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INDONESIA
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Published by Universitas Medan Area
ISSN : 25496247     EISSN : 25496255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT 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 .
Arjuna Subject : -
Articles 436 Documents
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Face Recognition System to RPA Software Design and Implementation Suwarno Suwarno
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i2.3106

Abstract

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.
Perhitungan Metode Fuzzy Sugeno Dan Antropometri Dalam Memprediksi Status Gizi Indeks Massa Tubuh Dinur Syahputra; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 1 (2018): EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v2i1.1676

Abstract

Antropometri merupakan ilmu yang mempelajari berbagai ukuran tubuh manusia. Dalam bidang ilmu gizi digunakan untuk menilai status gizi. Metode yang digunakan adalah fuzzy sugeno, yaitu aturan yang direpresentasikan dalam bentuk IF-THEN dengan output berupa konstanta untuk melakukan perhitungan terhadap status gizi indeks massa tubuh. Dalam hal memprediksi status gizi indeks massa tubuh dilakukanlah perhitungan dengan menggunakan metode fuzzy sugeno dan antropometri.
Classification of Wheat Seeds Using Neural Network Backpropagation Algorithm Juliansyah Putra Tanjung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 2 (2021): EDISI JANUARY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i2.4449

Abstract

There are various types of wheat scattered in the world. Usually it takes a long time to recognize the type of wheat seed by manual method because wheat germ has a physical appearance that looks the same as others. One method that can be used is an Artificial Neural Network. In this study, the data used were secondary data which consisted of data from the variable physical characteristics of wheat germ. The types of wheat seeds that are classified are 3. The Artificial Neural Network architecture used in this study is 5. By comparing the 5 Artificial Neural Network architectures, it is concluded that the architecture consisting of 3 layers and 4 layers is more precise in the classification of wheat germ types. The accuracy obtained by the 2 Artificial Neural Network architectures is 90% and 90%, respectively.
Moodle Web-Based Learning Constraints toward Student Learning Interest Using C4.5 Algorithm during Covid-19 Pandemic N. PRIYA DHARSHINNI; Aisyah Hikmasari Sitepu; Rezza Youan Syuhada; Damanik Barasa; Andy Christanto Wijaya
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5301

Abstract

The Learning System during the Covid-19 pandemic shifted from offline learning to online learning which made many campuses use various E-Learning platforms. However, most campuses use Moodle Web-Based Learning because it provides many features that can support lecturers and students in the online learning process and can be accessed via a laptop or smartphone. The problem is, some students experience constraints in following this learning model that affects the ups and downs of student interest in learning, so it is necessary to find the obstacle factors that hinder students during Moodle Web-Based Learning. The C4.5 algorithm generates a decision tree that can be used to predict good results and provide accurate information. The purpose of this study was to find the relationship between the contraints experienced by students while following the Moodle Web-Based Learning model toward students' interest in learning using the C4.5 algorithm. The results showed the main contraint that affects the decrease in student learning interest is influenced by the learning features used by lecturers at a time when online learning is incomplete, network quality is not good, students consider Moodle Web-Based Learning less interesting while the increasing interest in student learning is influenced by the learning features used by lecturers at the time of online learning is very complete , good network quality, students use laptops or computers in following moodle Web-Based Learning and students find Moodle Web-Based Learning interesting.
Comparison of Neural Network Algorithms, Naive Bayes and Logistic Regression to predict diabetes Dwi Yuni Utami; Elah Nurlelah; Fuad Nur Hasan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5201

Abstract

Diabetes is a disease that affects many people with the characteristics of high blood sugar levels. The International Diabetic Federation (IDF) estimates the number of Indonesians aged 20 years and over, suffering from diabetes at 5.6 million people in 2001, and increasing to 8.2 million people in 2020. The problem that occurs is that many people do not know that they suffer from diabetes because they do not have basic knowledge about diabetes and the existing methods to detect diabetes are time consuming. In this study, three data mining methods were compared, namely the neural network algorithm, naïve Bayes, and logistic regression using the rapid miner application by applying the Confusion Matrix Evaluation (Accuracy) and the ROC Curve. The result of this research is that logistic regression method is a fairly good method in predicting early diagnosis of diabetes compared to the naïve Bayes method and the neural network. From the evaluation and validation, it is known that logistic regression has the highest accuracy and AUC values among the comparable methods, namely 75.78% and AUC 0.801, followed by the naïve Bayes algorithm which is 74.87% and AUC 0.799, and the neural network is 69.27% and AUC 0.736. has the lowest accuracy.
Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta Firmansyah Firmansyah; agus yulianto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 2 (2021): EDISI JANUARY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i2.4539

