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International Journal Of Computer, Network Security and Information System (IJCONSIST)

ijconsist
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Published by Universitas Pembangunan Nasional Veteran Jawa Timur

ISSN : - EISSN : 26863480 DOI : https://doi.org/10.33005/ijconsist.v3i1

Core Subject : Science,

Computer Science & IT

Focus and Scope The Journal covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data

Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer

Articles 56 Documents

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Maturity Level for IT Implementation Measurement Focus on Business Goal 14 and 15 at Diskominfo Province Siti Mukaromah Mukaromah; Eva Y Puspaningrum; Doddy Riddwandono
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

How Information Technology and Information System (IT/IS) has been implemented at an organization or institution needs to measure to evaluate IT/IS implementation. Nowadays, IT is compulsory in business processes. It helps the employee to handle some of their duty. But it needs to measure to monitor how IT/IS aligns with the business. So, that management could decide whether IT/IS is maintained or need to be upgraded or else. There are many ways to measure IT/IS implementation; this article will discuss the maturity level based on Cobit 4.1. It will measure per level from level 0 to level 5. The scope of this research is at business goal 14 and business goal 15 and a case study of this research in Diskominfo at one of the provinces in Indonesia. The result of this research is the maturity level of Business Goal 14, and Business Goal 15 is in Level 3, it means that Define Process.

QR-Barcode Application for Barrier Gate Opener based on Android Okta Merdana
IJCONSIST JOURNALS Vol 2 No 1 (2020): September
Publisher : International Journal of Computer, Network Security and Information System

The use of Android-based smartphones is a personal assistant who is often a primary need in helping human activities. Based on this, this study uses Android to create a control device for access rights using QR-Code to open the barrier gate where the data will be added to the database server, and the system is called Q-BaGOS (QR-Code Based Barrier Gate Opening System). The design of this access system utilizes a QR-code scanner application that is made in-house and modified with coding to instruct Android as a medium to open a barrier gate containing a QR-code. In the application, there are menus such as login username and password, status and barcode data. If you already have user data, it can be used as an access right to unlock the barrier gate. In this case, to manage it all, a web server is built that will communicate with Arduino Uno to control the entire security system, by storing user access rights data, recording user data that performs scanners and entering the port. Barcode or barcode is a set of data that is described by lines and spacing (space). The working principle of this tool when opening a scanner application on Android, users are required to log in the username and password data first. After logging in, it automatically opens the QR-code scanner camera. After the scan process automatically enters the data on the web database server, then the servo door automatically opens. After the user enters, the ultrasonic sensor works, and the barrier gate is automatically closed again

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method Putri Taqwa Prasetyaningrun; Irfan Pratama; Albert Yakobus Chandra
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used four different machines learning algorithms such as KNN (Neighbor K-Nearest), NaÃ¯ve Bayes, Decision Tree, Random Forest plus two ensemble technique namely stacking and bagging. Results are expressed in terms of classic metrics and algorithms that produce the best result for the available data sets is the Random Forest classifier. It reveals the best withdrawals (0,88) as good as the stacking and bagging method with the same value

Classification of Toddler Nutritional Status Based on Antrophometric Index and Feature Discrimination using Support Vector Machine Hyperparameter Tuning Much. afif masykur mughni; Tresna Maulana Fahrudin; Made Kamisutara
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Nutritional status is the study of food and is related to health. Nutritional status is a benchmark to assess the health development of toddler. The nutritional status of toddler is assessed according to three index, such as body weight to age (BW / A), body height to age (BH / A), body weight to body height (BW / BH). The issue of nutrition is still a major factor in the growth and development of toddler in Indonesia. Public Health Center (Puskesmas) and Integrated Healthcare Center (Posyandu) as public health services work together to control the growth and development of toddler in Indonesia. To help control the growth and development of toddler, we proposed a research to classify the nutritional status of toddler based on anthropometric index. The nutritional status of toddler dataset was formed into a classification model using SVM Hyperparameter Tuning. SVM is a machine learning which the classification model used a hypothesis space in the form of linear functions in a high dimensional feature space. Adjustment of the hyperparameter was involved to reach a model that can optimally solve machine learning problems. We implemented feature selection using Fisher's Discriminant Ratio as a preprocessing stage, which the most important features were body weight (BB) and height (BH). The experimental results showed the classification model using SVM on training and testing data with a ratio of 70:30 reached accuracy of 84%, while SVM Hyperparameter Tuning with parameter of Cost = 100 parameters, Gamma = 0.01, Kernel = RBF reached accuracy of 97%. They represented a significant accuracy difference of 13%.

