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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
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Object Detection and Monitor System for Building Security Based on Internet of Things (IoT) Using Illumination Invariant Face Recognition Chatisa, Ivan; Syahbana, Yoanda Alim; Wibowo, Agus Urip Ari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1622

Abstract

Theft and intrusion are crimes that often occur in neighborhoods when there is opportunity or negligence by owners and security personnel. Many studies have been carried out to improve environmental security by applying cameras as a surveillance medium. However, the camera is not optimal in detecting objects when the lighting conditions are lacking. Therefore, in this study, a monitoring and object detection system was built by applying the Illumination Invariant model. This model is used to improve the appearance of the image from light and shadow reflections. The process of detecting and identifying objects is done by using human facial features (face detection) captured by the camera. The camera used is a Logitec C270 Webcam 720p which is connected via a USB port on the Raspberry Pi 4. The Raspberry Pi 4 processes human face image data and sends the processing results to a MySQL database using the HTTP protocol. Data transmission is done using the Python Flask web framework. The system was successfully run 100% by using black box testing of all functional requirements. Tests on the object detection feature were carried out based on different lighting conditions 15 times by comparing the original image and the results of the Illumination Invariant implementation. Based on the test results obtained object detection accuracy of 86.7%.
Water Level Detection for Flood Disaster Management Based on Real-time Color Object Detection Saddami, Khairun; Nurdin, Yudha; Noviantika, Fina; Oktiana, Maulisa; Muchallil, Sayed
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1635

Abstract

Currently, the water level monitoring system for a river uses instruments installed on the banks of the river and must be checked continuously and manually. This study proposes a real-time water level detection system based on a computer vision algorithm. In the proposed system, we use color object tracking technique with a bar indicator as a reference’s level. We set three bar indicators to determine the status of the water level, namely NORMAL, ALERT and DANGER. A camera was installed across the bar level indicators to capture bar indicator and monitoring the water level. In the simulation, the monitoring system was installed in 5-100 lux lighting conditions. For experimental purposes, we set various distances of the camera, which is set of 40-80 centimeters and the camera angle is set of 30-60 degrees. The experiment results showed that this system has an accuracy of 94% at camera distance is in range 50-80 centimeters and camera angle is 60o. Based on these results, it can be concluded that this proposed system can determine the water level well in varying lighting conditions.
Power Quality Improvement in Micro Hydro Power Plant Based-ELC and VSI using Fuzzy-PI Controller Pakaya, Ilham; Zulfatman; Faruq, Amrul; Irfan, 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.v10i1.1913

Abstract

The stability of frequency and voltage in micro hydro power plants (MHPP) depends on the ability to maintain balance between active and reactive power while managing load variations. Active power is typically regulated by an Electronic Load Controller (ELC), while reactive power is managed by a Voltage Source Inverter (VSI), with the VSI specifically compensating for reactive power induced by inductive loads. This study aims to enhance the control of active and reactive power in an MHPP system under varying load conditions by improving the ELC and VSI using Fuzzy-PI controller. The Fuzzy-PI controller applied in the ELC ensures a more precise TRIAC firing angle, enabling accurate control of the ballast load to balance the active power. Similarly, Fuzzy-PI controller applied in the VSI provides precise reactive power compensation to counteract inductive load effects. The performance of the proposed Fuzzy-PI-based ELC and VSI was evaluated using a complete MHPP model simulated in Matlab. Results demonstrated that the improved ELC and VSI effectively enhanced the system performance. Specifically, the Fuzzy-PI controller enabled the ELC to achieve accurate active power balance, while the VSI delivered suitable reactive power compensation. Consequently, the system achieved improved frequency and voltage stability under load variations, leading to enhanced power quality in the MHPP.
How HEXAD Types Influence Systemic and Finer-Grained Experiences in Gameful Educational Media: An Exploratory Study Sugiarto; Atmaja, Pratama Wirya; Mandyartha, Eka Prakarsa
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.v10i1.1985

Abstract

Education in the 21st century demands technology support, in which gameful media, such as educational games, can provide. Providing this support also requires the media to accommodate the different needs of the players, which can be identified by classifying the players’ type using HEXAD typology. However, the effect of HEXAD type classification on players’ experience in gameful media is still vague. This study aims to adress this vagueness by exploring the implementation of HEXAD in a more systemic and fine-grained manner using a playtest of an educational role-playing game. We measured the playtesters’ gameplay and learning experiences (n = 60) through a questionnaire developed based on HEXAD scale, GUESS, and EGameFlow. We also measured the correlation between the playtesters’ HEXAD types and their gameplay and learning experiences. Our analysis of the correlations uncovers exciting findings, including that the “achiever” type strongly appreciates playability features and that playability is among the essential gameplay factors for HEXAD types. We also propose design principles that can guide future research and development of the media.
Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition Sugiarto, Edi; Budiman, Fikri; Fahmi, Amiq
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.v10i1.2019

