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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 60 Documents
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Search results for , issue "Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA Purwanto, Arie; Sartono, Bagus; Notodiputro, Khairil Anwar
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/barekengvol19iss1pp227-236

Abstract

Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem. The research was conducted by utilising Education Management Information System (EMIS) data, which is related to the incidence of school dropout. The data used consists of 2 data, namely MTs and MA dropout data. The initial testing procedure was carried out using the random forest algorithm for each data set, then the data was evaluated using the double random forest method. From this study, the underfitting case can be overcome well using the double random forest algorithm, while in the fit case, the difference in the goodness-of-fit value of the model is relatively the same. The results obtained show that MTs prioritise school quality more than MA, although family factors are more important at the MA level. Although the total number of factors used is basically the same, it should be noted that the two school levels have different relevance variables. It should be noted that no forecasting was done in this study given that the methodology used two different types of data.
VOLATILITY ANALYSIS AND INFLATION PREDICTION IN PANGKALPINANG USING ARCH GARCH MODEL Dalimunthe, Desy Yuliana; Kustiawan, Elyas; -, Khadijah; Halim, Niken; Suhendra, Helen
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/barekengvol19iss1pp237-244

Abstract

One of the concerns of both developed and developing countries, as well as in a region, is the amount of inflation that occurs. Inflation is a serious problem. Inflation is a macroeconomic variable that affects people's welfare and is defined as a complex phenomenon resulting from general and continuous price increases. This research aims to analyze the volatility and projected value of the inflation rate, especially in Pangkalpinang City, using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. This research uses time series data on inflation rate of Pangkalpinang, Bangka Belitung Island Province from January 2014 to May 2024. This data was obtained through publications from the Central Statistics Agency of Bangka Beliltung Islands Province. The ARCH model is used to handle heteroscedasticity in data, while the GARCH model is a development of the ARCH model and serves as a generalization of the volatility model. This research shows that the predicted inflation rate in Pangkalpinang City from June 2024 to November 2024 tends to decrease with a MAPE prediction accuracy level of 200.04%. The high MAPE value is caused by actual data moving toward 0.
THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE Siregar, Indra Rivaldi; Nugraha, Adhiyatma; 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/barekengvol19iss1pp245-258

Abstract

Coal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing renewable energy adoption. As one of the top coal exporters, Indonesia must adjust to these changes. This study aims to forecast future coal prices using historical data from Indonesia's Ministry of Energy and Mineral Resources (KESDM), applying and comparing Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models. While BiLSTM has shown advantages in other contexts and studies, its effectiveness for coal price forecasting remains underexplored. To ensure robust predictions, we employ walk-forward validation, which divides the data into six segments and evaluates 90 hyperparameter combinations across all segments. The BiLSTM model consistently outperforms the LSTM model, achieving lower average RMSE and MAPE values. Specifically, BiLSTM records an average MAPE of 7.847 and RMSE of 10.485, compared to LSTM's 10.442 and 11.993, respectively. The Diebold-Mariano (DM) test using squared error and absolute error loss functions further corroborates these findings, with most segments showing significant improvements in favor of BiLSTM, indicated by negative DM-test statistics and p-values below 0.01 or 0.10. This superior performance continues into the testing data, where BiLSTM maintains lower error metrics and a significant result of the DM test, underscoring its reliability for forecasting. In the final stage, the forecasts from both models indicate a nearly linear downward trend in coal prices over the next 18 months, aligning with the International Energy Agency's 2023 projection of a structural decline in coal demand driven by the sustained growth of clean energy technologies.
A STUDY ON THE STRUCTURE OF MATRICES RELATED TO THE VECH*, VECP*, AND VEC OPERATORS Hidayah, Nurul; Yanita, Yanita; Nazra, Admi
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/barekengvol19iss1pp259-270

Abstract

The vec operator is an essential tool in matrix algebra that transforms a matrix into a column vector based on specific rules. This paper introduces two new operators, namely and , which take the main diagonal and supra-diagonal elements of the matrix, respectively. In this paper, we obtain the general form of the matrix , which transform to , with as a matrix of size . In addition, we also develop the general forms of matrices and , which transform into and into , with as a symmetric matrix of size . This study also explores the properties and relationships between these matrices and their relevance to duplication and commutation matrices, providing deeper insights into the structure and operations of matrices.
KNOT OPTIMIZATION FOR BI-RESPONSE SPLINE NONPARAMETRIC REGRESSION WITH GENERALIZED CROSS-VALIDATION (GCV) Al Barra, Andre Fajry; Saputro, Dewi Retno Sari
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/barekengvol19iss1pp271-280

