JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
JURNAL TEKNIK INFORMATIKA, JITE (Journal of Informatics and Telecommunication Engineering) is a journal that contains articles / publications and research results of scientific work related to the field of science of Informatics Engineering such as Software Engineering, Database, Data Mining, Network, Telecommunication and Artificial Intelligence which published and managed by the Faculty of Informatics Engineering at the University of Medan Area .
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436 Documents
<![CDATA[CLASSIFICATION OF FACIAL EXPRESSIONS USING SVM AND HOG ]]>
<![CDATA[Tanjung, Juliansyah Putra]]>;
<![CDATA[Muhathir, Muhathir]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3182
<![CDATA[The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.]]>
<![CDATA[MODEL CLASSIFICATION OF NOMINAL VALUE AND THE ORIGINAL OF IDR MONEY BY APPLYING EVOLUTIONARY NEURAL NETWORK ]]>
<![CDATA[Al-Khowarizmi, Al-Khowarizmi]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3284
<![CDATA[Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better]]>
<![CDATA[INITIAL CENTROID OPTIMIZATION OF K-MEANS ALGORITHM USING COSINE SIMILARITY ]]>
<![CDATA[Azmi, Fadhillah]]>;
<![CDATA[Utama, Kevin]]>;
<![CDATA[Gurning, Oki Thomas]]>;
<![CDATA[Ndraha, Syukurman]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3211
<![CDATA[Clustering salah satu metode yang sering digunakan di berbagai bidang yang melakukan analisis data, termasuk penggalian data, pengambilan dokumen, segmentasi gambar, dan klasifikasi pola. Adapaun tujuan dari metode tersebut adalah untuk mengelompokkan data ke dalam suatu cluster sehingga kesamaan antara anggota data dalam suatu data informasi yang telah di-cluster yang sama adalah maksimal, di sisi lain untuk kesamaan di antara anggota data yang lain berbeda cluster minimal. Ada beberapa pendekatan metode untuk mengurangi kesalahan pada saat centroid awal yang dipilih selama proses pengelompokan berlangsung. Disini data yang digunakan adalah data acak yang dibuat secara manual yatu 30 data dan 5 atribut, sehingga diperoleh hasil akurasi clustering dalam centroid dengan menggunakan metode K-Means memiliki signifikan 86.67%, sedangkan menggunakan K-Means dengan cosine similarity tidak jauh berbeda yaitu sebesar 89.7%, maka dari itu hasilnya cukup baik.]]>
<![CDATA[FACE RECOGNITION SYSTEM TO RPA SOFTWARE DESIGN AND IMPLEMENTATION ]]>
<![CDATA[Suwarno, Suwarno]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3106
<![CDATA[The purpose of this research is to build a face recognition system, and implement it into an RPA (Robotic Process Automation) software to expand automation capabilities. The system is built using the Python programming language. The face recognition algorithm that is used is an open-source library that has been pre-trained and developed beforehand along with a library called OpenCV. The client side of the system is desktop based, and requires a stable internet connection. Users of the system are able to register faces into the system, and then later detect and extract information from them using only images of faces with an average speed of 500 ms for every frame, with an accuracy of ~98% with tolerance set at the default value of 0.6. The system is also capable of automatically registering any new faces that it encounters.]]>
<![CDATA[MOTION MONITORING SYSTEM BASED ON IOT ]]>
<![CDATA[Susilawati, Susilawati]]>;
<![CDATA[Sembiring, Zulfikar]]>;
<![CDATA[Muhathir, Muhathir]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3326
<![CDATA[Every object is always moving, and movement of an object can occur at any time. The value of the motion which produced from an object can be very small to very large, and the impact of the movement can be at risk until very risky. For this reason, the movement of objects at risk must be observed whenever changes and observations for data retrieval can be done remotely. The purpose of this research to design an Internet of Things (IoT) devices that can observe and detect changes in the motion of an object. The device is designed to be small, around 44 x 48 millimeters with very low power consumption. The design phase begins with recording motion data using the MPU6050 accelerometer sensor as a motion detector, arduino nano as a control device, WiFi ESP8266 as a communication medium for sending data from a receiver apllication motion data with UDP protocol. The test results show that this device is very sensitive to detect changes in the motion and angle of X, Y and Z of an object.]]>
<![CDATA[NETWORK DEVICE MONITORING SYSTEM BASED ON GEOGRAPHIC INFORMATION SYSTEM DAN SIMPLE NETWORK MANAGEMENT PROTOCOL ]]>
<![CDATA[Hizriadi, Ainul]]>;
<![CDATA[Shiddiq, Radea]]>;
<![CDATA[Jaya, Ivan]]>;
<![CDATA[Prayudani, Santi]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3187
<![CDATA[Network infrastructure monitoring is an important part of an institute to maintain the stability of computer network devices. One of the functions of computer network monitoring is to find out the data traffic generated in network application. Simple Network Management Protocol is one of protocols for monitoring the data traffic in network device. However, network device administrators still have problems when they want to monitor their network infrastructure, such as device location and data traffic information of network device that is only temporarily stored in the monitoring system, and physical location of network device is not contained in the monitoring system. In order to make it easier for them how to monitor network devices, the Researcher intends to combine Geographic Information Systems (GIS) and SNMP into a web-based monitoring application. Geographic Information System application can display the physical location of network devices, whilethe SNMP application using for monitoring the data traffic in network device,finding out the data traffic that can be generated in real time, and displaying data traffic of network device that has been monitored in to graphical form based on the time and network device used.]]>
