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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 10 Documents
 1 
Search results for , issue "Vol. 11 No. 1 (2025): March" : 10 Documents clear
Solution Stirring Design Using Magnetic Stirrer on DC Motor with PLC-Based PID Method Natawangsa, Hari; Furizal, Furizal; Ma'arif, Alfian; A. Salah, Wael
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.26534

Abstract

Along with the development of the times, the industrial and manufacturing world also develops. One of the activities that is widely carried out in the industrial and manufacturing world is stirring production raw materials, either in the form of solutions or liquids. The purpose of the stirring process is to get a perfectly mixed (homogeneous) stirring. For this reason, a device is needed that can stir the solution as desired. One type of tool that can be used is a magnetic stirrer placed on a DC motor. However, when the DC motor is given a load, the DC motor tends to become unstable so a controller is needed. To solve this problem used PID controller. PID controllers use control constants in the form of PB, Tick, and Tdk. To obtain the controlling constant, a process of trial and error is carried out. The most stable results obtained from the testing process were PB = 600%, Tik = 1.2 s, and Tdk = 0.2 s. With system response in the form of rise time 0.7778 s, peak time = 5s. settling time 5.4286 s, overshoot = 2.8571 RPM and steady state error = 0%. The setpoint used is 700 RPM with a sampling time of 60 ms. The developed system successfully achieves stable and well-controlled stirring. The results of this research contribute to the improvement of solution stirring processes in the industrial and manufacturing domains. The developed system can be effectively utilized for stirring solutions, enhancing the efficiency and quality of production processes.
Early Mobilization Therapy Robot for Medical Rehabilitation Purpose Sutyasadi, Petrus; Parikesit, Elang; Widodo, Bernardinus Sri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30383

Abstract

Impairments in ambulation may result from neurological dysfunction. The expense of therapy constitutes a substantial obstacle to recovery following neurological disorders. An uncomplicated and cost-effective two-degree-of-freedom early mobilization trainer robot has been conceived and constructed. This device is intended for early training or adaptation before ready for mobilization training on the ground. The early mobilization trainer assists persons with mobility impairments during their early therapy phase. This research analyses the design and construction of an early mobilization trainer positioned within the patient's bed. The experimental findings indicate that in the condition with load at the hip joint, the output of this device can follow the trajectory input precisely. For the knee joint, the output of this device can follow the trajectory input, but with 0.9 degree of a steady-state error. This amount of steady state error does not affect the therapy because it is too small in term of knee movement precision during therapy.
Malware Classification and Detection using Variations of Machine Learning Algorithm Models Maslan, Andi; Hamid, Abdul
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30477

Abstract

Malware attacks are attacks carried out by an attacker by sending malicious codes to various files or even many packages and servers. Therefore, reliable network operations are a factor that needs to be considered to prevent attacks as early as possible in order to avoid more severe system damage. Types of attacks can be Ping of Death, flooding, remote-controlled attacks, UDP flooding, and Smurf Attacks.  Attack data was obtained from the ClaMP dataset, which has an unbalanced data set, and has very high noise, so it is necessary to analyze data packets in network logs and optimize feature extraction which is then analyzed statistically with machine learning algorithms. The purpose of the study is to detect, classify malware attacks using a variety of ML Algorithm models such as SVM, KNN and Neural Network and testing detection performance. The research stage starts from pre-Processing, extraction, feature selection and classification processes and performance testing. Training and testing data in the study used a mixed model, namely data division, split model and cross validation. The results of the study concluded that the best algorithm for detecting malware packages is the Neural Network for the Feature Combination category with an accuracy rate of 96.91%, Recall of 97.35% and Precision of 96.78%. So that the study can have implications for cyber experts to be able to prevent malware attacks early. While further research requires a special algorithm to improve malware attack detection, in addition to KNN, SVM and Neural Network. And another research challenge is to focus on feature extraction techniques on datasets that have unbalanced or varied features with the Natural Language Processing (NLP) approach. So this research can be used as a reference for researchers who are conducting research in the same field.
The Use of Attention-RNN and Dense Layer Combinations and The Performance Metrics Achieved in Palm Vein Recognition Indriani, Indriani; Syukriyah, Yenie
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30517

