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Jambura Journal of Mathematics
Published by Universitas Negeri Gorontalo
ISSN : 26545616     EISSN : 26561344     DOI : https://doi.org/10.34312/jjom
Core Subject : Education,
Mathematics
Jambura Journal of Mathematics (JJoM) is a peer-reviewed journal published by Department of Mathematics, State University of Gorontalo. This journal is available in print and online and highly respects the publication ethic and avoids any type of plagiarism. JJoM is intended as a communication forum for mathematicians and other scientists from many practitioners who use mathematics in research. The scope of the articles published in this journal deal with a broad range of topics, including: Mathematics; Applied Mathematics; Statistics; Applied Statistics.
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Articles 16 Documents
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Search results for , issue "Vol 7, No 1: February 2025" : 16 Documents clear
Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham Simamora, Fandi Presly; Purba, Ronsen; Pasha, Muhammad Fermi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.27166

Abstract

The accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it requires testing all possible combinations of hyperparameter configurations. This research aims to find the optimal hyperparameter configuration for the BiLSTM model using Bayesian Optimization. The study was conducted using three blue-chip stocks from different sectors, namely BBCA, BYAN, and TLKM, with two scenarios of search iterations. The test results show that Bayesian Optimization was able to find the optimal hyperparameter configuration for the BiLSTM model, with the best MAPE values for each stock: BBCA 1.2092%, BYAN 2.0609%, and TLKM 1.2027%. Compared to previous research on Grid Search-BiLSTM, the use of Bayesian Optimization-BiLSTM resulted in lower MAPE values.
Penentuan Hiperstruktur Aljabar dan Karakteristiknya dalam Masalah Pewarisan Biologi Alamsyah, Alifa Raida; Kurniadi, Edi; Triska, Anita
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28270

Abstract

This article discusses the application of mathematics in biological inheritance problems, which are closely linked to mathematical studies, particularly in algebraic hyperstructures, including hypergroupoids, hypergroups, and -semigroups. The research aims to determine types of algebraic hyperstructures arising from genetic crossing in inheritance issues, with the crossing results represented in a set where two distinct hyperoperations are applied. Findings indicate that under the first hyperoperation, the algebraic hyperstructures formed include a commutative hypergroup, a regular hypergroup, a cyclic hypergroup, and an -semigroup with one idempotent element, three identity elements, and one generator. Under the second hyperoperation the resulting algebraic hyperstructures include a commutative hypergroup, a regular hypergroup, a cyclic hypergroup, and an -semigroup without idempotent elements, with three identity elements and three generators. Future research could develop various alternative hyperoperations on biological inheritance problems, generating a greater variety of algebraic hyperstructures. The results of this study indicate that the algebraic hyperstructure of a set depends on its hyperoperation.
Forecasting the Rupiah Exchange Rate Influenced by Several Factors Using the Improve Grey Model (1,3) Pratiwi, Dian Meilin; Firdaniza, Firdaniza; Kusuma, Dianne Amor
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.27954

Abstract

The rupiah exchange rate is one of the important indicators of a country's economic stability, but the rupiah exchange rate often fluctuates following the factors that influence it. Forecasting the rupiah exchange rate is very important for economic planning, it helps the government make monetary policy decisions to maintain the stability of the rupiah exchange rate. Common methods used to forecast the rupiah exchange rate are ARIMA, FTS Markov Chain, and exponential smoothing. These methods are widely used to show the relationship between variables, but these methods have the disadvantage that they must meet the assumptions of data patterns. The contribution of this research is the use of the improve Grey model (1,3) predict the rupiah exchange rate in 2024, which is influenced by inflation and the balance of payments. The improve Grey model (1,3) was chosen because it does not require data distribution assumptions and can consider several external factors, thus providing more specific results for certain fields. The improve Grey model (1,3) uses the Grey model (1,1) to calculate the parameter values of the independent variables in the calculation of the improve Grey model (1,3) whitening equation. The calculation of the improve Grey model (1,3) whitening equation is calculated using a first-order ordinary differential equation. The use of the improve Grey (1,3) model for forecasting the rupiah exchange rate is considered accurate based on the Mean Absolute Percentage Error (MAPE) value. The rupiah exchange rate influenced by inflation and the balance of payments using the improve Grey model (1,3) for 2024 is predicted to increase from the previous year to Rp. 18.076, which indicates a weakening in value. This weakening has a positive impact on the balance of payments and a negative impact on inflation.
Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java Setyowati, Silfiana Lis; Qalbi, Asyifah; Aristawidya, Rafika; Sartono, Bagus; Firdawanti, Aulia Rizki
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28736

