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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 20 Documents
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Search results for , issue "Vol 5, No 1: February 2023" : 20 Documents clear
Pola Faktor Keragaman pada Respons Dikrit Fitri Nurjanah; Budi Suharjo; Hadi Sumarno
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1700.194 KB) | DOI: 10.34312/jjom.v5i1.15556

Abstract

In social research, respondents are usually given several questions or indicators for assessment. Responses between respondents may differ even if the same questions or indicators are given. This is one of the causes of the diversity of responses. The diversity of responses is one of the factors that cause response bias in conducting social research. The diversity of responses can come from differences in the thought processes of each respondent. There are three main aspects in the thought process, namely cognition, affection, and conation. This paper aims to analyze the source of the diversity of responses in the aspects of cognition, affection, and conation. The first thing to do in this research is to design a questionnaire by developing indicators into three aspects (cognition, affection, and conation). The study involved 100 respondents using OVO with a purposive sampling method. Respondents assess indicators of aspects of cognition, affection, and conation. The assessment options given are discrete assessments 1-5 with a description of the assessment adjusted to the indicators. Then, the respondent's assessment data were analyzed by calculating the standard deviation, analysis of variance, further test (Tukey HSD) and the distribution of the assessment of each indicator. The main result obtained is that there are three consecutive indicators with the largest standard deviation values in each aspect. These indicators are the source of the diversity of responses in aspects of cognition, affection, and conation. The results of the analysis also show that the conation aspect is the most diverse aspect with the largest standard deviation value. This research is useful as a reference for making social research questionnaires in measuring aspects related to cognition, affection, and conation.
Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis Aldila Dinanti; Joko Purwadi
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3017.921 KB) | DOI: 10.34312/jjom.v5i1.17098

Abstract

This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reduction using PCA produces 3 main components of the 8 variables in the original data, namely PC1, PC2, and PC3. Then classification result using K-Nearest Neighbor shows that by choosing 3 closest neighbor parameters (K), for K = 3, K = 5, and K = 7. The result for K = 3 has an accuracy of 67,53%, for K = 5 had an accuracy is 72,72%, and for K=7 had an accuracy of 77,92%. Thus, it was concluded that the best accuracy performance for the classification of diabetes was achieved at K=7 with an accuracy of 77.92%.
Synthetic Minority Oversampling Technique Pada Model Logit dan Probit Status Pengangguran Terdidik Fatimah Fatimah; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.17050

Abstract

Educated unemployment is caused by a misalignment of educational development planning and employment development, resulting in underemployed graduates from various educational institutions. Unemployment data in DKI Jakarta shows an unequal class. Unbalanced data is a severe problem of modeling because it can cause prediction errors that affect the accuracy of the resulting model. Using SMOTE to handle unbalanced data will likely increase the model’s accuracy. This study aims to find the best model for identifying the factors influencing the status of educated unemployment using logit and probit models and handling unbalanced data using SMOTE. The results showed that the independent variables that affect the status of educated unemployment in the logit and probit models are the same: age group and participation in training. The independent variables that affect the status of educated unemployment in the logit and probit models with SMOTE are also the same: age group, marital status, and participation in training. Unbalanced data handling using SMOTE can increase the balanced accuracy value significantly. Balanced accuracy values for the logit and probit models with SMOTE are higher than the logit and probit models without SMOTE. The logit model with SMOTE is the best because it has the highest balanced accuracy value compared to other models. According to the logit model with SMOTE, the educated unemployed in DKI Jakarta are young and have never married. There is a need for the government to play a role in improving the quality of educational institutions in producing graduates who meet company qualifications and can be hired by employers. Unemployed people who have attended the training, despite having a higher education, may also become unemployed. The training provided has not been able to reduce the unemployment rate. As a result, the government should be able to provide training to improve entrepreneurship skills while also providing capital in the form of business loans to reduce educated unemployment.
Model ETAS Spatio-Temporal pada Analisis Pemetaan Intensitas Kegempaan di Wilayah Sumatera Andreas Rony Wijaya
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3670.036 KB) | DOI: 10.34312/jjom.v5i1.17359

