cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota malang,
Jawa timur
INDONESIA
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Published by Universitas Muhammadiyah Malang
ISSN : 25032259     EISSN : 25032267     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control was published by Universitas Muhammadiyah Malang. journal is open access journal in the field of Informatics and Electrical Engineering. This journal is available for researchers who want to improve their knowledge in those particular areas and intended to spread the knowledge as the result of studies. KINETIK journal is a scientific research journal for Informatics and Electrical Engineering. It is open for anyone who desire to develop knowledge based on qualified research in any field. Submitted papers are evaluated by anonymous referees by double-blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully within 4 - 8 weeks. The research article submitted to this online journal will be peer-reviewed at least 2 (two) reviewers. The accepted research articles will be available online following the journal peer-reviewing process.
Arjuna Subject : -
Articles 536 Documents
 47   48   49   50   51 
Classification of Sleep Disorders using Support Vector Machine Nuraeni, Nenden; Faisal, Muhammad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2054

Abstract

Sleep disorders become a severe concern in our busy modern lifestyles, which are often overlooked and can cause significant negative impacts on an individual's health and quality of life. This research explores the implementation of machine learning, specifically Support Vector Machine, to facilitate quick and accurate sleep disorder diagnosis. Data shows that sleep deprivation or disturbed sleep is becoming common in society, with 62% of the adult population experiencing dissatisfaction with their sleep quality. This has a significant economic impact and affects the health and productivity sectors. This study uses Kaggle Sleep Health and Lifestyle dataset of 400 data samples, applying Support Vector Machine to classify sleep disorders using three testing scenarios. The results showed an accuracy rate of 92%, confirming that Support Vector Machine can potentially improve the diagnosis of sleep disorders, enabling early intervention and better treatment for patients. Thus, this research contributes to understanding and treating sleep disorders, improving people's overall quality of life.
A Hybrid Encryption using Advanced Encryption Standard and Arnold Scrambling for 3D Color Images Sari, Wellia Shinta; Astuti, Erna Zuni; Jatmoko, Cahaya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2058

Abstract

Digital security ensuring the confidentiality and integrity of visual data remains a paramount challenge. The escalating sophistication of cyber threats necessitates robust encryption methods to safeguard sensitive information from unauthorized access and manipulation. Despite the development of various encryption techniques, inherent vulnerabilities exist within conventional methods that can be exploited by attackers. Therefore, this research aims to investigate the effectiveness of the combined approach of Arnold Scrambling and Advanced Encryption Standard (AES) in mitigating these vulnerabilities and providing a more secure solution. The primary goal of this research is to enhance the security of digital images by mitigating vulnerabilities associated with conventional encryption methods. Arnold Scrambling introduces chaotic mapping to disperse pixel values, while Advanced Encryption Standard (AES) provides robust cryptographic strength through its substitution-permutation network. By combining these methods in an ensemble fashion, the encryption process achieves heightened resilience against various cryptographic attacks. The proposed methodology was evaluated by using standard metrics including Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and entropy analysis. Results indicate consistent performance across multiple test images, namely: Lena, Mandrill, Cameraman, and Plane with Unified Average Changing Intensity (UACI) averaging 33.6% and Number of Pixels Change Rate (NPCR) nearing 99.8%. Entropy values approached maximum, affirming the efficacy of the encryption in generating highly randomized outputs.
Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification Hidajat, Moch. Sjamsul; Wibowo, Dibyo Adi; Mintorini, Ery
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2059

Abstract

Ikat weaving is a rich traditional heritage of Kota Kediri, Indonesia, with a diverse array of intricate motifs that reflect the cultural richness of the region. As new motifs emerge and information about older designs fades, manual identification becomes time-consuming and difficult. This study leverages machine learning technology, specifically XGBoost, Random Forest, and Neural Network algorithms, to automate the classification of these weaving patterns. The dataset consisted of 600 images, split into 480 images (80%) for training and 120 images (20%) for testing, representing four distinct weaving motifs: "Gumul Weaving, Bolleches Weaving, Kuda Kepang Weaving, and Sekar Jagad Weaving." The study achieves high accuracy, with precision, recall, and F1-score all reaching 100%, underscoring its potential to not only improve the efficiency of motif identification, but also play a crucial role in preserving and promoting Indonesia's cultural heritage. Future research should focus on further optimizing these algorithms and expanding datasets to capture a broader range of ikat motifs. Additionally, enhancing the application of this model can contribute to a deeper understanding and broader appreciation of Kota Kediri’s cultural wealth through digital platforms.
Aspect-based Multilabel Classification of E-commerce Reviews using Fine-tuned IndoBERT Ihtada, Fahrendra Khoirul; Alfianita, Rizha; Aziz, Okta Qomaruddin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2088

