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All Journal Variance : Journal of Statistics and Its Applications Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Surya, Annisa Cahyani
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Computer Science & IT Mathematics

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ARCH MODEL FOR FORECASTING BCA BANK STOCK PRICE VOLATILITY Surya, Annisa Cahyani; Ariyanto, Adisty Syawalda; Napitupulu, Leonard Andreas; Sihaloho, Ryantoni; S, Mika Alvionita; Muthoharoh, Luluk
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page147-154

Abstract

This research analyzes the Autoregressive Conditional Heteroskedasticity (ARCH(p) model to predict the BCA Bank share price in the range of January 2013 to November 2023. BCA Bank's share price, as one of the shares traded on the Indonesian Stock Exchange, requires accurate volatility modeling. Researchers use the ARIMA(0,1,2) model as the initial approach, but because of heteroscedasticity, they apply the ARCH(8) model to overcome it. The results show that the ARCH(8) model performs best, with the lowest AIC values for volatility. BCA Bank's daily stock price as of December 1, 2023, showed high volatility, signaling significant risk to investors.
Community Detection of Singers in Spotify Rock Playlists Using Louvain Method Surya, Annisa Cahyani; Nadeak, Christyan Tamaro
JITTER: Jurnal Ilmiah Teknologi dan Komputer Vol. 7 No. 1 (2026): JITTER, Vol.7, No.1, April 2026
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

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Abstract

Spotify is one of the leading music streaming platforms, allowing users to create playlists and select songs based on their preferences. Rock music has remained a prominent genre on Spotify, especially in Indonesia, where it holds historical and cultural significance and serves as a medium for social and political expression. This study investigates how user preferences shape the network structure among Indonesian rock artists. Using the Louvain community detection method, artists were grouped based on their co-occurrence in playlists to uncover community patterns within the genre. Data were collected by scraping playlists using the keyword “Rock Indonesia.” The optimal configuration was found with a k-core value of 3 and an edge weight threshold of 0.39, resulting in a modularity score of 0.4782. Three main communities were identified, differentiated by subgenre, active period, and record label.
Co-Authors Ariyanto, Adisty Syawalda Christyan Tamaro Nadeak Mika Alvionita Sitinjak Muthoharoh, Luluk Napitupulu, Leonard Andreas Sihaloho, Ryantoni