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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 601 Documents
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Export Commodity Price Forecasting in Indonesia Using Decision Tree, Random Forest, and Long Short-Term Memory Shadifa Auliatama Harjanto; Siti Sa'adah; Gia Septiana Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25242

Abstract

Gross Domestic Product (GDP) is an indicator that becomes a benchmark for a country's economic performance. One of the factors that significantly affect GDP is export activity. However, the problem that occurs is that the export value is relatively fluctuating, this is because commodity prices are always changing every time. Therefore, we need a system that can predict commodity prices accurately. It is hoped that this system can help the government to make appropriate export policies based on predictions of commodity prices in the future. The contribution of this study is to compare Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) performance in forecasting several export commodities in Indonesia. In this study, the commodities forecasted are the main commodities from each sector that dominates exports in Indonesia, namely palm oil from the manufacturing sector, coffee from the agricultural sector, and coal from the mining sector. The experiments in this study were conducted by testing several hyperparameters of each method to determine the best model. The performance of models is measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that LSTM has the lowest error among Decision Tree and Random Forest with MAPE of 0.121, 0.494, and 0.282 in forecasting coal, coffee, and palm oil price respectively. Therefore, LSTM has proven to be the best method among Random Forest and Decision Tree in forecasting export commodity prices in Indonesia.
Optimal Scheduling of Electric Vehicle Charging: A Study Case of Bantul Feeder 05 Distribution System Candra Febri Nugraha; Jimmy Trio Putra; Lukman Subekti; Suhono Suhono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25287

Abstract

The growing popularity of electric vehicles (EVs) has the potential to complicate distribution network operations. When a large number of electric vehicles are charging at the same time, the system load can significantly increase. This problem is exacerbated when charging is done concurrently in the evening, which coincides with peak load times. To prevent the increase in peak load and distribution operation stress, EV charging must be coordinated to achieve financial and technical objectives. This study seeks to evaluate the impact of financially driven EV charging scheduling algorithms. The contribution of this study is that the scheduling algorithm considers EV usage behavior based on real data as well as considers the state-of-charge (SoC) target set by EV owners. The proposed algorithm seeks to minimize the total charging cost incurred by EV owners using mixed-integer linear programming (MILP). The impact of the coordinated charging scheduling on the system demand profile and real distribution system operation metrics are also evaluated. The simulation result tested on the Bantul Feeder 05 system demonstrates that coordinated charging can reduce the charging costs by 57.3%. Furthermore, the peak load is reduced by 5.2% while also improving the load factor by 3.5% as compared to uncoordinated scheduling. Based on the power flow simulation, the proposed algorithm can reduce distribution transformer loading by 0.5% and improve voltage quality by 0.1% during peak load. This demonstrates that coordinated EV charging benefits not only the EV users but also the distribution system operator by preventing system operation issues.
Performance of Lexical Resource and Manual Labeling on Long Short-Term Memory Model for Text Classification Mardhiya Hayaty; Aqsal Harris Pratama
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25375

Abstract

Data labeling is a critical aspect of sentiment analysis that requires assigning labels to text data to reflect the sentiment expressed. Traditional methods of data labeling involve manual annotation by human annotators, which can be both time-consuming and costly when handling large volumes of text data. Automation of the data labeling process can be achieved through the utilization of lexicon resources, which consist of pre-labeled dictionaries or databases of words and phrases in sentiment information. The contribution of this study is an evaluation of the performance of lexicon resources in document labeling. The evaluation aims to provide insight into the accuracy of using lexicon resources and inform future research. In this study, a publicly available dataset was utilized and labeled as negative, neutral, and positive. To generate new labels, a lexicon resource such as VADER, AFINN, SentiWordNet, and Liu & Hu was employed. An LSTM model was then trained using the newly generated labels. The performance of the trained model was evaluated by testing it on data that had been manually labeled. The study found manual labeling led to highest accuracy of 0.79, 0.80, and 0.80 for training, validation, and testing respectively. This is likely due to manual creation of test data labels, enabling the model to learn and capture balanced patterns. Models using lexicon resources (VADER and AFINN) had lower accuracy of 0.54 and 0.56. SentiWordNet had lowest accuracy among all models with 0.49, and Liu&Hu model had the lowest testing score of 0.26. Our research indicates that lexicon resources alone are not sufficient for sentiment data labeling as they are dependent on pre-defined dictionaries and may not fully capture the context of words within a sentence, thus, manual labeling is necessary to complement lexicon-based methods to achieve better result.
Visible Light Communication System Design Using Raspberry Pi4B, LED Array, and MQTT Synchronization Protocol Teuku Alif Rafi Akbar; Apriono Catur
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25431