Abstract

Batik as a cultural heritage is one of the heritages that needs to be preserved so that it continues to be recognized from generation to generation. Efforts to preserve batik can be made by using technology that can recognize batik motifs. Pattern recognition is a branch of science related to the identification, classification, and interpretation of patterns. Deep learning is one of the technologies that can be used very well for pattern recognition, especially for syllable and image recognition. Convolutional neural network (CNN) is one of the most popular deep learning methods and the most established algorithm for deep learning models. The main advantage of CNN over the preceding methods is its ability to automatically detect features, making the feature extraction and classification process highly organized. This study aims to apply CNN for batik pattern recognition. The batik patterns used were geometric patterns, divided into 7 batik classes. Experiments were conducted on 3100 data, consisting of 3000 for training set and 100 for testing set. At the preprocessing stage, the batik image was resized to 28x28, and the color was changed to grayscale. Training was carried out on 100, 200, and 300 epochs. The classification results prove that CNN can recognize batik patterns well with an accuracy rate of 95%.
Analyzing Perceptron Algorithm for Global Gold Price Prediction using Quantum Computing Approach Solikhun; Siregar, Muhammad Rahmansyah
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.v10i1.2024

Abstract

The price of gold has garnered significant attention in the world of finance and investment due to its role as a safe haven asset and an indicator of global economic stability. An inherent risk of investing in gold is the daily fluctuation in prices, which can rise, fall, or remain stable. Investors are constantly seeking accurate ways to predict gold price movements in order to make informed investment decisions. While classic algorithms like artificial neural networks have been used for gold price prediction, they often struggle with analyzing complex data and identifying the hidden patterns within large datasets. It is widely acknowledged that accurately and consistently predicting the gold price movements, exchange rate, and whether the gold price will rise or fall is very challenging. To address this challenge, this study explored the use of quantum perceptron algorithm for predicting global gold prices. This approach harnesses the principles of quantum computing to improve the efficiency and performance of neural network models. Quantum computers can perform multiple computations simultaneously, enabling the solution of problems that are difficult for classical computers. This study utilized global gold data from January 2018 to December 2022, with 80:20 split of training and testing data; data from January 2018 to December 2021 for training and data from January 2022 to December 2022 for testing. This study aims to offer insights into the potential and application of quantum algorithms in predicting gold prices. The research involved an analysis of global gold price predictions using the quantum perceptron algorithm and quantum computing.
Detecting Acute Lymphoblastic Leukemia in Blood Smear Images using CNN and SVM Adiwijaya, Nelly Oktavia; Ardiansyah, Sultan; Swasono, Dwiretno Istiyadi
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.v10i1.2027

Abstract

Acute Lymphoblastic Leukemia (ALL) is a common and aggressive subtype of leukemia that predominantly affects children. Accurate and timely diagnosis of ALL is critical for successful treatment, but it is hindered by the limitations of manual examination of peripheral blood smear images, which are prone to human error and inefficiency. This study proposes an improved diagnostic approach by integrating the EfficientNet architecture with a Support Vector Machine (SVM) classifier to enhance classification accuracy and address the performance inconsistencies of standalone EfficientNet models. Additionally, a novel CNN-based model with a reduced number of parameters is developed and evaluated. A dataset comprising 3.256 peripheral blood smear images across four classes (benign, early, pre and pro) was used for training and testing. The EfficientNet-SVM models achieved a peak accuracy of 97.35% using the EfficientNet-B3 architecture, surpassing previous studies. The improved CNN model achieved the highest accuracy of 99.18% while reducing parameters by 59.5% compared to the best prior models, with a negligible accuracy decrease of only 0.67%. These findings highlight the potential of combining EfficientNet with SVM and the efficiency of the improved CNN model for automated ALL detection, paving the way for more reliable, cost-effective, and scalable diagnostic tools.
Design and Implementation of Two-phase Boost Inverter using Interleaved Method to Increase Output Current Setiyawan, Fahrul Indra; Pratomo, Leonardus Heru
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.v10i1.2046