Abstract

High seismic activity forces Indonesia to mitigate natural disasters to minimize the impact of these disasters. One of the mitigation efforts that can be done is to know the possibility of an earthquake occurring in an area. Earthquakes, which are random phenomena, cannot be determined with certainty. Therefore, studies in seismological statistics can be used. One of the studies in seismological statistics that discusses the occurrence of earthquakes is the epidemic-type aftershock sequence (ETAS) model. This model describes the main seismic activity followed by aftershocks. The ETAS model, which only considers the parameters of time and event magnitude, was developed into a Spatio-temporal ETAS model, which also describes spatial parameters or locations. This study used the Spatio-temporal ETAS model to analyze the Sumatra region’s seismic activity from 2000-2022. Earthquake data sources are obtained from the United State Geological Survey (USGS). Based on data analysis, the Spatio-temporal ETAS model expressed in terms of the conditional intensity function shows that earthquakes that occur in the Sumatera region with a large magnitude tend to produce a large conditional intensity function as well. The spatial component of the spatio-temporal ETAS model can be explained by mapping the peak ground acceleration (PGA). The results of the PGA mapping show that seismic activity in the Sumatera region is prone to occur along the north and west coasts of the Sumatra region, with PGA values ranging from 0.2 g to 0.9 g. The larger the PGA value means, the higher the risk of an earthquake occurring in the area marked with a reddish-yellow dot or contour.
Mathematical Model of Coastline Changes in the Ujung Pangkah Gresik Using the Polynomial Lagrange Approach Hendrata Wibisana; Primasari Cahya Wardhani; Novie Handajani
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (6898.664 KB) | DOI: 10.34312/jjom.v5i1.17077

Abstract

Ujung Pangkah Gresik is an area where there is an estuary of a vast river, the Bengawan Solo river. This area annually sends sediment loads transported by the Bengawan Solo river to the Ujung Pangkah estuary so that gradually accumulation occurs, which results in changes in the morphology of the coast on the edge of the Ujung Pangkah coast, Gresik. This study seeks to see the side of shoreline changes on the Ujung Pangkah coast for five years. Then take the change model as a mathematical model that can describe the speed of shoreline change per year using the Lagrange polynomial interpolation method for degrees 1, degrees 2, and degrees 3. The results of shoreline changes that occur are obtained by the Lagrangge polynomial algorithm of degree 3 with R2 of 0.3747 which has a better correlation than degree 1 (0.0313) and degree 2 (0.3741). The results of this study obtained a mathematical model with a Lagrange polynomial approach where degree 3 has the best correlation among other models. This study concludes that by using a mathematical model, an overview of the process of change or natural phenomena can be obtained, where the existing model can predict future changes in the existing coastline.
Peramalan Return Saham Menggunakan Model Integrated Moving Average Rizki Apriva Hidayana; Budi Nurani Ruchjana
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3360.988 KB) | DOI: 10.34312/jjom.v5i1.17381

Abstract

A popular investment that is in great demand among investors is stocked. Stocks are another type of financial instrument offering returns but carrying a higher risk level. Price time series are more difficult to manage than return time series. To equip investors with the knowledge to forecast future stock prices, mathematical models can be used to simulate stock price fluctuations. The time series method, especially the Integrated Moving Average (IMA) model, is a model that can be used to observe changes in stock prices. The Integrated Moving Average (IMA) model will be used in this study to simulate stock returns. The Integrated Moving Average (IMA) model is a Moving Average model that is carried out with a differencing process or an Autoregressive Integrated Moving Average model with a value of Autoregressive being zero. This study uses secondary data simulations from secondary sources, such as data on daily business stock prices for one year, to conduct a literature review and test experiments. The Integrated Moving Average (IMA) model is used in data processing, especially to test the differencing data process. The results obtained are the IMA (1,1) model with the following equation Zˆt = Zt-1 + 0, 5782at-1, which can be used to anticipate future stock returns. Based on these results, it is expected that investors can predict the value of shares within a certain period of time.
Optimasi Portofolio Saham Syariah Menggunakan Model Indeks Tunggal dan VaR Berbasis GUI Matlab Lindrawati Abdjul; Resmawan Resmawan; Agusyarif Rezka Nuha; Nurwan Nurwan; Djihad Wungguli; La Ode Nashar
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.18570