Abstract

Visible light communication emerged as the solution to overcome limitations exist in RF-based communication system. Although many research has been done on VLC, there are still a lot room for improvements, especially in the design of the VLC itself. This study discusses a simple visible light communication system design that transmits temperature and humidity information. The system uses Array 2×2 LED configuration to transmit data and photodiode to receive the optical signal. Raspberry Pi is used as the signal processor. The research carried out variations in the color of LED used, variations in the method of synchronization, and variations in the data rate transmission with BER value as the main parameter to be analyzed. The research contribution is to propose a simple visible light communication design that transmit and receive information in reference to room temperature and humidity using Raspberry Pi and DHT-11 sensor, while also implementing two synchronization methods to maximize synchronization in transmission thus minimizing the BER value in higher bit rate. The LED used is blue with an average voltage of 0.0423 V for bit ‘1’ and 0.00448 V for bit ‘0’. The throughput can be achieved are within range 1bps to 10 kbps with BER 0.5 as a threshold. The implementation of the synchronization method decreases the average BER value by 0.0945 with the implementation of transmission calibration synchronization and decreases the average BER value by 0.1221 using the MQTT communication protocol. In conclusion, the design has limitations through the component used in the transmitting and receiving end with BER values relatively high. Further research for system development can be done by implementing Forward Error Correction to minimize errors that occur in the transmission and collaborating with vendors with same research field for the latest components for VLC system design.
Application of the Machine Learning Method for Predicting International Tourists in West Java Indonesia Using the Averege-Based Fuzzy Time Series Model Sri Nurhayati; Syahrul Syahrul; Riani Lubis; Mochamad Fajar Wicaksono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25475

Abstract

The purpose of this study is to propose whether an average-based fuzzy time series model is appropriate for use in predicting the number of foreign tourists coming to West Java, Indonesia. Machine learning is a branch of artificial intelligence where machines are designed to learn on their own without human direction. One of the machine learning methods used by data science is for prediction processes, such as predicting the number of tourists. Tourism is one of the economic sectors that has a direct impact on the community's economy. Based on data from the Badan Pusat Statistik (BPS), the number of tourists coming to West Java Indonesia fluctuates, meaning that the number can increase and decrease every month and year. Changes in the number of tourists that fluctuate are one of the problems that have an impact on tourism actors. Therefore, the solution given to answer this problem is that an appropriate model is needed to predict the number of tourists visiting West Java. The contribution of this research is to help related parties in predicting the number of foreign tourists so that it can be used as one to make policies related to tourism preparation and planning efforts in West Java, Indonesia.  The method used in this research is a case study approach, where the case study is taken from data on foreign tourists visiting West Java from 2017 to 2020. For the prediction process, the method used is the fuzzy time series method and the average length-based algorithm as the determinant of the interval length. Effective interval length can affect prediction results with a higher level of accuracy. Based on the prediction test results, the Mean Absolute Percentage Error (MAPE) value is 14.71%. These results indicate that the fuzzy time series model based on the average interval length is good for prediction.
The Combination of C4.5 with Particle Swarm Optimization in Classification of Class for Mental Retardation Students Sausan Hidayah Nova; Budi Warsito; Aris Puji Widodo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25520

