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 10 Documents
 1 
Search results for , issue "Vol. 9, No. 2, May 2024" : 10 Documents clear
Hybrid Fuzzy-PID Design Based on Flower Pollination Algorithm for Frequency Control of Micro-Hydro Power Plant Hakim, Ermanu Azizul; Norazizah; Zulfatman; Setyawan, Novendra
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1755

Abstract

Micro-Hydro Power (MHP) Plant System is the renewable energy resource that utilizes water potential energy. In MHP, the energy flows depend on the rotation speed of the generator which cause instability and nonlinearity in the frequency of electrical power. It is also supported by the fluctuation on the electricity load. Therefore, this study used Fuzzy Logic Controller combined with FPA-tuned PID to control the power frequency of the load. This test consisted of 4 stages, namely testing the system without a controller, testing the system using PID, testing the MHP system with a PID controller tuned to the Flower Pollination Algorithm, and testing the system using a Fuzzy PID tuned by the Flower Pollination Algorithm. Based on these tests, the Micro-Hydro Power Plant system response using a Fuzzy PID-tuned FPA controller performed best, especially in accelerating the time to a steady state, reducing overshoot and undershoot with the fastest rise time. As for the output signal from the controller used in the MHP, optimizing the Flower Pollination Algorithm for the Kp, Ki, and Kd parameters is effective and smooth in improving all elements in the Micro-Hydro Power Plant frequency stabilization. Meanwhile, the role of the fuzzy logic controller (FLC) is not very significant, and there is relatively a lot of noise in the output signal of the Fuzzy PID controller itself in terms of stabilizing the load frequency on the Micro-Hydro Power Plant.
Multi-Label Classification of Indonesian Qur'an Translation using Long Short-Term Memory Model Akbar, Ismail; Faisal, Muhammad; Chamidy, Totok
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1901

Abstract

Studying the Quran is an integral act of worship in Islam, necessitating a nuanced comprehension of its verses to ease learning and referencing. Recognizing the diverse thematic elements within each verse, this research pioneers in applying Deep Learning techniques, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), coupled with Word Embedding methods like Word2Vec and FastText, to refine the multi-label classification of the Quran's translations into Indonesian. Targeting core thematic categories such as Tawheed, Worship, Akhlaq, and History, the study aims to elevate classification accuracy, thereby enhancing the textual understanding and educational utility of the Quran's teachings. The employment of Bi-LSTM in conjunction with FastText and meticulous hyperparameter optimization has yielded promising results, achieving an accuracy of 71.63%, precision of 64.06%, recall of 63.60%, and a hamming loss of 36.17%. These outcomes represent a significant advancement in the computational analysis of religious texts, offering novel insights into the complex domain of Quranic studies. Furthermore, the research accentuates the critical role of selecting suitable word embedding techniques and the necessity of precise parameter adjustments to amplify model performance, thereby contributing to the broader field of religious text analysis and understanding. Through such computational approaches, this study not only fosters a deeper appreciation of the Quran's multifaceted teachings but also sets a new precedent for the interdisciplinary integration of Islamic studies and artificial intelligence.
Classification of Arrhythmic and Normal Signals using Continuous Wavelet Transform (CWT) and Long Short-Term Memory (LSTM) Yunidar, Yunidar; Melinda, Melinda; Azmi, Ulul; Bashir, Nurlida; Nurbadriani, Cut Nanda; Taqiuddin, Zulfikar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1917