Abstract

Nonparametric regression is a statistical method used to model relationships between variables without making strong assumptions about the functional form of the relationship. Nonparametric regression models are flexible and can capture complex relationships that may not be adequately represented by simple parametric forms. Spline is one of the approaches used in nonparametric regression. Splines have the disadvantage of having to use optimal nodes in the data. Therefore, this article discusses the retrieval of optimal knot points using the generalized cross-validation method in the nonparametric bi-response spline regression model. The research results showed that the generalized-cross validation method is the best method for selecting nodes from other methods such as CV, AIC, BIC, RSS, or a more explicit validation-based approach method because of the development of the Cross Validation (CV) method which automatically selects the optimal number of nodes based on the balance between bias and variance. The process of optimizing knot points with Generalized Cross Validation (GCV) on bi-response spline nonparametric regression is implemented using Python can provide optimization at optimal knot points. Based on the results of the generalized cross-validation model analysis, it is concluded that GCV can effectively optimize knot points for spline fitting, ensuring a balanced and efficient model in capturing data patterns without overfitting.
FORECASTING EGG PRICES WITH CONVOLUTIONAL LONG SHORT-TERM MEMORY IN INDONESIA’S HIGH STUNTING PREVALENCE PROVINCE Pangastuti, Sinta Septi; Agam, Muhammad Restu; Santika, Ananda Hilmi; Rosadi, Juzma Fawwaza
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/barekengvol19iss1pp281-290

Abstract

The Sustainable Development Goals (SDGs) are a series of 17 goals fixed by the United Nations and adopted by 193 countries in 2015, including Indonesia. By 2030, to end all forms of malnutrition, targeting on stunting and wasting in children under 5 years of age is one of the targets from Goals Number 2. One affordable source of protein and nutrition used as a solution to overcome malnutrition problems such as stunting is eggs. Egg price modeling was carried out to see the affordability of prices for the community. Weekly dataset of egg price in NTT Province from 2018 to 2023 used to modeling with Convolutional LSTM. The Convolutional LSTM components are Adam optimizer, ReLU activation function, Huber loss function, with batch size and neurons of 32. The MAPE value obtained from the model is relatively small, with MAPE for training, validation, and testing of 1.97%, 1%, and 1.19% respectively. The results of egg price forecasting for December 11, 2023, to January 8, 2024, show that egg prices tend to continue to decline per week. Thus, a decrease in egg prices can be a good thing in providing more affordable nutrition for the community.
THE EXPLOITATION STATUS OF WORKING SCHOOL-AGE CHILDREN IN INDONESIA: A MULTILEVEL BINARY LOGISTIC REGRESSION ANALYSIS Ariansyah, Setiawan; Siagian, Tiodora Hadumaon
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/barekengvol19iss1pp291-302

Abstract

Many children in Indonesia are exploited in the workforce. In 2022, 12.22 percent of school-age children worked more than 40 hours per week. Children are considered exploited if they work more than 20 hours a week. Children who work for a long time have serious impacts. This study aims to determine a general picture of the exploitation of working school-age children in Indonesia and its influence factors. This study uses the March 2023 Socioeconomic Survey (SUSENAS) data by utilizing multilevel analysis specifically the two-level binary logistic regression method. The study results showed that 54.22 percent of school-age children are working and exploited in Indonesia. The individual and regional contextual factors that are significantly associated with the exploitation status of working school-age children are age, sex, education level, education of household head, sex of household head, employment status of household head, Smart Indonesia Programme (PIP) ownership status, family size, expected years of schooling (HLS), and poverty level. This study finds that increasing age, male sex, lack of access to the PIP, low household head education, female-headed households, unemployed household heads, and larger household sizes increased the likelihood of child exploitation. Moreover, children residing in districts with lower HLS scores had a higher chance of being exploited. These findings highlight the importance of considering both individual and regional contextual factors when addressing child exploitation. A two-level binary logistic regression model with random effects provides a better fit than the intercept-only model. Therefore, it is recommended to prioritize interventions for children without access to the PIP and those from household heads with low education levels. Furthermore, programs emphasizing the importance of education for children should be strengthened.
SOLVING CUTTING STOCK PROBLEM USING PATTERN GENERATION METHOD ON 2-DIMENSIONAL STOCK Hasbiyati, Ihda; Latifa, Indri; Rahman, Abdul; Ahriyati, Ahriyati; Gamal, Moh Danil Hendry
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/barekengvol19iss1pp303-318