Abstract

Sharia-based investment is an investment by the community to obtain profits in accordance with Islamic principles and law. This study aims to calculate the optimal portfolio return value using the Single Index Model, calculate risk with VaR (Value at Risk), and then implement it with Matlab’s GUI (Graphical User Interface). The data used is closing stock price data on the JII (Jakarta Islamic Index) using 30 stocks for two consecutive years. Furthermore, these stocks are selected which have a positive average return value. The study results show that 14 stocks are candidates for optimal portfolios with positive return values, namely: ACES, ADRO, ANTM, BRPT, BTPS, CTRA, EXCL, INCO, MDKA, MNCN, SCMA, TPIA, UNTR, and WIKA. Then the optimal portfolio of the 14 stocks is determined using the Single Index Model considering the ERB (Excess Return to Beta) value ≥ cut-off point value (C*). Based on the value, 4 shares were obtained that belong to the optimal portfolio, namely: MDKA, BRPT, BTPS, and ANTM. Furthermore, VaR calculations are performed on the 4 optimal portfolios to obtain optimum VaR consistency values with 500 repetitions. The VaR calculation results with a 95% confidence level show that the average VaR result is in the range of -0.14704 to -0.3420 so that when investors invest in 4 optimal stocks, the losses experienced by investors are no more than 34%.
Near-Modul Kuat Faktor Meryta Febrilian Fatimah; Darmawati Darmawati
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.17595

Abstract

Near-ring is a generalization of ring. In ring theory, let R be a ring over addition and multiplication operation with unity element and G be a commutative group under addition operation. Then G together with scalar multiplication of R and holds several axioms called module over ring. Let N be a near-ring, here we can construct the module over near-ring called near-module. We have three definitions of module over near-ring, such that, near-module, modified near-module, and strong near-module. Then we showed that strong near-module can be constructed into strong near-module factor as well as module factor in G. Furthermore, we generalized the fundamental theorem of homomorphism in module over ring to strong near-module over strong near-ring.
Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali Windy Lestari; Adika Setia Brata; Alfian Anhar; Suci Rahmawati
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.18681

Abstract

Poverty is a crucial problem that often occurs in Indonesia, including in the province of Bali. The poverty rate rate in Bali has increased significantly over the last 3 years, so further analysis is needed so that this poverty rate can be reduced. The pattern of poverty between regions is different because it depends on the geographical and socio-cultural conditions in each area. The effect of location on poverty cases can be identified by global and local spatial autocorrelation methods. This study aimed to analyze the spatial autocorrelation of poverty data in Bali Province using the global autocorrelation test with the Moran’s and Geary’s C indices as well as the local spatial autocorrelation test with the Local Indicator of Spatial Association (LISA) and Getis-Ord G to obtain an overview of the spatial distribution of poverty data. Based on the global autocorrelation test, it is concluded that using Moran’s index there is a negative spatial autocorrelation in the 2020-2022 data for a=10%. Similar results were also obtained when using the Geary’s C test. In the local autocorrelation test using LISA, it was found that districts had negative spatial autocorrelation, namely in 2020 Buleleng Regency (high-low) and Klungkung (low-low), for 2021 there is Buleleng Regency (high-low) and Jembrana (low-low), while for 2022 only Buleleng Regency (high-low) has negative spatial autocorrelation. For local autocorrelation testing with the Getis-Ord G test, it was found that there were no districts/cities that showed spatial grouping or that there was no spatial autocorrelation locally.
Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network Hikmah Hikmah; Asrirawan Asrirawan; Apriyanto Apriyanto; Nilawati Nilawati
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5265.505 KB) | DOI: 10.34312/jjom.v5i1.17496

Abstract

Extreme weather modeling is a challenge for modeling experts in Indonesia and the world. Extreme weather prediction is a complex problem because the chances of it happening are very small, so the developed models often have a low level of accuracy. The purpose of this research is to combine the classic model, Autoregressive Integrated Moving Average (ARIMA), recurrent neural network (RNN) model using Adam and SGD estimation (RNN-Adam and RNN-SGD) with the reLU, tanh, sigmoid and gaussian activation functions. In addition, the ARIMA-RNN mix model was also demonstrated in this study. These models are applied to monthly period extreme weather data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of West Sulawesi Province which are converted into training data and test data. The RMSE value is used to see the level of prediction accuracy in both training data and test data. Based on the research results, the best model obtained for modeling Indonesia’s extreme weather is the ARIMA-RNN-Adam mix model with the reLU activation function based on the RMSE value on the training and test data. At n = 50, the smallest RMSE and MSE values of the third model are the ARIMA-RNN-Adam model which is 0.23212 using the reLU activation function, then the ARIMA-RNN-SGD model which is 0.25432 with the same activation function, while the ARIMA value is 0.3270. At n=100 it can be seen that the smallest RMSE and MSE values of the three models are the ARIMA-RNN-Adam model which is equal to 0.25149 using the reLU activation function, then the ARIMA-RNN-SGD model which is equal to 0.25256 with the same activation function, while the ARIMA value is 0.2644.

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