Article Per Year (5 Year)
p-Index From 2021 - 2026
3.699
P-Index
This Author published in this journals
All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika SAINSMAT Jurnal Gaussian CAUCHY: Jurnal Matematika Murni dan Aplikasi JURNAL PETERNAKAN JUITA : Jurnal Informatika 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 Jurnal Veteriner dan Biomedis Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Laily Nissa Atul Mualifah
Program Studi Statistika Dan Sains Data, Sekolah Sains Data Matematika Dan Informatika, IPB University, Jl. Raya Dramaga, Kabupaten Bogor 16680, Jawa Barat, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology 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

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 28 Documents
Search

 1   2   3 
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.
Performance Evaluation of ARIMA and GRU Models for Forecasting Chili Price in East Jawa Windi Pangesti; Nabila Syukri; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26445

Abstract

Time series forecasting plays a crucial role in predicting future conditions based on historical data, particularly in the food sector, which is highly susceptible to price fluctuations. This study compares two approaches: the conventional ARIMA method and the deep learning method GRU, to forecast the price of red chillies in East Java. East Java was chosen because it is the largest national producer of chilies, thus the stability of its prices has a broad impact. The research results indicate that the GRU model outperforms the ARIMA model with a MAPE value of 19.80% compared to a MAPE of 27.63% for the latter. The benefit of this research is to contribute to the literature on developing agricultural commodity price forecasting models as a basis for enhancing food security policies and stabilizing commodity prices, particularly in East Java Province, Indonesia
Bayesian Vector Autoregressive Modeling on Macroeconomic Variables in Indonesia Indra Mahib Zuhair Riyanto; Muhammad Firlan Maulana; Nur Anggraini Fadhilah; Laras Suprapti; Salsabila Fayiza; Eliza Rahmadania; Bulan Cahyani Suhaeri; Anang Kurnia; Laily Nissa Atul Mualifah; Aulia Akhrian Syahidi
Indonesian Journal of Statistics and Applications Vol 10 No 1 (2026): Vol 10 Issue 1 June 2026
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v10i1p105-118

Abstract

This research studies a Bayesian Vector Autoregressive (BVAR) model to analyze the dynamic interactions among the rupiah exchange rate, exports, imports, gold futures prices, and inflation in Indonesia during the 2015-2024 period. The BVAR method was chosen to overcome the limitations of conventional VAR models on overparameterization problem by utilizing hierarchical Minnesota priors and Markov-Chain Monte Carlo (MCMC) estimation. Data were stationary through first order differencing and normalized using z-score. Lag selection based on the Akaike Information Criterion (AIC) showed that lag 6 is optimal. Model evaluation using Mean Absolute Percentage Error (MAPE) shows good overall model performance on training data, especially on the gold price variable (MAPE 10,09%) and inflation (MAPE 3,74%). On test data, the model struggles to perform well on prediction due to the high uncertainty of the test data period. Impulse Response Function (IRF) analysis is used to reveal short-term responses between variables, such as the effect of exchange rate depreciation on inflation and the impact of export value on a temporary decline in import value. The result highlights the BVAR model’s ability to capture general macroeconomic relationships, especially when many parameters need to be estimated and the available data is limited.
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Laily Nissa Atul Mualifah; Hari Wijayanto
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.
Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index Andi Illa Erviani Nensi; Mahda Al Maida; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33379

Abstract

This study evaluates the performance of ARIMA, LSTM, and hybrid ARIMA-LSTM models in predicting the closing and opening prices of the Indonesia Stock Exchange Composite Index (IHSG) over various periods (2007-2020, 2007-2022, and 2007-2024). For the LSTM model, a lag of 1 was chosen based on MAPE analysis, showing strong dependence on the previous day’s price. Different learning rates (0.01, 0.001, 0.0001) and batch sizes (16, 32) were tested on various network architectures. Results indicate that while ARIMA effectively captures linear patterns, LSTM consistently outperforms with lower MAPE values—2.27% for closing and 2.02% for opening prices—especially with a simple (1-50-1) architecture and a learning rate of 0.001. The hybrid ARIMA(0,1,1)-LSTM(1-50-1) model showed competitive results, achieving MAPE of 2.00% for closing and 1.74% for opening prices using batch size 16. However, its success depends on ARIMA’s ability to model linear components. Key findings emphasize LSTM’s dominance in accuracy, the importance of parameter tuning, and the effectiveness of simple network structures. The hybrid approach holds promise when linear and nonlinear data components are clearly separable. This research offers methodological insights for optimizing stock price prediction models and practical guidance for model configuration, contributing to the advancement of financial market forecasting.
PERBANDINGAN PERFORMA MODEL ARIMA-GARCH DAN LSTM DALAM MERAMALKAN JUMLAH KUNJUNGAN WISATAWAN DANAU KASTOBA Laily Nissa Atul Mualifah; Dalilah Husna; Jasmita Yasmin; Avrel Chesia Berbina; Fadhilah Yumna; Muhammad Ali Uraidly; Adelia Putri Pangestika
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.314-324

Abstract

Kastoba Lake, located on Bawean Island, East Java, is a unique natural tourist destination with significant potential for further development. To enhance strategic tourism management, predicting tourist visit numbers is necessary. This study aims to assess the performance of the ARIMA-GARCH and Long Short-Term Memory (LSTM) models in predicting daily tourist arrivals to Kastoba Lake, based on data collected between March 2023 and July 2024. These two methods were specifically selected because the dataset exhibits nonlinear patterns and heterogeneous variance. The ARIMA-GARCH model was employed to handle heteroscedasticity within the data, while LSTM was chosen for its ability to effectively learn and represent long-term patterns. The findings indicate that both models deliver comparable performance and are highly capable of identifying the underlying data trends. Moreover, each model is effective in forecasting short-term tourist visits, particularly over a 7-day horizon (one week). Consequently, these models are reliable tools for predicting and analyzing tourism trends at Kastoba Lake.
Kerangka SPIKR untuk Mengajarkan Keterampilan Kolaborasi Antardisiplin Ilmu bagi Statistisi dan Data Saintis Indonesia Laily Nissa Atul Mualifah; Eric Alan Vance
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.
Co-Authors Abdurahman, Harits Adelia Putri Pangestika Ahdiat, Keyzha Mutiara Alahmad, Ali Omar Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Anang Kurnia Andi Illa Erviani Nensi Aulia Akhrian Syahidi Avrel Chesia Berbina Billy, Billy Bulan Cahyani Suhaeri Cici Suhaeni Cici Suhaeni Cintani, Meavi Dalilah Husna Dzulkharifah, Indah Eliza Rahmadania Eric Alan Vance Fachry Abda El Rahman Fadhilah Yumna Fitrianti, Dwi Ghiffary, Ghardapaty Ghaly Hakim, Bashir Ammar Hari Wijayanto Hasanah, Lailatul Hasanah, Mauizatun Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indahwati Indra Mahib Zuhair Riyanto Isnaini, Mardatunnisa Jasmita Yasmin Karunia, Nia Kefi Amtiran, Chandraone Putra Kurnadipare, Aleytha Ilahnugrah Laras Suprapti Magfirrah, Indah Mahda Al Maida Maulana, Muhammad Fahrezi Maulidiyah, Wildatul Muhammad Ali Uraidly Muhammad Firlan Maulana MY, Hadyanti Utami Nabila Syukri Nizar, Yeky Abil Nugraha, Adhiyatma Nur Anggraini Fadhilah Nurdin, Achmad Raihan Pangesti, Windi Pangestika, Adelia Putri Pratiwi, Windy Ayu Putra, Varel Geo Syah Putri, Mega Ramatika Rahmasari, Hazelita Dwi Rahmawati, Elke Frida Rasyid, Baharun Riska Yulianti, Riska Riyanto, Indra Mahib Zuhair Rizki, Anwar Fajar Sachnaz Desta Oktarina Salsabila Fayiza Setyowati, Silfiana Lis Siregar, Indra Rivaldi Sri Wahyuni Syam, Ummul Auliyah Ulfia, Ratu Risha Wijaya, Joshua Bryan Windi Pangesti Yanuari, Eka Dicky Darmawan Yenni Angraini Zulhijrah, Zulhijrah