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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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Review of Peer-to-Peer (P2P) Lending Based on Blockchain Victory, Timotius; Yazid, Setiadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27671

Abstract

Peer-to-Peer (P2P) lending is a financing business model that has gained popularity in recent years due to the ease of loan application, disbursement, and repayment processes. The volume of Peer-to-Peer (P2P) Lending transactions have a significant growth. One of the reasons for the popularity of Peer-to-Peer (P2P) lending is its utilization of technology in both the application and loan repayment processes. One such technology gaining traction in Peer-to-Peer (P2P) lending is blockchain technology. The popularity of blockchain technology lies in its ability to enhance the transparency of the transaction process. This literature study aims to address three main questions: What are the characteristics of blockchain suitable for Peer-to-Peer (P2P) lending , the benefits of implementing blockchain technology in Peer-to-Peer (P2P) lending and the challenges of Peer-to-Peer (P2P) lending based on blockchain. The findings reveal that there are characteristics of blockchain that can be applied to Peer-to-Peer (P2P) lending, bringing numerous benefits to the overall Peer-to-Peer (P2P) lending process. However, challenges persist in the implementation of blockchain technology in Peer-to-Peer (P2P) lending. The insights gained from this literature review are intended to guide researchers interested in studying the application of blockchain technology in the context of Peer-to-Peer (P2P) lending.
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting Pebrianti, Dwi; Kurniawan, Haris; Bayuaji, Luhur; Rusdah, Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27712

Abstract

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method.
Software Design of Autocorrection Essays on the Website and Application Pasaribu, Novalanza Grecea; Alif, Menara; Budiman, Gelar; Akhyar, Fityanul
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27758

Abstract

Traditional essay assessment methods are often time-consuming and prone to subjectivity. This study proposes a novel Automated Essay Scoring (AES) system, "Essay Mathematic Auto Correction (Emath Toco)," featuring web and mobile app interfaces. Emath Toco leverages visual stimuli and deep learning algorithms like 1D CNN, NasNet Mobile, and GoogleNet to offer objective and efficient essay evaluation. Extensive testing on a 40/60 training/testing data split yielded accurate data classification, validating successful implementation on Flutter-built Android applications and a Firebase-powered web interface. User experience surveys revealed positive feedback on Emath Toco's ease of use, visually appealing interfaces, and effective data collection, confirming its user-friendliness. Emath Toco's innovative use of visual stimuli and deep learning algorithms significantly reduces subjectivity and improves the accuracy of essay evaluation. Emath toco is promising technology with the potential to revolutionize essay assessment and educational methodologies. The research contributes to the field of automated essay scoring in two key ways. First, by integrating visual stimuli as a novel approach, Emath Toco expands the range of factors considered in scoring, potentially leading to more comprehensive and efficient. Second, the successful implementation of the system on both web and mobile platforms demonstrates its flexibility and accessibility, offering educators a versatile tool regardless of technological limitations.
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning Kurniati, Florentina Tatrin; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan; Huizen, Roy Rudolf
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27842

Abstract

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.
Determinants of Sugar Imports, Sugar Consumption and Production in Indonesia (2000 – 2019 Study Case) Al Azam, Nasrudin Ahmad; Antriyandarti, Ernoiz; Adi, Raden Kunto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27887

Abstract

Indonesia was a prime sugar exporter country in the past, but since year 1967 has been importing sugar from other countries. Sugar import volume has been increasing every year so Indonesia has become second largest sugar importer in the recent years. Based on those problems, this research aims to analyze the factors that affect Indonesian sugar imports. The data used for analysis is secondary data in the form of time series in the range of 20 years (2000 – 2019), which were collected from various related agencies. Data analysis uses Seemingly Unrelated Regression (SUR), which was used to analyze the effect of sugar production, GDP, sugar consumption, domestic sugar prices, International sugar prices, and rupiah exchange rate on the volume of Indonesian sugar imports. The result shows that only sugar consumption affects significantly sugar imports, while sugar production, GDP, domestic sugar prices, international sugar prices, and the rupiah exchange rate do not significantly affect sugar imports. Sugar consumption is affected by GDP and domestic sugar prices, while sugar production is affected by domestic sugar prices. In addition, sugar imports volume shows that trend imports grew positively with an estimated trend of 197.978 tons per year in the 2000 – 2019 period. According to the sugar import trend, can be concluded that there will be growth of sugar import volume in the coming years. Based on SUR analysis, sugar imports growth is caused by consumption growth and consumption growth is affected by the growth of GDP and the decrease in domestic sugar prices. One policy that can be implemented by the government to resolve the sugar import problems is the policy on sugar prices, because high sugar prices decrease sugar consumption, and on the other hand also increase domestic sugar production.
Design of Application Framework for Vital Monitoring Mobile-Based System Rizky Ananda, Muhammad; Faisal, Mohammad Reza; Herteno, Rudy; Nugroho, Radityo Adi; Abadi, Friska
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28416

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Comparative Evaluation of Feature Selection Methods for Heart Disease Classification with Support Vector Machine Bidul, Winarsi J.; Surono, Sugiyarto; Kurniawan, Tri Basuki
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28647

