MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
MATICS is a scientific publication for widespread research and criticism topics in Computer Science and Information Technology. The journal is published twice a year, in March and September by Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia. The journal publishes two regular issues per year in the following areas : Algorithms and Complexity; Architecture and Organization; Computational Science; Discrete Structures; Graphics and Visualization; Human-Computer Interaction; Information Assurance and Security; Information Management; Intelligent Systems; Networking and Communication; Operating Systems; Platform-Based Development; Parallel and Distributed Computing; Programming Languages; Software Development Fundamentals; Software Engineering; Systems Fundamentals; Social Issues and Professional Practice.
Articles
237 Documents
<![CDATA[Mengukur Performa Model TSK Fuzzy Logic Menggunakan Faktor Eksternal untuk Peramalan Laju Inflasi ]]>
<![CDATA[Sari, Nadia Roosmalita]]>;
<![CDATA[Mahmudy, Wayan Firdaus]]>;
<![CDATA[Wibawa, Aji Prasetya]]>
<![CDATA[ MATICS Vol 9, No 1 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i1.3932
<![CDATA[Pertumbuhan ekonomi merupakan salah satu tolak ukur menilai perkembangan ekonomi negara. Inflasi merupakan kecenderungan naiknya harga barang secara umum dan terjadi terus-menerus. Sehingga inflasi dapat dijadikan sebagai tolak ukur untuk menilai perkembangan suatu negara. Inflasi merupakan salah satu permasalahan yang sering menjadi topik pembahasan di kalangan pakar ekonomi. Inflasi dapat dipengaruhi oleh berbagai faktor, misalnya pola konsumtif masyarakat yang tinggi. Perekonomian Indonesia akan menurun jika inflasi tidak dikendalikan dengan baik. Untuk mengendalikan laju inflasi dibutuhkan sebuah peramalan terhadap laju inflasi di Indonesia. Hasil peramalan digunakan sebagai informasi bagi pemerintah untuk menyiapkan kebijakan agar laju inflasi tetap dalam keadaan stabil. Penelitian ini mengusulkan Takaghi Sugeno Kang (TSK) fuzzy logic untuk peramalan laju inflasi. Penelitian ini bertujuan untuk mengukur performa sistem dengan menggunakan faktor-faktor yang mempengaruhi laju inflasi. Data yang digunakan pada penelitian ini adalah data historis dan faktor eksternal sebagai parameter. Untuk mengevaluasi hasil peramalan digunakan teknik analisis Root Mean Square Error (RMSE). Hasil penelitian menunjukkan bahwa penggunaan parameter time series dan faktor eksternal CPI memiliki performa sistem yang lebih baik dibandingkan faktor-faktor lain dengan RMSE sebesar 1.328.]]>
<![CDATA[The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang ]]>
<![CDATA[Santika, Gayatri Dwi]]>;
<![CDATA[mahmudy, wayan f]]>
<![CDATA[ MATICS Vol 9, No 1 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i1.3968
<![CDATA[This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE) of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.]]>
<![CDATA[Pendukung Keputusan Penentuan Jumlah Order Menggunakan Fuzzy Mamdani ]]>
<![CDATA[Sonalitha, Elta]]>
<![CDATA[ MATICS Vol 9, No 2 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i2.4373
<![CDATA[To get customer satisfaction, a restaurant should always provide raw materials in accordance with the menu. Each raw material has a different demand based on an uncertain customer interest. Purchasing managers have difficulty in determining the number of orders for each raw material, due to the uncertainty of demand and supply. Therefore we built decision support system for determining the number of orders using fuzzy mamdani. From decision support system we get ROP and recommendation of the number of orders accompanied by the total purchase price for each raw material. This system helps the purchasing managers in determining the amount of orders quickly and precisely by considering the losses, especially in the field of financial management.]]>
<![CDATA[Penerapan Fitur Warna Untuk Identifikasi Plasmodium Falciparum pada Sediaan Apus Darah Menggunakan MK-Means dan Jaringan Backpropagation ]]>
<![CDATA[hamid, mustamin]]>
<![CDATA[ MATICS Vol 8, No 2 (2016): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v8i2.3730
