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).
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<![CDATA[Pemodelan Akreditasi SMK di Provinsi Banten dengan Menggunakan Logika Fuzzy Metode Mamdani ]]>
<![CDATA[Syamsuri Syamsuri]]>;
<![CDATA[Indiana Marethi]]>
<![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.2018.4.1.42-48
<![CDATA[This article aims to describe an accreditation model of vocational schools in Banten province that accredited for 2009-2011 using method of Mamdani of fuzzy logic. The data used were obtained from Banten Accreditation Board for Schools/Madrasah (BAP-S/M), 275 expertise in vocational programs are accredited by the BAP-S/M Banten during 2009-2011. In the accreditation model using fuzzy logic assumes that: (1) there are strong correlation among content standards, process standards, competency standards, and assessment standards, so that we use score of process standards in modelling, (2) Standard educators and staff, as well as management standard strongly correlated, so that we choose educators, and (3) standards of infrastructure and financing have strong correlation, so that only one representing one standard, namely : standard of infrastructure. The model can be used in predicting the outcome of a vocational accreditation by just looking scores from the process standard, educators standard, and infrastructures standard. The resulting models have about 68% accuracy rate.]]>
<![CDATA[Pemodelan Tingkat Okupansi Penumpang Kereta Api dari Surabaya dengan Metode S-Sur (Spatial-Seemingly Unrelated Regression) ]]>
<![CDATA[Kuzairi Kuzairi]]>;
<![CDATA[Anwari Anwari]]>;
<![CDATA[Fariz Fadillah Mardianto]]>
<![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.2018.4.1.7-15
<![CDATA[Kereta api merupakan sarana transportasi yang populer di Pulau Jawa yang terdiri dari kelas ekonomi, bisnis atau ekonomi plus, dan eksekutif. Tingkat okupansi dari masing-masing kelas untuk jurusan yang sama juga berbeda. Tingkat okupansi penumpang kereta api yang berangkat dari Surabaya menarik untuk diteliti karena ruang lingkup asal penumpang lebih luas daripada penumpang kereta di Jabodetabek. Asal penumpang kereta api di Surabaya tidak hanya penumpang yang berasal atau memiliki kepentingan di kota Surabaya saja melainkan kabupaten dan kota disekitarnya, sampai Pulau Madura. Dalam penelitian ini dilakukan prediksi tingkat okupansi penumpang kereta api untuk tiap kelas berdasarkan faktor-faktor yang berpengaruh terhadap tingkat okupansi semua kereta api lintas kota yang berangkat dari Stasiun Surabaya Gubeng, dan Pasar Turi menggunakan metode Spatial-Seemmingly Unrelated Regression (S-SUR). Metode S-SUR digunakan karena mampu mengakomodasi efek spasial pada seluruh pengamatan. Penelitian ini terdiri atas 30 pengamatan rute tujuan dari Surabaya, 8 prediktor, dan 3 respon yang saling berkorelasi spasial berdasarkan pengujian Morans I. Hasilnya adalah prediktor yang berpengaruh signifikan terhadap tingkat okupansi penumpang kereta api untuk semua kelas yaitu prediktor yang terkait dengan kependudukan.]]>
<![CDATA[Analisis Angka Harapan Lama Sekolah di Indonesia Timur Menggunakan Weighted Least Squares Regression ]]>
<![CDATA[Arifin M Kahar]]>
<![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.2018.4.1.32-41
<![CDATA[Human Development Index (HDI) is one of the data and information used by the local government to measure the achievement of human development with the basic components of quality of life that is life expectancy that represents health dimension, expected years of schooling (EYS) and mean years of schooling (MYS) represents the educational dimension, and purchasing power parity that represents decent living dimension. HDI especially in eastern Indonesia in the last three years has continued to increase but the figure is always below from the national figure even left behind if compared with West Indonesia. One dimension that is still low achievement is the educational dimension. EYS is one of the indicators on the educational dimension that is still a low achievement. Therefore, this research would like to know the influence of percentage of poor people, Gross Regional Domestic Product (GRDP) per capita, net enrollment rate (NER) of junior high school, and a ratio of educational facilities to EYS in eastern Indonesia. Using Weighted Least Squares (WLS) method concluded that the four predictor variables used were able to influence EYS in Eastern Indonesia.]]>
<![CDATA[Text Mining dengan K-Means Clustering pada Tema LGBT dalam Arsip Tweet Masyarakat Kota Bandung ]]>
<![CDATA[Eko Yulian]]>
<![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.2018.4.1.53-58
<![CDATA[The movement of LGBT is growing rapidly through social media so that LGBT ideas can be freely expressed. The tweeter is one of the media that is often used for that purpose. Comments or "cuitan" about LGBT on twitter certainly many in number. The amount of information available in cyberspace makes development efforts to extract information from online databases rapidly, one of which is text mining. One of the statistical techniques that can be used to utilize the results of text mining is clustering. Clustering used in this study is K-Means clustering. This study uses 5 clusters to group comments on The twitter associated with LGBT in the city of Bandung. Of the five clusters formed in the K-means process, it is found that the tendency of Tuet Tweeter users of LGBT related bands in general, is still related to the religious perspective which is marked by the emergence of the word religion very often.]]>
<![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[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[Identifikasi Citra Daging Ayam Berformalin Menggunakan Metode Fitur Tekstur dan K-Nearest Neighbor (K-NN) ]]>
<![CDATA[Faris Muslihul Amin]]>
<![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.2018.4.1.68-74
<![CDATA[The research aimed to create a fresh chicken meat identification system to detect differences between formalin and non-formalin chicken meat based on the image of raw chicken meat. Feature extraction method used is the Feature Texture method which is included in the statistical method where the statistical calculation uses a gray degree distribution (histogram) by measuring the level of contrast, granularity, and roughness of an area from the neighboring relationships between pixels in the image then feature extraction, results feature extraction is then classified by K-NN. With the classification using K-NN results obtained high classification accuracy. The K-NN method is a very good method of dealing with the problem of recognizing complex patterns in the form of data training and processing calibration, based on very fast and high accurate literature methods more than other methods. Observation images will be carried out at various distances between the smartphone camera and chicken meat samples.]]>
