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Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango, Gorontalo, Indonesia
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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 179 Documents
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Implementasi Model Autoregressive Integrated Moving Average pada Proyeksi Komoditas Ekspor Timah Desy Yuliana Dalimunthe; Herman Aldila
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.18853

Abstract

The Bangka Belitung Archipelago is a potential area in the mining sector because many soils contain tin minerals and minerals that are spread evenly. Based on this phenomenon, this study uses the ARIMA model to analyze the prediction of the number of tin export commodities in the Bangka Belitung Islands Province. The time series data used in this study begins in January 2020 and ends in September 2022, with projected results ending in June 2023. Based on the analysis results, it is found that the ARIMA model (1,1,0) is the best model that can be used to project the value of tin export commodities in the Bangka Belitung Islands Province. This model was selected through the results of the correlogram test, which shows that the data is cut off at the second lag for the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. This projection analysis was carried out after passing the stationarity test first through the Augmented Dicky Fuller (ADF) test. Through this test, it is found that the data is stationary at the first difference, and the prob value is 0.0003 with the projected result that there will be an increase in the number of exports of tin commodities with a total increase of 0.03%. The results of this analysis can certainly be part of preventive actions for the government to be able to assist the country in increasing the country’s foreign exchange through increasing export commodities.
Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index Yogi Anggara; Arif Munandar
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20407

Abstract

RNN is a type of artificial neural network used to handle problems that require sequential data processing. ANFIS is a method that combines the advantages of fuzzy logic and artificial neural networks to create a system, so can adapt the parameters it uses according to the obtained data so that it can build an automated inference system. In this research, we make combination of RNN in ANFIS, which makes ANFIS able to accept input in the form of time series data so that ANFIS can recognize patterns contained in the time series data and its suitable for forecasting cases in the Jakarta Islamic Index. The membership functions used are three Gaussian functions. The results of the RNN-ANFIS Hybrid model training provide an interpretation that the first membership function represents the trend change indicator value, the second membership function represents the closing price change value in the last eight days, and the third membership function represents the pattern change value in the trend. The model for the Jakarta Islamic Index provides very good predictions with an MSE value of 0.001 and an MAE of 0.246.
Prediksi Spot Price Komoditas Emas Berjangka dengan Pendekatan Vector Error Correction Model Izma Fahria; Desy Yuliana Dalimunthe; Ririn Amelia; Ineu Sulistiana; Baiq Desy Aniska Prayanti
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.18737

Abstract

Time series data usually exhibit non-stationary behavior and involve interrelated variables. Thus, we need a model that can obtain good forecasting results from non-stationary time series data with multivariate variables. The Vector Error Correction Model (VECM) is a multivariate time series model which is a vector form of Vector Autoregressive Regression (VAR) for time series data that are non-stationary and have a cointegration relationship. This research was conducted to model the cointegration relationship in providing clarity on the long-term relationship of the influence of future prices and the Covid-19 pandemic on price movements of gold futures commodities and to predict spot price prediction modeling for gold futures commodities. The results of the research using the VECM (2) model, which is the best model, show that the future price of the gold commodity is quite dominant in influencing the value of the spot price of gold. The Covid-19 variable does not have a significant effect on the spot gold price variable.
On the Solution of Volterra Integro-differential Equations using a Modified Adomian Decomposition Method Kabiru Oyeleye Kareem; Morufu Olayiwola; Oladapo Asimiyu; Yunus Akeem; Kamilu Adedokun; Ismail Alaje
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.19029

Abstract

The Adomian decomposition method’s effectiveness has been demonstrated in recent research, the process requires several iterations and can be time-consuming. By breaking down the source term function into series, the current work introduced a new decomposition approach to the Adomian decomposition method. As compared to the conventional Adomian decomposition approach, the newly devised method hastens the convergence of the solution. Numerical experiments were provided to show the superiority qualities.
Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM Rahadi Ramlan; Neva Satyahadewi; Wirda Andani
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20860

Abstract

Twitter is one of the social media with the most active users, which is 24 million active users. Information published on twitter contains comments from users on an object. Sentiment analysis is used to determine whether the data includes negative comments or positive comments because the comments taken on twitter are textual data. The method used in this sentiment analysis is Support Vector Machine (SVM) about public comments on fuel price increases on twitter. The comment data used was 258 data on September 4, 2022 because on that date it was exactly the day after the fuel price increase. First, preprocessing is done to remove unnecessary words or information. Then the data is divided into training data by 80% and testing data by 20%. The accuracy rate is 82.69%, sensitivity is 100%, and specificity is 79.07%. Then from the results of testing 52 data obtained the results of 43 negative comments and 9 positive comments so that it can be concluded that more people disagree with the increase in fuel prices.
Unsupervised Feature Selection Based on Self-configuration Approaches using Multidimensional Scaling Ridho Ananda; Atika Ratna Dewi; Maifuza Binti Mohd Amin; Miftahul Huda; Gushelmi Gushelmi
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20397

