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Deny Zainal Arifin
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matics@uin-malang.ac.id
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+6285646744340
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matics@uin-malang.ac.id
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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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INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol 10, No 1 (2018): MATICS" : 6 Documents clear
Cover MATICS Arifin, Deny Zainal
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (85.046 KB) | DOI: 10.18860/mat.v10i1.5601

Abstract

Modeling of Situation Response Time in Vehicular Ad-Hoc Network AKINYEDE, Raphael
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (809.069 KB) | DOI: 10.18860/mat.v10i1.4785

Abstract

— In Vehicular Ad-Hoc Networks (VANETs), wireless-equipped vehicles form a network spontaneously while traveling along the road. The direct wireless transmission from vehicle to vehicle makes it possible for them to communicate even where there is no telecommunication infrastructure; this emerging new technology provide ubiquitous connectivity to vehicular nodes while on the move, The main idea is to provide ubiquitous connectivity to vehicular nodes while on the move, and to create efficient vehicle-to-vehicle communications that enable the Intelligent Transportation Systems (ITS). This is achieved by allowing nodes within certain ranges to connect with each other in order to exchange information. Since accident happens in split seconds, to avoid communication inefficiency, there is need for this information to get to the intended vehicle on time. To solve this problem, this work models each vehicle in a chain of others and how it responds to the traffic around it using Microscopic (also known as car-following) method for modeling traffic flow; driver- to-driver and driver-to-road interactions within a traffic stream and the interaction between a driver and another driver on road were considered. The essence of this modeling is to determine the minimum response time required for a vehicle in VANET to respond and communicate situations on the road. A simulated scenario was carried out for two vehicles, a leading vehicle and following vehicle. The result shows that with an average of 32 meters apart with average difference in velocity of   1.23m/s, a minimum of 0.9secs is required for efficient situation response communication to ensue between them.
Optimasi dan Reinisiasi Sistematis Pembelajaran Elektronik Vijaya, Annas
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (541.11 KB) | DOI: 10.18860/mat.v10i1.5471

Abstract

One of the IT applications implemented in higher education and quite rich in renewable IT features is e-learning. However, facts show that in Indonesia it appears that there are not many universities are moved to apply e-learning, in addition, from the other side for others who have implemented e-learning still leads to a pattern that has not been systematic according to its allocation.This paper will discuss the optimization and re-initiation of e-learning using a case study. In addition to reviewing the aspects of human resources involved in e-learning, the following features and functions can be developed in e-learning, such as collaboration, social networking, and so forth. This review is conducted considering that in addition to e-learning included in the top 10 topics in higher education, e-learning also has the potential as acceleration in the learning process and collaboration on campus..
Optimization Improved K-Means on Centroid Initialization process using Particle Swarm Optimization for Tsunami Prone Area Groupings Santika, Gayatri Dwi; Sari, Nadia Roosmalita; S, M Zaki; Mahmudy, Wayan Firdaus
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.109 KB) | DOI: 10.18860/mat.v10i1.3836

Abstract

Tsunami is a high wave caused by tectonic earthquakes, volcanic eruption or landslides in the ocean.  Indonesia is one of the countries that has thousands of islands. Lots of towns is a city on the banks or waterfront city. Indonesia becomes Tsunami prone areas. Tsunami can affect damage in various sectors, namely land degradation and infrastructure, environmental damage, fatalities, even the psychological impact on the victims themselves. Therefore, it takes a clustering of tsunami-prone areas. The result of clustering can give information to the public to remain alert to the danger of the tsunami. Also, clustering of the tsunami can be used by a government to prepare policies in overcoming the danger of the tsunami. Improved K-Means is an approach that proposed in this study to clustering the tsunami prone areas. In selecting the initial centroid must be done properly to produce a high accuracy. We proposed a method to determine the initial centroid appropriately, so that can increase the accuracy. The proposed method is Particle Swam Optimization (PSO). This study also uses comparison methods, such as K-Means, K-Means Improved, and K-Means Improved PSO. This study uses silhouette coefficient to test the accuracy of the system. The result showed that the proposed method has higher accuracy than the comparison method. Silhouette coefficient generated at 0.99924223 with smaller computing time
Perancangan Sistem Informasi Manajemen Kas Berbasis Web Studi Kasus: RS dr. Etty Asharto Batu Supriyono, Supriyono; Muslimah, Endah
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (817.378 KB) | DOI: 10.18860/mat.v10i1.4302

