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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Published by Universitas Ahmad Dahlan
ISSN : 23383070     EISSN : 23383062     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.
Arjuna Subject : -
Articles 16 Documents
 1   2 
Search results for , issue "Vol 7, No 3 (2021): December" : 16 Documents clear
Comparing Machine Learning and Human Judge in SATU Indonesia Awarding Processes Onno W. Purbo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22201

Abstract

For more than ten years, SATU Indonesia Awards, with PT. Astra International Tbk's support is given to inspiring young Indonesians. Every year, more than 10,000 nominations must be short-listed to 90 nominations within one week with five (5) assessment parameters. The research contributions are (1) creating a machine learning mechanism for the awarding process from ten years of the SATU Indonesia Awards nomination archive, (2) creating two (2) models of training data for the five (5) assessed parameters, namely motivation, obstacle, outcome, outreach, and sustainability, and (3) compare machine learning prediction with 2021 judge's assessment. TEMPO Data and Analysis Center (PDAT) extracts the corpus training data from ten years' SATU Indonesia Awards data in six months. The corpus training data contains nomination texts with Judges' scores on motivation, obstacle, outcome, outreach, and sustainability. Two (2) corpus training data and two models were generated with, namely, (1) the average Judges' parameter value per instance and (2) the Judges' smallest value and stored in two (2) corpus of 1220 instances each. The classification model was generated by Random Forest, which has the slightest error among the classification algorithms tested. The first model aims to predict the nomination assessment parameters. The second model is to detect the outlier in the incoming nominees for extraordinary nominees. The machine learning predictions were compared and found to be similar to the 2021 judge's assessment in the awarding processes at SATU Indonesia Awards. The average Judges' pre-final 2021 nominees' scores are compared to the Random Forest's predictions and found to be reasonably similar, with a small RMSE error around 1.1 to 1.6 for all assessment parameters. The smallest RMSE was obtained in the Sustainability parameter. The Obstacle parameter was found to have the largest RMSE.
Design and Prototyping of Electronic Load Controller for Pico Hydropower System Supriyanto Praptodiyono; Hari Maghfiroh; Muhammad Nizam; Chico Hermanu; Arif Wibowo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22271

Abstract

A hydroelectric power plant is an electrical energy generator that utilizes water energy to drive a water turbine coupled to a generator. The main problem in hydroelectric power plants is the frequency and voltage fluctuations in the generator due to fluctuations in consumer loads. The purpose of this research is to make a prototype of the Electronic Load Controller (ELC) system at the Pico Hydropower Plant. The main part of ELC is the frequency sensor and gating system. The first part is made by a Zero Crossing Detector, which detects the generator frequency. The gating system was developed with TRIAC. The method used is the addition of a complement load which is controlled by delaying the TRIAC. Load control is intended to maintain the stability of the electrical energy produced by the generator. The PID algorithm is used in frequency control. The results of the frequency sensor accuracy test are 99.78%, and the precision is 99.99%. The ELC system can adjust the frequency automatically by setting the firing delay on the TRIAC to distribute unused power by consumer loads to complementary loads so that the load used remains stable. The ELC is tested with increasing and decreasing load. The proposed ELC gives a stable frequency at 50Hz. Whereas at the first test, the mean voltage is 183V, and in the second test is 182.17V.
Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era Anggunmeka Luhur Prasasti; Iftitahrira Aulia Rahmi; Syarifah Faisa Nurahmani; Ashri Dinimaharawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22012

Abstract

Every human being faces various episodes of events that can cause changes in mental health conditions, including this coronavirus pandemic disease. The ups and downs of the psychological turmoil dynamics resulted, and also traumatic feelings can occur continuously or for a certain period. It can cause an adverse response for those who experience it and even cause anxiety or mental disorders. The implementation of restrictions on community activities during these pandemic circumstances makes people who want to check their mental health condition difficult to meet the experts or professionals such as psychologists. Therefore, the application that can detect these anxiety disorders as early as possible to minimize unwanted effects was developed. In the making of the application, an expert system is used to determine the results of the diagnosis. The expert system requires knowledge that is produced from experts, especially in the psychology field. The data that has been obtained will be processed and then yield the results determined from the classification of anxiety types using several methods in Artificial Intelligence. Several tests were carried out 50 times using Certainty Factor methods to obtain an accuracy rate of 96%. It has similar accuracy compared to the Naïve Bayes method. This application called Mental Health Helper has a validity and reliability test to prove that this application is valid and reliable. It has better performance than previous researches, which still only has two classes of diseases.
Secure Key Exchange Against Man-in-the-Middle Attack: Modified Diffie-Hellman Protocol Mostefa Kara; Abdelkader Laouid; Muath AlShaikh; Ahcène Bounceur; Mohammad Hammoudeh
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22210

Abstract

One of the most famous key exchange protocols is Diffie-Hellman Protocol (DHP) which is a widely used technique on which key exchange systems around the world depend. This protocol is simple and uncomplicated, and its robustness is based on the Discrete Logarithm Problem (DLP). Despite this, he is considered weak against the man-in-the-middle attack. This article presents a completely different version of the DHP protocol. The proposed version is based on two verification stages. In the first step, we check if the pseudo-random value α that Alice sends to Bob has been manipulated! In the second step, we make sure that the random value β that Bob sends to Alice is not manipulated. The man-in-the-middle attacker Eve can impersonate neither Alice nor Bob, manipulate their exchanged values, or discover the secret encryption key.
The Development of Real-Time Mobile Garbage Detection Using Deep Learning Haris Imam Karim Fathurrahman; Alfian Ma'arif; Li-Yi Chin
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22295

