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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 22 Documents
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Search results for , issue "Vol. 10 No. 2 (2024): June" : 22 Documents clear
Movie Recommender System Using Cascade Hybrid Filtering with Convolutional Neural Network Arsytania, Ihsani Hawa; Setiawan, Erwin Budi; Kurniawan, Isman
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.v10i2.28146

Abstract

The current technological advancements have made it easier to watch movies, especially through online streaming platforms such as Netflix. Social media platforms like Twitter are used to discuss, share information, and recommend movies to other users through tweets. The user tweets from Twitter are utilized as a film review dataset. Film ratings can be used to build a recommendation system, incorporating Collaborative Filtering (CF) and Content-based Filtering (CBF). However, both methods have their limitations. Therefore, a hybrid filtering approach is required to overcome this problem. The filtering approach involves CF and CBF processes to improve the accuracy of film recommendations. No current research employs the Cascade Hybrid Filtering method, particularly within the context of movie recommendation systems. This study addresses this gap by implementing the Cascade Hybrid Filtering method, utilizing the Convolutional Neural Network (CNN) as the evaluative instrument. This research presents a significant contribution by implementing the Cascade Hybrid Filtering method based on CNN. This research uses several scenarios to compare methods to produce the most accurate model. This study's findings demonstrate that the application of Cascade Hybrid Filtering, incorporating CNN and optimized with RMSProp, yields a movie recommendation system with notable performance metrics, including an MAE of 0.8643, RMSE of 0.6325, and the highest accuracy rate recorded at 86.95%. The RMSprop optimizer, facilitating a learning rate of 6.250551925273976e-06, enhances accuracy to 88.40%, showcasing a remarkable improvement of 6.00% from the baseline. These outcomes underscore the significant contribution of the paper in enhancing the precision and effectiveness of movie recommendation systems.
Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model Kurniawan, Johanes Dian; Trihandaru, Suryasatriya; Parhusip, Hanna Arini
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.v10i2.28490

Abstract

This study introduces the AIOT-Particle, a compact device designed for comprehensive air quality and environmental monitoring in Tegalrejo, Salatiga, Indonesia. Addressing the need for real-time, multi-parameter environmental data, the device simultaneously tracks PM1.0, PM2.5, temperature, humidity, pressure, and altitude, utilizing a built-in data fusion algorithm to ensure accurate and coherent data collection. Air pollution standards classify air quality as "good" (0–50), "moderate" (51–100), "unhealthy" (101-200), "very unhealthy" (201-300), and "hazardous" (>300). The research contribution is the development and validation of the AIOT-Particle using the ARIMA model for precise environmental monitoring. The methods involved deploying the device in Salatiga and applying the ARIMA model to analyze the collected data for accuracy. The results demonstrated promising accuracy: for PM1.0, the RMSE was 8.13 with an MAE of 6.04; for PM2.5, the RMSE was 6.60 with an MAE of 4.49. Environmental data analysis showed an RMSE of 0.74 for temperature (MAE 0.43), 2.11 for humidity (MAE 1.36), 0.25 for pressure (MAE 0.19), and 2.18 for altitude (MAE 1.70). These findings highlight the device's potential to enhance environmental surveillance and public health assessments, advance the understanding of air quality dynamics, and support targeted interventions to mitigate environmental risks. The novelty of this study lies in the integration of multiple environmental parameters into a single monitoring device, validated for accuracy using the ARIMA model.
Effect of SMOTE Variants on Software Defect Prediction Classification Based on Boosting Algorithm Aflaha, Rahmina Ulfah; Herteno, Rudy; Faisal, Mohammad Reza; Abadi, Friska; Saputro, Setyo Wahyu
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.v10i2.28521