Abstract

The utilization of palm veins in vascular biometrics is widely recognized, offering significant potential and challenges for advancing individual recognition technology. Deep learning has played a crucial role in enhancing the accuracy of these recognition systems. In this study, we proposed combining Attention-RNN and Dense Layer. To validate this proposed method, three deep learning model scenarios were implemented: (1) a combined Dense Layer with RNN, (2) an Attention-RNN model, and (3) a combined Attention-RNN with a Dense Layer for palm vein recognition. Experimental results demonstrated that the Attention-RNN combined with the Dense Layer achieved the highest accuracy, outperforming the other two models. The model’s performance was evaluated on two datasets, achieving 95% accuracy on the Kaggle dataset and 83% on the CASIA dataset, confirming its effectiveness in palm vein recognition.
Comparative Analysis of Deep Learning Models for Retrieval-Based Tourism Information Chatbots Af'idah, Dwi Intan; Dairoh, Dairoh; Handayani, Sharfina Febbi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30373

Abstract

Despite significant advancements in deep learning models for chatbots, comprehensive analyses tailored to the tourism sector remain limited. This study addresses the gap by comparing the performance of six prominent models—MLP, RNN, GRU, LSTM, BiLSTM, and CNN—in creating chatbots designed to address traveler needs such as information about facilities, ticket prices, activity suggestions, and operational details. The methodology includes key stages such as data collection, preparation, model training, and evaluation using accuracy, precision, recall, F1-score, and qualitative assessments. The dataset, derived from interviews with managers of 11 tourism destinations, captures critical details to replicate real-world user interactions. The results indicate that the CNN model performed the best, achieving the highest accuracy (0.98), precision (0.99), recall (0.98), and F1-score (0.98), showcasing its ability to effectively handle user queries by identifying relevant patterns in data. While MLP achieved strong accuracy (0.94), its simpler design limited its capacity to manage complex questions. The RNN model had the lowest accuracy (0.82), highlighting its challenges in understanding structured information. These findings confirm CNN as the most effective model for retrieval-based chatbots in tourism, balancing accuracy and practicality. This research offers valuable insights for improving AI-driven tourism tools, providing guidelines for selecting optimal models and enhancing chatbot performance to enrich the traveler experience.
System Identification Position Error in Panoramic Radiography: a Review Nafiiyah, Nur; Astuti, Eha Renwi; Putra , Ramadhan Hardani; Asymal , Alhidayati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30598

Abstract

The professionalism of the radiologist greatly influences the results of radiological images. The quality of panoramic radiography greatly influences accurate clinical diagnosis. The correct patient position is one of the many factors that affect high-quality and accurate panoramic radiography. The process of taking radiographic images causes radiation exposure to the patient, so that when taking radiographic images repeatedly it is very bad for the patient. A review research is needed to reduce radiation exposure by improving the quality of panoramic radiography. This research conducted a literature review by proposing the questions (1) What types of position errors in panoramic radiography have been researched? (2) How is the process of identifying position errors in panoramic radiography that have been researched? The results of the review research showed that the types of position errors in panoramic radiography that have been researched are the head turning, the tongue not sticking to the palate, the chin down, the chin not resting on the support. The process of evaluating position errors in panoramic radiography is mostly done manually, there is only one research that identifies position errors in panoramic radiography automatically using SVM. That there is a great opportunity to create an automatic system for identifying position errors in panoramic radiography to be more precise and time efficient.
Factors Influencing 5G Adoption in Java: A Theory of Consumption Value and Stimulus-Organism-Response Approach Kesumahadi, Lisdianto Dwi; Nuruzzaman, Muhammad Taufiq; Sugiantoro, Bambang; Sumarsono , Sumarsono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30557

Abstract

The rapid advancement of information and communication technology has led to a significant transformation in telecommunication networks, particularly with the introduction of 5G technology, which offers high speed, low latency, and extensive device connectivity. However, the adoption of 5G in Indonesia, particularly in Java, remains challenging due to unequal network distribution and disparities in purchasing power between urban and rural areas. This study examines the key factors influencing consumer acceptance of 5G services in Java using the Theory of Consumption Value (TCV) and Stimulus-Organism-Response (SOR) framework. A descriptive quantitative approach was applied, collecting primary data from 200 respondents through purposive sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that Safety Affordance and Facilitation Conditions significantly influence consumption value, whereas Visibility Affordance and Guidance Affordance do not. These results highlight the importance of security perceptions and supporting infrastructure in 5G adoption. This study contributes to the theoretical understanding of technology adoption by integrating TCV and SOR in the context of 5G and provides practical recommendations for policymakers and service providers to enhance 5G implementation, particularly by addressing infrastructure gaps in rural areas.
Prediction of Purchase Volume Coffee Shops in Surabaya Using Catboost with Leave-One-Out Cross Validation Nariyana, Calvien Danny; Idhom, Mohammad; Trimono, Trimono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30610