Abstract

This article discusses the solution of the 2-dimensional stock cutting problem using the Branch and Bound modified pattern generation method. The pattern generation method will produce a feasible cutting pattern matrix which is then converted into a mathematical model with a linear program equation with an objective function to minimize the use of initial stock materials. The research is a case study located at the Handal Karya Buana Store which is engaged in cutting glass of different sizes, thicknesses and types of glass. In this case, 3 types of initial stock will be used with the same thickness, and type but have different area sizes, and one of the consumer demand data will be used, namely 3 types of requests with different sizes and many requests. By using the pattern generator method, 10 cutting patterns are generated with each different cutting residue. By using the simplex method, the optimal solution is obtained for the amount of initial stock needed, the pattern used and the remaining cuts produced. So using the pattern generator method can produce a feasible cutting pattern, and can be used as an alternative to solve the stock cutting problem.
COMPARISON OF SARIMA AND SARIMAX METHODS FOR FORECASTING HARVESTED DRY GRAIN PRICES IN INDONESIA Yulianti, Riska; Amanda, Nabila Tri; 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/barekengvol19iss1pp319-330

Abstract

Harvested dry grain (HDG) is a vital commodity for rice availability and plays a strategic role in Indonesia’s agricultural economy. Farmers typically sell HDG to rice millers post-harvest, yet disparities between farm-level selling prices and consumer-level purchase prices. This price gap can lead to financial losses for farmers, highlighting the need for accurate forecasting can lead to potential losses for farmers. SARIMA models are effective in capturing seasonality and trends but often fail to incorporate external factors influencing the dependent variable, resulting in less accurate forecasts when such factors have significant impacts. SARIMAX models, however, can include exogenous variables like the government purchase price (GPP), which supports farmer income by establishing a price floor for HDG and directly influencing farm-level price dynamics. This study aims to compare the SARIMA and SARIMAX models in forecasting HDG prices at the farm level in Indonesia, using GPP as an exogenous variable. The dataset, obtained from Statistics Indonesia, covers January 2008 to March 2024, and the forecasting accuracy is measured using Mean Absolute Percentage Error (MAPE). The findings indicate that the best model is the SARIMAX model (1,1,1)(0,1,2)12, achieving a MAPE of 10.919%. The forecasted results show that HDG prices in 2024 are expected to remain stable, with only a gradual increase throughout the year.
DEVELOPMENT OF NONPARAMETRIC PATH FUNCTION USING HYBRID TRUNCATED SPLINE AND KERNEL FOR MODELING WASTE-TO-ECONOMIC VALUE BEHAVIOR Rohma, Usriatur; Fernandes, Adji Achmad Rinaldo; Astutik, Suci; Solimun, Solimun
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/barekengvol19iss1pp331-344

Abstract

Waste management remains a challenge, including in Batu City, East Java, Indonesia. Rapid population growth and economic activities in the city have resulted in a substantial increase in waste volume. One of the key factors in solving waste problems is the mindset of the community towards waste management. The application of statistical analysis methods can be an effective approach to solving problems related to waste management from an economic point of view. Nonparametric path analysis is a statistical method that does not rely on the assumption that the curve is known. Nonparametric path analysis is performed if the data does not fulfill the linearity assumption. This study aims to determine the best nonparametric path function with a hybrid truncated spline and kernel approach among EV values of 0.5; 0.8; and 1. In addition, this study also aims to test the significance of the best path function obtained. The data used in this study are timer data obtained from the Featured Basic Research Grant. The results showed that the best model of hybrid truncated spline and kernel nonparametric path analysis is a hybrid model of truncated spline nonparametric path of linear polynomial degree 1 knot and kernel triangle nonparametric path at EV 0.5. In addition, the significance of the best nonparametric truncated spline and kernel hybrid path function estimation using jackknife resampling shows that all exogenous variables have a significant effect on endogenous variables as evidenced by a p-value smaller than (0.05).

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