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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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Metaverse Adoption in Public Sector: A Bibliometric Analysis Mailangkay, Adele; Napitupulu, Darmawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1891

Abstract

The objective of this study is to map the landscape and knowledge structure of the public sector metaverse through bibliometric analysis. Following this, a descriptive overview of the current state of public sector research related to the metaverse, including the leading ten authors and sources, most relevant keywords, and primary research topics, will be presented. The selection of the Scopus database was based on its status as the largest repository of its kind and its provision of an extensive compilation of abstracts and citations for peer-reviewed articles, proceedings, and journals. The initial search yielded 369 documents from Scopus; after eliminating duplicate and irrelevant articles, 354 documents remained. The results demonstrate the substantial development of the metaverse in the public sector, with an annual growth rate of 14.89%. This indicates that the adoption of virtual technology and the metaverse is gaining importance in the public sector. Castronova ranks first in terms of the quantity of studies published, with five articles, followed by De Kool and Zhang. "Metaverse," "virtual reality," and "virtual world" are the most pertinent keywords. Due to the extensive research that has been conducted on these phrases, they represent fundamental concepts in the field. Additional frequently occurring terms include "social networking (online)," "interactive computer graphics," "government data processing," and "internet." These terms underscore the significance of governance, human factors, technology, and data in shaping the trajectory of digital government services. They also exemplify the interdisciplinary character of metaverse applications within the public sector.
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 %.
Service Quality Analysis of Unej Digital Library Using M-S-QUAL and Importance Performance Analysis Methods Prasetyo, Beny; Ramadhani, Firlia Dwi Cahya; Nurman Arifin, Fajrin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1918

Abstract

A lot of library services are impacted by the improvement of technologies. With this modification, the traditional library services are now entirely digital. To carry out this digitalization, the Unej Digital Library (UnejDigiLib) application was created by the University of Jember's library. The purpose of developing this application is to improve the effectiveness of library services, which were previously hindered by the COVID-19 epidemic. The UnejDigiLib developer has not yet evaluated the quality of its services since the application's release, so they are unsure of whether the current services satisfy the user needs. The goal of this study is to combine Mobile Service Quality (M-S-QUAL) and Importance Performance Analysis (IPA) in order to assess the UnejDigiLib service's quality based on users perceptions. The M-S-QUAL is used to determine the service quality indicators and examine the gap between their performance and importance. After that, the service indications are mapped using the IPA based on their priority level. The M-S-QUAL dimensions that used are: compensation, privacy fulfillment, content, system availability, efficiency, and privacy. Data collection was carried out through online surveys and interview. The respondents are Unej students who had used and borrowed e-book from UnejDigiLib. The sample was determined using simple random sampling and obtained 287 respondents. The findings indicate that the user expectations have not been met by the UnejDigiLib service's performance. Meanwhile, the IPA analysis's findings indicate that the following indicators are found in quadrant 1: C3 (completeness of the book collection), C5 (suitability of the book collection with the curriculum), C6 (updates to the book collection), and F3 (download speed). This quadrant's indicators are the primary focus for the improvement. The conclusion from these improvement suggestions is that application service providers must coordinate with stakeholders to complete the e-book collection according to customer needs and also require technical updates starting from the features and internal application system to minimize errors due to the system.
Spatial Interpolation Long-Term Patterns Capacity of Solar Energy in Sumatera Vatresia, Arie; Utama, Ferzha Putra; Daratha, Novalio; Lestari, Etika Dwi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1929

