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Redaksi BAREKENG: Jurnal ilmu matematika dan terapan, Ex. UT Building, 2nd Floor, Mathematic Department, Faculty of Mathematics and Natural Sciences, University of Pattimura Jln. Ir. M. Putuhena, Kampus Unpatti, Poka - Ambon 97233, Provinsi Maluku, Indonesia Website: https://ojs3.unpatti.ac.id/index.php/barekeng/ Contact us : +62 85243358669 (Yopi) e-mail: barekeng.math@yahoo.com
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BAREKENG: Jurnal Ilmu Matematika dan Terapan
Published by Universitas Pattimura
ISSN : 19787227     EISSN : 26153017     DOI : https://search.crossref.org/?q=barekeng
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation
BAREKENG: Jurnal ilmu Matematika dan Terapan is one of the scientific publication media, which publish the article related to the result of research or study in the field of Pure Mathematics and Applied Mathematics. Focus and scope of BAREKENG: Jurnal ilmu Matematika dan Terapan, as follows: - Pure Mathematics (analysis, algebra & number theory), - Applied Mathematics (Fuzzy, Artificial Neural Network, Mathematics Modeling & Simulation, Control & Optimization, Ethno-mathematics, etc.), - Statistics, - Actuarial Science, - Logic, - Geometry & Topology, - Numerical Analysis, - Mathematic Computation and - Mathematics Education. The meaning word of "BAREKENG" is one of the words from Moluccas language which means "Counting" or "Calculating". Counting is one of the main and fundamental activities in the field of Mathematics. Therefore we tried to promote the word "Barekeng" as the name of our scientific journal also to promote the culture of the Maluku Area. BAREKENG: Jurnal ilmu Matematika dan Terapan is published four (4) times a year in March, June, September and December, since 2020 and each issue consists of 15 articles. The first published since 2007 in printed version (p-ISSN: 1978-7227) and then in 2018 BAREKENG journal has published in online version (e-ISSN: 2615-3017) on website: (https://ojs3.unpatti.ac.id/index.php/barekeng/). This journal system is currently using OJS3.1.1.4 from PKP. BAREKENG: Jurnal ilmu Matematika dan Terapan has been nationally accredited at Level 3 (SINTA 3) since December 2018, based on the Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia, with Decree No. : 34 / E / KPT / 2018. In 2019, BAREKENG: Jurnal ilmu Matematika dan Terapan has been re-accredited by Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia and accredited in level 3 (SINTA 3), with Decree No.: 29 / E / KPT / 2019. BAREKENG: Jurnal ilmu Matematika dan Terapan was published by: Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura Website: http://matematika.fmipa.unpatti.ac.id
Arjuna Subject : Matematika - Matematika Terapan
Articles 1,248 Documents
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GRAPH ENERGY OF THE COPRIME GRAPH ON GENERALIZED QUATERNION GROUP Miftahurrahman, Miftahurrahman; Wisnu Wardhana, I Gede Adhitya; Alimon, Nur Idayu; Sarmin, Nor Haniza
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0031-0040

Abstract

This paper investigates the Degree Square Sum Energy , Degree Exponent Energy , and Degree Exponent Sum Energy of the coprime graph on generalized quaternion group ​. This research is quantitative study using previous study as the literature review to construct the new theorem. These energy methods provide new insights into the spectral properties of graphs by their vertex degree distributions into eigenvalue computations. Using spectral graph theory, the general formulas for the , , and of ​ are formulated for for every positive integer . Furthermore, we explore the implications of these methods in understanding the algebraic and spectral characteristics of ​. Numerical results are presented for specific cases to validate the previous theorem. This study contributes to the broader analysis of graph energies, offering a framework for studying other algebraic structures.
PARTIAL LEAST SQUARES - MULTIGROUP ANALYSIS ON THE EFFECT OF LEADERSHIP ON WORK CULTURE AND LECTURER PERFORMANCE Rahman, Hairur; Dwi Mulyanto, Angga; Harini, Sri; Rozi, Fachrur; Widjanarko Otok, Bambang; Dwi Trijoyo Purnomo, Jerry
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0041-0054

Abstract

Higher education institutions depend on leadership to build their organizational culture and achieve better employee performance. However, there remains limited understanding of how the impact of leadership may vary across institutional contexts. This research employs Partial Least Squares - Multigroup Analysis (PLS-MGA) to explore the effects of leadership on work culture and lecturer performance in two Indonesian universities: UIN Maulana Malik Ibrahim and IAIN Ponorogo. A multistage random sampling of 272 lecturers was conducted. The methodological approach allowed for a robust comparative analysis between the institutions. The results reveal that leadership exhibits powerful positive relationships with work culture and lecturer performance in both institutions. Leadership explains 38.7% of work culture variability at IAIN Ponorogo, but only 18.6% at UIN Maulana Malik Ibrahim. These findings underscore the need for context-sensitive leadership development strategies and provide a foundational contribution for future research in higher education leadership and performance.
LSTM AND GRU IN RICE PREDICTION FOR FOOD SECURITY IN INDONESIA Hendrawati, Triyani; Marthendra, Kennedy; Simanjuntak, Brian Riski Jayama; Pravitasari, Anindya Aprilianti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0055-0068

