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Deny Zainal Arifin
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Jurusan Teknik Informatika Fakultas Sains dan Teknologi Universitas Islam Negeri Maulana Malik Ibrahim Malang Jalan Gajayana 50 Malang, Jawa Timur, Indonesia 65144
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MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
Published by Universitas Islam Negeri Maulana Malik Ibrahim Malang
ISSN : 1978161X     EISSN : 24772550     DOI : https://doi.org/10.18860/mat
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 237 Documents
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Ekstraksi Ciri Sinyal EEG Untuk Gangguan Penyakit Epilepsi Menggunakan Metode Wavelet ANI, WIWIT PUTRI
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v9i2.4376

Abstract

Abstrak- Epilepsy terjadi karena ada gangguan sistem saraf otak pada manusia, yang terekam dari sinyal Elektroensephalogram. Sinyal Elektroensephalogram memiliki informasi aktivitas listrik pada otak, termasuk kondisi gangguan kelistrikan dan pikiran pada syaraf. Sinyal Elektroensephalogram mempiliki bentuk yang kompleks, mudah tertimbun noise , amplitudo kecil dan tidak memiliki pola yang baku, sehingga analisa secara visual tidak mudah[1] Untuk meningkatkan akurasi dan menghilangkan noise dari sinyal EEG, penelitian ini menggunakan metode Wavelet sebagai proses ekstraksi ciri dan Backpropagation untuk klasifikasi. Data sinyal Elektroensephalogram didapat dari Universitas Bonn yang terdiri dari 5 kelas dataset yaitu A, B, C, D, dan E. Tiap dataset berisi 100 segmen EEG saluran tunggal dengan durasi selama 23.6 detik. Peneliti menggunakan dataset B dan E. Pada tahap pelatihan (training) menggunakan 80 naracoba , sedangkan pada tahap pengujian (testing) menggunakan 100 naracoba. Proses ini dilakukan setelah ekstraksi ciri sinyal EEG dengan Wavelet. Hasil ekstraksi ciri digunakan sabagai nilai input, pada penelitian ini menggunakan metode back propagation (16-35-2) yaitu 2 input sinyal EEG,  satu hidden layer dengan 35 unit dan dua target epilepsy dan non epilepsi . dari pengujian data tersebut didapat nilai akurasi sebesar 100%. Kata kunci : Backpropagation, Wavelet, epilepsy, EEG
Detection System Milkfish Formalin Android-Based Method Based on Image Eye Using Naive Bayes Classifier hadini, fadhil muhammad
MATICS Vol 9, No 1 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.957 KB) | DOI: 10.18860/mat.v9i1.4054

Abstract

In this study was researcher trying to make an android-based application that can identify fish with formalin. The method used in researcher methods naïve Bayes classifier as a detector (detector) with the object input in the form of fish eye image. The steps in the study include the training and testing process. In the training process used to build the model naïve classifier and estimation parameters. While testing process, implement the results of the model and parameter estimation have been built to detect fish formalin or not formalin. The trial results demonstrate the ability-based applications using the naïve Bayes 98.3% for object dimensions 10x10 image
Aplikasi MATLAB untuk Mengenali Karakter Tulisan Tangan mahmudi, ali
MATICS Vol 9, No 1 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (839.953 KB) | DOI: 10.18860/mat.v9i1.4128

Abstract

Handwriting recognition is one of the very interesting research object in the field of image processing, artificial intelligence and computer vision. This is due to the handwritten characters is varied in every individual. The style, size and orientation of handwriting characters has made every body’s is different, hence handwriting recognition is a very interesting research object. Handwriting recognition application has been used in quite many applications, such as reading the bank deposits, reading the postal code in letters, and helping peolple in managing documents.This paper presents a handwriting recognition application using Matlab. Matlab toolbox that is used in this research are Image Processing and Neural Network Toolbox. 
Mengukur Performa Enterprise Architecture Framework Menggunakan Fuzzy Tsukamoto Nur Azizi, Fakri Fandy
MATICS Vol 8, No 2 (2016): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (719.014 KB) | DOI: 10.18860/mat.v8i2.3557