Abstract

Indonesia possesses considerable capacity for renewable energy as a result of its plentiful natural resources, including geothermal, solar, wind, hydro, and biomass. However, the nation's existing energy composition is predominantly dependent on non-renewable resources, with fossil fuels constituting approximately 95% of its overall energy consumption. Recently, Indonesia has made notable advancements in augmenting its renewable energy output in years. Nevertheless, there is still obscurity about the identification of suitable regions for the installation of solar power plants in order to facilitate the development of solar energy. This study employed a methodology to investigate and forecast the solar energy potential in Sumatra, Indonesia. The data utilized consists of MERRA-2 reanalyzing information spanning from 1980 to 2019, collected on a daily basis. The data is analyzed and shown using Inverse Distance Weighting and ARIMA techniques to visualize the spatial variation of solar energy potential in Sumatra. ARIMA is employed as a supplementary method to the interpolation technique in order to get long-term projections of solar energy potential for the period spanning from 2020 to 2029. The analysis of the best interpolation method for estimating solar energy potential reveals that the IDW approach with a power of 5 yields the most accurate findings, with an RMSE value of 28.33. For long-term prediction of solar potential in Aceh province, the ARIMA (1,0,0) method is recommended, which has a MAPE value of 0.0219. The findings indicated that Lampung and Bengkulu frequently experience the distribution of solar energy with an intensity ranging from 1400 to 1450 kWh. In addition, the forecast of the potential over Sumatera Island yielded encouraging results using the GAM model, with a root mean square error rate of 0.05103.
Comparative Study of Classification of Eye Disease Types Using DenseNet and EfficientNetB3 Jatmoko, Cahaya; Lestiawan, Heru; Agustina, Feri; Erawan, Lalang
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1931

Abstract

The purpose of this research is to build a classification model that can perform the eye disease identification process so that the diagnosis of eye disease can be known and medical action can be taken as early as possible. This research used a dataset which has a total of 4217 eye image data and had 4 main classes namely cataract, diabetic retinopathy, glaucoma, and normal. With the data distribution of 1038 cataract images, 1098 diabetic retinopathy images, 1007 glaucoma images, and 1074 normal images, which of this data will be divided with a data percentage scheme of 50:10:40, 60:10:30, and 70:10:20, to see the results of which dataset division can produce optimal accuracy. In this study, the classification process will use 2 CNN transfer learning architectures, namely DenseNet, and efficientnetb3, which are both trained using the ImagiNet dataset. The results obtained after completing the testing process on the model built using the DenseNet architecture get optimal accuracy when using data division as much as 60:10:30, which is 78.59% while using the efficientnetb3 architecture optimal accuracy results when using the data division of 70:10:20, which is 95.66%. In research on the classification that had previously been done, it is very rare to find a classification process for eye disease types, therefore, in this study, the classification process will be carried out and provide an overview of the eye disease classification process with the CNN transfer learning method with more optimal accuracy results.
Performance Comparison between Double Exponential Smoothing and Double Moving Average Methods in Seasonal Beef Demand Khusnul Khotimah, Bain; Setiani; Wulandari, Ana Yuniasti Retno; Anamisa, Devie Rosa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 4, November 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i4.1934

Abstract

Beef demand relies on seasonal patterns because it depends on feed supplies, especially in the rural areas, that still rely on natural feeds. Beef supply is regulated by the government as it is one of the highly demanded commodities. It is a livestock product containing nutritional value to meet the protein needs of the community. The supply is influenced by several factors such as beef production, beef consumption, and the people's income level. In order to anticipate the increasing demand for beef, it is necessary to conduct a forecast to estimate the demand for meat in the future. In forecasting, various methods were examined to choose the method with the lowest error rate. This research compared the Mean Absolute Percentage Error (MAPE) resulted from Double Exponential Smoothing (DES) and Double Moving Average (DMA) methods. Based on the test results and analysis on beef supplies in Madura, it can be concluded that the method with the lowest MAPE value is Double Exponential Smoothing, i.e. 9.50% with an alpha parameter of 0.5. Meanwhile, the test using the Double Moving Average method to determine the best MAPE value, resulted the best time order of 2 with a MAPE value of 29.8408%. After finding the parameter with the lowest MAPE value, that parameter was used for the data testing. In the measurement, the data used for the testing were the data of 1-year, 2-year, 3-year, and 4-year period. Each method has a level of error value that increases the same; the number of data entered can affect the MAPE value. Therefore, the more data entered, the lower the error value.
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.

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