Abstract

Random Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. This study aims to optimize Random Forest parameters for classifying school-age students' eligibility for the Kartu Indonesia Pintar (KIP) in West Java, based on economic factors. The research uses secondary data from the 2023 National Socio-Economic Survey (SUSENAS) of West Java, with a sample size of 13,044 individuals. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is applied. Hyperparameter tuning through grid search identifies the optimal combination of parameters, including the number of trees (ntree), random variables per split (mtry), and terminal node size (node_size). Model performance is evaluated using balanced accuracy, sensitivity, and specificity. Results indicate that the optimal parameters (mtry = 5, ntree = 674, node_size = 26) yield a balanced accuracy of 65.47%. Significant variables include PKH status, floor area of the house, source of drinking water, and building material type. The model accurately identifies students in need of educational assistance. In conclusion, optimizing Random Forest parameters improves the accuracy of KIP eligibility classification, supporting educational equity policies in West Java. These findings provide a foundation for developing more effective beneficiary selection systems for educational aid.
Penerapan Metode I-CHAID Menggunakan SMOTE pada Data Tidak Seimbang untuk Klasifikasi Durasi Studi Mahasiswa Akor, Umar D.; Payu, Muhammad Rezky Fiesta; Nashar, La Ode
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.27978

Abstract

The issue of delayed graduation is often encountered in various universities, including in the Statistics Study Program at Universitas Negeri Gorontalo, for graduates between 2018 and 2023. Among them, 162 students (76.5%) experienced delayed graduation, and 5 students (2.35%) dropped out. This delay in graduation is caused by various factors, necessitating a classification method capable of identifying the most dominant factors. The classification method used in this research is Improved Chi-Square Automatic Interaction Detection (I-CHAID) with the Synthetic Minority Oversampling Technique (SMOTE) approach. SMOTE is employed to address imbalanced data. Based on the I-CHAID classification tree with the SMOTE approach, the significant factors influencing the duration of study completion are the GPA in the fifth semester (67.2%) and the mentoring method (87.5%). As for the classification performance from the 40% testing data, the accuracy achieved was 40.6%, meaning that out of 32 samples, 13 were correctly classified. The sensitivity value was 6.25%, indicating the success rate of classifying data for students who graduated on time. The specificity value was 75%, showing the success rate in classifying data for students who did not graduate on time. The precision value was 20%, reflecting the accuracy of predicting students who actually graduated on time, and the F-measure was 9.52%, indicating the balance between precision and sensitivity.
Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification Indah, Yunna Mentari; Aristawidya, Rafika; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28290

Abstract

Machine learning classification is an effective tool for categorizing data based on patterns, which is particularly useful in analyzing the Human Development Index (HDI) in Indonesia. HDI serves as a key indicator of regional development progress, making it crucial to classify HDI categories at the regency/city level to support targeted development planning. This study aims to compare the performance of three ensemble-based classification methods—Random Forest, XGBoost, and LightGBM—in classifying HDI categories in Indonesia. Data from the Central Bureau of Statistics (BPS) in 2023, comprising 514 observations across nine variables, was used for analysis. The study applied these algorithms to analyze the most influential variables affecting HDI. The results show that LightGBM outperformed both Random Forest and XGBoost, achieving an accuracy of 0.937 without outlier handling and 0.944 with outlier handling. Additionally, per capita expenditure was identified as the most influential factor in predicting HDI. These findings contribute to the field of statistical modeling by demonstrating how ensemble methods can improve classification accuracy and provide valuable insights for data-driven policymaking, thus enhancing regional development planning and supporting future HDI-related research.
Derivasi di Pseudo BG-aljabar Putri, Ayuni; Gemawati, Sri; Syamsudhuha, Syamsudhuha
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28306