Abstract

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.
Detection of Banana and Its Ripeness Using Residual Neural Network Erwin Dhaniswara; Yosi Kristian; Esther Irawati Setiawan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.4844

Abstract

Automatic fruit detection utilizing computer vision techniques has been carried out to help the agriculture and plantation industries. This study researches smart systems to detect bananas and ripeness classification utilizing residual neural networks. The method used to detect bananas is transfer learning from pretraned Model VGG-19. Whereas, in the bananas ripeness classification process, residual neural networks, which are trained from the start, are used. Sliding Windows is used to detect the position of bananas followed by Non-Max Suppression to summarize the results of several detected bananas. Previous studies were limited to the level of ripeness, but in this study, bananas are detected and followed by the level of bananas ripeness (raw, ripe, and overripe). This study’s data uses bananas which were mixed with other kinds of fruit. There two kinds of bananas detection architecture used in this study, VGG-19 and Restnet. After they were used to detect bananas, it was found that VGG-19 was more suitable. The results of this study are very satisfying as it is seen from the bananas detection testing percentage using VGG-19 architecture which shows 100% ripe bananas, 99 % raw bananas, and 100% overripe bananas.Keywords: Detection of banana, banana ripeness, Non-Max suppression, residual block.
Automation of Aquaponic Choy Sum and Nile Tilapia Using Arduino Microcontroller Arif Widi Atmaja; Daniel Rudiaman Sijabat; Febry Eka Purwiantono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 2 (2021): EDISI JANUARY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i2.4395

Abstract

This study aims to build an aquaponics automation tool to simplify the control of fish and vegetable cultivation. The objects that were taken in this study were nile tilapia and choy sum. In this study, testing was carried out in an aquarium and hydroponic pipe to control nutrition, water turbidity, light, pH, feed, and temperature. The main tools used to build this automation include Arduino ESP-32, GY-302 Ambient Light Intensity Sensor, DFRobot Gravity Analog pH Sensor, DS18B20 temperature sensor, 3-6V DC R140 DC motor, Relay Module6 Chanel 12V, RTC Module. DS130 and SR04 Ultrasonic Sensor. After the system testing process, it can be concluded that this tool can support the process of cultivating nile tilapia and choy sum properly and make it easier for farmers to monitor aquaponics.
Comparison of C4.5 and Naïve Bayes Algorithms for Assessment of Public Complaints Services Martin Martin; Lala Nilawati
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5292

Abstract

Public service is one type of service provided by the government. The National Commission on Human Rights as a state institution, one of its functions is to provide services for complaints of cases of human rights violations. The purpose of this study was to find the most appropriate algorithm method by looking at the results of the accuracy and the Area Under Curve (AUC) value. The data used is data from questionnaires regarding assessments related to complaints of cases of human rights violations by the public in 2018, totaling 1750 records. The data is processed using the C4.5 algorithm and Naïve Bayes with the Rapid Miner tools. The results showed that the C4.5 Algorithm has a better accuracy of 99.49% compared to Naïve Bayes of 95.66%. The AUC value produced by the C4.5 algorithm is better at 0.998 and Naïve Bayes by 0.996. In this study, the rule generated by C4.5 will be the basis for making a questionnaire assessment application in the form of visual programming, to help provide an assessment of the satisfaction of complaint services at Komnas HAM. The system is built based on web, using PHP framework, database using MySQL and editor tools using notepad ++.
Expert System For Diagnose Covid19 Using Certainty Factor Method Lukman Nulhakim; Doni Andriansyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5069

Abstract

AbstractAt the end of 2019 the world was shocked by the emergence of a new type of virus from the Corona family, namely the Novel Coronavirus (2019-nCoV) which had never been previously identified in humans, later known as Coronavirus disease 2019 (Covid-19). Early symptoms in people with Covid-19 include fever, cough and shortness of breath, similar to flu and cough symptoms in general, making it difficult to detect early. The certainty factor method can measure a certainty and uncertain thing. Research with certainty factor methods has been carried out to diagnose a disease based on the symptoms experienced. Types of diseases are focused only on types of diseases with almost the same symptoms, namely Upper Respiratory Tract Infection, Pneumonia, and Covid-19. The purpose of this study is to build an expert system application that can be accessed online to detect early symptoms experienced by sufferers. Based on the results and discussion, the expert system application can run well and the results of manual CF calculations are the same as the results of CF calculations on the system.

Page 16 of 44 | Total Record : 436

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