Abstract

Some researchers often collect features so the principal information does not lose. However, many features sometimes cause problems. The truth of analysis results will decrease because of the irrelevant or repetitive features. To overcome it, one of the solutions is feature selection. They are divided into two, namely supervised and unsupervised learning. In supervised, the feature selection can only be carried out on data containing labels. Meanwhile, in unsupervised, there are three approaches correlation, configuration, and variance. This study proposes an unsupervised feature selection by combining correlation and configuration using multidimensional scaling (MDS). The proposed algorithm is MDS-Clustering, which uses hierarchical and non-hierarchical clustering. The result of MDS-clustering is compared with the existing feature selection. There are three schemes in the comparison process, namely, 75\%, 50\%, and 25\% feature selected. The dataset used in this study is the UCI dataset. The validities used are the goodness-of-fit of the proximity matrix (GoFP) and the accuracy of the classification algorithm. The comparison results show that the feature selection proposed is certainly worth recommending as a new approach in the feature selection process. Besides, on certain data, the algorithm can outperform the existing feature selection.
Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa Yuveinsiana Crismayella; Neva Satyahadewi; Hendra Perdana
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.18790

Abstract

Colleges provide higher education as the benchmark of education quality and evaluate higher education syllabi. Graduation rates and enrollment capacity are essential for graduation assessment and decision-making. Unfortunately, some students majoring in statistics failed to finish their studies on time, impacting the accreditation of the study program. It is necessary to examine the characteristics of students who managed and failed to complete their studies on time using the data mining classification method, namely Algorithm C5.0. In this study, Adaboost algorithm and Algorithm C5.0 was employed to classify graduation rates accurately. Graduation data of the Statistics Study Program of Universitas Tanjungpura Batch 1 of 20217/2018 to Batch II of 2022/2023 School years were regarded in this study. First, the entropy, gain, and gain ratio values were measured. After that, each data was given equal weight, and iteration was performed 100 times. The analysis using Algorithm C5.0 showed School Accreditation as the variable with the highest gain ratio, indicating that School Accreditation has the most decisive influence on graduation rates with an accuracy percentage of 70%. This percentage then increased to 82.14% after the boosting using the Adaboost algorithm. Adaboost Algorithm is regarded as good in improving the accuracy of algorithm C5.0. The results of this study can provide insight for colleges in designing policies to increase on-time graduation based on the factors that influence student graduation.
Teori Titik Tetap untuk Tipe Kannan yang Diperumum dalam Ruang b-Metrik Modular Lengkap Afifah Hayati; Noor Sofiyati; Dwiani Listya Kartika
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20571

Abstract

Some generalizations of Banach's contraction principle, which is a fixed-point theorem for contraction mapping in metric spaces, have developed rapidly in recent years. Some of the things that support the development of generalization are the emergence of mappings that are more general than contraction mappings and the emergence of spaces that are more general than metric spaces. The generalized Kannan type mappings are one of the mappings that are more general than contraction mappings. Furthermore, some of the more general spaces than metric spaces are b-metric spaces and modular b-metric spaces, which bring the concept of b-metric spaces into modular spaces. The fixed-point theorems for generalized Kannan-type mappings on b-metric spaces have been given. Therefore, this research aims to define generalized Kannan-type mappings on modular b-metric spaces and provide fixed point theorems for the generalized Kannan-type mappings on complete modular b-metric spaces. The definition of generalized Kannan type mapping in modular b-metric spaces is given by generalizing generalized Kannan type mappings in b-metric spaces. Then, the proof of fixed-point theorems for that mapping in modular b-metric spaces is carried out analogously to the proof of the fixed-point theorems for that mapping given in b-metric space. In this article, we obtain the definition of Kannan-type mappings and fixed-point theorems for generalized Kannan-type mappings in modular b-metric spaces and some consequences of the fixed-point theorem. In proving the theorem, a property of altering distance functions in b-metric spaces is generalized into modular b-metric spaces.
The Comparison between Ordinal Logistic Regression and Random Forest Ordinal in Identifying the Factors Causing Diabetes Mellitus Assyifa Lala Pratiwi Hamid; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20289

Abstract

Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people. Therefore, this problem needs further research regarding the factors that cause it. Further analysis can be done using ordinal logistic regression and random forest. Both methods were chosen to compare the modelling results in determining the factors causing diabetes conducted in the CDC dataset. The best model obtained in this study is ordinal logistic regression because it generates an accuracy value of 84.52%, which is higher than the ordinal random forest. The four most important variables causing diabetes are body mass index, hypertension, age, and cholesterol.
Efek Perlindungan Mangsa dan Daya Dukung Variabel pada Sistem Mangsa-Pemangsa dengan Fungsi Respon Beddington-DeAngelis Hanifah, Aisyiah Kholifatul; Abadi, Abadi
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.24288

Abstract

Several species in the world have experienced extinction. To save species from extinction, a system needs to be formulated, one of which is protecting prey. Changes in physical and biological processes also have a role in the environment, namely resulting in dynamically changing carrying capacity. The rate of prey consumption by the average predator, i.e. the response function is also important in the system formulation. In this study, a prey-predator system was constructed with a Beddington-DeAngelis response function that considers prey protection and variable carrying capacity as well as a predation process that takes into account the predator population. In this research, the equilibrium point for prey-predator extinction, predator extinction, and coexistence was determined and then continued to analyze its stability. With a large prey protection value, the predator extinction equilibrium point is asymptotically stable and the coexistence equilibrium point is asymptotically stable, if and only if within a certain range of prey protection parameter values. In addition, simulations were carried out and it was concluded that at certain prey protection values, changes occurred in the stability of the system which depended on the limits of its carrying capacity.

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