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
11 Documents
Search results for
, issue
"Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics"
:
11 Documents
clear
<![CDATA[Penerapan Non-Linier Support Vector Machine pada Penggunaan Alat Kontrasepsi di Provinsi Maluku Utara ]]>
<![CDATA[Muhamad Budiman Johra]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (263.199 KB)
|
DOI: 10.15642/mantik.2018.4.2.137-142
<![CDATA[The objective of BKKBN is to reduce the rate of population growth because the high population growth rate causes a high population quantity as well. According to the Departemen Kesehatan RI (2013), married women aged 15-49 years who are not use contraseption mostly in eastern Indonesia, one of them is Provinsi Maluku Utara. According to BKKBN Provinsi Maluku Utara, the birth rate increased from 57.4 to 57.9. This happens because many KB participants are drop out, contraceptive failure and side effects, the need for family planning is served 9.1 in 2007 to 8.5 in 2012 with a target of 5 in 2014. Therefore, it important to know determinant factors that affect women to use contraceptives. There are several methods in the classification, one of which is the Support Vector Machine (SVM). SVM has advantages over other classification methods because the Support Vector Machine not only minimizes errors in the trainset, but also has a high generalization capability. This is reflected in maximal margin selection. This study shows the Support Vector Machine can describe the decision of women to use contraception or not. The best kernel in this study is a radial base kernel with cost 1 and gamma 0.14286.]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (284.718 KB)
|
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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (378.41 KB)
|
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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (332.218 KB)
|
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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (529.818 KB)
|
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[Regresi Nonparametrik dengan Pendekatan Deret Fourier pada Data Debit Air Sungai Citarum ]]>
<![CDATA[Intaniah Ratna Nur Wisisono]]>;
<![CDATA[Ade Irma Nurwahidah]]>;
<![CDATA[Yudhie Andriyana]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (368.256 KB)
|
DOI: 10.15642/mantik.2018.4.2.75-82
<![CDATA[River discharge is one of the factors that affect the occurrence of floods. It varies over time and hence we need to predict the flood risk. Since the plot of the data changes periodically showing a sines and cosines pattern, a nonparametric technique using Fourier series approach may be interesting to be applied. Fourier series can be estimated using OLS (Ordinary Least Square). In a Fourier series, nonparametric regression the level of subtlety of its function is determined by their bandwidth (K). Optimal bandwidth determined using the GCV (Generalized Cross Validation) method. From the calculation results, we have optimal bandwidth which is equal to 16 with R2 is 0.7295 which means that 72.95% of the total variance in the river discharge variable can be explained by the Fourier series nonparametric regression model. Comparing to a classical time series technique, ARIMA Box Jenkins, we obtained ARIMA (1,0,0) with RMSE 83.10 while using Fourier series approach generate a smaller RMSE 50.51.]]>
<![CDATA[Analisis Faktor-Faktor yang Mempengaruhi Jumlah Kematian Ibu dan Bayi di Provinsi Jawa Tengah Menggunakan Regresi Bivariat Poisson ]]>
<![CDATA[Mutiara Widhika Astuti]]>;
<![CDATA[A’yunin Sofro]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (308.634 KB)
|
DOI: 10.15642/mantik.2018.4.2.110-115
<![CDATA[Maternal and infant mortality are two correlated subjects, because during pregnancy the mother's placenta distributes nutrients to the fetus so the baby born is affected by the condition of his mother. Central Java has significant maternal and neonatal mortality rates in Indonesia. In this case, need a research to analyze the factors that influence maternal and infant mortality using Bivariate Poisson Regression (BPR) method. BPR is the right method because it can reconfirm two data that are correlated with Poisson distribution. This study produced three models. The first model is the maternal mortality rate has several significant factors, including pregnant women implementing the K1 and K4 program, vitamin A to postpartum mothers, pregnant women getting Fe tablets, and midwifery handle complications. The second model is the infant deaths that have factors pregnant women implementing the K4 program, helped assistance by medical team, postpartum mothers receiving vitamin A, pregnant women getting Fe tablets, complications handled by midwifery, and KB participants. The final model involves maternal and infant mortality. Significant factors are pregnant women implementing the K1 program, pregnant women implementing the K4 program, giving vitamin A to postpartum mothers, and KB participants.]]>
