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All Journal Jurnal Gaussian Science and Technology Indonesia Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal BPPK : Badan Pendidikan dan Pelatihan Keuangan Jurnal Kreativitas PKM Sulolipu: Media Komunikasi Sivitas Akademika dan Masyarakat Jurnal Informatika dan Rekayasa Elektronik Unri Conference Series: Community Engagement Operations Research: International Conference Series Jurnal Ketenagakerjaan Parameter: Journal of Statistics Journal of Community Engagement Research for Sustainability Jurnal Ilmiah Pengabdian Masyarakat Bidang Kesehatan Sulolipu: Media Komunikasi Sivitas Akademika dan Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Gustriza Erda
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Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Investigating Long-Run and Short-Run Dynamics of Palm Oil Production with Key Factors Using the VECM Method Lathifah Zahra; Gustriza Erda
Operations Research: International Conference Series Vol. 6 No. 4 (2025): Operations Research International Conference Series (ORICS), December 2025
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v6i4.429

Abstract

This study investigates the long-run and short-run relationships among palm oil production, rainfall, the number of bunches per palm (NOB), and average bunch weight (BTR) using the Vector Error Correction Model (VECM). Monthly data from 2015 to 2024 obtained from PT Perkebunan Nusantara IV (PTPN IV) Regional III, Sei Rokan Estate, were analyzed. Descriptive statistics indicate high variability in rainfall and relatively balanced distributions for production, NOB, and BTR. The Augmented Dickey-Fuller (ADF) test confirmed that all variables became stationary after first differencing, and the Johansen cointegration test identified three cointegrating relationships, suggesting both short-run and long-run linkages among variables. The VECM estimation results reveal positive long-run relationships for palm oil production (ECT = 0,052), rainfall (ECT = 0,090), and NOB (ECT = 0,042), indicating that these variables move toward long-run equilibrium in the same direction. In the short run, previous rainfall significantly affects both current palm oil production and NOB, with coefficients of 0,203 and 0,178, respectively, highlighting the critical role of rainfall fluctuations in influencing short-term productivity and fruit development. Model evaluation using the Root Mean Square Error (RMSE) shows low prediction errors across all variables, with rainfall having the highest RMSE (1,334) and NOB the lowest (0,962), confirming the model’s strong predictive performance. Overall, the findings demonstrate that the VECM approach effectively captures both long-run equilibrium and short-run dynamics among key determinants of palm oil productivity in the Sei Rokan plantation.
Palm Oil Production Forecasting Using the SARIMA Model at the Terantam Plantation of PTPN IV Regional III in 2025 Eky; Erda, Gustriza
Operations Research: International Conference Series Vol. 6 No. 4 (2025): Operations Research International Conference Series (ORICS), December 2025
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v6i4.430

Abstract

Palm oil is one of the important plantation commodities that plays a major role in the Indonesian economy because it contributes to state revenues, making palm oil production crucial. Forecasting palm oil production is essential to support effective planning and decision-making in plantation management. This study aims to forecast palm oil production at the Terantam Plantation of PTPN IV Regional III for the year 2025 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The data used consist of monthly production data based on volume (kg) from January 2014 to December 2024. The results of the analysis indicate that the best model obtained is SARIMA(0,1,4)(0,1,1)12 with the smallest Akaike Information Criterion (AIC) value. Diagnostic tests show that the model residuals behave as white noise and are normally distributed, indicating that the model is suitable for forecasting. The Mean Absolute Percentage Error (MAPE) value of 8.02% indicates a very good level of accuracy. The forecasting results reveal a seasonal pattern in palm oil production, with the highest production in September 2025 amounting to 15,108,145 kg, and the lowest in February 2025 at 9,347,573 kg. Overall, the SARIMA model is able to capture both trend and seasonal patterns effectively, making the forecast results useful as a reference for production planning and operational management at the Terantam Plantation. Furthermore, the findings of this study are expected to serve as a reference for applying similar forecasting methods to other plantation commodities.
STATISTICAL MODELING FOR DOWNSCALING USING PRINCIPAL COMPONENT REGRESSION AND DUMMY VARIABLES: A CASE OF SIAK DISTRICT Adnan, Arisman; Alika, Elsa Riesta; Silalahi, Divo Dharma; Aulia, Felia Rizki; Erda, Gustriza
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1643-1658