Abstract

The advancement of technology is rapidly evolving, particularly in the field of electronics, namely power electronics. One of the applications is the use of new and renewable energy. The converters required in new and renewable energy are inverters with good quality and performance. The step-down (buck) inverter is commonly used in this application. Different from the normal inverter, the step up (boost) inverter is proposed to be analyzed, simulated, and implemented in this paper. The proposed inverter uses a two-phase interleaved boost inverter (TP DC-AC IBI) consisting of a full bridge inverter and dual AC-AC interleaved boost converter. The inverter part always converts DC voltage to AC voltage, while the dual AC-AC interleaved boost converter part serves to increase the output voltage. The inverter consists of three arms: the first and second arms are controlled by Sinusoidal Pulse Width Modulation (SPWM) using 180° phase-shifted carrier signal, and the third arm is controlled by a zero-crossing detector.  Pulse Width Modulation (PWM) is used to control dual AC-AC interleaved boost converter. By combining this inverter with dual AC-AC interleaved boost converter, a new topology is created. This study specifically investigated the strategy to control this new topology using current controls. The actual current was obtained by installing an HX-10P current sensor on the output side. The output current was compared with the reference current, and the next stage was controlled using a proportional plus integral controller. The control signals output was modulated using SPWM signals on the inverter side and PWM at the AC-AC interleaved boost converter side to drive many power switches. To guarantee that the desired current control can always be achieved, the actual current and reference current must always match. The proportional plus integral controller was chosen due to its simplicity, high accuracy, and quick response time. The analysis involved verifying simulation tests using Power Simulator (PSIM) software. The hardware implementation was conducted in the laboratory and tested using standardized equipment. A couple of inductors were installed to reduce harmonic current on the output side and obtained THD of 3.3%, which according to the IEEE 519-2014, has met the standard as it was less than 5%. Thus, this new topology can be used in new and renewable energy for its good performance.
Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Ghozi, Wildanil
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.v10i1.2051

Abstract

The rapid advancement of malware poses a significant threat to devices, like personal computers and mobile phones. One of the most serious threats commonly faced is malicious software, including viruses, worms, trojan horses, and ransomware. Conventional antivirus software is becoming ineffective against the ever-evolving nature of malware, which can now take on various forms like polymorphic, metamorphic, and oligomorphic variants. These advanced malware types can not only replicate and distribute themselves, but also create unique fingerprints for each offspring. To address this challenge, a new generation of antivirus software based on machine learning is needed. This intelligent approach can detect malware based on its behavior, rather than relying on outdated fingerprint-based methods. This study explored the integration of machine learning models for malware detection using various ensemble algorithms and feature selection techniques. The study compared three ensemble algorithms: Gradient Boosting, Random Forest, and AdaBoost. It used Information Gain for feature selection, analyzing 21 features. Additionally, the study employed a public dataset called ‘Malware Static and Dynamic Features VxHeaven and VirusTotal Data Set’, which encompasses both static and dynamic malware features. The results demonstrate that the Gradient Boosting algorithm combined with Information Gain feature selection achieved the highest performance, reaching an accuracy and F1-Score of 99.2%.
An Ensemble Learning Layer for Wayang Recognition using CNN-based ResNet-50 and LSTM Irawan, Candra; Rachmawanto, Eko Hari; Hadi, Heru Pramono
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.v10i1.2053

Abstract

Wayang is commonly used to tell epic stories of Mahabharata and Ramayana, as well as local legends and myths. There are various types of wayang, such as wayang kulit (made of buffalo or goat leather), wayang golek (made of wood), and wayang klithik (combination of leather and wood). Although it indicates cultural richness, such diversity also makes it difficult for the general public to identify the character of wayang they are seeing because each type has unique characteristics and details. Recognizing   wayang characters is a challenging task due to their intricate designs and subtle variations. This research addresses this problem by leveraging machine learning technology, specifically CNN-based classification methods, to accurately identify wayang characters. This study proposed a novel method that integrates ResNet-50 transfer learning with LSTM, enhancing the model's ability to capture both spatial and sequential features of wayang images. The proposed model achieved an impressive accuracy of 97.92%, with precision, recall, and F1-scores all reaching 100%. Despite the extended training time of 188 minutes and 21 seconds, the results demonstrate the model's superior performance. This advancement can significantly aid in the preservation and educational dissemination of Indonesian cultural heritage. Future research can focus on optimizing the training process to reduce the time while maintaining or even improving the accuracy, potentially expanding the model's application scope and effectiveness.

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