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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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Time Series Intervention Analysis With Gradual Impact Function A Case Study Of Railway Passenger Volume In Java Island Zulhijrah, Zulhijrah; Isnaini, Mardatunnisa; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774742025

Abstract

Java Island has been significantly impacted by the COVID-19 pandemic, which started in March 2020. This study aims to analyze the impact of the pandemic on the volume of railway passengers’ volume with a time series approach using an interventional ARIMA model. The data used is the number of monthly passengers from 2015 to 2024. Initial modeling on data before the pandemic produced the best model, namely ARIMA (0,2,1). To measure the impact of the pandemic, a gradual step intervention function is used which represents the gradual effect of the event. The estimation results show that the ARIMA (0,2,1) model with a gradual step intervention function is able to provide more accurate forecasting results, with a MAPE value of 18.39%. This model effectively captures changes in mobility patterns due to the pandemic, especially in the post-intervention recovery phase. The findings make an important contribution to transportation policy evaluation and future strategic planningKeywords: Time Series, ARIMA  Intervention, Gradual Function, Railway 
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Mualifah, Laily Nissa Atul; Wijayanto, Hari
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5211

Abstract

This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.
Kerangka SPIKR untuk Mengajarkan Keterampilan Kolaborasi Antardisiplin Ilmu bagi Statistisi dan Data Saintis Indonesia Mualifah, Laily Nissa Atul; Vance, Eric Alan
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.82940

Abstract

Di abad ke-21, dengan pesatnya peningkatan volume data dan kompleksitas masalah, statistisi dan data saintis tidak lagi mampu menyelesaikan permasalahan hanya berdasarkan bidang keilmuan mereka saja. Mereka kini dituntut untuk berkolaborasi dengan profesional dari berbagai disiplin ilmu guna mendorong inovasi dan kreativitas dalam pemecahan masalah. Keterampilan kolaborasi ini bukanlah keterampilan yang spesifik pada disiplin ilmu tertentu, melainkan keterampilan umum yang dapat diajarkan dan dipelajari oleh berbagai pihak dari semua bidang ilmu. Penelitian ini memperkenalkan kerangka SPIKR, yaitu suatu kerangka yang dirancang untuk mengajarkan keterampilan kolaborasi antardisiplin ilmu kepada statistisi dan data saintis Indonesia. Kerangka SPIKR terdiri dari lima komponen utama: Sikap, Pola Pertemuan, Isi Proyek, Komunikasi, dan Relasi. Hasil penelitian kami menunjukkan bahwa setiap komponen dalam SPIKR memiliki peran yang sangat penting dalam meningkatkan keterampilan kolaborasi antardisiplin ilmu di kalangan statistisi dan data saintis Indonesia. Untuk mengajarkan SPIKR dengan efisien, kami menemukan bahwa metode pembelajaran berbasis kelompok dan memfasilitasi mahasiswa untuk melakukan kolaborasi nyata dengan mitra kolaborasi dari disiplin ilmu yang berbeda terbukti menjadi pendekatan yang sangat efektif dalam meningkatkan keterampilan non-teknis kolaborasi.
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.
SARIMA-GARCH and LSTM Performance for Broiler Meat Price Forecasting: A Case Study in West Sumatra Wijaya, Joshua Bryan; Dzulkharifah, Indah; Putra, Varel Geo Syah; Abdurahman, Harits; Mualifah, Laily Nissa Atul; Pangesti, Windi
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1497

Abstract

The price of broiler chicken meat in West Sumatra is characterized by strong seasonality and high volatility. As a primary source of animal protein and a key contributor to regional inflation, accurate forecasting of these price fluctuations is essential for economic stability and policymaking. This study aims to compare the forecasting performance of the SARIMA-GARCH hybrid model against the Long Short-Term Memory (LSTM) model. The dataset consists of 1,198 daily observations spanning from 15 July 2022 to 24 October 2025, sourced from the National Food Agency (Badan Pangan Nasional). The results demonstrate that the SARIMA-GARCH model outperforms the LSTM model in terms of point forecast accuracy, as evidenced by lower prediction error metrics. Furthermore, the hybrid model successfully satisfies the statistical diagnostic criteria for volatility modeling by effectively resolving ARCH effects, ensuring the statistical validity of the residuals. While the LSTM model produces smoother long-term forecasts, the SARIMA-GARCH model effectively captures daily price fluctuations and indicates a modest upward trend over the next 28 days. These findings suggest that SARIMA-GARCH provides a more realistic depiction of short-term price movements for this specific regional market, offering a localized framework for stakeholders in West Sumatra to anticipate future market changes and maintain price stability.
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