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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 12 Documents
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Search results for , issue "Vol. 10 No. 1 (2024): March" : 12 Documents clear
Enhancing Speed Estimation in DC Motors using the Kalman Filter Method: A Comprehensive Analysis Setiawan, Muhammad Haryo; Ma'arif, Alfian; Rekik, Chokri; Abougarair, Ahmed J.; Mekonnen, Atinkut Molla
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.26591

Abstract

The accurate estimation of speed is crucial for optimizing the performance and efficiency of DC motors, which find extensive applications in various domains. However, the presence of noise ripple, caused by interactions with magnetic or electromagnetic fields, poses challenges to speed estimation accuracy. In this article, we propose the implementation of the Kalman Filter method as a promising solution to address these challenges. The Kalman Filter is a recursive mathematical algorithm that combines measurements from multiple sources to estimate system states with improved accuracy. By employing the Kalman Filter, it becomes possible to estimate the true speed of DC motors while effectively reducing the adverse effects of noise ripple. This research focuses on determining the optimal values for the Kalman Filter parameters and conducting experiments on a DC motor to evaluate the performance of the proposed approach. The experimental results demonstrate that the Kalman Filter significantly improves the control of speed oscillations and enhances the stability of the DC motor system. Furthermore, a comprehensive analysis of the system's response and parameter tuning reveals the impact of different parameter combinations on settling time, overshoot, and rise time. By carefully selecting appropriate parameters, the proposed approach contributes to accurate speed estimation and effective control of DC motors, advancing the understanding and application of the Kalman Filter in various relevant fields. Overall, this research provides valuable insights into enhancing the performance and efficiency of DC motors through the integration of the Kalman Filter method.
Advanced Control for Quadruple Tank Process Kasiyanto, Iput; Firdaus, Himma; Lailiyah, Qudsiyyatul; Kusnandar, Nanang; Supono, Ihsan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.27124

Abstract

In the realm of control systems, the last three decades have witnessed significant advancements in model predictive control (MPC), an advanced technique renowned for its ability to optimize processes with constraints, handle multivariate systems, and incorporate future references when feasible. This paper introduces an innovative offset-free MPC approach tailored for the control of a complex nonlinear system—the quadruple tank process (QTP). The QTP, known for its deceptively simple yet challenging multivariate behavior, serves as an ideal benchmark for evaluating the efficacy of the proposed algorithm. In this work, we rigorously compare the performance of the PID and MPC controller when applied to both linear and nonlinear models of the QTP. Notably, our research sheds light on the advantages of MPC, particularly when confronted with constant disturbances. Our novel algorithm demonstrates exceptional capabilities, ensuring error-free tracking even in the presence of persistent load disturbances for both linear and nonlinear QTP models. Compared to the PID control, the proposed method can reduce the overall set point tracking error up to 32.1%, 27.6%, and 38.54% using the performance indices ISE, ITAE, and IAE, respectively, for the linear case. Furthermore, for the nonlinear case, the overall set point tracking error reduction is up to 93.4%, 94.9%, and 91.5%. This work contributes to bridging the gap in effective control strategies for nonlinear systems like the QTP, highlighting the potential of offset-free MPC to enhance control and stability in a challenging process industry involving automatic liquid level control.
Analysis of IoT-LoRa to Improve LoRa Performance for Vaname Shrimp Farming Monitoring System Adi, Puput Dani Prasetyo; Ardi, Idil; Plamonia, Nicco; Wahyu, Yuyu; Mariana L, Angela; Novita, Hessy; Mahabror, Dendy; Zulkarnain, Riza; Wirawan, Adi; Prastiyono, Yudi; Waryanto, Waryanto; Susilo, Suhardi Atmoko Budi; Rahmatullah, Rizky; Kitagawa, Akio
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.27598