Abstract

The purpose of this study is to compare the effectiveness of a variety of feature selection techniques to enhance the performance of Support Vector Machine (SVM) models for classifying heart disease data, particularly in the context of big data. The main challenge lies in managing large datasets, which necessitates the application of feature selection techniques to streamline the analysis process. Therefore, several feature selection methods, including Logistic Regression-Recursive Feature Elimination (LR-RFE), Logistic RegressionSequential Forward Selection (LR-SFS), Correlation-based Feature Selection (CFS), and Variance Threshold were explored to identify the most efficient approach. Based on existing research, these methods have shown a great impact in improving classification accuracy. In this study, it was found that combining the SVM model with LR-RFE, LR-SFS, and Variance Threshold resulted in superior evaluation, achieving the highest accuracy of 89%. Based on the comparison of other evaluation results, including precision, recall, and F1-score, the performance of these models varied depending on the feature selection method chosen and the distribution of data used for training and testing. But in general, LR-RFE-SVM and Variance Threshold-SVM tend to provide better evaluation values than LR-SFS-SVM and SVM-CFS. Based on the computation time, SVM classification with the Variance Threshold method as the feature selection method obtained the fastest time of 118.1540 seconds with the number and retention of 23 important features. Therefore, it is very important to choose a suitable feature selection technique, taking into account the number of retained features and the computation time. This research underscores the significance of feature selection in addressing big data challenges, particularly in heart disease classification. In addition, this study also highlights practical implications for healthcare practitioners and researchers by recommending methods that can be integrated into real-world healthcare settings or existing clinical decision support systems.
Study of Indirect Vector Control Induction Motor Based on Takagi Sugeno Type Fuzzy Logic on Rotational Speed Control Primary Surveillance Radar Setiawan, Paulus; Dharmawan, Muchamad Wizdan; Santoso, Prasidananto Nur; Pratiwi, Elisabeth Anna; Dinaryanto, Okto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28689

Abstract

During the rainy season and bad weather, strong winds blowing at an airport can cause dynamic changes in performance of the primary surveillance radar (PSR) antenna which is driven by an induction motor (IM). Changes in dynamic performance that occur in this IM can be in the form of changes in PSR rotation speed, changes in torque values, and changes in stator current values. In this article, we propose the application of the Takagi Sugeno method to fuzzy logic indirect vector control of IM as a solution that can reduce changes in the dynamic performance of motor as PSR drivers during bad weather. The contribution of this research is the application of the Takagi Sugeno method in a fuzzy inference system (FIS), where this fuzzy logic control system replaces the conventional proportional integral (PI) controller for indirect vector control IM. Takagi Sugeno method is computationally efficient and works well with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for dynamic nonlinear systems. Takagi Sugeno type FIS uses weighted average to compute the crisp output, so the Sugeno’s output membership functions are either linear or constant. Furthermore, Takagi Sugeno method has better processing time since the weighted average replace the time consuming defuzzification process. The results obtained after simulation in MATLAB Simulink environment showed that fuzzy logic using the Takagi Sugeno method which is used as a substitute controller for indirect vector control can provide better performance when compared to conventional PI controllers. These results can be seen from the response values of rotor rotation speed, electromagnetic torque, and stator current. Overall, this research provides discourse on improving the dynamic performance of IM through the application of the Takagi Sugeno fuzzy logic indirect vector control method.
Usage of Unsupported Technologies in Websites Worldwide Nugroho, Pascal Alfadian; Putra, Raymond Chandra; Maulana, Rajasa Cikal; Tandra, Vinson
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28692

Abstract

Websites using unsupported 3rd party technologies (libraries, frameworks, plugins, etc) are generally not recommended, especially due to security issues that are left unfixed. However, upgrading to supported technologies is also challenging, hence not all web maintainers upgrade their technology dependencies. Measuring the existence of unsupported technologies in the wild may contribute to the sense of urgency in keeping technologies updated. Our research proposed a method to measure the existence of unsupported technologies in international websites, using HTTP Archive as the data source. The contribution from our research is the method as well as the snapshot result from January 2023 data. The method is composed of four steps, namely: identify the list of websites, identify technologies used, group by technology names and retrieve currently supported versions, and compare versions between usage and supported versions. From the January 2023 data, we found several interesting results. One is that the higher the website rank is, the higher the number of supported technologies used. Another finding was that worldwide websites also generally use more supported versions of technologies, compared to Indonesian websites. Further research may be performed for longitudinal analysis of technology support evolution.
Adaptive Traffic Light Signal Control Using Fuzzy Logic Based on Real-Time Vehicle Detection from Video Surveillance Fahrunnisa, Zulfa; Rahmadwati, Rahmadwati; Setyawan, Raden Arief
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28712

Abstract

Intersections often become the focal points of congestion due to poor traffic signal management, reduced productivity, increased travel duration, gas emissions, and fuel consumption. Existing traffic light systems maintained constant signal duration regardless of traffic situations, resulting in green signals for lanes with no vehicle queues that increased waiting times in other lanes. Therefore, a real-time traffic signal optimization system using Fuzzy Logic control, utilizing vehicle queue and flow rate real-time data from video surveillance, is needed. This research used recorded video from surveillance cameras in Banten Province, Indonesia, during daylight conditions. Vehicle queues and flow rate data were used as parameters to determine traffic light signals. The YOLO algorithm obtained these parameter values, then served them as inputs for the Fuzzy Logic system to determine signal duration. The accuracy of the traffic situation estimation system fluctuated within a range of 40% to 100%. Simulation results showed an improvement of approximately 18% by evaluating the total number of vehicles that exited the queue and reduced vehicle waiting time by about 21% compared to the existing system on intersection efficiency. Consequently, the proposed system can reduce pollution and fuel consumption, contributing to urban sustainability and public well-being enhancement. Despite the improvements over the previous systems, the accuracy of the vehicle detection system may vary with traffic density based on the extent of occlusions present, which is an area that needs further refinement. This research's contributions include utilizing real-time video footage from surveillance cameras above traffic lights to obtain real traffic conditions and identify potential errors such as occlusion of overlapping vehicle due to very congested roads. Another contribution is the adjustment of the Fuzzy membership function based on the vehicle detection system's ability to ensure precise determination of green signal duration, even when the input data contains errors.

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