Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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.
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<![CDATA[Opinion Spam Classification on Steam Review using Support Vector Machine with Lexicon-Based Features ]]>
<![CDATA[Rafif Taqiuddin]]>;
<![CDATA[Fitra A. Bachtiar]]>;
<![CDATA[Welly Purnomo]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1323
<![CDATA[Steam is a video game digital distribution platform developed by Valve Software. Steam provides a user review feature, where users can write about criticism or comments on games that can contain positive or negative sentiments. Based on the questionnaire that the author conducted to Steam users from all over Indonesia, the user review feature provided by Steam was not sufficient. This is because there are fake reviews that allow biased opinions from certain parties so that a phenomenon called review bombing often occurs where users review only to drop or raise the image of a product, not to review it sincerely. From these problems, a solution design is needed that can classify fake reviews on the Steam service. The Support Vector Machine (SVM) classification method was chosen as the model in combination with lexicon-based feature retrieval and Term Frequency – Inverse Document Frequency (TF-IDF) weighting. Of the 236 classification test data conducted by SVM, it produced 105 reviews which were categorized as Valid Reviews. Meanwhile, those categorized as Opinion Spam by SVM are 131 reviews. The accuracy level of the data classification model using Support Vector Machine method is of 81% by dividing training data by 70% and test data by 30% with a random state level of 109. A dashboard in the form of a web application has also been made that contains the classification model to be used for buying reference for Steam user.]]>
<![CDATA[Optimization of System Authentication Services using Blockchain Technology ]]>
<![CDATA[Imam Riadi]]>;
<![CDATA[Herman Herman]]>;
<![CDATA[Aulyah Zakilah Ifani]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1325
<![CDATA[With the development of the era, one thing that must be considered for security is the Login System. In most cases, user login information is stored on the server. This gives access to sensitive informatio, many hackers easily break into data from users. Based on these problems, this research focuses on data security authentication in the form of usernames and passwords in the login system. Authentication using blockchain is used to reduce malicious access and increase security for the authentication process. One of the innovative technologies that can solve these problems is Blockchain Technology. Using blockchain technology, hackers will find it difficult to change and modify the same data on all computers at the same time because it takes a very long time to crack the encryption code on each block of data in the entire computer network. Data storage or transactions in the blockchain are stored in the form of hashes. This makes it difficult for hackers to break into it. Tests in this study using Wireshark tools and network miner. Based on the research conducted, the test was conducted as many as 5 times with two scenarios, namely authentication of the login system before using the blockchain and after using the blockchain. The results obtained. The, system built using blockchain can secure data. The test results obtained that data in the form of usernames and passwords were converted into hashes and with the immutable nature of the blockchain, data from users could not be changed or replaced by anyone.]]>
<![CDATA[Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction ]]>
<![CDATA[Tyas, Fitri Ayuning]]>;
<![CDATA[Setianama, Mamur]]>;
<![CDATA[Fadilatul Fajriyah, Rizqi]]>;
<![CDATA[Ilham, Ahmad]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1330
<![CDATA[One of the oldest known predictive analytics techniques is time series prediction. The target in time series prediction is use historical data about a specific quantity to predicts value of the same quantity in the future. Multivariate time series (MTS) data has been widely used in time series prediction research because it is considered better than univariate time series (UTS) data. However, in reality MTS data sets contain various types of information which makes it difficult to extract information to predict the situation. