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All Journal 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 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 Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Veterinary Other

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COMPARING FORECASTS OF AGRICULTURAL SECTOR EXPORT VALUES USING SARIMA AND LONG SHORT-TERM MEMORY MODELS Kurnadipare, Aleytha Ilahnugrah; Amaliya, Sri; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp385-396

Abstract

Indonesia's agricultural sector plays a crucial role in the national economy, with significant export potential and supporting the livelihoods of the majority of the population. As part of the government's vision to make Indonesia the world's food barn by 2045, increasing the volume and value of agricultural product exports is a primary focus, making export value forecasting essential for supporting strategic decision-making. Sequential data analysis is an important approach in analyzing data collected over a specific period. This study aims to compare two popular methods in forecasting the export value of the agricultural sector, namely the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model and the Long Short-Term Memory (LSTM) model. Monthly agricultural export data from West Java Province from January 2013 to February 2024 were used as the dataset. The best SARIMA model generated was (1,1,1)(0,1,1)12, while the optimal parameters for the LSTM model were neuron: 50, dropout rate: 0.3, number of layers: 2, activation function: relu, epochs: 500, batch size: 64, optimizer: Adam, and learning rate: 0.01. Evaluation was performed using the Root Mean Squared Error (RMSE) method to measure the accuracy of both models in forecasting the export value of the agricultural sector. The results showed that the LSTM model outperformed the SARIMA model, with a lower RMSE value (SARIMA: 4182.133 and LSTM: 1939.02). This research provides valuable insights for decision-makers in planning agricultural sector export strategies in the future. With this comparison, it is expected to provide better guidance in selecting forecasting methods suitable for the characteristics of the data.
THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES Pratiwi, Windy Ayu; Rizki, Anwar Fajar; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp397-406

Abstract

In the financial markets, accurately forecasting the closing prices of gold futures is crucial for investors and analysts. Traditional methods like ARIMA (Autoregressive Integrated Moving Average) have been widely used for this purpose, particularly for their effectiveness in short-term stable data forecasting. However, the inherent complexity and dynamic nature of financial data, coupled with trends and seasonal patterns, present significant challenges for long-term forecasting with ARIMA. Conversely, advanced methods such as Recurrent Neural Networks (RNN) have shown promise in handling these complexities and providing reliable long-term forecasts. This research seeks to evaluate and compare the performance of ARIMA and RNN in forecasting daily gold futures closing prices using forecast accuracy tests namely RMSE and MAPE, aiming to identify the optimal method that balances accuracy, stability, and adaptability to trends and seasonal variations in the financial market. The daily data for this analysis is sourced from Investing.com (https://www.investing.com).
COMPARATIVE PERFORMANCE OF SARIMAX AND LSTM MODEL IN PREDICTING IMPORT QUANTITIES OF MILK, BUTTER, AND EGGS Ghiffary, Ghardapaty Ghaly; Yanuari, Eka Dicky Darmawan; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp407-418

Abstract

This study evaluates how well SARIMAX and LSTM models predict monthly imports of HS-04 commodities (butter, eggs, and milk) in Indonesia. Data were provided by BPS Statistics Indonesia, Bank Indonesia, Ministry of Trade, Trade Map, and Indonesia National Single Window and used from January 2006 to February 2024. The SARIMAX model included exogenous variables such as inflation rates, USD/IDR exchange rates, and major Indonesian holidays (Eid al-Fitr, Eid al-Adha, Christmas, and Lunar New Year). The results show that the SARIMAX and LSTM models predict the import volumes of butter, eggs, and milk with good accuracy. However, the SARIMAX model demonstrated superior forecasting accuracy, achieving a lower RMSE of 7547.89 and a MAPE of 13.16 compared to the LSTM model, which yielded an RMSE of 8787.73 and a MAPE of 14.89. The SARIMAX model performed significantly better when the lunar new year was added as an exogenous variable. In order to support price stability and economic growth in Indonesia, this research provides policymakers and industry stakeholders with critical information to optimize import management strategies for butter, eggs, and milk commodities. Accurate forecasts can contribute to price stability, enhanced food security, and sustainable economic development in Indonesia by enabling informed decisions on import quotas, tariff adjustments, investment in domestic production, and strategic reserves.
The Impacts of Knowledge, Attitudes, and Actions on the Implementation of Biosecurity in the Management of Foot and Mouth Disease in Kuta Baro Subdistrict, Aceh Besar Regency Rasyid, Baharun; Karunia, Nia; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Mualifah, Laily Nissa Atul; Hasanah, Lailatul
Jurnal Peternakan Vol 22, No 2 (2025): September 2025
Publisher : State Islamic University of Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jupet.v22i2.36826

Abstract

.  Foot and Mouth Disease (FMD) is an animal disease caused by a virus from the Picornaviridae family with the genus Aphthovirus. This study aimed to assess the extent of knowledge, attitudes, and actions of cattle farmers in Kuta Baro Subdistrict, Aceh Besar District, in implementing biosecurity on their farms to prevent FMD. The sample used was 45 cattle farmers in four villages in Kuta Baro Subdistrict, Aceh Besar District, namely Cut Preh, Cut Beut, Lam Seunong, and Ujong Blang. This study used a questionnaire instrument and the data were analyzed using binary logistic regression analysis. The statistics exhibited that the percentages of farmers with poor knowledge, attitude, and action were 71.1%, 66.7%, and 68.9%, respectively. Furthermore, the results of the analysis revealed that there was a significant relationship between the attitudes and actions of farmers towards the infection of FMD virus in livestock. Meanwhile, the farmer’s knowledge did not have a significant role in handling FMD. The odds ratio showed that the odds of FMD cases decrease 0.593 times if there is an increase in farmers' attitudes towards biosecurity, and the odds of FMD cases decrease 0.666 times if there is an increase in farmers' actions towards biosecurity. The accuracy of this model reached 68.9%. Enhancements in farmers’ knowledge, attitudes, and actions towards implementing biosecurity have the potential to reduce the incidence of Foot and Mouth Disease (FMD) in livestock.
COMPARISON OF SARIMA AND HIGH-ORDER FUZZY TIME SERIES CHEN TO PREDICT KALLA KARS MOTORBIKE SALES Syam, Ummul Auliyah; Irdayanti, Irdayanti; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.197-208

Abstract

Forecasting sales time series data is essential for companies to support effective planning and decision-making processes. This study evaluates the strengths of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and High-Order Fuzzy Time Series Chen (FTS Chen) models in predicting motorbike sales at Kalla Kars Company, a prominent automotive dealer in Sulawesi, Indonesia. SARIMA is renowned for accurately capturing seasonal patterns, while the FTS Chen model excels in handling data uncertainties and incorporating complex relationships through high-order fuzzy logic. Weekly sales data from January 2020 to February 2024 were analyzed, with 205 weeks used for training and 13 weeks for testing. The results indicate that the third-order FTS Chen model outperforms SARIMA, achieving a Root Mean Square Error (RMSE) of 1.88 and a Mean Absolute Percentage Error (MAPE) of 4.64%. Forecasts for the next eight weeks using the third-order FTS Chen model suggest a decline in sales, contrasting with the SARIMA model, which predicts a slight increase. These results show that Chen's FTS model is more accurate and reliable, making it an effective choice for forecasting Kalla Kars motorbike sales.
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.
COMPARATIVE EVALUATION OF ARIMA AND GRU MODELS IN PREDICTING RUPIAH DOLLAR EXCHANGE RATE Fitrianti, Dwi; Ulfia, Ratu Risha; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.1-12

Abstract

This study evaluates the effectiveness of the ARIMA (Autoregressive Integrated Moving Average) and GRU (Gated Recurrent Unit) models in forecasting the USD–Rupiah exchange rate. Exchange rate fluctuations influence overall economic stability, making accurate forecasting crucial. Monthly data from January 2001 to March 2024 were analyzed. The ARIMA model, a traditional statistical approach, combines autoregressive (AR), differencing (I), and moving average (MA) components to capture linear patterns, while the GRU model, a deep learning approach, captures nonlinear and complex temporal relationships using update and reset gate mechanisms to retain long-term information. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The GRU model achieved a MAPE of 1.74%, lower than the ARIMA model’s 1.94%, and generated a forecast of Rp. 16,399.91 for April 2024, closer to the actual value of Rp. 16,249.00 compared to ARIMA’s Rp. 15,857.68. The findings demonstrate the GRU model’s superior forecasting performance and provide empirical evidence of its effectiveness in modeling volatile exchange rate data, particularly the Rupiah–USD rate.
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 
National Milk Production Dynamics: Interactions Among Dairy Cattle Population, Milk Imports and Exports in Indonesia 2020–2024 Kefi Amtiran, Chandraone Putra; Alahmad, Ali Omar; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul; Indahwati, Indahwati
JURNAL ILMIAH PETERNAKAN TERPADU Vol. 13 No. 3 (2025)
Publisher : DEPARTMENT OF ANIMAL HUSBANDRY, FACULTY OF AGRICULTURE, UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jipt.v13i3.p789-801

Abstract

The dairy cattle sub-sector plays a vital role in fulfilling the national demand for animal protein; however, domestic milk production has yet to meet the increasing demand. This study analyzes the impact of dairy cattle population, as well as milk import and export, on milk production in Indonesia from 2020 to 2024. Panel data from various provinces were analyzed using a fixed effects model to identify significant variables. Results indicate that the dairy cattle population has a positive and significant effect on national milk production, with variations across island regions. Conversely, milk import and export showed no significant impact on domestic production. These findings emphasize the importance of region-based development strategies, increasing dairy cattle productivity, and implementing appropriate import protection and substitution policies to enhance national milk production self-sufficiency. This study is expected to provide a basis for policymaking and strategic interventions aimed at sustainable development of the dairy industry.
Pengaruh Pemberian Salep Chlorella vulgaris Terhadap Penyembuhan Luka Sayatan pada Mencit (Mus musculus albinus) Wahyuni, Sri; Notodiputro, Khairil Anwar; Oktarina, Sachnaz Desta; Mualifah, Laily Nissa Atul
Jurnal Veteriner dan Biomedis Vol. 2 No. 1 (2024): Maret
Publisher : Sekolah Kedokteran Hewan dan Biomedis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jvetbiomed.2.1.16-21.

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh salep Chlorella vulgaris terhadap proses penyembuhan luka sayatan mencit (Mus musculus albinus) berdasarkan waktu yang dibutuhkan untuk menyembuhkan luka dan perubahan morfologi luka dibandingkan kontrol. Penelitian ini menggunakan metode Rancangan Acak Lengkap (RAL) dengan menggunakan 25 ekor mencit sebagai hewan uji yang dibagi menjadi 5 kelompok yaitu; 3 kelompok perlakuan (C. vulgaris salep 5%, C. vulgaris salep 10%, C. vulgaris salep 15%) dan 2 kelompok kontrol (plasebo dan proses penyembuhan normal). Mencit dilukai dengan scalpel-blade sepanjang 1 cm sampai fascia. Luka diolesi salep C. vulgaris dua kali sehari dan diamati setiap hari dari hari ke 1 sampai hari ke 14. Semua data kuantitatif diuji secara statistik menggunakan ANOVA dan data kualitatif disajikan secara deskriptif. Hasil penelitian menunjukkan bahwa terdapat perbedaan yang signifikan pada 5 kelompok (P>0,05). Terdapat perbedaan antara kelompok perlakuan (C. vulgaris salep 5%, C. vulgaris salep 10%, C. vulgaris salep 15%) dan kelompok kontrol. Hasilnya salep C. vulgaris berpengaruh terhadap proses penyembuhan luka sayatan mencit (M. m. albinus) dibandingkan kelompok kontrol dengan kandungan ekstrak C. vulgaris 10% paling baik untuk menyembuhkan luka dengan cepat.
Co-Authors Ahdiat, Keyzha Mutiara Al Maida, Mahda Alahmad, Ali Omar Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Billy, Billy Cici Suhaeni Cintani, Meavi 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 Pangestika, Adelia Putri Pratiwi, Windy Ayu 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 Yanuari, Eka Dicky Darmawan Yenni Angraini Zulhijrah, Zulhijrah