Jurnal Matematika: MANTIK
Jurnal Matematika MANTIK is a mathematical journal published biannually by the Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya. Journal includes research papers, literature studies, analysis, and problem-solving in Mathematics (Algebra, Analysis, Statistics, Computing and Applied).
Articles
119 Documents
<![CDATA[Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia ]]>
<![CDATA[Rini Silvi]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.1.22-31
<![CDATA[Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed even though outliers often contain important information. HIV/AIDS is a serious challenge for global public health since HIV/AIDS is an infectious disease attacking body’s immune system that in turn lowering the ability to fight infections which in the end causing death. HIV/AIDS indicators data in Indonesia contain outliers. This research uses gap statistic to define the number of clusters based on HIV/AIDS indicators that groups Indonesia provinces into 7 clusters. By comparing Sw/Sb ratio, Centroid Linkage clustering is more homogenous than K-means clustering. Using clustering, the government shall be able to create a better policy for fighting HIV/AIDS based on the dominant indicators in each cluster.]]>
<![CDATA[Optimisasi Perencanaan Produksi Pupuk Menggunakan Firefly Algorithm ]]>
<![CDATA[Dinita Rahmalia]]>;
<![CDATA[Awawin Mustana Rohmah]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.1.1-6
<![CDATA[In Indonesia, there are many farmers as a livelihood because of fertile soil for agriculture and the demand for food. Production planning is the important part of managing cost spent by the company. In production planning, there are many constraints which have to be satisfied such as the number of productions, the number of workers, and the number of inventory. In previous research, constrained optimizations have been solved by exact method or heuristic method. In this research, production planning optimization will be solved by Firefly Algorithm (FA). FA works as a behavior of Firefly. One of firefly behavior used is less bright firefly will move toward brighter firefly. The simulation results show that FA method can find an approaching optimal solution of production planning like production cost, worker cost, and inventory holding cost satisfying the constraints of the number of productions, workers, and inventory.]]>
<![CDATA[Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika ]]>
<![CDATA[Maulida Nurhidayati]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.1.16-21
<![CDATA[Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, whereas each regime following an autoregressive (AR) model. The division of the regime based on the score of the delay and threshold of the data itself. The number of SETAR model parameters not only resulted from the best model search process but also resulted in a SETAR model that is not yet optimum. Based on these findings, this study used Genetic Algorithm (GA) to produce the best and optimum SETAR model. In this research, using SETAR simulation data modeling and return data of Bank Rakyat Indonesia (BRI) were performed. The method used to model the data is Grid Search (GS) and Genetic Algorithm (GA). The result of analysis of SETAR simulation data shows that GA method gives better modeling result than GS method. The GA motive AIC value for the amount of 200 data is -3.976178 which is smaller than the AIC GS method of 1.361723. For the amount of data of 500 AIC values, GA method is also smaller than AIC GS method. In BRI stock return data, GA method also gives better modeling result compared to GS. It is marked by the GA AIC method value of -11147.66 less than -11146.26 which is the AIC method of GS. Thus, the result of analysis of SETAR model simulation data and BRI stock return shows that GA method gives better modeling result compared to GS method based on generated AIC value.]]>
<![CDATA[Aplikasi Jaringan Bayes pada Pembuatan Butir Soal Tes ]]>
<![CDATA[Wahyu Hartono]]>;
<![CDATA[Tonah Tonah``]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.1.49-52
<![CDATA[The course of differential calculus is essential because it is a prerequisite material in most classes at the next level. From experience, most of the students have not been able to master the prerequisite topic. These conditions will disrupt the teaching and learning process. Information about the students' initial knowledge will be useful for applying appropriate learning models. This research describes Bayes network application on the manufacture of items about the fixed and adaptive test related to differential calculus courses. The research method is an experiment. The sample used is the students of mathematics education program as many as 98 students who already finish differential calculus course. The results showed that the performance of adaptive test design in predicting student ability is better than fixed test design, especially after the fifth question. The performance of the fixed test items sorted from easy to difficult is better than other fixed test designs. This study is useful for making diagnostic test questions in mapping/predicting students' initial knowledge as well as evaluating their abilities. The suggestion for further research is to make the performance of fixed test design is equivalent to adaptive test in diagnostic capability.]]>
<![CDATA[Penyelesaian Sistem Persamaan Linier Fully Fuzzy Menggunakan Metode Dekomposisi Nilai Singular (SVD) ]]>
<![CDATA[Corry Corazon Marzuki]]>;
<![CDATA[Agustian` Agustian`]]>;
<![CDATA[Dewi Hariati]]>;
<![CDATA[Junitis Afmilda]]>;
<![CDATA[Nurul Husna]]>;
<![CDATA[Putra Nanda]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.2.143-149