<![CDATA[Abstract - This research proposed a system to identify Plasmodium falciparum on blood smear  using the neural network  backpropagation. Modified K-Means (MK-Means) is used to separate between the object with the background image because that method was able to equalize the value of fitness at all Center cluster so there is no dead center and can also cope with the local minimum value. The extraction of the features used in this study consists of color features i.e. calculation of the mean, standard deviation, skewness, curtosis and entropy of co-occurent matrix with the purpose to get the values of all the trait value image, obtained are then used to train a neural network with the backpropagation training algorithm. Method of backpropagation networks capable of acquiring knowledge even though there is no certainty, able to perform a generalization and extraction of a specific data pattern.                       The image of the preparations blood smear are classified using the method of neural network Backpropagation. The test results obtained from Tropozoit with the accuracy 100%, scizon 80% and gametocytes 80%. Identification is then obtained outcomes the introduction with an average accuracy of 86,66%.]]>
<![CDATA[Eggs Fertilities Detection System on the Image of Kampung Chicken Egg Using Naive Bayes Classifier Algorithm ]]>
<![CDATA[Diantoro, Aris]]>;
<![CDATA[Santoso, Irwan Budi]]>
<![CDATA[ MATICS Vol 9, No 2 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i2.4198
<![CDATA[Losses in chicken eggs hatchery make breeders income declined. The main cause of these things because it is less effective and efficient in distinguishing the state of fertilities in the eggs. The detection of fertile and infertile eggs will automatically provide ease of selection and removal of the eggs are fertile and infertile eggs. This will bring more profits for breeder as well as time efficiency more and selling power. Infertile eggs will give breeders the sale price if it is known as early as possible in order not to fail hatching. A method fuzzy c means and naive bayes classifier is designed to identify the state of the fertility of eggs. By putting eggs near the source light and black background in a dark room, then taked of image with a high qualities camera. From the resulting camera image, then extracted features or take characteristics that distinguish between fertile and infertile eggs. The total amount of data used in this study of 450 eggs image sourced from the field survey. Training data is used  250 data, 125 fertile eggs image data and 125 infertile eggs image data. As for testing the data using the 200 data, the image data 150 fertile eggs and 50 infertile eggs image data. Based on trial results of training data is obtained the best accuracy is equal to 80% at intervals of 5, 86.4% at intervals of 5 and dimensions 70x60, and 99.6% on 1x2 resize. The accuracy of the results obtained by 78%, 82% and 94% in trials testing data.]]>
<![CDATA[Sistem Identifikasi Kandungan Boraks pada Bakso Daging Sapi Berbasis Android Menggunakan Algoritma Naive Bayes Classifier ]]>
<![CDATA[Purwanto, Sofi Dwi]]>;
<![CDATA[Santoso, Irwan Budi]]>
<![CDATA[ MATICS Vol 9, No 1 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i1.3954
<![CDATA[Poin pertama yang menjadi fokus dalam pemenuhan keamanan adalah keamanan dalam bidang pangan (food security). Di Indonesia, masih terdapat beberapa fenomena seperti penggunaan boraks sebagai zat aktif kimia pada bakso. Hal ini masih menjadi tren yang menjadi hambatan dalam pemenuhan hak manusia dalam mewujudkan keamanan pangan. Penelitian ini mengimplementasikan metode naïve bayes classifiersebagai pendeteksi (detektor) dengan melakukan grayscale dan melakukan estimasi parameter distribusi fitur objek untuk data citra proses training. Sedangkan proses testing juga akan melalui tahap grayscale, selanjutnya proses identifikasi dengan menggunakan fungsi diskriminan dan hasil estimasi parameter distribusi. Jumlah data yang digunakan dalam penelitian sebesar 840 citra meliputi 780 bakso yang dibuat secara mandiri dan 60 data diperoleh dari hasil survey dilapangan. Hasil uji coba menunjukkan hasil terbaik diperoleh dengan tingkat akurasi sebesar 82.7778% untuk dimensi citra 3x4 dengan jumlah data yang diidentifikasi secara benar adalah sebanyak 149 dari 180 data yang digunakan.]]>
<![CDATA[KLASIFIKASI DAN IDENTIFIKASI JUMLAH KOLONI PADA CITRA BAKTERI DENGAN METODE K-NEAREST NEIGHBOR ]]>
