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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 412 Documents
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Penerapan Metode Convolution Neural Network (CNN) Pada Aplikasi Automatic Lip Reading Nimatul Mamuriyah; Jason Sumantri
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7523

Abstract

AbstractA Prototype of Automatic Lip-Reading or a prototype of automatic lip-reading is a device that is needed by people with hearing disabilities or the Deaf. The prototype will help people with hearing impairment move independently without depending on others. Advances in Nano technology have driven the development of Computer Vision and software that enables the creation of prototypes of Automatic Lip-Reading. The image processing applied to this prototype is focused on the movement of the lips, tongue, and the area around a person's mouth. Furthermore, the results of the image recognition will compare with the database that has been provided to produce certain words. The prototype design consists of a camera, Python with Tensorflow used as an image processing programming language with the Convolutional Neural Network (CNN) method as an image recognition method, and Machine Learning Technology used as processing and decision-making systems. This study, numbers and alphabets were used as trials or predictions of the Automated Lip-Reading system. By using CNN and Machine Learning methods, the test results show that in general, the system designed can predict numbers and alphabet with not quite high or less than 35%.   
Application of Artificial Intelligence Chi-Square Model and Classification Of KNN in Heart Disease Detection Rosdiana Rosdiana; Vera Novalia; Ilham Saputra; Mutammimul Ula; Muhammad Danil
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7343

Abstract

Cardiovascular disease is a problem in the blood vessels that do not run smoothly into the heart. This is fatal in patients with a history of heart disease. This problem often occurs in the flow of blood pumps into the heart. The problem examined in this study is how to complete the level of accuracy of each data set and the reduction of each attribute in heart disease. The purpose of this study is to analyze heart disease and classify heart disease using the chi-square and K-Nearest Neighbor algorithms. The results of the study with patient age 57, gender LK, cp 3, trestbps 200, chol 564, fbs 1, restecg 2, thalach 202, oldpeak 6.2, slope 2, ca 4, and the value of thal 3 for the target is there is disease heart 0 or 1 is detected without heart disease when the max-min data is normalized. while to measure the performance of the algorithm with the value of the confusion matrix with the actual class value of 1, prediction class 1 value 44, actual class 0 and prediction class value 6. while the actual class value 0-1, prediction class 1 value 5 and 0-0 value 36. the final stage value of the accuracy measure is 0.87912, the recall value is 0.89797 and the precision value is 0.85714. The implication of the application of the test has an optimal test, the accuracy value with data K = 303 then it can be concluded that based on the test the calculation of the KNN model obtained an accuracy of 91%
Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction Ignatius Wiseto Prasetyo Agung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7158

Abstract

The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively. 
Analysis Of Right And Wrong Use Of Mask Based On Deep Learning Rico Wijaya Dewantoro; Sonni Yudha Nugraha Arfan; Reyhan Achmad Rizal
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7582

Abstract

Pandemic COVID-19 makes it important to apply the proper and correct use of masks. The correct use of a mask where its use can cover the nose mouth and chin. One of the problems in using masks is that there are still many people who have not used masks properly and correctly. The importance of the correct use of masks because the transmission of the Covid-19 itself does not only occur through splashes when sneezing or coughing between humans but can also occur when talking or breathing by spreading through fluid particles less than 0.0002 inches (5 microns) in diameter called aerosols that are emitted when people talk. From these problems  it is necessary to have a computational-based analysis system to be able to identify patterns and make decisions and perform certain tasks automatically so that the results obtained are more efficient and objective. In this study, a deep learning method with a resnet  50 will be used to obtain the correct and incorrect results of using masks. The results of this study indicate that the deep learning method with resnet 50 is able to achieve 98.41% accuracy in classifying the correct and incorrect use of masks.
Broadband Channel Based on Polar Codes At 2.3 GHz Frequency for 5G Networks in Digitalization Era Khoirun Ni'amah; Reni Dyah Wahyuningrum; Solichah Larasati
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7310

Abstract

This research using a polar code and without polar codes -based broadband channel that is affected by human blockage using one of the 5G cellular network frequencies at 2.3 GHz, 99 MHz bandwidth, 128 blocks of Fast Fourier Transform (FFT) with Cyclic prefix-Orthogonal Frequency Division Multiplexing ( CP-OFDM) and Binary Shift Keying (BPSK) modulation. The use of high frequencies causes the technology to be sensitive to the surrounding environment and attenuation such as human blockage. The purpose of this research is to determine the performance results and analyze the BER parameters that use polar codes and without polar codes on 5G network broadband channels that are affected by human blockage. Broadband channel modeling on a 5G network is presented in a representative Power Delay Profile (PDP) with the influence of human blockage, which is obtained as many as 41 paths which have multiple delays of 10 ns on each path. This research also uses the scaling method on representative PDP because the use of FFT will produce 128 blocks, and the results of this scaling show that there are 9 lanes with multiple delays of 50 ns. The results of this study are close to the average Bit Error Rate (BER) of 10-4. BER performance without polar code is affected by human blockage requires Signal to Noise (SNR) of 30 dB, for theory BER on BPSK modulation requires SNR of 34.5 dB and BER performance using polar code only requires SNR of 23 dB. These results indicate that using a polar code can reduce or save power usage by 7 dB without a polar codes. Polar codes can minimize errors in the 5G network system, because polar codes are one of the strong codes and are one of the channel coding recommended by ITU to be applied to 5G network systems
Hybrid RF-Energy Harvester for IoT Smart Device Applications Low Power Consumption Nurfitri Nurfitri; Elyas Palantei; Intan Sari Areni
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7271