Abstract

An electrocardiogram (ECG) can detect heart abnormalities through signals from the rhythm of the human heartbeat. One of them is arrhythmia disease, which is caused by an improper heartbeat and causes failure of blood pumping. In reading ECG signals, a common problem encountered is the uncertainty of the prediction results. An accurate and efficient heart defect classification system is needed to help patients and healthcare providers carry out appropriate therapy or treatment. Several studies have developed algorithms that are more effective in Machine Learning (ML) in automatically providing initial screening of patients' heart conditions. This study proposed the Long Short-Term Memory (LSTM) method as a classifier of heart conditions experienced by humans and Continuous Wavelet Transform (CWT) as a feature extractor to eliminate noise during data collection. CWT and LSTM methods are believed to perform well in feature extraction and classification of ECG signals. The dataset used in this study was taken from the MIT-BIH Arrhythmia Database. The results of this study successfully extracted ECG signals using CWT, thus improving the understanding of ECG characteristics. This research also succeeded in classifying ECG signals using the LSTM method, which obtained an accuracy training value of 98.4% and an accuracy testing value of 94.42 %.
Sensorless Field-Oriented Control (FOC) using Sliding Mode Observer for BLDC Motor Arif, Abdul Hafid; Muslim, Muhammad Aziz; Yudaingtyas, Erni
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1937

Abstract

Motor Brushless Direct Current (BLDC) has become the preferred choice in various engineering applications. However, BLDC motor control involves high complexity, and motor performance depends on the control algorithms used. This research discusses the use of sensorless control methods, specifically the Sliding Mode Observer (SMO) for rotor position and speed estimation in BLDC motors within the context of Field-Oriented Control (FOC), validated through simulations using Matlab/Simulink. Simulation results indicate that SMO provides rapid dynamic response to current changes, albeit with slight delays at high speeds. Rotor position estimation with SMO is also reasonably accurate in both steady-state and transient conditions, affirming the iveness of SMO in sensorless control for BLDC motors. SMO can be experimentally implemented to enhance sensorless control in BLDC motors by reducing the cost of installing Hall sensors while maintaining comparable performance.
Tomato Leaf Diseases Classification using Convolutional Neural Networks with Transfer Learning Resnet-50 Muslih; Krismawan, Andi Danang
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1939

Abstract

This research delves into the critical domain of Tomato Leaf Disease classification using advanced machine learning techniques. Specifically, a comparative evaluation was conducted between a Base CNN model devoid of ResNet-50 integration and a Proposed Method harnessing the capabilities of ResNet-50. The results elucidated a notable enhancement in performance metrics when leveraging ResNet-50, with the Proposed Method consistently achieving exceptional accuracy scores of 99.96%, 99.98%, and 99.96% across data splits of 90:10, 80:20, and 70:30, respectively. Furthermore, the ResNet-50 integration significantly augmented key metrics, including recall, precision, and F1-Score, thereby accentuating its pivotal role in enhancing sensitivity and positive predictive value for tomato leaf disease classification. As for prospective research trajectories, this study highlights potential avenues for refinement, encompassing the exploration of ensemble techniques amalgamating diverse architectural frameworks, advanced data augmentation methodologies, and broader disease classification scopes. Collectively, this research underscores the transformative potential of ResNet-50 in agricultural diagnostics, advocating for continued exploration and innovation to fortify global food security and sustainable farming practices. Future research could explore ensemble techniques, advanced data augmentation, broader disease classification scopes, and interdisciplinary collaborations to develop comprehensive diagnostic tools for sustainable farming practices and global food security.
Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD Salsabil, Adinda Arwa; Setiawan, Erwin Budi; Kurniawan, Isman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1940

Abstract

The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research  utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.
Movie Recommender System on Twitter Using Weighted Hybrid Filtering and GRU Valentino, Nico; Setiawan, Erwin Budi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1941

Abstract

The development of the industry in the film sector has experienced rapid growth, marked by the emergence of film streaming platforms such as Netflix and Disney+. With the abundance of available films, users face difficulty in choosing films that suit their preferences. Recommender systems can be a solution to this problem for users. Recommender systems rely on user reviews, making Twitter a platform that can be used to collect user reviews of a film. This study will develop a recommender system that has the potential to provide item recommendations to users using the weighted hybrid filtering and GRU methods. The weighted hybrid filtering used is a combination of collaborative filtering and content-based filtering methods. The dataset used in this study was obtained by crawling tweets relevant to the feedback of specific accounts regarding a film. The dataset resulting from the data crawling consists of a total of 854 films, 45 users and 34,086 tweets consisting of film reviews from Twitter users. The GRU model classification is performed on the results of weighted hybrid filtering with model optimization involving testing various test size scenarios and optimizer methods. The test sizes used are 40%, 30%, and 20%. The optimizer methods used include Adam, Nadam, Adamax, Adadelta, Adagrad, and SGD. The research results show that the optimal outcome is obtained using the Nadam optimization method. The performance evaluation yielded 85.74% precision, 88.63% recall, 88.63% accuracy, and 86.30% F1-score.
Optimizing Android Program Malware Classification Using GridSearchCV Optimized Random Forest Hakim, Luqman; Sari, Zamah; Aristyo, Ananda Rizaldy; Pangestu, Syahrul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1944