Abstract

Pengelolaan data keuangan perusahaan merupakan hal yang perlu mendapatkan penanganan yang tertib, terstruktur dan transparan. Penerapan sistem informasi manajemen keuangan dapat dijangkau melalui situs lokal yang memberikan rincian data penerimaan kas dan pengeluaran kas. Metodologi yang digunakan didalam penelitian berikut menggunakan metode waterfall. Hasil dari penelitian berikut ini berupa sebuah perancangan sistem informasi yang mampu mengelola pemasukan kas dan pengeluaran kas. Sistem informasi pengelolan kas masuk dan kas keluar dirancang menggunakan database MySQL, bahasa pemrograman PHP, dengan tampilan user interface menggunakan Bootstrap dan kerangka kerja menggunakan framework Codeigniter. Dengan menggunakan analisa dan perancangan sistem serta rekayasa kebutuhan dapat menghasilkan sistem informasi yang handal dan tepat guna. Sistem informasi pengelolaan kas masuk dan kas keluar pada Rumah Sakit dr. Etty Asharto Batu mampu memberikan manfaat bagi pegawai dalam proses pencatatan.
Klasifikasi Jenis Golongan Darah Menggunakan Fuzzy C-Means Clustering (FCM) dan Learning Vector Quantization (LVQ) Hariri, Fajar Rohman
MATICS Vol 10, No 1 (2018): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.709 KB) | DOI: 10.18860/mat.v10i1.5356

Abstract

Abstract— Blood is an important part of the body. Blood is divided into several groups A, B, O, and AB. Conventionally, detect blood group by dripping anti-A serum and anti-B serum into the blood to be recognized and direct measurement of the serum droplet reaction. This study will compare the processes that use segmentation and  without using segmentation to know the various segmentation information in introduction of human blood type image. From the test results that segmentation increase accuracy of recognition between 10% -24% of each test. By using JST Learning Vector Quantization (LVQ) as a classifier and Fuzzy C-Mean as segmentation, the optimal result on the system averages 92% to 98%.. Index Terms—Blood, Segmentation, Classification Abstrak–- Darah merupakan salah satu bagian penting dalam tubuh. Darah dibedakan menjadi beberapa golongan yaitu A, B, O, dan AB. Secara konvensional, mendeteksi golongan darah dengan cara meneteskan serum anti-A dan serum anti-B ke darah yang akan dikenali kemudian melakukan pengamatan langsung terhadap reaksi tetesan serum tersebut.  Penelitian ini akan membandingkan antara proses pengenalan yang menggunakan segmentasi dengan proses pengenalan tanpa menggunakan segmentasi untuk mengetahui seberapa besar pengaruh metode segmentasi dalam pengenalan citra golongan darah manusia. Dari hasil pengujian didapatkan bahwa dengan adanya metode segmentasi akurasi system pengenalan bertambah antara 10%-24% setiap uji coba. Dengan menggunakan JST Learning Vector Quantization (LVQ) sebagai pengklasifikasi dan Fuzzy C-Mean sebagai segmentasi citra darah dapat diperoleh hasil yang optimal pada sistem pengenala golongan darah manusia dengan prosentase keberhasilan rata rata 92% hingga 98%. Kata Kunci—Darah, Segmentasi, Klasifikasi Klasifikasi Jenis Golongan Darah MenggunakanFuzzy C-Means Clustering (FCM) dan Learning Vector Quantization (LVQ)

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