Abstract

Enterprise Architecture (EA) adalah deskripsi dari misi stakeholder yang menggambarkan rencana pengembangan sebuah sistem atau sekumpulan sistem untuk mencapai sebuah misi organisasi melalui performansi optimal dari proses bisnis dalam sebuah lingkungan TI yang efisien. Untuk bisa menerapkan EA dalam sebuah organisasi, dibutuhkan kerangka kerja yang bersifat fundamental dan satu set alat pendukung yang digunakan untuk mengembangkan suatu EA. Pengukuran performa EA framework dirasa perlu, untuk mengetahui EA framework yang applicable pada kondisi saat ini.  Sehingga dibutuhkan sebuah decision support untuk membantu memilih EA framework berdasarkan kriteria penilaian dari sisi artifact, governance, strategy, consistency, requirement, guidelines, dan continual. Pada makalah ini dibahas pembuatan decission support system untuk mengukur performa EA framework menggunakan Sistem Inferensi Fuzzy Tsukamoto. Parameter yang digunakan untuk batasan fungsi keanggotaan fuzzy berdasarkan data yang diperoleh dari pakar yaitu artifact, governance, strategy, consistency, requirement, guidelines, dan continual. Akurasi sistem dihitung berdasarkan hasil perbandingan dari keluaran sistem dengan hasil penilaian pakar.
Pendeteksian Ketidaklengkapan Kebutuhan Dengan Teknik Klasifikasi Pada Dokumen Spesifikasi Kebutuhan Perangkat Lunak Nurfauziah, Suci
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.237 KB) | DOI: 10.18860/mat.v9i2.4291

Abstract

Dokumen Spesifikasi Kebutuhan Perangkat Lunak (SKPL) dihasilkan dari proses rekayasa kebutuhan dan merupakan tahapan yang kritis pada pengembangan perangkat lunak. Kesalahan yang terjadi pada proses rekayasa kebutuhan akan mempengaruhi ketidakberhasilan produk tersebut. Dokumen SKPL sering kali ditulis dengan bahasa alamiah. Salah satu karakteristik spesifikasi kebutuhan yang baik adalah lengkap. Kualitas spesifikasi kebutuhan bisa dinilai berdasarkan pernyataan kebutuhan atau dokumen kebutuhan. Spesifikasi kebutuhan yang lengkap secara jelas mendefinisikan semua situasi yang dihadapi sistem dan dapat dipahami tanpa melibatkan atau terkait pada kebutuhan lain. Penelitian ini bertujuan untuk membangun model klasifikasi pendeteksian ketidaklengkapan kebutuhan pada dokumen spesifikasi kebutuhan perangkat lunak yang ditulis dengan bahasa alamiah. Penelitian ini membuat corpus kebutuhan yang berisi pernyataan kebutuhan lengkap dan pernyataan kebutuhan tidak lengkap. Corpus ditulis secara manual oleh tiga orang ahli. Dari Corpus akan dilakukan ekstraksi fitur, pemilihan fitur yang valid, dan pembangkitan kata kunci.  Nilai performansi Gwet’s AC1 digunakan untuk mengetahui apakah classifier yang dibangun dapat diandalkan dan dapat mendeteksi adanya ketidaklengkapan pada dokumen spesifikasi kebutuhan perangkat lunak.Berdasarkan hasil ujicoba dengan menggunakan kombinasi metode adaboost dan C4.5 diperoleh rata-rata indek kesepakatan pada level moderate dengan nilai tertinggi 0.52 pada saat penggunaan enam fitur teratas. Enam fitur teratas yang paling berpengaruh antara lain bad_jj, bad_rb, jml_kt_penegasan, jml_kt_penghubung, bad_prp dan jml_kt_negatif.
Analisa Penempatan Kamera CCTV Menggunakan Metode Simple Additive Weighting (SAW) Untuk Smart Monitoring Widodo, Gianto; ., Rahmadwati; Santoso, Purnomo Budi; Kurniawan, Fachrul
MATICS Vol 8, No 2 (2016): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.606 KB) | DOI: 10.18860/mat.v8i2.3782

Abstract

Abstract-monitoring technology is an area or region now isgrowing rapidly, this refers to the interest to be used.Closed Circuit Television (CCTV) is a type of camera thatwas used for the supervisor of the building or room, but itcan also be used to monitor the condition of congestion androad conditions. The problems are the installation ofCCTV is not always right on target because it is onlyinstalled for trend following, without looking at theconditions that will be installed, so it becomes less thanoptimal. Many of the problems in need of supervision bythe Government. Problems such as road density, accidentprone area, business area, area schools, parking area andpopulation density at the location of the road is a problemthat requires supervision. In order for the supervision canbe done with the optimal technological devices are usedthen the information and communication technology, theutilization for surveillance of a region commonly referredto with the Smart Monitoring, the device that can be usedis CCTV. In order to target the right CCTV installationrequires a calculation and analysis of the right against theconditions of the point to be fitted, to allow the installationof CCTV can be right on target and not just follow thetrend of development of the technology. Many of theproblems that have to be analyzed before the installationof CCTV, to find the solution of many problems, thisresearch using methods MCDM method Simple AdditiveWeighting (SAW). Based on the results of the calculationof 40 data points of observation in the city of XYZ with 10categories, problems and preferences 3 weights usedproduce a value Vi maximum 4 point, and that point isrecommended for CCTV installed.Keywords: SAW, CCTV
Segmentasi Paru-Paru pada Citra X-Ray Thorax Menggunakan Distance Regularized Levelset Evolution (DRLSE) Hariyadi, M Amin
MATICS Vol 9, No 1 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (650.924 KB) | DOI: 10.18860/mat.v9i1.4130