Abstract

Shrimp farming requires a touch that must be right on the side of water quality; water is a fundamental factor that must be met to achieve maximum yields. Many factors affect the quality of the water, but some things cause changes in water quality caused by external and internal factors causing death in shrimp. Disease conditions in shrimp can attack at any time, coupled with external factors such as extreme climate change, and cause changes in water components such as water pH, CaMg or hardness, and other factors that cause death in shrimp. Water turbidity oxygen demand (DO) in water determines the life of shrimp. It is coupled with microorganisms that must be maintained to maintain water quality for the growth of a Vaname shrimp. This research raises the Aquaculture System, specifically in the process of intelligent monitoring of water quality in shrimp nurseries to the shrimp harvest process, especially vaname shrimp from the results of observations use three sensors connected to LoRaWAN is able to provide real-time data from pond water and transmit it to LoRa Server or Internet Server, and the realtime data can be read through a Smartphone. This research analyzes in detail the ability of LoRaWAN to send multi-sensor data and Quality of Service LoRaWAN communication at different distances. This research also discusses how the LoRa antenna design can be developed to improve the performance of LoRa as transmitting devices or Radio Frequency 920-923 MHz for sending sensor data for Aquaculture.The contribution of this research is shown in the real-time monitoring system of the water environment, namely water pH, ammonia, turbidity, DO, salinity, water temperature, and nitrate in vaname shrimp ponds. The following contribution is the development of LoRaWAN with Tago IO servers capable of being used in Smart Aquaculture for contributions to The Things Network community or LoRaWAN Community.
Throughput and Coverage Evaluation on The Use of Existing Cellular Towers for 5G Network in Surakarta City Affandi, Muhammad Afif; Riyadi, Munawar Agus; Prakoso, Teguh
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.27719

Abstract

Currently, telecommunication operators must deploy 5G networks to cope with the exponential growth in internet-access demand. To minimize capital expenditure, existing 4G cell towers are being used to install new 5G base stations (gNodeB). However, 5G has different key performance indicators (KPI), frequency and bandwidth values, and propagation models compared to 4G hence an evaluation of this approach’s effectiveness is needed. This paper analyzes 5G network performance with frequency of 3.5 GHz, bandwidth of 100 MHz, and using existing cellular towers in Surakarta City. The city has a total area of 46.8 km2, mostly flat topography and not many tall buildings therefore propagation models with line-of-sight urban macro (UMa) and urban micro (UMi) are representative. KPI parameters for throughput include 75% of the area served with at least 100 Mbps for downlink and at least 50 Mbps for uplink. KPI parameter for signal strength targets at least 90% of the area covered with -100 dBm or higher. Our Atoll simulations show that the optimistic scenario (UMa) produces average throughput of 153.59 Mbps (downlink) and 117.88 Mbps (uplink), 89.43% served with at least 100 Mbps (downlink) and 100% experience at least 50 Mbps (uplink), average signal strength is -83.99 dBm and 79.71% area covered with at least -100 dBm. The pessimistic scenario (UMi) predicts throughput of 141.32 Mbps (downlink) and 117.88 Mbps (uplink), 86.52% provided with at 100 Mbps (downlink) and 100% served with 50 Mbps (uplink), average signal strength of -90.73 dBm and 75.13% area covered with at least -100 dBm. It can be concluded that the 5G network installed at existing 4G towers can conform to KPI parameters on throughput but still experience drawbacks in signal coverage. A non-Standalone 5G network is suitable for early deployment, but gNodeB installation at new locations is needed in the following years.
Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method Dakwah, Muhammad Mujahid; Firdaus, Asno Azzawagama; Furizal, Furizal; Faresta, Rangga
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28070

Abstract

This research addresses the challenges of marketplace customer feedback, which is an important aspect in today's era of online transactions. Marketplaces often receive many unsatisfactory comments from their customers through social media platforms. One approach that can be used to address this is sentiment analysis. This research contributes new insights as recommendations for marketplaces based on customer opinions on available services and delivery. The sentiment analysis methods used are Naive Bayes and Support Vector Machine because they are considered the best methods in training text-based classification models. Before being classified, the data goes through preprocessing stages such as cleaning, case folding, filtering, stemming, and tokenizing, as well as feature extraction stages using Term Frequency - Inverse Document Frequency (TF-IDF). The objects analyzed are divided into several well-known marketplaces in Indonesia such as Tokopedia, Lazada, and Shopee in discussing services and delivery of goods. The data used in this study comes from Twitter (X) social media accessed on August 27, 2023, using crawling techniques and successfully obtained as much as 2057 Tweet data. The best accuracy is obtained in the SVM method when compared to the Naive Bayes method. Words obtained based on service talks include price, service, application service, feedback, independence, and others. As for the delivery of goods, common words such as COD, delivery, package, courier, cheap, price, and others appear. Both methods used have good accuracy and can be recommended for use in similar research.
Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms Muflikhah, Lailil; Bachtiar, Fitra A.; Ratnawati, Dian Eka; Darmawan, Riski
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28107