Abstract

Energy independence and long battery lifetime, the vision of 5G technology is one of them for IoT smart devices. Focusing on the challenges in this technological vision, the solution for this system is to harvest the energy sources around it as a backup or eliminate the use of batteries. The output of the hybrid energy harvester method as a solution to energy independence using radio frequency (RF) waves has been optimized in this study. The double-T patch microstrip antenna based on CSRR metamaterial as an RF energy harvester works at the ISM band frequency of 2.45 GHz and has return loss, gain and bandwidth values that are in accordance with the concept of the RF-Energy harvester system. This antenna is capable of absorbing energy using a DC converter circuit at a maximum distance of 6.79 V using 2 access points at the position of the transmitting antenna facing each other with a conversion efficiency of 6.68%. One method to maximize the output of the energy harvester is also carried out in this study by doing a hybrid with a mini solar cell, which is able to carry out constant voltage and can charge the load in the form of a cellphone. The hybrid RF-Energy harvester system designed in this research can be used as a source of energy in smart devices with small power requirements.
A Good Accuracy in Apple Fruits Quality Based on Back Propagation Neural Network and Feature Extraction Ajib Susanto; Ibnu Utomo Wahyu Mulyono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.6938

Abstract

Apel merupakan varietas buah yang memounyai banyak jenis. Aple ang secara visual tampak sama, bisa jadi merupakan variates yang berbeda misalnya jenis apel Grany smith, Apel golden dan apel malang. Ketiga apel tersebut sama-sama berwarna hijau. Secara kasat mata warna saja tidak cukup untuk mengklasifikasi, sehingga perlu adanya pemnafaatan teknologi yang dapat membantu proses klasifikasi menjadi lebih akurat. Pengolahancitra digital, lewat proses segmentasi warna menggunakan algoritma Back Propagation Neural Network (BPNN) dapat digunakan untuk proses klasifikasi jenis apel. Dalam penelitian ini digunakan 12 macam apel yaitu apel Golden, apel Grany Smith, apel Braeburn, apel Red delicious, apel Malang, apel Red Yellow, apel Red, apel Anna, apel Golden Delicious, apel Fuji, apel Gala, dan aepl Honeycrisp. Pemnafaatan fitur ekstraksi warna dan ciri membuktikan bahwa akurasi dapat mencapai nilai optimal hingga 93%., presisi 94% dan recal 94% dengan menggunakan ekstraksi fitur RGB
Support Vector Machine Method with Word2vec for Covid-19 Vaccine Sentiment Classification on Twitter Muktar Sahbuddin; Surya Agustian
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7534

Abstract

Covid-19 has been a dangerous outbreak for the world that has lasted more than 2 years. Covid-19 has evolved or developed into several new variations, such as delta which is more dangerous than its initial variant. Vaccines became the world's solution to defend against Covid-19. In Indonesia, at the early stages of implementing mass vaccination programs, people had been involved in many pros and cons, to support or against the program. On social media such as Twitter, public opinions about vaccines are very diverse. This study investigates public sentiment towards the early stage of vaccination program conducted by the government. The classification method used in the sentiment analysis is the Support Vector Machine (SVM), among the positive, negative and neutral classes, with word embeddings extraction features. Data was collected and labeled by 12 crowd sourced annotators. The training and parameter tuning process was carried out to find the model that produced the best accuracy of validation data. From 400 testing data, the application of this optimal model resulted in an F1-score of 65% and an accuracy of 69%, higher than several machine learning methods in the same study.
ANIMATION INTRODUCTION OF PROFILE OF SMK NEGERI 5 JAYAPURA USING MULTIMEDIA ANIMATION COMPUTACION METHOD Mayor M. H. Manurung; Halomoan Edy Manurung; Kostantina Wandamani
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.6249

Abstract

Advances in science and technology can help an institution to support information and promotion, such as creating school profile recognition animations. Sala one vocational secondary in Papua, SMK Negeri 5 Jayapura at this time for the process of introducing school profiles using a website in the form of text so that it would be better if using multimedia components such as animation to clarify school profiles. The purpose of this research is to design animation to help promote SMK Negeri 5 Jayapura to the community to help the school to be able to provide better promotion and service to the community. This animation was created by the multimedia development life cycle (MDLC) development model of Luther Sutopo's multimedia development model which consists of six stages namely Concept, Design, Material Collection, Manufacturing, Testing and Distribution. The software used in the animation process is Blender V 2.79b, Photoshop CS5 and Adobe Audition CS6. The results of the study in the form of animated videos are 6 minutes long and not interactive. The profile creation of SMK Negeri 5 Jayapura has been successfully built to help the school to provide information about SMK Negeri 5 Jayapura, especially the introduction of school profiles to the public.
Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos Fadlisyah Fadlisyah; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.6614

Abstract

Fish is one of the foods that are often consumed by humans to complete the protein in the body. Indonesia is rich in animal protein so some rogue traders to avoid losses due to rotten fish, sellers process fish to make it look fresh by fishing with formalin liquids so that buyers think the fish is still fresh, this problem is often found in the market so that it takes the right solution. The solution offered is to apply Machine Learning (Boosting Algorithm) to classify fresh fish and fish that have been planned with formalin by utilizing the extraction of GLCM features. The findings of this study indicate a variation of boosting algorithms can provide solutions to this problem. Accuracy, precision, recall, f1-score, f2-score, and Jaccard score in tilapia fish with a value of 0.95, 0.95, 0.95, 0.95, 0.95, 0.95 this result is obtained by extreme gradient boosting variations with the highest achievements compared to variations Other things are also similar to Tamban fish with a value of 0.78, 0.775, 0.78, 0.825, 0.77, 0.632. The effect of boosting algorithm on the measurement of model performance looks very increased in tilapia fish while in tamban fish do not provide maximum results, but these findings are better

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