Abstract

Hunger in Indonesia remains a serious challenge, especially in the face of food price instability, particularly rice as the main staple food. In order to achieve SDG 2 “Zero Hunger” by 2030, policies that support price stability and more effective food distribution are needed. This study aims to assess the predictive power of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for Indonesian rice prices. The dataset, consisting of 1,424 observations from early 2021 to late 2024, was collected from official sources and preprocessed using normalization techniques. The data was then divided into training, validation, and testing sets. Each model was trained and evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. LSTM, a type of Recurrent Neural Network (RNN), uses three gates and cell memory to identify long-term patterns in time series data. GRU, with a simpler structure involving only two gates, is more efficient in modeling temporal relationships. The results show that the LSTM model achieved MAPE 3.49%, while the GRU model outperformed it with MAPE 1.08%. Overall, the GRU model demonstrated higher accuracy in forecasting rice prices.
THE GENERALIZED SPACE-TIME ARIMA (GSTARIMA) MODEL FOR PREDICTING NITROGEN MONOXIDE TO MITIGATE EID AL- FITR AIR POLLUTION IN SURABAYA Khaulasari, Hani; Rini Novitasari, Dian Candra; Setyawati, Maunah; Maulana, Jeneiro; Mohd Fauzi, Shukor Sanim
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0069-0086

Abstract

Air quality is a crucial factor due to its significant impact on environmental sustainability and public health. One of the major pollutants affecting air quality is Nitrogen Monoxide (NO), especially during periods of increased human mobility such as Eid al-Fitr. Monitoring and predicting NO levels are essential for early mitigation efforts. This study aims to evaluate the performance of the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model with three types of spatial weighting schemes and compare it with other forecasting methods, namely ARIMA, VARIMA, and Support Vector Regression (SVR), in predicting NO concentrations in Surabaya for April 2024. The data used in this study consist of daily NO concentration measurements obtained from the Surabaya City Environment Agency’s monitoring stations located at SPKU Tandes, SPKU Wonorejo, and SPKU Kebonsari, covering the period from January 2023 to March 2024. The GSTARIMA model was selected for its capability to capture both spatial and temporal dependencies across monitoring locations. As an extension of the ARIMA model, GSTARIMA incorporates spatial weight matrices to model spatial heterogeneity. Parameter estimation was conducted using the Ordinary Least Squares (OLS) method. The results indicate that the GSTARIMA model with Inverse Distance Weighting (IDW) and order (3,1,0)₁ in the first spatial order yields the most accurate predictions, outperforming ARIMA, VARIMA, and SVR models. The model produced the lowest Symmetric Mean Absolute Percentage Error (sMAPE) of 0.93% and Root Mean Square Error (RMSE) of 5.32. A notable spike in NO concentrations was observed between April 23 and 25, 2024, coinciding with the post-Eid al-Fitr return flow, indicating a surge in population mobility.
ANALYSIS OF FACTORS AFFECTING PNEUMONIA IN INDONESIAN TODDLERS USING NONPARAMETRIC REGRESSION WITH LEAST SQUARE SPLINE AND FOURIER SERIES METHODS Saifudin, Toha; Suliyanto, Suliyanto; Nurdin, Nabila; Christiano Ginzel, Bryan Given; Oktavia, Sabrina Salsa; Ariyawan, Jovansha; Ubadah, Mohammad Noufal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0087-0104

