruhan mengalami peningkatan dan inflasi tertinggi pada tahun 2021 terjadi pada bulan Desember sebesar 0,92% dengan tingkat akurasi peramalan berdasarkan Root Mean Square Error (RMSE) sebesar 0,069399.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2021-09-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/14284</dc:identifier>
	<dc:identifier>10.20956/j.v18i1.14284</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 18 No. 1 (2021): September 2021; 78-92</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/14284/7307</dc:relation>
	<dc:relation>10.20956/j.v18i1.14284.g7307</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>