Abstract

-- Lung is one in control in the circulatory system of air (oxygen) in the human body, so the detection of disorders of the human respiratory urgently needed to detect any disturbance in the lungs used X-ray beam, from the results of x-ray image of the thorax contained information used to analyze and determine the shape of an object from the lungs, in order to obtain such information, a process of segmentation. In this study used methods Distance regularized Levelset Evolution (DLRSE), this method region based models which is an improvement of edge-based models. The purpose of this study to implement segmentation methods DRLSE the lungs of the results of x-ray image of the thorax. The trial results with the system DLRSE method performed on the 20 data from X-ray image of the thorax obtained an average result accuracy of 87.90%, a sensitivity of 76.27% and a specificity of 93.98%.
Implementasi Manajemen Bandwidth Dengan Disiplin Antrian Hierarchical Token Bucket (HTB) Pada Sistem Operasi Linux Nugraha, Muhammad; Utama, Soffin Nahwa
MATICS Vol 8, No 2 (2016): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (665.239 KB) | DOI: 10.18860/mat.v8i2.3558

Abstract

Important Problem on Internet networking is exhausted resource and bandwidth by some user while other user did not get service properly. To overcome that problem we need to implement traffic control and bandwidth management system in router. In this research author want to implement Hierarchical Token Bucket algorithm as queue discipline (qdisc) to get bandwidth management accurately in order the user can get bandwidth properly. The result of this research is form the management bandwidth cheaply and efficiently by using Hierarchical Token Bucket qdisc on Linux operating system were able to manage the user as we want.
Performance Comparison of Rule Generation Method Substractive Clustering and Fuzzy C-Means Clustering on Sugeno's Inference for Stroke Risk Detection Mardi Putri, Rekyan Regasari; Santoso, Edy
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.178 KB) | DOI: 10.18860/mat.v9i2.4587

Abstract

Abstract - Fuzzy Inference is one method that cansolve the problem of uncertainty in a decision-makingor classification well. In inference, fuzzy rules thatrepresent the need of expert knowledge in the relevantfields, so that the classification given decision or beappropriate expert knowledge. However there are timeswhen experts are less able to represent the rules of theappropriate knowledge or knowledge that there is needof too many rules, so we need a method that cangenerate rules based on the data given expert.At issue troke s disease risk detection, it also occursbecause of the research that has been done by taking thedirect rule of experts, it turns out less than the maximumaccuracy, still 82.89%. Substractive methodsClustering and Fuzzy C-Means (FCM) could generaterules by grouping algorithm, in which the existingtraining data are grouped in common and the rules ofthe group raised. Differences in the two methods are indetermining the center of the cluster and assign eachincoming data which groups.Based on research that has been done, substractiveaverage Clustering membrika better accuracy is84.46%, while 73.81% FCM. However, in theprocessing time FCM faster at 16.75 seconds to give anaverage processing time of 13:02 seconds.
Perancangan Decision Support System Penilaian Kinerja Dosen Berdasarkan Penilaian Prestasi Kerja Pegawai dan Beban Kinerja Dosen Ramadhan, Rizal Furqan; Tolle, Herman; Muslim, Muhammad Aziz
MATICS Vol 8, No 2 (2016): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (629.739 KB) | DOI: 10.18860/mat.v8i2.3555

Abstract

The lecturer is one of the essentialcomponents in the Higher Education system. Performanceassessment of lecturer needs to be conducted to measurethe lecturer capability based on the Tri Darma’s HigherEducation concept. Related to the nowadays technologydevelopment, to conduct performance assessment oflecturer can use the Decision Support System based onseveral criteria as the assessment material. The providedcriteria in this paper seem to be the obtained criteria fromP2KP and BKD component. P2KP is performanceassessment of lecturer under the Badan KepegawaianNegara (BKN) supervision. Meanwhile BKD isperformance assessment of lecturer under the DIKTIsupervision. The lecturer criteria are taken from those twocomponents because the lecturers’ status cannot beseparated from the officer under BKN and educator underthe DIKTI support. It is expected that the criteria comingfrom both components integration will be able to produceperformance assessment of lecturer objectively. Themethod to proceed the assessment was Weighted Product(WP). The examined data of the lecturers were theBrawijaya University lecturers’ data. The finalexamination data was conducted by taking the datarandomly from 20 Brawijaya University lecturers. Thefinal output from this Decision Support System is thelecturers which are selected from three categories, whichare, less, normal, and good. It is expected that DecisionSupport System is able to categorize the standard eligiblelecturer (Normal/medium category), and the lecturersurpassing the standard (good category).

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