Determining Students Preparation for College Entrance Examinations in Indonesia From Twitter Data Using Exploratory Data Analysis Kartika Maulida Hindrayani; Tresna Maulana F; Prismahardi Aji R; Kartini
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Nowadays, educational data can be learned not only for those in Education but also in Information Technology. This happened because education and technology can no longer be separated. Senior high school graduates will take College Entrance Examination to be admitted to public institutions in Indonesia. Sometimes, they share their progress, target, and complain on social media. In this research, we collected data from Twitter. We explore the data to determine student's preparation using Exploratory Data Analysis. The results are positive words in both English and Indonesia, word count, word cloud, and geographical data plot.

Imam Safii Heart Disease Classification using Gain Ratio Feature Selection with Hidden Layer Modification in Extreme Learning Machine Imam Safii; Made Kamisutara; Tresna Maulana Faahrudin
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Heart disease is a non-communicable disease that causes a high mortality rate and is still a problem both in developed and developing countries. This disease often occurs because of the narrowing of blood vessels which causes the functioning of the heart is disturbed. The number of cases of heart disease in Indonesia is still quite high, making medical staff require a fairly in diagnosing the patient's conditional. The research proposed to implement Gain Ratio in selecting the most important feature that influences heart disease and building the classification models based on the modification of hidden layer weight on Extreme Learning Machine. The research collected the heart disease dataset which was obtained from Kaggle UCI Machine Learning consist of 1.025 samples, 14 attributes, and 2 labels. The data preprocessing include using data cleaning and normalization to find out dirty data or missing values. The experiment reported that Gain Ratio succeeds to generate the attribute ranking of heart disease dataset, then Gain Ratio score was added to the weighting of the hidden layer input on learning methods. The research used various validation sampling using the splitting test between training data and testing such as 70:30, 80:20, 90:10%, and set up 1500 hidden layers. The accuracy average performance of Extreme Learning Machine with modification using Gain Ratio reached 100% for the training phase and 97.67% for the testing phase. Keyword: Heart Disease, Gain Ratio, Modification, Classification, Extreme Learning Machine

Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Kartika Maulida Hindrayani; Mohammad Idhom
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.

Classification of color features in butterflies using the Support Vector Machine (SVM) Dhian Satria Yudha Kartika; Hendra Maulana
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2% Keywordsâ€”image processing; classification; butterflies; color features; features extraction

Brain Tumor Classification with Hybrid Algorithm Convolutional Neural Network-Extreme Learning Machine Radical Rakhman Wahid; Fetty Tri Anggraeni; Budi Nugroho
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Brain tumor is a disease that attacks the brains of living things in which brain cells grow abnormally in the area around the brain. Various ways have been done to detect this disease, one of which is through the anatomical approach to medical images. In this study, the authors propose a Convolutional Neural Network (CNN)-Extreme Learning Machine (ELM) hybrid algorithm through Magnetic Resonance Imaging (MRI). ELM was chosen because of its superiority in the training process, which is faster than iterative machine learning algorithms, while CNN was chosen to replace the traditional feature extraction process. The result is CNN-ELM, which has 8 filters in the convolution layer and 6000 nodes in the hidden layer, has the best performance compared to CNN-ELM another model which has different number of filters and number of nodes in the hidden layer. This is evidenced by the average value of precision, recall, and F1-score which is 0.915 while the accuracy of the test is 91.4%.

Traffic Sign Detection Using Region And Corner Feature Extraction Method Hendra Maulana; Dhian Satria Yudha Kartika; Agung Mustika Riski; Afina Lina Nurlaili
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.

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Contact Name
-

Contact Email
ijconsist@upnjatim.ac.id

Phone
+6281999471017

Journal Mail Official
ijconsist@upnjatim.ac.id

Editorial Address
https://ijconsist.org/index.php/ijconsist/about/editorialTeam

Location
Kota surabaya,

Jawa timur

INDONESIA