Abstract

The growing number of smartphones, particularly Android powered ones, has increased public awareness of the security concerns posed by malware and viruses. While machine learning models have been studied for malware prediction in this field, methods for precise identification and classification still require improvement for the perfect detection of malwares and minimizing the cracks on machine learning based classification. Detection accuracy that ranges from 93% to 95% has been observed in prior research, indicates room for improvement.  In order to maximize the hyperparameters, this paper suggests improving the Random Forest method by introducing the grid search algorithm which isn’t present in previous studies. A significant increase in classification accuracy is the main aim of the research. We exhibit an outstanding 99% accuracy rate in detecting malware contaminated programs, demonstrating the significance of our technique. The proposed method can be seen as a huge improvement over existing models, achieving near perfection in detection, in contrast to which typically obtained by previous models with the accuracy rate of 95% max on the same dataset. Our approach achieves such high accuracy and provides a novel remedy for the limits of the Android based platforms, particularly when program processing resources are limited. This study confirms the effectiveness of our improved Random Forest algorithm, points to a paradigm shift in malware detection, and heightened cybersecurity measures for the rapidly growing smartphone market.
Design of SEPIC Converter for Battery Charging System using ANFIS Suryono; Sudiharto, Indhana; Anggriawan, Dimas Okky; Jufriyadi, Mohammad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1954

Abstract

Rechargeable batteries are the most widely used medium for storing energy today. One type of rechargeable battery that is widely used is lithium-ion batteries. The large use of lithium-ion batteries in society requires companies to conduct research so that the life time of these batteries can last a long time and charging can take place quickly. Charging system at this time is less efficient in charging lithium batteries where the time needed is still quite long where when lithium batteries are charged with a long time can cause the battery to heat up quickly and can reduce the life time of the battery. To overcome this, a system is needed that can control the battery charger process so that the output voltage and current are constant and battery charging is faster. It is hoped that the SEPIC converter system can help many people who forget to unplug the power supply during the charging process so as to maintain the life time of the battery. Setting the output voltage and current in the DC-DC converter can be done using an Adaptive Neuro Fuzzy Inference System which aims to keep the output of SEPIC stable according to the setting point. In this system, the DC-DC converter used is a SEPIC converter which can increase and decrease the output voltage for battery charging. The battery charging process uses the CC-CV method. In the test, the average error is 0.025% where when the SOC is 60% to 80% the average error is 0.04% and when the SOC is 80% to 95% the average error is 0.0005%.
Enhancing Qur'anic Recitation Experience with CNN and MFCC Features for Emotion Identification Syafa'ah, Lailis; Prasetyono, Roby; Hariyady, Hariyady
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.2007

Abstract

In this study, MFCC feature extraction and CNN algorithms are used to examine the identification of emotions in the murottal sounds of the Qur'an. A CNN model with labelled emotions is trained and tested, as well as data collection of Qur'anic murottal voices from a variety of readers using MFCC feature extraction to capture acoustic properties. The outcomes show that MFCC and CNN work together to significantly improve emotion identification. The CNN model attains an accuracy rate of 56 percent with the Adam optimizer (batch size 8) and a minimum of 45 percent with the RMSprop optimizer (batch size 16). Notably, accuracy is improved by using fewer emotional parameters, and the Adam optimizer is stable across a range of batch sizes. With its insightful analysis of emotional expression and user-specific recommendations, this work advances the field of emotion identification technology in the context of multitonal music.

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue 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