Abstract

Mental retardation or brain weakness is a condition of children who experience mental disorders. There are several characteristics to know the child has mental retardation. When entering a school, teachers are expected to be able to determine the right class for mental retardation students according to their category. Data mining is the process of finding patterns in selected data using artificial intelligence and machine learning. Algorithm C4.5 is one of the classification techniques in data mining. C4.5 can be used to create decision trees and classify data that has numeric, continuous, and categorical attributes. But C4.5 has the disadvantage of reading large amounts of data and cannot rank every alternative. PSO is an optimization algorithm for feature selection that can improve performance in data classification. Therefore, this study proposes an algorithm that can overcome the weaknesses of C4.5 by combining PSO. This study aims to classify a class of new mental retardation students using a combination of C4.5 as a classification and PSO as a feature selection to determine the attributes that affect the level of accuracy. The contribution of this research is to make it easier for the school to determine the new class of mental retardation students so that it is appropriate and according to their needs. The classification process in this study uses a combination of C4.5 and PSO. The validation used in this model is 10-fold cross-validation, and the evaluation uses a confusion matrix. This study resulted in an accuracy of C4.5 before using PSO of 91%. While the accuracy of C4.5 uses a PSO of 93%. Of the 20 attributes, there are 6 attributes that affect the level of accuracy. This study shows that PSO can be used to implement feature selection and increase the accuracy value of C4.5 by 2%.
Development of Novel Machine Learning to Optimize the Solubility of Azathioprine as Anticancer Drug in Supercritical Carbon Dioxide Arya Adhyaksa Waskita; Stevry Yushady CH Bissa; Ika Atman Satya; Ratna Surya Alwi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25608

Abstract

Supercritical carbon dioxide (Sc-CO2) has thus been proposed as an appropriate solvent for diluting the pharmaceuticals to increase particle size. The use of supercritical fluids (SCFs) in various industrial applications, such as extraction, chromatography, and particle engineering, has attracted considerable interest. Recognizing the solubility behavior of various drugs is an essential step in the pharmaceutical industry's pursuit of the most effective supercritical approach. In this work, four models were used to predict the solubility of Azathioprine in supercritical carbon dioxide, including Ridge regression (RR), Huber regression (HR), Random forest (RF), and Gaussian process regression (GPR). The R-squared scores of all four models are 0.974, 0.6518, 0.966, and 1.0 for Ridge regression (RR), Huber regression (HR), Random forest (RF), and Gaussian process regression (GPR) models, respectively. The RMSE error rates of 2.843 ×10-13, 7.036 ×10-12, 5.673 ×10-13, and 1.054 ×10-30 for the RR, HR, RF, and GPR models, respectively. MAE metrics of 1.205 ×10-6, 2.151  ×10-6, 5.997 ×10-7 and 9.419 ×10-16 errors were also found in the RR, HR, RF, and GPR models, respectively. It was found that Ridge regression (RR), Random forest (RF), and Gaussian process regression (GPR) models can be used to predict any compound's solubility in supercritical carbon dioxide.
Motorcycling-Net: A Segmentation Approach for Detecting Motorcycling Near Misses Rotimi-Williams Bello; Chinedu Uchechukwu Oluigbo; Oluwatomilola Motunrayo Moradeyo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25614