Abstract

Indonesia's coffee consumption grew from 265,000 tons in 2015 to 294,000 tons in 2020. Averaging 2% annual growth with a projected 368,000 tons by 2024. One of the coffee businesses is coffee shops, Coffee shop businesses often struggle to attract customers quickly, risking low purchase volume within their first five years. In their first year, challenges include management, company size, service quality, and customer preferences.  This study adopts a quantitative approach and new solutions to develop a purchase prediction application based on machine learning and strategy to enhance purchase volumes for three coffee shops in Surabaya. It utilizes CatBoost, with LightGBM as a comparison, across multiple coffee shop locations. LOOCV (Leave-One-Out Cross-Validation) is used in this model to address research limitations, such as data overfitting and biases, while enhancing evaluation accuracy. As a result, the study established CatBoost as the superior model for purchase prediction, providing insights and practical applications in business forecasting. The Catboost model achieved an MAE of 0.91 and MAPE of 15%, outperforming LightGBM’s MAE of 1.13 and MAPE of 18%. These results confirmed CatBoost’s effectiveness for the coffee shop industry with good accuracy. This research also contributes to helping coffee shop owners in Surabaya understand market characteristics, such as the most profitable coffee types and high-customer-density locations. Additionally, it aids in optimizing purchase volume to leverage profit by developing new strategies based on prediction result.  In conclusion, CatBoost accurately predicts purchase volume, helping coffee shops identify target markets and refine strategies based on customer preferences.
Adaptive Cooling System Control in Data Center with Reinforcement Learning Dinata, Ericha Septya; Hertiana, Sofia Naning; Sugesti, Erna Sri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30671

Abstract

Data center cooling system is consuming large amounts of power, which requires effective control to reduce operational costs and deliver optimal server performance. The high power consumption occurs because traditional cooling methods struggle to adapt dynamically to workloads, causing wasteful power consumption. Therefore, this study aimed to explore the use of machine learning methods to improve energy efficiency for data center cooling system. For the experiment, an RL (Reinforcement Learning) model was designed to adjust cooling parameters with dynamic environmental changes. The method focused on optimizing energy efficiency while maintaining stable temperature and humidity control. By applying RL-based control method to PAC system, this study contributed original results that validated the effectiveness of RL-simulated data center environments. Specifically, the stages included developing system model, creating simulations using the PAC control system, and training an RL model with environmental conditions. Data were collected from simulations and analyzed to test the model performance, and the outcomes were presented using a real-time monitoring interface with Flask. The results showed that the RL model achieved an average reward of 4.76 (between -5 and 5), a convergence rate 13.2, a sampling efficiency 10.15, and a stability score 2.6. The model effectively reduced temperature and increased humidity during stressed data center operations. When compared with a fixed cooling system, RL showed superior adaptability to workload variations and reduced unnecessary energy consumption. However, scalability to real data center remained an issue, which required more than simulation validation. In conclusion, the RL-based method optimized efficiency of cooling system, showing the potential to improve energy savings and operational resilience in data center environments.
Integration of Pixy2 Camera Sensor and Coordinate Transformation for Automatic Color-Based Implementation of a Pick-and-Place Arm Robot Sitompul, Erwin; Yaqin, Muhammad Teguh Ilham; Tarigan, Hendra Jaya; Tampubolon, George Michael; Samsuri, Faisal; Galina, Mia
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30717

Abstract

Technology related to robotics has developed rapidly in recent years. In manufacturing production lines, an industrial pick-and-place robot is used to efficiently move objects from one location to another. In most approaches, this robot automates the repetitive task from one exact start position. However, the task of collecting objects from various positions in the robot workspace still introduces challenges in terms of object positional detection and movement accuracy. In this paper, an arm robot system equipped with automatic color-based object recognition and position control was proposed. The robot was able to detect multiple target object positions automatically without any need to plan a fixed movement beforehand. In the construction of the experiment platform, a Pixy2 camera sensor with color recognition ability was integrated into a 4-DoF Dobot Magician arm robot. Furthermore, a coordinate transformation was derived and implemented to achieve an accurate positional robot movement. The coordinate transformation performed a mapping from the Camera Coordinate System (CCS), which was initialized from image pixel values to the Robot Coordinate System (RCS), which was finalized to the robot’s actuator input signals. Prior to the implementation, the robot underwent a color calibration and position calibration. Thereafter, a set of color signatures was obtained and any object position in the camera’s field of view can be matched with any end-effector position in the robot’s workspace. Three experiment setups were conducted to evaluate the proposed system. Limited to one lighting condition, the robot was commanded to pick-and-place objects based on the criteria of all 3 colors, 1 specific color, and 2 specific colors. The robot performed perfectly to pick and place the objects, achieving a 100% success rate in terms of object color detection and pick-and-place. The positive results encouraged further investigation in different actuator actions and greater work areas.

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