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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 401 Documents
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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.
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.
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.
Newspaper Ad Submission and Payment Website Measurement Analysis Using McCall and PIECES Muhammad Nazar Gunawan; Friska Abadi; Dodon Turianto Nugrahadi; Irwan Budiman; Setyo Wahyu Saputro
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30355

Abstract

The transition to digital platforms in the media industry requires robust systems to ensure efficiency and user satisfaction. As with Digital Iklan Radar Banjarmasin, the Newspaper ad submission and payment website, there is a need for evaluation to comprehensively ensure software feasibility and quality. This research evaluates the quality of the Newspaper ad submission and payment website using the McCall and PIECES frameworks, comparing their strengths and identifying areas for improvement. This research contributes to determining the most suitable evaluation methods for such types of websites while offering actionable insights for developers to improve the quality of systems and services. Data collection involved online surveys with 106 respondents and 38 Likert-scale questions mapped to McCall and PIECES frameworks. Statistical tests, including validity, reliability, and an independent t-test, were applied to compare results. McCall's evaluation rated the system at 68% (Good), with low scores in Usability (38.5%), Reliability (36.77%), and Efficiency (38.15%), indicating areas needing significant improvement. PIECES evaluation scored 80.4% (Good), with Performance (81%) and Service (82.39%) rated Very Good, though Control and Security (78.55%) required enhancement. Statistical analysis with independent t-test confirmed significant differences between the two methods, indicating that both methods measure aspects of software quality from different perspectives, thus providing complementary insights for evaluation. The study highlights the complementary nature of McCall and PIECES in software quality evaluation. Recommendations include improving usability, system stability, and security for better user experiences. Future research should involve broader demographic samples and different system types to validate findings and enhance generalizability.
Enhancing Soybean Fertilization Optimization with Prioritized Experience Replay and Noisy Networks in Deep Q-Networks Fakhrezi, Alfian; Budiman, Gelar; Perdana, Doan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30690

Abstract

This study focuses on the optimization of reinforcement learning in the Deep Q Network algorithm. This is achieved using the prioritized experience replay algorithm and Noisy Network optimization. The main goal is to optimize fertilization so that it can adapt to its environment and avoid over-fertilization. This study uses the prioritized experience replay algorithm and Noisy Network optimization to create an agent in RL that is able to explore and exploit optimally so that it can improve the precision of fertilization in soybeans. This methodology includes several steps, including data preparation, creating an environment that matches real-world conditions, and validating changes in soil nutrient conditions.  The RL model was trained with PER and NN, with performance evaluated using cumulative reward, convergence speed, action distribution, and Mean Squared Error (MSE). The main results of the study show that DQN-PER NN achieves the highest cumulative reward, approaching 600,000 in 1000 episodes, outperforming standard DQN, A2C, and PPO. It also converges faster at episode 230, indicating superior adaptability. In addition, the results of this study indicate that the model that has been created is able to recommend a dose of SP36 fertilizer of 150 kg/ha, urea fertilizer of 100 kg/ha, and KCL fertilizer of 125 kg/ha. Compared with the A2C and PPO methods, the dose of urea fertilizer is reduced by 14%, KCL fertilizer is reduced by 33%, while for SP36 the difference is 23%. In Conclusion this model effectively distributes actions based on environmental conditions, which supports sustainable agriculture. In conclusion, the integration of PER and NN into DQN significantly improves exploration and decision making, and optimizes soybean fertilization. This model not only improves harvest efficiency but also encourages sustainable agricultural practices.
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.

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