Abstract

Detecting software defects early on is critical for avoiding significant financial losses. However, building accurate software defect prediction models can be challenging due to class imbalance, where the data for defective modules is much less than for standard modules. This research addresses this issue using the imbalanced dataset NASA MDP. To address this issue, researchers have proposed new methods that combine data level balancing approaches with 14 variations of the SMOTE algorithm to increase the amount of defective module data. An algorithm-level approach with three boosting algorithms, Catboost, LightGBM, and Gradient Boosting, is applied to classify modules as defective or non-defective. These methods aim to improve the accuracy of software defect prediction. The results show that this new method can produce a more accurate classification than previous studies. The DSMOTE and Gradient Boosting pair with 0.9161 has the highest average accuracy (0.9161). The DSMOTE and Catboost model achieved the highest average AUC value (0.9637). The ADASYN kernel and Catboost showed the best ability to perform the average G-mean value (0.9154). The research contribution to software defect prediction involves developing new techniques and evaluating their effectiveness in addressing class imbalance.
Energy Saving Analysis on Distribution Network with Incorporation of D-STATCOM Using Firefly Algorithm and Power Loss Index Olabode, Olakunle Elijah; Ajewole, Titus Oluwasuji; Ariyo, Funso Kehinde
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.v10i2.28862

Abstract

This present work investigated the effects of reactive power compensation with the use of a Distribution Static Synchronous Compensator (D-STATCOM) on a practical distribution network. In the approach proposed, the network steady-state parameters were obtained with a backward forward sweep power flow technique, the possible sites for D-STATCOM were predetermined with power loss index while the firefly algorithm was employed for determining the optimal sizes and sites respectively. Three different levels of D-STATCOM penetrations were investigated and their effects on voltage profile enhancement, active power loss reduction, cost of energy savings, payback times, and cost of procurement were assessed. The best optimal sites and sizes obtained after several simulations for case I, case II, and case III are (6, 1000kVar); (12, 349.69kVar; 22, 867.29kVar) and (5, 1200kVar; 14, 424.34kVar; 21, 350kVar) respectively. Also, the percentage improvements at the bus with minimum voltage magnitude for cases I to III are 0.6, 0.78, and 0.79% while the accompanied active power loss reductions are 59.03, 70.57 and 91.78 %. From the economic perspective, the cost of procurement ($), annual energy savings ($), and the payback time (years) for the three cases examined are (5,303.5, 1,461.00, 3.63); (6,454.25, 1,746.66, 3.69); (10,471, 2, 271.58, 4.61) respectively. Also, results validation showed that the approach proposed outsmarts particle swarm optimization and network feeder reconfiguration. The outcome of this work findings application in performance enhancement of real-life distribution networks.
Random Search-Based Parameter Optimization on Binary Classifiers for Software Defect Prediction Ali, Misbah; Azam, Muhammad Sohaib; Shahzad, Tariq
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.v10i2.28973

Abstract

Machine learning classifiers consist of a set of parameters. The efficiency of these classifiers in the context of software defect prediction is greatly impacted by the parameters chosen to execute the classifiers. These parameters can be optimized to achieve more accurate results. In this research, the efficiency of binary classifiers for software defect prediction is analyzed through parameter optimization using random search technique. Three heterogeneous binary classifiers i.e., Decision tree, Support vector machine, and Naïve Bayes are selected to examine the results of parameter optimization. The experiments were performed on seven publicly available NASA Datasets. The dataset was split into 70-30 proportions with class preservation. To evaluate the performance; five statistical measures have been implemented i.e., precision, recall, F-Measure, the area under the curve (AUC), and accuracy. The findings of the research revealed that there is significant improvement in accuracy for each classifier. On average, decision tree improved from 88.1% to 95.4%; support vector machine enhanced the accuracy from 94.3% to 99.9%. While Naïve Bayes showed an accuracy boost from 74.9% to 85.3%. This research contributes to the field of machine learning by presenting comparative analysis of accuracy improvements using default parameters and optimized parameters through random search. The results presented that he performance of binary classifiers in the context of software prediction can be enhanced to a great extent by employing parameter optimization using random search.
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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