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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 505 Documents
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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.
Master Plan for Electricity Distribution Networks Based on Micro-Spatial Projection of Energy Demand Adri Senen; Christine Widyastuti; Oktaria Handayani
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.v7i3.22244

Abstract

The existing method of the Master plan for electricity distribution networks is sectoral and macro-based, which means it is unable to show load centers in micro-grids. The inaccurate and bias results lead to the failure of determining the capacity of transformers, the total of transformers, and the locations of distribution substations, and thus it will complicate the master planning of the distribution network. Therefore, a micro-spatial-based method in electricity master planning is needed, as it will generate more accurate forecasting, energy projection and estimate the numbers of load centers at each grid based on the geographical structure. The research contribution is to produce a master planning of distribution network that will help in determining transformer capacity, the placement of substations and distribution substations, evaluation, and orientation of electricity distribution system development to a smaller area. The results of the load growth become the basis for determining the capacity and the total of transformers in the area. The methodology developed in this research has analyzed the transformer rating, transformer capacity, total of transformers, and the location of transformer with growing energy demand in the smaller range. The results can be developed into the design planning of distribution network systems with better accuracy.
Evaluation of Product Quality Improvement Against Waste in the Electronic Musical Instrument Industry Hendra Hendra; Indra Setiawan; Hernadewita Hernadewita; Hermiyetti Hermiyetti
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.v7i3.21904

Abstract

Customer satisfaction is one of the main factors that must be considered by every company to compete in the global market. One way to increase customer satisfaction is to reduce defective products and reduce waste. This method is a continuous improvement that must be carried out by every manufacturing industry. The Musical Instrument Industry is one of the producers of electronic musical instruments such as pianos. The production process has occurred several problems such as low product quality and waste (ineffective processes, inappropriate layouts, overproduction, and poor production quality). The biggest problem with this company is the large number of products that are not following company standards, such as not achieving quality standards and production not achieving targets. To improve the quality of its production, companies need to make the production process streamlined so that it will create a more effective and efficient production line. A balance between effectiveness and efficiency can be made by reducing waste. Lean Six Sigma is an approach that focuses on improving quality and eliminating waste and sustainable strategy to improve product quality while reducing waste. The company's strategy to be able to compete in the global market must be able to improve product quality and eliminate waste. This study aims to improve product quality and analyze the effect of improving quality on waste. The method used is Lean Six Sigma with dynamo stages, namely pre-study, measurement, analysis, and implementation. The results showed that the quality of the product had increased. Quality improvement has a significant effect on waste.
The Comparison Performance of Digital Forensic Tools Using Additional Root Access Options Aljo Leonardo; Rini Indrayani
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.v7i3.22381

Abstract

This research used MiChat and SayHi as materials for forensic investigations using three different tools, namely MOBILedit, Magnet Axiom, and Belkasoft. These three tools will show each performance in the forensic process. We also added a rooting process as an option if data cannot be extracted optimally even when using these three applications. The result of this study shows that the cases studied with processes without root access and with root access have the aim of complementing each other in obtaining evidence. So that these two processes complement each other's shortcomings. The main contribution of this research is a recommendation of a tool based on the best performance shown during the forensic process with rooting access and without rooting access. Based on the comparison, Magnet Axiom is superior with a total of 34 items of data found without root access, while MOBILedit is 30 items and 30 items for Belkasoft. While comparison using root access, Magnet Axiom and MOBILedit are superiors with a total of 36 items found in Magnet Axiom without root access, while MOBILedit is 36 items and 33 items for Belkasoft. Based on the test results, it can be concluded that the recommended tool according to the used scenario is Magnet Axiom.
Battery Insulation Performance Analysis in Electric Vehicles for the Improvement of Battery Lifetime Lora Khaula Amifia; Nuansa Dipa Bismoko; Philip Tobianto Daely
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.v7i3.22199

Abstract

The battery is the main component both as an energy provider and as an interface for several systems in an electric vehicle. It has three important parameters: current, voltage, and temperature that must be maintained as the battery can have a harmful reaction that can lead to overcurrent. The battery must also not overcharging or discharging for too long because it can cause damage and affect its lifetime. Another error that can arise is sensor failure due to the interference or noise that can cause an error in data reading. To prevent this problem, it needs protection by means of isolation in operating the battery. In this research, planning in optimizing battery work was conducted by designing the process of detection and isolation of faults that occurred in batteries, particularly lithium polymer battery to reach their more optimal and good performance. Battery modeling was needed as the parameter identification, and the Kalman Filter algorithm was applied to help to reduce the detection rate and fault isolation. The results of detection and isolation of overcurrent and sensor failure using Kalman Filter were found quite accurate. In overcurrent isolation, a discharge current of 6A was obtained from the maximum current limit of 10 A, and for sensor failure isolation, the Kalman Filter algorithm succeeded in improving the results of the previous reading.
LoRaWAN Technology in Irrigation Channels in Batu Indonesia Puput Dani Prasetyo Adi; Akio Kitagawa; Dwi Arman Prasetya; Rahman Arifuddin; Stanislaus Yoseph
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.v7i3.22258

Abstract

Currently, agricultural technology or Farming development is increasingly sophisticated by applying LoRaWAN-based IoT technology, ignoring quality agricultural products. LoRaWAN used in this research uses Long-Range Frequency 915 MHz and 920 MHz. The case study in this research is a case of river water quality that enters agricultural land or irrigation in Temas, Batu City, where the river water has been contaminated by household waste. The prototype installed on this farm uses an Arduino and Dragino LoRa 915 MHz microcontroller as transceivers and input and output devices consisting of ultrasonic sensors and water pH sensors, and outputs such as Solenoid valves mounted on tub one and tub 2. In contrast, tub 3 is a unique tub for distributing water to agricultural land with normal water pH quality. In this research, real-time monitoring, especially on the conditions of water turbidity, water pH, and water level.
Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters Mungki Astiningrum; Vivi Nur Wijayaningrum; Ika Kusumaning Putri
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.v7i3.22010

Abstract

The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.

Page 17 of 51 | Total Record : 505

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