Abstract

Nephropathy is a severe diabetic complication affecting the kidneys that presents a substantial risk to patients. It often progresses to renal failure and other critical health issues. Early and accurate prediction of nephropathy is paramount for effective intervention, patient well-being, and healthcare resource optimization. This research used medical records from 500 datasets of diabetic patients with imbalanced classes. The main goal of this study is to get high-performance predictive models for nephropathy. So, this study suggests a new way to deal with the common problem of having too little or too much data when trying to predict nephropathy: adding more data through adaptive synthetic sampling (ADASYN). This technique is particularly pertinent in ensemble machine-learning methods like Random Forest, AdaBoost, and bagging (Adabag). By increasing the number of instances of minority classes, it tries to reduce the bias that comes with imbalanced datasets, which should lead to more accurate and strong predictive models in the long run. The experimental results show an improving 4% rise in performance evaluation such as precision, recall, accuracy, and f1-score, especially for the ensemble methods. Two contributions of this research are highlighted here: first, the utilization of adaptive synthetic sampling data to improve the balance and diversity of the training dataset. The second contribution is incorporating ensemble methods within machine learning algorithms to enhance the accuracy and robustness of diabetic nephropathy detection.
Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods Nilla, Arliyanna; Setiawan, Erwin Budi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28113

Abstract

Managing large amounts of data is a challenge faced by users, so a recommendation system is needed as an information filter to provide relevant item suggestions. Twitter is often used to find information about movie reviews that can be used a basis for developing recommendation systems. This research contributes to applying content-based filtering in the context of Convolutional Neural Network (CNN). To the best of the researcher's knowledge, there has been no research addressing this combination of method and classification. The main focus is to evaluate the development of a recommendation system by integrating and comparing similarity identification methods using the RoBERTa and TF-IDF approaches. In this research, Roberta and TF-IDF as vectorizer and classification methods are applied to form a model that can recognize patterns in data and produce accurate predictions based on its features. The total data used is 854 movies and 34086 film reviews from 44 Twitter accounts. The SMOTE method was applied as a technique to overcome data imbalance. The research was conducted three times with increasing accuracy results. The first experiment TF-IDF as baseline, SMOTE on CNN classification. The second experiment, applying baseline, SMOTE, embedding on CNN classification. The third experiment applied baseline, SMOTE, embedding, and optimizer to CNN classification. The experimental results show that TF-IDF as baseline, SMOTE, embedding and SGD optimizer with the best learning rate on CNN classification can provide optimal results with an accuracy rate of 86.41%. Thus, the system can provide relevant movie recommendations with good prediction accuracy and performance.
Uncovering Security Vulnerabilities in Electronic Medical Record Systems: A Comprehensive Review of Threats and Recommendations for Enhancement Wijayanti, Dian; Ujianto, Erik Iman Heri; Rianto, Rianto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28192