Abstract

This article presents near misses as corrective and preventive measures to safety events. The article focuses on the risk factors of commercial motorcycling near misses, which we address by proposing a near miss detection framework based on a hybrid of YOLOv4-DeepSort and VGG16-BiLSTM models. We employed YOLOv4-DeepSort model for the detection and tracking tasks, and the tracked images and identity information were stored. The sequence of image was fetched into the VGG16-BiLSTM model for extraction of image feature information and near misses recognition respectively. Video streams of near miss datasets containing motorcycling in different scenes were collected for the experiment. We evaluate the proposed methods by testing 444 sequential video frames of motorcycling near misses in urban environment. The detection models achieved 96% accuracy for motorcycle, 89% for car, and 81% for person with lower false-positive rates on the test datasets while the tracking models achieved 34.3 MOTA on the test set and MOTP of 0.77. The results of the study indicate practicality for automatic detection of motorcycling near misses in urban environment, and it could assist in providing resourceful technical reference for analyzing the risk factors of motorcycling near misses. The research contributions are: (1) A hybrid of YOLOv4 and DeepSort model to enhance object detection and tracking in a complex environment and (2) A hybrid of YOLOv4 and DeepSort model to optimize the extraction of image feature information and near misses recognition respectively for overall system performance.
Optimization of Wind Farm Yaw Offset Angle using Online Genetic Algorithm with a Modified Elitism Strategy to Maximize Power Production Kurniawan Kurniawan; Aris Triwiyatno; Iwan Setiawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25747

Abstract

The wake interaction in a wind farm occurs when the front turbines block the flow of wind to the turbines behind them, causing a total power loss of approximately 10–25%. Wake interactions can be redirected to reduce bad impacts by optimizing the yaw offset angles. Optimization of the yaw offset angle can increase the total power of the wind farm by approximately 6–9%. However, the fluctuating wind flow angle in the environment causes the behavior of the wake interaction to change, making it difficult to optimize the yaw offset angles. Therefore, this study proposes an online genetic algorithm with a modified elitism strategy to overcome this problem. The contribution of this study is to improve the performance of the genetic algorithm by modifying the elitism strategy in order to optimize the yaw offset angle for each turbine adaptively to a wind farm operating in a dynamic environment. The optimal yaw offset angles are stored in the elite population for various wind flow angles and then reinserted into the search population in each generation according to the actual wind flow angles. A Gaussian-based analytical wake interaction model under a yawed condition developed by Shapiro is employed in this study to evaluate the total power of a wind farm. This study resulted in a convergence speed that was 3.8 times faster than the classical elitism strategy. At several wind flow angles of 270°, 315°, and 360°, an average power increase of 10.52% was obtained. This study shows that the modification of the elitism strategy can increase the convergence speed to adaptively track the optimal yaw offset angle at various wind flow angles, so that the average increase in wind farm power is 1.94% higher than in previous studies.
Gazebo Semar: An Android-based Farmer Education Platform for Agricultural Waste Management Mahdaviqia Dharmawan; Lusia Dara Sari; Jericho Pandita Gunawan; Ernoiz Antriyandarti; An Duong
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25757

Abstract

Agricultural waste and lack of knowledge about agricultural waste management is an environmental problem in Karanganyar Regency. Farmers in Karanganyar only handle agricultural waste, such as rice straws, husks, and corn stalks by burning them. Therefore, this study attempts to create innovation by educating farmers on properly treating agricultural waste. This study implemented mix method, namely application development and application evaluation by conducting survey. The Gazebo Semar application is built using a Kodular service begins with concept planning, interface design, features, and coding. Application evaluation were collected from a survey using a questionnaire of 120 farmers in Karanganyar Regency to evaluate the usability testing using USE (Usefulness, Satisfaction, and Ease to use) questionnaires and use the Likert scale for measurement. Gazebo Semar is a solution to provide information on agricultural waste types, waste management, and marketing of processed agricultural waste products. This application allows the users to easily access information about agricultural waste and Zero Waste without visiting multiple websites or blogs. The Gazebo Semar App has nine main features: Home Screen, Zero Waste, SDGs, Waste Source, Waste Classification, Waste Management, Gazebo Semar Store, Quiz, and About Us. Gazebo Semar provides a number of novelties in terms of substance and features, so that it is expected to have an impact on local farmers especially in the field of agricultural waste processing. The results show that the score of Usefulness, Satisfaction, and Ease of Use is above 71%, which means Gazebo Semar has provided the application that fits with the needs of farmers. The research contribution is to improve the mindset of farmers in processing agricultural waste into other forms that are more economically valuable. The significance of this study is to increase public knowledge about agricultural waste, zero waste, and waste management. 

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