<![CDATA[Peramalan Kebutuhan Daya Listrik Menggunakan Model ARIMA dan Fungsi Transfer (Studi Kasus: PT. PLN (Persero) Area Sumbawa) ]]>
<![CDATA[Mikhratunnisa Mikhratunnisa]]>;
<![CDATA[Tri Susilawati]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (277.487 KB)
|
DOI: 10.15642/mantik.2018.4.2.122-127
<![CDATA[Energy is one of the basic need of human being. One of the vital energy is electricity. The need of electricity in NTB is increase along with the citizen economic development in NTB especially in Sumbawa regency. Therefore, there is a need for the right way in adjusting the amount of electrical capacity to match customer demand. One way that can be done is to forecast/ predict the need for electricity. The forecast can be used by using the ARIMA and Transfer Function models. The results of the study show that using the ARIMA model is estimated to require electricity in 2018 experienced an increase of 18,21% from the previous year, while using the transfer function model is estimated to increase by 18,18% from the previous year.]]>
<![CDATA[Klasifikasi Alzheimer dan Non Alzheimer Menggunakan Fuzzy C-Mean, Gray Level Co-Occurence Matrix dan Support Vector Machine ]]>
<![CDATA[Dian C. Rini Novitasari]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (370.119 KB)
|
DOI: 10.15642/mantik.2018.4.2.83-89
<![CDATA[Based on the Alzheimer's Charter, 2-3 million cases of dementia by Alzheimer's disease occur every year. People with Alzheimer's disease experience memory and cognitive disorders progressively for 3 to 9 years. Patients experience confusion in understanding the question and have a chaotic sequence of memory, which can interfere with daily activities and unchecked well, it cause death. The classification system is based on Alzheimer's and non-Alzheimer's disease Magnetic Resonance Imaging (MRI) using Support Vector Machine (SVM). The feature data segmentation using Fuzzy C-Means (FCM) and feature extraction using Gray Level Co-Occurrence Matrix (GLCM) and give accuracy result of 93.33%.]]>
<![CDATA[Analisis Regresi Kuantil B-Splines Monoton Naik pada Hubungan Rata-Rata Lama Sekolah dan Pengeluaran Rumah Tangga Per Kapita di Provinsi Yogyakarta ]]>
<![CDATA[Yogo Aryo Jatmiko]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (480.936 KB)
|
DOI: 10.15642/mantik.2018.4.2.128-136
<![CDATA[The multidimensional problem in various countries that is always become the government's attention is the problem of poverty, Indonesia is no exception. Poverty is often associated with the education sector due to the function of education as a driving force of the transformation of society to break the chain of poverty. The pattern of relations between poverty and the education sector can be seen from the relationship between the level of education (mean years of schooling) and poverty level (per capita household expenditure). DI Yogyakarta is still the province with the largest percentage of poverty on the Java island despite showing a downward trend since 2007. This study aims to look at the relationship between the level of education (mean years of schooling) and poverty level (per capita household expenditure) in DI Yogyakarta Province 2016. The model that is suitable for determining household characteristics is quantile regression with the Increased monotone B-Splines method that links the mean years of schooling and per capita household expenditure. Estimation results based on the quantile regression model with Increased monotone B-Splines method found that households with the lowest education level are said to be very poor households if monthly per capita expenditure is less than 322,205 rupiah and is said to be a poor household if monthly per capita expenditure is between 322,205 rupiah to 426,666 rupiah. Meanwhile, households with the highest level of education are said to be very poor households if monthly per capita expenditure is less than 3,410,965 rupiahs and is said to be a poor household if monthly per capita expenditure is between 3,410,965 rupiahs up to 4,676,718 rupiahs]]>