Abstract

The problem of garbage in the world is a serious issue that must be solved. Good garbage management is a must for now and in the future. Good garbage management is accompanied by a system of classification and sorting of garbage types. This study aims to create a mobile-based application that can select the type of garbage and enter the garbage data into a database. The database used is a Google SpreadSheet that will accommodate data from the output issued by the garbage detection mobile application. The image data used in this study amounted to 10108 images and was divided into six different garbage classes. This study uses a deep learning platform called densenet121 with an accuracy rate of 99.6% to train the image data. DenseNet121 has been modified and added an optimization based on a genetic algorithm. The genetic algorithm applied in the optimization uses four generations. The model resulting from the training of the two approaches is converted into a model that mobile applications can access. The mobile application based on a deep learning model accommodates the detection data of the type of garbage, the level of detection accuracy, and the GPS location of the garbage. In the final experiment of the mobile application, the delay time in sending data was very fast, which was less than one second (0.86s).
Efficient MAC Adaptive Protocol on Wireless Sensor Network Adim Firmansah; Aripriharta Aripriharta; Sujito Sujito
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22053

Abstract

Wireless Sensor Networks (WSNs) have attracted a lot of attention from the research community and industry in recent years. WSNs maintenance associated with battery replacement can increase system operating costs, especially for wireless sensor networks located in hard-to-reach and dangerous places. In this study, an adaptive Medium Access Control (MAC) is proposed that can regulate the period of data acquisition and transmission. In contrast to conventional MAC, the applied adaptive MAC regulates the data transmission period based on the estimated energy use in the previous cycle. This study focuses on comparing energy efficiency between conventional and adaptive MAC. Energy usage information is retrieved directly on the sensor node. In star topology, the proposed MAC can increase the lifetime of the sensor network up to 6.67% in a star topology. In the hierarchical topology, the proposed MAC can increase network energy efficiency up to 9.17%. The resulting increase in network throughput is 17.73% for the Star network and 33.81% for the Hierarchy network. The star topology without implementing adaptive MAC has the lowest throughput of 0.188 kb/s. The highest throughput is achieved by a hierarchical topology that applies MAC with a throughput of 2.157 kb/s.
Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model Purwono Purwono; Alfian Ma'arif; Iis Setiawan Mangku Negara; Wahyu Rahmaniar; Jihad Rahmawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22237

Abstract

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.
Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot Muhammad Auzan; Roghib Muhammad Hujja; M Ridho Fuadin; Danang Lelono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.21017

Abstract

Differential drive wheeled mobile robot (DDWMR) is one example of a robot with a constrained movement, Multiple Input Multiple Output (MIMO), and nonlinear system. Designing a low resource position and heading controller using linear MIMO methods such as LQR became a problem because of the linearization of robot dynamics at zero value. One of the solutions is to design a MIMO controller using a Single Input Single Output (SISO) controller. This work design a controller using PID for DDWMR Jetbot and selects the best feedback gain using different scenarios. The designed controller manipulates both motors by using calculated control signal to achieve a complex task such as path tracking with robot position in x-Axis, y-Axis, and heading angle as the feedback. The priority between position and heading angle can be adjusted by changing three feedback gains. The controller was tested, and the best gain was selected using Integral Absolute Error (IAE) metrics in a path tracking task with four different path shapes. The proposed methods can track square, circle, and two types of infinity shape paths, with the less well-formed shape being the four edges square path.
Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis Md Shofiqul Islam; Md Munirul Hasan; Md Abdur Rahim; Ali Muttaleb Hasan; Mohammad Mynuddin; Imran Khandokar; Md Jabbarul Islam
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22327

Abstract

The growth of the entertainment industry around the world may be seen in the creation of new genres and the influx of artists and musicians into this field. Every day, a large amount of music is generated and released. The classification of these music based on genres and the recommendation of music to users is a crucial task for various music streaming platforms. Many artificial intelligence methods have been created to overcome this. Inadequate data for training is one of the biggest issues when it comes to building machine learning algorithm. A certain dataset contains a large number of redundant features, which may lead the models to overfit. Data filtering could be used to solve this issue. On the GTZAN data for music genre classification, this article constructed numerous Artificial Intelligence (AI) models and used a data filtering strategy. This study does a comparative analysis and discusses the results. The models developed and evaluated are Naive Bayes, Stochastic Gradient Descent, KNN, Decision trees, Random Forest, Support Vector Machine, Logistic Regression, Neural Nets, Cross Gradient Booster, Cross Gradient Booster (Random Forest) and XGBoost.  Accuracy gained by Naive Bayes is 51.95%, Stochastic Gradient Descent 65.53%, KNN 80.58%, Decision trees  63.997%, Random Forest is 81.41% , Support Vector Machine 75.41%, Logistic Regression 69.77%, Neural Nets 67.73%, Cross Gradient Booster 90.22%, Cross Gradient Booster (Random Forest) 74.87%.Finally, XGBoost is the best performed machine learning with accuracy of 90.22%. This research outcomes will help to analyse music in different areas.
Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization Rista Azizah Arilya; Yufis Azhar; Didih Rizki Chandranegara
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22080

Abstract

At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before.

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