Abstract

A BG-algebra  is defined as a non-empty set  that includes a constant 0 and a binary operation  which adheres to the following axioms: (𝐵G1) , (𝐵G2) , and (𝐵G3)  for all . Pseudo BG-algebra is a generalization of BG-algebra, which is an algebra  that satisfies the following axioms: (pBG1) , (pBG2) , and (pBG3)  for all . In BG-algebra introduced an (l, r)-derivation, an (r, l)-derivation, and left derivation. This article aims to discuss and develop the concept of derivations in pseudo BG-algebras by introducing two new operations,  and , within the structure of pseudo BG-algebra . These operations are defined as  and  for each . In this research, the  operation in BG-algebra derivations replaced with the  and  operations under certain conditions, leading to the formulation of new types of derivations. Through this approach, three main types of derivations in pseudo BG-algebras are identified: (l, r)-derivation, (r, l)-derivation, and left derivation of type 1 and type 2. The results reveal several significant properties, including a formula for , the role of the special element 0, regularity in derivations, and the relationship between regular derivations and  as the identity function. This study contributes to advancing the theory of pseudo BG-algebras and its potential applications in other algebraic structures.
Optimized Approach to Electric Vehicle Routing Problem with Time Windows Using Grasshopper Optimization Algorithm Aksyarafah, Adifa Yasin; Kurdhi, Nughthoh Arfawi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.30664

Abstract

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is a complex logistics issue that involves optimizing delivery routes for electric vehicles while adhering to strict time limits, managing limited battery capacity, and addressing recharging needs. In this research, we introduce an optimized method to tackle the EVRPTW using the Grasshopper Optimization Algorithm (GOA), a metaheuristic inspired by the swarming behavior of grasshoppers. We utilize the Solomon dataset, a recognized benchmark in logistics and vehicle routing, to assess the effectiveness of our proposed algorithm. Our focus is on minimizing the total distance traveled while ensuring timely deliveries and effectively managing battery logistics and recharging. Comparative analysis indicates that the GOA surpasses traditional methods in route efficiency, reducing travel distances, and enhancing logistical operations. This study highlights the potential of GOA as a valuable tool for overcoming the challenges associated with electric vehicle logistics, paving the way for more sustainable and efficient transportation systems.
Analysis of Optimal Portfolio Formation Using Multi-Objective Optimization Method and Nadir Compromise Programming Aliwu, Randa Resvitasari; Rahmi, Emli; Nuha, Agusyarif Rezka; Yahya, Lailany; Wungguli, Djihad; Arsal, Armayani
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.29065

Abstract

A portfolio is a collection of financial assets in the stocks owned by a company or individual. An optimal portfolio is a selected portfolio that aligns with the investor's preferences, drawn from a set of efficient portfolios that have been formed. This research aims to create an optimal portfolio using the Multi-Objective Optimization method and the Nadir Compromise Programming (NCP) method. Additionally, Value at Risk (VaR) analysis is applied to determine the maximum risk an investor will bear for the portfolio. The data used consists of closing stock prices on the IDX30 Index from February 2022 to July 2023. The findings indicate that the optimization approach produces portfolios that align with investor risk-return preferences. The comparison of Multi-Objective Optimization and NCP methods provides insights into their effectiveness in portfolio selection. Furthermore, the VaR analysis helps investors understand potential risk levels, offering a comprehensive perspective on portfolio performance.
The Hamiltonian and Hypohamiltonian of Generalized Petersen Graph (GP_(n,9)) Susilawati, Susilawati; Nasfianti, Iis; Agustiarini, Efni; Nasution, Dinda Khairani
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.30053

Abstract

The study of Hamiltonian and Hypohamiltonian properties in the generalized Petersen graph GP_{n,k} is interesting due to the unique structure and characteristics of these graphs. The method employed in this study involves searching for Hamiltonian cycles within the generalized Petersen graph GP_{n,9}. Not all of GP_{n,9} graphs are Hamiltonian. For certain values of n, if the graph does not contain a Hamiltonian cycle, then one vertex should be removed from the graph to become Hamiltonian or neither. This research specifically investigates the Hypohamiltonian property of GP_{n,9}. The results show that for n ≡ 3 (mod 19) and n ≡ 5 (mod 19), GP_{n,9} is Hamiltonian. Meanwhile, for n ≡ 0 (mod 19), GP_{n,9} is Hypohamiltonian. Furthermore, for n ≡ 1 (mod 19), n ≡ 2 (mod 19), and n ≡ 4 (mod 19), GP_{n,9} is neither Hamiltonian nor Hypohamiltonian.

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