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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi JURNAL PETERNAKAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH PETERNAKAN TERPADU IJID (International Journal on Informatics for Development) Jambura Journal of Mathematics International Journal of Advances in Data and Information Systems Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Veteriner dan Biomedis
Mualifah, Laily Nissa Atul
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Veterinary Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Forecasting the Number of Passengers for the Jakarta-Bandung High-Speed Rail using SARIMA and SSA Models Mualifah, Laily Nissa Atul; Riyanto, Indra Mahib Zuhair; Rahmawati, Elke Frida; Maulana, Muhammad Fahrezi; Ahdiat, Keyzha Mutiara; Nurdin, Achmad Raihan; Pangestika, Adelia Putri
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10720

Abstract

Time series forecasting is essential for analyzing past data to predict future trends, supporting planning, and decision-making. The SARIMA model is widely used for seasonal data but may be less effective for highly fluctuating or non-stationary data, which can impact forecast accuracy. As an alternative, Singular Spectrum Analysis (SSA) offers a flexible approach, decomposing time series into trend, seasonal, and noise components without strict parametric assumptions, making it effective for complex data patterns. This study compares SARIMA and SSA models in forecasting daily passenger counts on the Jakarta-Bandung high-speed rail, using data from November 1, 2023, to September 30, 2024. The results show that the performance of SSA is more stable compared to SARIMA in the term of MAPE, where SSA provides lower MAPE then SARIMA in all three scenarios of data splits. These results are expected due to the non-linear pattern that appears in the data. Moreover, the predictions on both methods show that slight increment of passengers in the end of 2024 to the beginning of 2025. This finding suggests that the government needs to consider implementing interventions if they wish to change the current trend, such as offering discounts or year-end holiday promotions.
Application of SARIMA, GRU, and Prophet for Capturing Seasonal Patterns in Consumer Price Inflation Mualifah, Laily Nissa Atul
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11802

Abstract

Seasonal dynamics make inflation forecasting challenging in emerging economies where holiday effects, regulated prices, and supply shocks interact. This study models Indonesia’s monthly consumer price inflation (CPI) using official data from Statistics Indonesia (May 2006–April 2025) and evaluates three forecasting paradigms: a classical seasonal baseline (SARIMA), a decomposable model with trend–seasonality components (Prophet), and a neural sequence learner (GRU). A 10-fold sliding window design is employed to preserve temporal order. Performance is assessed with RMSE, MAE, and MASE, summarized across folds with boxplots and statistical descriptives (means, standard deviations, and 95% confidence intervals). Across folds and metrics, Prophet consistently achieves the lowest error and the tightest dispersion, GRU ranks second with competitive accuracy and stable variance, and SARIMA remains a transparent yet weaker benchmark. MASE values below one for Prophet (and generally for GRU) indicate improvements over a naïve baseline. Practically, Prophet’s decompositions support policy communication by linking forecast movements to interpretable components (e.g., Ramadan/Eid and year-end effects), while GRU is useful during more nonlinear or volatile periods; SARIMA remains valuable for diagnostics in stable regimes.
Co-Authors Abdurahman, Harits Ahdiat, Keyzha Mutiara Al Maida, Mahda Alahmad, Ali Omar Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Billy, Billy Cici Suhaeni Cintani, Meavi Dzulkharifah, Indah Fachry Abda El Rahman Fitrianti, Dwi Ghiffary, Ghardapaty Ghaly Hakim, Bashir Ammar Hari Wijayanto Hasanah, Lailatul Hasanah, Mauizatun Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indahwati Isnaini, Mardatunnisa Karunia, Nia Kefi Amtiran, Chandraone Putra Kurnadipare, Aleytha Ilahnugrah Magfirrah, Indah Maulana, Muhammad Fahrezi MY, Hadyanti Utami Nensi, Andi Illa Erviani Nizar, Yeky Abil Nugraha, Adhiyatma Nurdin, Achmad Raihan Pangesti, Windi Pangestika, Adelia Putri Pratiwi, Windy Ayu Putra, Varel Geo Syah Putri, Mega Ramatika Rahmawati, Elke Frida Rasyid, Baharun Riska Yulianti, Riska Riyanto, Indra Mahib Zuhair Rizki, Anwar Fajar Sachnaz Desta Oktarina Setyowati, Silfiana Lis Siregar, Indra Rivaldi Sri Wahyuni Syam, Ummul Auliyah Ulfia, Ratu Risha Vance, Eric Alan Wijaya, Joshua Bryan Yanuari, Eka Dicky Darmawan Yenni Angraini Zulhijrah, Zulhijrah