<![CDATA[Ihsan, Ihsan]]>
<![CDATA[ MATICS Vol 8, No 2 (2016): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v8i2.3723
<![CDATA[Abstract – This study proposes a system for classification and counting the number of bacterial colonies using a photo image of bacteria. The system uses several image pretreatment process. Including Contrast Stretching, Extended-Maxima Transform, and Regionprops. The main purpose of this system is to determine the category of colonies of bacteria in large quantities can not be done manually. To build the algorithms necessary features must be determined such as diameter, perimeter and roundness method of determining the categories using KNN (K-Nearest Neighbor). As a results of this research is classify three types of bacteria such as Lactobacillus Bulgaricus, Streptococcus thermophiles, and bifidobakterium Precision with a percentage of 97,97% and 87,09% F-MeasureKeywords: Contrast Stretching, Lactobacillus, Regionprops, K-Nearest Neighbor]]>
<![CDATA[Aplikasi Market Matching Berbasis Fuzzy sebagai Penunjang Keputusan Ekspor Produk UMKM ]]>
<![CDATA[Nurdewanto, Bambang]]>
<![CDATA[ MATICS Vol 9, No 2 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i2.4372
<![CDATA[Determining the exact location of the export market with the right amount in the marketing process is expected to reduce the number of losses due to the stagnancy of product turnover. Appropriate target market system using fuzzy control on MSMEs. Fuzzy control method is used to overcome the determination of a market that is influenced by the subjectivity of marketing actors. Online market matching application which is the right decision support system of the right export destination and the right amount so efficient. The result of market matching application of fuzzy method is recommendation of destination and quantity that can be exported.]]>
<![CDATA[Identifikasi Pola Penggunaan Lahan pada Sektor Perikanan dan Peternakan Berbasis Sistem Informasi Geografis ]]>
<![CDATA[Auliasari, Karina]]>;
<![CDATA[Agustine, Thesalonika Nameta]]>
<![CDATA[ MATICS Vol 8, No 2 (2016): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v8i2.3590
<![CDATA[Monitoring and evaluation the quantity of the three sectors, agriculture, livestock and fisheries is conducted independently by each local government through the statistics and annual reports. But the development of information fishery and livestock sectors served from annual reports and statistical results have not been able to provide geographic information systems commodities in each district. The results of the development of the geographic information systems indicates that the category feature helps the user to view a visualization mapping fisheries sector and livestock commodities. Based on the visualization mapping analysis results, fisheries sector shows that six of the districts in Barito Utara that has not only a maximum production of fish from the river and three sub-categories of the category of the lake. For the results of visualization mapping livestock sector analysis in cattle category shows that six districts (Lahei, Teweh Tengah, Teweh Timur, Montalat, Gunung Timang and Gunung Purei) is able to fulfill the food needs of beef as a whole districts.]]>
<![CDATA[Penanganan Fuzzy Time Window pada Travelling Salesman Problem (TSP) dengan Penerapan Algoritma Genetika ]]>
<![CDATA[Yuliastuti, Gusti Eka]]>;
<![CDATA[Mahmudy, Wayan Firdaus]]>;
<![CDATA[Rizki, Agung Mustika]]>
<![CDATA[ MATICS Vol 9, No 1 (2017): MATICS ]]>
Publisher : <![CDATA[Department of Informatics Engineering ]]>
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DOI: 10.18860/mat.v9i1.4072
<![CDATA[The route of the travel tour packages offered by travel agents is not considered optimum, so the level of satisfaction the tourist is not maximal. Selection of the route of the travel packages included in the traveling salesman problem (TSP). The problem that occurs is uncertain tourists visiting destinations at the best destinations timing hereinafter be referred to as the fuzzy time window problem. Therefore, the authors apply the genetic algorithm to solve the problem. Based on test results obtained optimum solution with the fitness value of 1.3291, a population size of 100, the number of generations of 1000, a combination of CR=0,4 and MR=0.6.]]>
