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Implementation of Auto ARIMA, PSO-LSTM, and PSO-GRU for Time Series Modeling of 3 Telecommunication Company Stock Prices LQ45 Index

Unnes Journal of Mathematics · Vol. 13 No. 1 (2025) · hlm. 29–37

DOI
10.15294/ujm.v13i1.11939
ISSN
2460-5859, 2252-6943
Terbit
2025-06-11
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

The Indonesia Stock Exchange (IDX) issues stock indices to make it easier for investors to choose company shares such as the LQ45 Index. This study focuses on forecasting the share prices of 3 telecommunications companies listed in the LQ45 Index, namely PT Telkom Indonesia Tbk with the stock code TLKM, PT Tower Bersama Infrastructure Tbk with the stock code TBIG and PT Sarana Menara Nusantara Tbk with the stock code TOWR in the future. The algorithms used for forecasting are Auto ARIMA, LSTM and GRU algorithms. In addition, the PSO method is used to find the optimal hyperparameters in the LSTM and GRU algorithms. The results of this study show that the GRU model has the best performance and produces the best model evaluation value compared to other models on TLKM and TBIG stock data, while on TOWR stock data the LSTM model is the best model. The GRU model on TLKM data results in an R Square value of 0,961, RMSE 122,291 on training data and MAPE 3,027% and an R Square value of 0,859, RMSE 114,703 and 2,109% on testing data. On TBIG data, the GRU model results in an R Square value of 0,984, RMSE 71,945 and MAPE 4,206% on training data and R Square an value of 0,967, RMSE 73,627 and 2,165% on testing data. The LSTM model on TOWR data results in an R Square value of 0,943, RMSE 43,824 and MAPE 4,274% on training data and an R Square value of 0,796, RMSE 42,597 and 3,117% on testing data.

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  • State University of Semarang
  • Universitas Halu Oleo
  • Universitas Airlangga
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Rekan penulisOrganisasiArtikel bersama
Zaenuri ZaenuriState University of Semarang3
Fajar Sodik PamungkasAfiliasi tidak diketahui3
Dedy KurniantoAfiliasi tidak diketahui3
St. Budi WaluyaAfiliasi tidak diketahui2
Jannatun Khustia LubisAfiliasi tidak diketahui2
Alif Aulia RahmanAfiliasi tidak diketahui2
Mohammad AsikinAfiliasi tidak diketahui2
Muhammad AshimAfiliasi tidak diketahui2
Nur HasanahUniversitas Halu Oleo2
Rohimatul AzizahAfiliasi tidak diketahui2
Bayu Dwi PrasetyaAfiliasi tidak diketahui2
Fatkhurokhman FauziAfiliasi tidak diketahui2
MulyonoAfiliasi tidak diketahui1
Noviana Dini RahmawatiAfiliasi tidak diketahui1
Alfina AkmalinaAfiliasi tidak diketahui1
Elya Gita AstutiAfiliasi tidak diketahui1
Mira Dwi UtamiAfiliasi tidak diketahui1
Nike Yustina OktavianiAfiliasi tidak diketahui1
Riska Nur FitrianingsihAfiliasi tidak diketahui1
Annisa IkrimahAfiliasi tidak diketahui1
Supriyono SupriyonoAfiliasi tidak diketahui1
Mohammad Jefrie Ilham AkbarAfiliasi tidak diketahui1
Faris Febri RahantoAfiliasi tidak diketahui1
Muhammad Nurul MuttaqinAfiliasi tidak diketahui1
Iwan JunaediState University of Semarang1
Sanianajiba Nugroho PutriAfiliasi tidak diketahui1
Hendrik SutrisnoAfiliasi tidak diketahui1
Yeni NurochmahAfiliasi tidak diketahui1
WardonoAfiliasi tidak diketahui1
MunaAfiliasi tidak diketahui1
Tri Sri Noor AsihAfiliasi tidak diketahui1
Rizki Ahid NurhasanahAfiliasi tidak diketahui1
Petronela Ivoni SusantyAfiliasi tidak diketahui1
Kartono KartonoUniversitas Airlangga1
Luluk Ulfa ChasaniAfiliasi tidak diketahui1
Tri Sri Noor AsihAfiliasi tidak diketahui1
Wardono WardonoAfiliasi tidak diketahui1
Nurlaela Eka CahyatiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Implementation of Auto ARIMA, PSO-LSTM, and PSO-GRU for Time Series Modeling of 3 Telecommunication Company Stock Prices LQ45 Index</dc:title>
	<dc:creator xml:lang="en">Astutiningtyas, Luthfiyah</dc:creator>
	<dc:creator xml:lang="en">Kharisudin, Iqbal</dc:creator>
	<dc:subject xml:lang="en">Auto ARIMA</dc:subject>
	<dc:subject xml:lang="en">Long Short Term Memory</dc:subject>
	<dc:subject xml:lang="en">Gated Recurrent Unit</dc:subject>
	<dc:subject xml:lang="en">Particle Swarm Optimization</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:description xml:lang="en">The Indonesia Stock Exchange (IDX) issues stock indices to make it easier for investors to choose company shares such as the LQ45 Index. This study focuses on forecasting the share prices of 3 telecommunications companies listed in the LQ45 Index, namely PT Telkom Indonesia Tbk with the stock code TLKM, PT Tower Bersama Infrastructure Tbk with the stock code TBIG and PT Sarana Menara Nusantara Tbk with the stock code TOWR in the future. The algorithms used for forecasting are Auto ARIMA, LSTM and GRU algorithms. In addition, the PSO method is used to find the optimal hyperparameters in the LSTM and GRU algorithms. The results of this study show that the GRU model has the best performance and produces the best model evaluation value compared to other models on TLKM and TBIG stock data, while on TOWR stock data the LSTM model is the best model. The GRU model on TLKM data results in an R Square value of 0,961, RMSE 122,291 on training data and MAPE 3,027% and an R Square value of 0,859, RMSE 114,703 and 2,109% on testing data. On TBIG data, the GRU model results in an R Square value of 0,984, RMSE 71,945 and MAPE 4,206% on training data and R Square an value of 0,967, RMSE 73,627 and 2,165% on testing data. The LSTM model on TOWR data results in an R Square value of 0,943, RMSE 43,824 and MAPE 4,274% on training data and an R Square value of 0,796, RMSE 42,597 and 3,117% on testing data.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-06-11</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/ujm/article/view/11939</dc:identifier>
	<dc:identifier>10.15294/ujm.v13i1.11939</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Mathematics; Vol. 13 No. 1 (2024): Unnes Journal of Mathematics Volume 1, 2024; 29-37</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v1i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujm/article/view/11939/4367</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Unnes Journal of Mathematics</dc:rights>
</oai_dc:dc>