Abstract

In recent years, e-commerce has experienced rapid growth. A significant change in consumer behavior is marked by the ease of access and time flexibility offered by e-commerce platforms, as well as the existence of the review feature to assess products and services. However, with the ever-increasing number of reviews, consumers and store owners face challenges in sorting out relevant information. This research focuses on the multilabel classification of Indonesian e-commerce reviews. This research was undertaken because the application of multilabel classification, especially for e-commerce reviews in Indonesia, has received little attention. This research compares three classification models: end-to-end IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM, to determine the most effective model for multilabel aspect classification of customer reviews. The multilabel classification method was applied to determine the aspect categories of the reviews, such as product, customer service, and delivery, using different thresholds for evaluation. Results show that 0.6 threshold is optimal, with the IndoBERT-LSTM model as the best-performing model for the multilabel aspect classification of these e-commerce reviews. Optimal classification of the model enables more precise information extraction from customer reviews. This can be useful for e-commerce businesses to gain insight from the reviews they get from customers. This insight can be used to find out which aspects need to be improved from the e-commerce business which leads to increased customer satisfaction and trust.
PID Controller-Based Simulations for Controlling Inverter Voltage to Enhance Power in a Microgrid Maulidin, Reza; Nugroho, Bayu Rahmad; Kusmantoro, Adhi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2106

Abstract

An inverter is a device that converts direct current (DC) into alternating current (AC), which is crucial in various applications, including solar power systems, uninterruptible power supplies (UPS), and electric motor control. Accurate and stable voltage control of the inverter is essential to ensure the performance and reliability of the system. The Proportional-Integral-Derivative (PID) control method is one of the most commonly used control techniques due to its simplicity and effectiveness across different control systems. This study focuses on the implementation of inverter voltage control using a PID controller. The PID controller is designed to regulate the inverter's output voltage, ensuring stability even in the presence of disturbances or load variations. In this research, the mathematical model of the inverter and the PID control system is developed and simulated using MATLAB/Simulink software. The simulation results demonstrate that the PID controller effectively maintains the inverter's output voltage, providing a rapid transient response with minimal overshoot. The application of the PID controller to the inverter also shows improvements in system stability and a reduction in steady-state error. Furthermore, precise tuning of the PID parameters is a key factor in achieving optimal control performance. This research makes a significant contribution to the field of inverter control by demonstrating the effectiveness of the PID controller in regulating the inverter's output voltage. The practical implementation of PID controllers on inverters is expected to enhance the efficiency and reliability of power systems that utilize inverters.
Improvement of AC Bus Voltage Stability with Current Control Inverter Nugroho, Bayu Rahmad; Maulidin, Reza; Kusmantoro, Adhi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2107

Abstract

This research focuses on the development and analysis of a current control method for inverters, which demonstrates superior performance compared to the more conventional voltage control method. Current control in inverters offers several significant advantages, including faster dynamic response, constant switching frequency, and the ability to effectively reduce harmonic distortion, which is often a challenge in modern power systems. Additionally, this method is capable of maintaining system stability even when it had complex load variations and fluctuating operating conditions. In this study, we implement a fuzzy logic approach to simulate current control in an inverter integrated with a photovoltaic (PV) renewable energy system. The simulation results indicate that the proposed current control method not only enhances overall energy efficiency, but also extends the operating range of the inverter, allowing the system to operate optimally under various load conditions.
Optimizing Connected Vehicle Routing Protocol for Smart Transportation Systems Bonari, Anggiet Harjo Baskoro; Bintoro, Ketut Bayu Yogha
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2118