Abstract

Pneumonia is the leading cause of death among children under five, with the highest prevalence in Indonesia found in West Papua Province (75%) and the lowest in North Sulawesi (0.3%). This study aims to analyze the factors influencing the prevalence of pneumonia in Indonesian toddlers using nonparametric regression approach by comparing Least Square Spline (LS-Spline) and Fourier Series. Data sourced from the Indonesian Ministry of Health website, consisting of 34 provinces in Indonesia in 2023, with one response variable (Y) and five predictor variables (X). The analyzed factors include the coverage of vitamin A supplementation, malnutrition rates, low birth weight prevalence, measles immunization coverage, and exclusive breastfeeding rates. The analysis was conducted by modeling with nonparametric Least Square Spline regression using up to three optimal knot points, then performing analysis using nonparametric regression with the Fourier series approach. The two methods were compared based on GCV and R², with the best model having lower GCV and higher R². The results showed that LS-Spline was better than Fourier Series, with a GCV value of 233.16 and a coefficient of determination of 92.5%. The findings reveal that the relationships between predictor factors and pneumonia prevalence are nonlinear, with varying influence patterns across different variable ranges. These results indicate that LS-Spline has a strong ability to explain data variability. The Fourier series is limited in this study because it is best suited for periodic data, unlike pneumonia data and its causal factors which do not show such patterns. The weakness of the Fourier Series in this study lies in its suitability for periodic data, while pneumonia cases and their causal factors do not follow such patterns. This study offers insights into health policy making to reduce pneumonia cases, improve their lives, in line with the SDGs target on Good Health and Well-being.
COMPARATIVE STUDY OF LSTM-BASED MODELS WITH HYPERPARAMETER OPTIMIZATION FOR SHORT-TERM ELECTRICITY LOAD FORECASTING Kharisudin, Iqbal; Arissinta, Insyiraah Oxaichiko; Aulia, Sabrina Aziz; Dani, Muhamad Abdul Qodir; Wijaya, Galih Kusuma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0105-0122

Abstract

This research is focused on the development and comparison of time series models for short-term electrical load forecasting, utilizing several variants of Long Short-Term Memory (LSTM) networks. The specific LSTM variants employed in this study include Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, and Convolutional Neural Network LSTM (CNN-LSTM). We used five years (2016-2020) of daily electricity load data from the Central Java-DIY system, provided by PT PLN (Persero). The primary objective is to ascertain the accuracy and evaluate the performance of these LSTM variants in the context of short-term load forecasting. This is achieved quantitatively through the computation of various error metrics, namely Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared. The results of the study reveal that the CNN-LSTM method outperforms the other variants in terms of the calculated metrics. Specifically, the CNN-LSTM method achieved the lowest values for all metrics: an MSE of 0.007 for training and 0.0010 for testing, an MAE of 0.0050 for training and 0.0062 for testing, and an RMSE of 0.083 for training and 0.099 for testing. Among the evaluated models, CNN-LSTM demonstrates the best trade-off between predictive accuracy and training efficiency, making it the most recommended for short-term electricity load forecasting. While BiLSTM achieves higher accuracy, particularly in terms of MAE, it requires a longer training time. In contrast, Stacked LSTM converges faster with slightly lower accuracy, making it a strong alternative when computational efficiency is prioritized..
MODELLING SCHOOL DROPOUT RATES IN WEST JAVA PROVINCE WITH MIXED GEOGRAPHICALLY TEMPORALLY WEIGHTED REGRESSION Rismawati Arum, Prizka; Maharani, Endang Tri Wahyuni; Fatimahthus Zahra, Diandra; Utami, Tiani Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0123-0136

Abstract

School dropout is a problem in the education sector that can hinder the progress of the quality of human resources and the competitiveness of the nation. West Java Province has the highest school dropout rates among all provinces in Indonesia. The data on school dropout rates exhibit spatial and temporal variations. Additionally, the potential differences between regions allow for the occurrence of diverse data that can be addressed locally and globally. Mixed Geographically Temporally Weighted Regression (MGTWR) is an extension of the GWR method that can produce parameters that are both local and global for each location and time. So, the objective of this research is to obtain factors that have a local and global influence on the school dropout rate in West Java Province using the Mixed Geographically Temporally Weighted Regression method. In this study, the data used includes school dropout rates in West Java Province from 2018 to 2022. The data used is sourced from the official statistical data website of the Ministry of Education, Culture, Research and Technology, and the official website of the West Java Province Central Statistics Agency. The results of the MGTWR modeling show that globally influential variables include the percentage of the poor population, population density, unemployment rate, and average length of schooling, which have local effects. Based on the MGTWR model, the Fixed Kernel Gaussian weighting function is the best model for modeling school dropout rates in regencies/cities in West Java, with an RMSE value of 0.0755 and R-squares of 92.09%.
UNILEVER STOCK PRICES FORECASTING WITH ENSEMBLE AVERAGING APPROACH ARIMA-GARCH AND SUPPORT VECTOR REGRESSION Pusporani, Elly; Nitasari, Alfi Nur; Salsabila, Fatiha Nadia; Indrasta, Irma Ayu; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0137-0154