<![CDATA[FACE RECOGNITION BASED FEATURE EXTRACTION USING PRINCIPAL COMPONENT ANALYSIS (PCA) ]]>
<![CDATA[Nasution, Muhammad Zulfahmi]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3132
<![CDATA[The human face is an entity that has semantic features. Face detection is the first step before face recognition. Face recognition technique is an identification process based on facial features. One feature extraction approach for facial recognition techniques is the Principal Component Analysis (PCA) method. The PCA method is used to simplify facial features and characteristics in order to obtain proportions that are able to represent the characteristics of the original face. The purpose of this research is to construct facial patterns stored in a digital image database. The process of pattern construction and face recognition starts from objects in the form of face images, side detection, pattern construction until it can determine the similarity of face patterns to proceed as face recognition. In this research, a program has been designed to test some samples of face data stored in a digital image database so that it can provide a similarity in the face patterns being observed and its introduction using PCA]]>
<![CDATA[THE IMPLEMENTATION OF THE RAD MODEL IN THE DEVELOPMENT OF TENDER SELECTION PROGRAMS USING THE AHP METHOD ]]>
<![CDATA[Rudianto, Rudianto]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3232
<![CDATA[The activity of selecting bidders is one of the most important activities carried out by companies to order goods or services for bidders, but at meetings of the company leadership it is difficult to select bidders to make business partners because there are only a few things that are not in accordance with who agree to end the shipment of goods, prices do not match and end the company that receives losses. That is caused by the lack of competence of selected tender partners. From the problems mentioned above, the tender selection process that has been conducted so far is still lacking and inaccurate. The purpose of this study is to create an application program using the Rapid Application Development Model (RAD Model) that can assist leaders in selecting business partners through a tender process by giving weights according to the criteria specified requirements. It is expected that the tender selection process is objective and accurate. Assessment method Decision making (SPK) Analytichal Hierarchy Process (AHP) method is one method in the process of resolution by completing each case by completing it first by using weighting criteria. In the end the selection application program can help lead in making objective, fast and accurate decisions.]]>
<![CDATA[INDONESIAN TEXT DATASET FOR DETERMINING SENTIMENT CLASSIFICATION USING MECHINE LEARNING APPROACH ]]>
<![CDATA[Syahputra, Indra Edy]]>;
<![CDATA[Tulus, Tulus]]>;
<![CDATA[Efendi, Syahril]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3153
<![CDATA[Advances in information technology encourage the emergence of unlimited textual information with the use of online media developing so rapidly that the emergence of the need for information presentation without reducing the value of the information presented. Basicaly the concept of the dataset is a general form of almost every discipline, where the dataset provides empirical basic information for research activities. Sentiment analysis is done to see opinions or feelings about a problem or identify and classify information trends from the problem. The dataset analysis in determining sentiment classification is a model of sentiment classification that has relevance to the dataset with the use of machine learning techniques with supervision that learns from experience to predict output from labeled input data and output from machine learning. The results of experiments and tests that have been carried out on machine learning techniques with supervision can classify sentiments in the tweet text properly and the level of accuracy can still be improved to a better direction with data namely baseline 100 (days) and 83 (weeks), naivebayes 100 (days) and 82 (weeks), maxent 100 (days) and 83 (weeks), and SVM 100 (days) and 83 (weeks).]]>
<![CDATA[THE APPLICATION OF FUZZY-ANALYTICAL HIERARCHY PROCESS METHOD FOR MAJORS SELECTION AT PUBLIC UNIVERSITIES ]]>
<![CDATA[Aditya, Addin]]>;
<![CDATA[Purwiantono, Febry Eka]]>
<![CDATA[ JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI ]]>
Publisher : <![CDATA[Universitas Medan Area ]]>
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DOI: 10.31289/jite.v3i2.3245
<![CDATA[The purpose of this research is to build a decision support model for prospective students in choosing undergraduate programs at state universities after they accomplished their high school. There are more than 800 undergraduate programs from science and technology departments from 40 state universities in Indonesia. In this research, we use the Fuzzy Analytical Hierarchy Process (F-AHP) to model a decision. This method is chosen because able to make comparisons between criteria more complex than other DSS (Decision Support System) methods. For the criteria used in this research is computer-based test result, student capacity in 2019 and the people who interest in those majors in 2018. The main result of this research is a ranking of science and technology departments at 40 state universities in Indonesia. The results of this research are expected to be able to assist students in choosing majors that will be taken after graduation in their school. In addition, the method of this research can also be continued in subsequent researches that are similar (theoretically). Whereas practically, this method can be implemented every year using different data.]]>