Abstract

The significant growth in integrating connected vehicles into intelligent transportation networks has underscored the importance of Vehicle-to-Vehicle (V2V) communication in optimizing route efficiency, reducing traffic congestion, and enhancing road safety. However, routing protocols such as AODV face substantial challenges in dynamic automotive environments characterized by high mobility and rapid topology changes, leading to issues like packet loss, delays, and network congestion. Reactive protocols like AODV often suffer from route discovery delays, while proactive protocols like DSDV, although reducing latency, increase bandwidth consumption, making them less effective in highly dynamic contexts. This study introduces the Learning Automata Ad Hoc On-Demand (LA-AODV) routing protocol, designed to improve relay node selection and V2V communication efficiency. The proposed method leverages real-time vehicle data to predict and select optimal relay nodes under dynamic traffic conditions, thereby enhancing packet delivery ratio, throughput, and reducing latency and routing overhead. The results demonstrate that LA-AODV significantly outperforms AODV and DSDV across various traffic scenarios, with an increase in packet delivery ratio up to 4% in high traffic conditions, throughput reaching 125 units, and a reduction in end-to-end delay within the range of 2E+10 to 6E+14. These improvements highlight LA-AODV's superior efficiency in handling packet loss and latency, making it a suitable protocol for data-intensive and safety-critical applications that demand reliable and efficient data transmission. This study contributes by developing the LA-AODV protocol, which significantly enhances V2V communication performance in dynamic traffic scenarios and provides a robust simulation model replicating real-world conditions, potentially reducing traffic accidents.
Performance Evaluation of Outgoing Interface Selection Method on Fortigate SD-WAN for Network Optimization Kholil Romadhoni, Mufti; Kenanga, Larynt Sawfa; Akbi, Denar Regata; Risqiwati, Diah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2120

Abstract

Reliable and high-performance network services are essential to facilitate communication between parent companies and subsidiaries as well as among the subsidiaries themselves. Challenges arise in managing and optimizing outgoing interface selection in an effective and reliable Software-Defined Wide Area Network (SD-WAN) environment. This research evaluates four outgoing interface selection methods, namely Manual, Best Quality, Lowest Cost, and Maximize Bandwidth (SLA), using a tree-based network topology simulated in GNS3 with FortiGate devices as part of the simulation. The results show that under simulated disturbances, such as limiting a single connection line to 10 kbps, the Manual, Best Quality, and Lowest Cost methods perform worse than the Maximize Bandwidth method. In contrast, the Maximize Bandwidth method outperformed the others, achieving only 0% packet loss, 22.275 ms one-way delay, and 7.03 ms jitter, while maintaining the ITU-T G.1010 standard at the preferred level. These findings highlight the reliability and effectiveness of the Maximize Bandwidth method in ensuring consistent data transmission even under fault conditions, while providing practical guidance for network engineers in configuring SD-WAN for uninterrupted high-quality network services in complex business environments.
Sentiment Analysis on Social Media Using CNN-RNN Hybrid: A Case Study of Indonesian Presidential Candidate Riyadi, Slamet; Fayyadh Daffa; Cahya Damarjati; Megat Syahirul Amin Megat Ali
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2125

Abstract

Research on sentiment analysis for Presidential Candidate 01 on social media cannot be ignored because there is no in-depth understanding of public perceptions and opinions circulating online. The CNN model is quite commonly used for sentiment analysis; however, this model still has quite low accuracy so modifications need to be made. This research aims to increase the accuracy of sentiment analysis through the application of a modified Convolutional Neural Network (CNN) method. The research process includes collecting tweet data related to Presidential Candidate 01 using crawling techniques, data preprocessing, sentiment labeling, data balancing, as well as dividing the dataset into training, validation and test data. The CNN model is modified with additional layers to improve the performance. The model is evaluated by measuring its accuracy, precision, recall, and F1 Score. The research results show that the modified CNN-RNN Hybrid model with the Upsampling method achieves an accuracy of 94% and F1 Score of 0.95, while the CNN-RNN Hybrid model has an accuracy of 86% and F1 Score of 0.82, the CNN Model has an accuracy of 90% and F1 Score of 0.88, and the RNN model has an accuracy of 88% and F1 Score of 0.84, which are higher compared to the Naïve Bayes and LSTM methods used in the previous research. Modifying the CNN method can significantly increase the accuracy of sentiment analysis for Presidential Candidate 01, so that it can become a more effective tool for understanding public perceptions and improving political campaign strategies.
Optimized BiLSTM-Dense Model for Ultra-Short-Term PV Power Forecasting Tjahyadi, Christianto; Sutarna, Nana; Oktivasari , Prihatin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2127