Abstract

Cybersecurity is a critical concern for healthcare organizations in the digital era, as patient data privacy faces significant risks from numerous vulnerabilities. Given the escalating cyberattacks in healthcare, understanding EMR system vulnerabilities has become imperative. This study aimed to find the main weaknesses in Electronic Health Record (EHR) systems and suggest proven methods to improve security and keep patient information private. Utilizing a cross-sectional analysis, we assessed the effectiveness of current security protocols against identified threats. We systematically reviewed 25 recent, high-quality articles (from 2020 to 2023) on EMR vulnerabilities, selected based on their relevance and the efficacy of their proposed solutions. Our analysis revealed that system architecture flaws and credential misuse represented the most significant threats, with hacking incidents most frequently targeting these weaknesses. The analysis identified six key threat categories to EMR security: compromised access, system architecture flaws, data sharing challenges, hacking, credential misuse, and non-compliance with regulations. This framework introduced a multi-layered defense strategy, unique in incorporating both technical and behavioral security measures. The study provided a novel framework combining technological and management safeguards, offering a fresh perspective on modern EMR vulnerabilities. The detailed threat categorization gave healthcare organizations a strategic basis for improved security planning and resource allocation. The actionable insights from this study could greatly enhance EMR security protocols in healthcare settings, potentially reducing data breaches and improving patient trust. Further research was warranted to test the effectiveness of the proposed framework across various healthcare environments.
Students Final Academic Score Prediction Using Boosting Regression Algorithms Muhammady, Dignifo Nauval; Nugraha, Haidar Aldy Eka; Nastiti, Vinna Rahmayanti Setyaning; Aditya, Christian Sri Kusuma
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28352

Abstract

Academic grades are crucial in education because they assist students in acquiring the knowledge and skills necessary to succeed in school and their future. Accurately predicting students' final academic performance grade score is important for educational decision-makers. However, creating precise prediction models based on students' historical data can be challenging due to the complex nature of academic data. This research analyzes student academic data totaling 649 Portuguese language course student data that has been processed according to data requirements which are then predicted using XGBoost Regressor, Light Gradient Boosting Machine (LGBM), and CatBoost. This research aims to develop a robust prediction model that can effectively predict students' final academic performance. This research offers valuable insights into the factors that influence academic success and provides practical implications for educational institutions looking to improve their decision-making processes. The prediction requires identifying key predictors of academic performance, such as previous grades, attendance records, and socio-economic background. The research makes a contribution by improving the matrix MAE in this research is less than the previous research from 2.2 average each algorithm to 0.22 average, this less MAE means the better model. The research achieved MAE score of 0.22 average. In conclusion, this research is expected to address the challenge of predicting student academic performance through the application of advanced machine learning techniques. The results provide valuable insights for decision-makers in education and highlight the importance of a data-driven approach to improving academic performance. By utilizing machine learning algorithms, educational institutions can effectively support student learning and success.
Harmonic Mitigation in Inverter Circuits Through Innovative LC Filter Design Using PSIM Usman, Habib Muhammad; Mahmud, Muhammad; Saminu, Sani; Ibrahim, Salihu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28398

Abstract

The increasing use of renewable energy sources, such as solar and wind power, and the growing ubiquity of High Voltage Direct Current (HVDC) transmission systems to improve power transmission efficiency are the main factors behind the increased deployment of inverter circuits. However, high harmonic distortions in the resultant sine wave are a major problem for inverter circuits and could jeopardise circuit efficiency if left unchecked. This study presents a novel, affordable, and effective LC filter intended to remove almost all harmonic content from inverter circuits. The study uses PSIM software to model, design, and control a three-phase inverter. Starting with the DC power supply, the study makes use of effective three-legged IGBT (insulated gate bipolar transistor) semiconductor devices as switch elements due to their high and current rating as well as faster operation. The switching gate pulses that turn inverter switches on and off at regular 60-degree intervals are produced by the pulse controller that controls the switches. This study's results show that the innovative LC filter in the inverter significantly reduced total harmonic distortion (THD) in all phases of the power signal. Specifically, THD decreased from 37.68% to 0.47% in the red phase, from 37.69% to 0.48% in the blue phase, and from 37.71% to 0.48% in the yellow phase. This reduction results in a notable improvement in power quality in all phases of the signal. Additionally, there is a noticeable increase in voltage magnitude, stabilizing and raising levels from 17.92 V to 23.83 V in the red phase, 17.93 V to 23.81 V in the blue phase, and 17.83 V to 23.81 V in the yellow phase due to the LC filter. These results demonstrated the effectiveness of the LC filter-equipped inverter for industrial, HVDC, and renewable energy applications.

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