Abstract

Indonesia, as a tropical country, is characterized by two primary seasons: the rainy season and the dry season. It is evident that meteorological shifts can exert considerable influence on the agricultural sector, a notable example being the cultivation of palm oil. Consequently, the ability to predict rainfall has emerged as a pivotal element in the broader endeavor to mitigate the adverse effects of climate change. This study employs statistical downscaling using the Principal Component Regression (PCR) approach to model rainfall predictions. The issue of multicollinearity, a common occurrence in Global Circulation Model (GCM) data, is addressed through the use of Principal Component Regression (PCR). This method has been demonstrated to stabilize the model structure and reduce variance in the regression coefficients. The data utilized encompass observed rainfall from LIBO Estate, which is owned by PT SMART Tbk (SMART Research Institute), for the period from 2013 to 2022. This data serves as the response variable, while the CMIP6 GCM simulation output data functions as the predictor variable. The findings indicated that the initial PCR model exhibited an RMSE value ranging from 97.06 to 131.69, along with an R² value ranging from 14.25% to 20.49%. The incorporation of dummy variables into the model resulted in a substantial enhancement in its performance, as evidenced by a decline in RMSE to 24.46–35.83 and an increase in R² to 89.02%–90.24%. The findings indicate that the use of PCR with dummy variables is an effective approach for enhancing the accuracy of rainfall modeling through statistical downscaling.
Modeling Cointegrated Nonstationary Air Pollution Data: A Forecasting Study of NO₂ and SO₂ in Indonesia (1950–2022) Adnan, Arisman; Erda, Gustriza; Wamiliana; Russel, Edwin
Science and Technology Indonesia Vol. 11 No. 1 (2026): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.1.161-173

Abstract

 Air pollution from nitrogen dioxide (NO2) and sulfur dioxide (SO2) poses serious threats to human respiratory health and contributes to environmental degradation through acid rain formation. In Indonesia, despite rapid industrialization and increasing emissions, studies examining the interrelated dynamics between NO2 and SO2 at the national level remain limited, with most research focusing only on provincial areas and short time periods. This study fills this gap by analyzing the dynamic relationship between NO2 and SO2 using comprehensive national-level time series data from 1950 to 2022. The analysis examines short-term adjustments, long-term equilibrium patterns, directional causality, and shock responses between the two pollutants. The analysis focuses on identifying the best statistical model to capture the interaction between the two variables. Granger causality tests, impulse response functions (IRFs), and forecast error variance decomposition are applied to examine causal links and response dynamics. The data exhibits nonstationary but cointegrated with rank r=1, indicating a long-run equilibrium correlation between two pollutants. Consequently, the Vector Error Correction Model, VECM(4), is selected as the most appropriate model. The study also provides 10-year forecasts for both pollutants insights into potential future air pollution trends in Indonesia, with NO2 rising from 5.29 to 8.09 million tons and SO2 from3.38 to 5.10 million tons, underscoring the urgent need for integrated emission control policies that address both pollutants simultaneously rather than in isolation.
Co-Authors Adia Syaputri Al Baihaqi, Habib Alika, Elsa Riesta Althoff Hibban Anisa, Rahmatul Anne Mudya Yolanda Arisman Adnan Aulia, Felia Rizki Ayu Sofia Dien, Zulfanita Dimas Abyan Fatkhin Al-Aswad Divo Dharma Silalahi Efendi, MHD Eky Erie Sadewo Fandi Gusriyanda Ferdinan, Tri Agus Finda, Ingla Fitri Ramadhani Goldameir, Noor Ell Gumanti Gunawan , Chairani Indra, Zul Kholilah Samosir Kurnia Ramadani Lafina, Melanie Maria Lathifah Zahra M. SYIFA RAMADHAN Mariza Mega Aulia, Sartika Miftahul Jannah Mistika, Lensi Musraini M Nurhaliza Nurhannifah Rizky Tampubolon Nusantri Purba Okta Bella Syuhada Pitri, Rizka Putri, Nelsa Rahmi, Afrilia Ramadani, Kurnia Rezzy Eko Caraka Rini Latifah Riris Putri Kinanti Russel, Edwin Rustam Efendi Safitri, Nuraini Sarasmita Apriyenti Simbolon, Veronika Amelia Sirait, Tesa Theresia Siregar, Nurainun Siti Farika Sari SITI SUHAILA Sukamto Syaftiani Dwi Astuti Syamsudhuha Syamsudhuha Syamsudhuha T, Masrina Munawarah T, Nurhannifah Rizky Tata, Tata Tatik Widiharih Thearas, Lia Titi Solfitri Usdika, Radhiatul Khaira Wamiliana Wawan Sugiyarto Yenita Roza Yuciana Wilandari Zahra, Lathifah Zulya Erda