Abstract

Investment, mainly in stock prices, plays a significant role in the Indonesian economy. Accurate stock price forecasting can help investors make informed decisions. Unilever Indonesia Tbk (UNVR) exhibits high volatility in its closing stock prices, making it crucial to develop a reliable forecasting model. This study applies an ensemble averaging method that integrates the ARIMA-GARCH model and Support Vector Regression (SVR) to predict UNVR's closing stock prices from January 6, 2019, to November 5, 2023. The results indicate that the data can be modeled using ARIMA (0,2,1). However, the squared residuals of the model show heteroscedasticity, necessitating variance modeling using the ARCH-GARCH approach. The best combination of mean and variance modeling is achieved with ARIMA (0,2,1) – GARCH (1,1), yielding a Mean Absolute Percentage Error (MAPE) of 2.865%. Additionally, a nonparametric SVR model with parameters C = 4 and ε = 0 is applied, resulting in a MAPE of 2.94%. An ensemble averaging approach is implemented to optimize forecasting accuracy further, combining ARIMA-GARCH and SVR models. This ensemble approach improves predictive performance, achieving a final MAPE of 1.682%. These findings demonstrate that ensemble averaging effectively enhances stock price forecasting accuracy by leveraging linear and nonlinear modeling techniques.
SPATIAL ASSESSMENT OF PEAT-LAND FIRES UTILIZING BINARY LOGISTICS REGRESSION IN WEST KALIMANTAN Debataraja, Naomi Nessyana; Kusnandar, Dadan; Simanjuntak, Martina
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0155-0166

Abstract

This study contributes to the understanding of forest fire susceptibility by applying a binary logistic regression model combined with a Geographic Information Systems (GIS) to map hotspot vulnerability in West Kalimantan, Indonesia, an approach not extensively explored in previous research. Forest fire is one of the environmental problems. In West Kalimantan, land fires are a routine disaster that is experienced almost every year. In this paper, a binary logistic regression model was used to identify land fire in west Kalimantan. In addition, mapping of confidence of hotspot susceptibility was carried out in West Kalimantan. The data used were 72 hotspots spread across in seven districts of West Kalimantan in 2020. The independent variables used were land cover, slope, topography, distance of hotspots to rivers, distance of hotspots to roads and distance of hotspots to settlements. While the dependent variable was the point which was classified into hotspots and non-hotspots. Results showed that the method identified that the variables significantly influencing land fires include the distance of the points to the river and the distance of the points to the road. The Binary Logistic Regression model of the land fire in West Kalimantan has a classification accuracy rate is 84.03%. From the results of weighting and visualization using GIS shown that the area that has a very high level of vulnerability is the city of Pontianak (42.97%). Meanwhile, areas that have a moderate level of vulnerability include Kayong Utara, Kubu Raya, Mempawah, Sambas, Sanggau, Sekadau and Sintang districs. Kubu Raya and Kayong Utara districts in the medium vulnerability level have the largest forest fire districts (43.70% and 41.25%). Meanwhile, districts that are in the very low vulnerability level are Bengkayang, Singkawang, Landak and Melawi districts.
LOSS INSURANCE MODEL OF RISK FOR AGRICULTURAL COMMODITY BASED ON MAXIMUM DAILY RAINFALL INDEX CONSIDERATION Muna, Siti Umamah Naili; Putu Purnaba, I Gusti; Setiawaty, Berlian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0167-0178

Abstract

Agricultural commodities in rainfed areas face significant risks of yield loss and crop failure due to uncertain rainfall patterns and intensities. Index-based crop insurance has been introduced as an adaptive strategy to simplify loss assessment using climate indicators. However, most existing schemes cover only a single peril, such as drought. This study aims to develop a loss model of risk for agricultural commodity using maximum daily rainfall index that accounts for both drought and flood risks. The model consists of two components: rainfall modelling and insurance modelling. Rainfall modelling identifies the appropriate probability distribution to define rainfall index parameters—trigger and exit—which represent thresholds for yield reduction and total crop failure, respectively. These parameters are derived through numerical integration and can be approximated using percentiles when crop-specific water requirement data are unavailable. Insurance modelling determines a benefit claim model based on rainfall probability and parameters of rainfall index, with three possible benefit claim conditions: full, partial, and none. A case study using maximum daily rainfall data (September–December, 1984–2014) for paddy in Dramaga, Bogor, indicates that the Burr Type XII distribution fits the data better than the GEV distribution. The estimated premium ranges from IDR 300000 to 300822.9 per hectare. In high-rainfall areas like Dramaga, premiums are primarily influenced by the probability of excess rainfall, while drought risk is negligible. Analysis over a 10-year actual maximum daily rainfall data (September–December, 2015–2024) shows that lower insured percentiles result in lower premiums. To improve accuracy, trigger and exit should ideally be determined based on the specific crop's water requirements. Despite data limitations, this model provides a conceptual model for developing more representative and actuarially fair loss model for agricultural commodity risk.

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