Abstract

The growing integration of photovoltaic (PV) systems into power grids poses challenges due to the inherent variability in PV output, particularly during rapid weather changes. While existing forecasting methods often struggle to capture these fluctuations, accurate ultra-short-term PV power prediction is critical for grid stability. The study aims to develop an optimized BiLSTM-Dense model that enhances forecasting accuracy by incorporating an additional dense layer. The model is designed to improve forecasting performance over a 30-second horizon. It utilizes a dataset of solar irradiance, PV output power, surface temperature, ambient temperature, humidity, and wind speed, collected in late 2023. Data preprocessing involved normalization and smoothing techniques to enhance robustness. Hyperparameter optimization was performed using grid search. Evaluation results demonstrate the superiority of the proposed model, achieving an MAE of 0.00271 and an RMSE of 0.00806 when paired with the Adam optimizer and Swish activation function. Compared to standard BiLSTM, the BiLSTM-Dense achieved MAE and RMSE improvements of 0.52% and 2.19%, respectively. It also outperformed the LSTM model with reductions of 4.00% in MAE and 2.65% in RMSE, and significantly surpassed ARIMA, reducing MAE by 98.87% and RMSE by 97.21%. These findings highlight the model’s ability to capture complex, non-linear dependencies in PV output data, outperforming conventional approaches like ARIMA, which rely on linear assumptions, and simpler architectures like LSTM, which lack bidirectional context integration.

Page 49 of 54 | Total Record : 536

 47   48   49   50   51 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol. 11, No. 1, February 2026 (Article in Progress) Vol. 10, No. 4, November 2025 Vol. 10, No. 3, August 2025 Vol. 10, No. 2, May 2025 Vol. 10, No. 1, February 2025 Vol. 9, No. 4, November 2024 Vol. 9, No. 3, August 2024 Vol. 9, No. 2, May 2024 Vol. 9, No. 1, February 2024 Vol. 8, No. 4, November 2023 Vol. 8, No. 3, August 2023 Vol. 8, No. 2, May 2023 Vol. 8, No. 1, February 2023 Vol. 7, No. 4, November 2022 Vol. 7, No. 3, August 2022 Vol. 7, No. 2, May 2022 Vol. 7, No. 1, February 2022 Vol. 6, No. 4, November 2021 Vo. 6, No. 3, August 2021 Vol. 6, No. 2, May 2021 Vol. 6, No. 1, February 2021 Vol. 5, No. 4, November 2020 Vol. 5, No. 3, August 2020 Vol. 5, No. 2, May 2020 Vol. 5, No. 1, February 2020 Vol 4, No 4, November 2019 Vol 4, No 3, August 2019 Vol 4, No 2, May 2019 Vol 4, No 2, May 2019 Vol 4, No 1, February 2019 Vol 4, No 1, February 2019 Vol 3, No 4, November 2018 Vol 3, No 4, November 2018 Vol 3, No 3, August 2018 Vol 3, No 3, August 2018 Vol 3, No 2, May-2018 Vol 3, No 2, May-2018 Vol 3, No 1, February-2018 Vol 3, No 1, February-2018 Vol 2, No 4, November-2017 Vol 2, No 4, November-2017 Vol 2, No 3, August-2017 Vol 2, No 3, August-2017 Vol 2, No 2, May-2017 Vol 2, No 2, May-2017 Vol 2, No 1, February-2017 Vol 2, No 1, February-2017 Vol 1, No 3, November-2016 Vol 1, No 3, November-2016 Vol 1, No 2, August-2016 Vol 1, No 2, August-2016 Vol 1, No 1, May-2016 Vol 1, No 1, May-2016 More Issue