<![CDATA[Linear equation system can be arranged into the AX = B matrix equation. Constants in linear can also contain fuzzy numbers and all their parameters in fuzzy numbers known as fully fuzzy linear equation systems. singular value decomposition (SVD) is a method that decomposes an A matrix into three components of the USVH. The SVD method can be used to find a solution to the fully fuzzy fully linear equation system that is also an inconsistent fully fuzzy linear equation system. The solution obtained from a fully fuzzy linear equation system that is consistent using SVD is a single solution and many solutions. Whereas, the solution obtained from a fully fuzzy linear equation system that is inconsistent using SVD is the best approach solution.]]>
<![CDATA[Sebarang Pembangun Subgrup Siklik Dari Suatu Grup (Zn,+) ]]>
<![CDATA[Indra Bayu Muktyas]]>;
<![CDATA[Samsul Arifin]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.2.116-121
<![CDATA[(Zn,+) is a group of the integer modulo n with an addition operation. A cyclic subgroup is a subgroup that is generated by one element of a group. In group (Zn,+), any cyclic subgroup can be determined through a generator which is a factor of n. The aim of this article is to get all generator of the cyclic subgroup of a group (Zn,+) using Python. The result of our study shows that by using Python, for any cyclic subgroup of (Zn,+) , we can get all their generator.]]>
<![CDATA[Pemodelan Kriminal di Jawa Timur dengan Metode Geographically Weighted Regression (GWR) ]]>
<![CDATA[Imanudin Nurhuda]]>;
<![CDATA[I Gede Nyoman Mindra Jaya]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.2.150-158
<![CDATA[Criminality constitutes all kinds of actions that are economically and psychologically harmful in violation of the law applicable in the state of Indonesia as well as social and religious norms, while the criminal data is the number of cases reported to the police institution. The higher the number of complainants the higher the number of criminals in the region. The greater the risk the community represents the more insecure a region is. This study aims to obtain the best model affecting crime or crime in East Java. The number of crimes in this study is limited to the number of theft cases (whether ordinary theft, theft by force, theft with theft, and the theft of motor vehicles). In this study, we use the Geographically Weighted Regression (GWR) model because this method is quite effective in estimating data that has spatial heterogeneity (uniformity in location / spatial). In essence, the model parameters in GWR can be calculated at the observation location with the dependent variable and one or more independent variables that have been measured at the sites where the location is known, where criminal acts in the research conducted in East Java involves the effects of spatial heterogeneity, with fixed kernel weighting function. The results showed that the variables affecting criminality in East Java Province are population density, economic growth, Gini Ratio, and poverty.]]>
<![CDATA[Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network ]]>
<![CDATA[Mertha Endah Ervina]]>;
<![CDATA[Rini Silvi]]>;
<![CDATA[Intaniah Ratna Nur Wisisono]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.2.90-99
<![CDATA[Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.]]>
<![CDATA[Prediksi Cuaca Kota Surabaya Menggunakan Autoregressive Integrated Moving Average (Arima) Box Jenkins dan Kalman Filter ]]>
<![CDATA[Nurissaidah Ulinnuha]]>;
<![CDATA[Yuniar Farida]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2018.4.1.59-67
<![CDATA[Season changes conditions in Indonesia cause many disasters such as landslides, floods and whirlwinds and even hail. Extreme weather conditions that occur, it is better to remain alert to anticipate the various possibilities that occur and to reduce and minimize the impact that can harm the people. The design of weather prediction system in this research using Autoregressive Integrated Moving Average ARIMA Box Jenkins model and Kalman filter with the aim to predict the increasingly extreme weather of Surabaya city at the end of 2017. In this research, weather prediction focused on humidity, temperature, and velocity wind with results 5 days later. The prediction of Surabaya city weather using ARIMA method - Kalman filter obtained the smallest error goal (error MAPE) of 0.000014 each for the prediction of humidity, 0.000037 for temperature prediction, and 0.0123 for wind speed prediction.]]>
<![CDATA[Analisis Faktor-Faktor yang Mempengaruhi Sektor Moneter Berdasarkan Jumlah Uang yang Beredar Pada Statistik Ekonomi dan Keuangan Indonesia (SEKI) ]]>
<![CDATA[Dian C. Rini Novitasari]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 3 No. 2 (2017): Mathematics and Applied Mathematics ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2017.3.2.105-111
<![CDATA[One of the impacts of the economic crisis that occurred in Indonesia was influenced by the monetary sector that moved the macroeconomy into the economy of the society such as currency rate of exchange and rate of interest policies applied in all banks in Indonesia. Analysis of the factors that influence the money supply is one step that can be used to analyze economic statistics and the financial condition of Indonesia. The analysis begins with finding the regression equation using a nonlinear regression model of several factors used such as foreign activities, bills to the central government, bills to the private sector, and the value of Gross Domestic Product (GDP). Other tests were also carried out such as the data normality test, heteroscedasticity test, and autocorrelation test to read data characteristics used in the 1999-2016 period using SPSS and then analyzed. Where the results obtained in the regression of the central government independent variables do not have a significant effect, the data normality test shows that the data is normally distributed, heteroscedasticity does not occur, and the data does not contain autocorrelation.]]>
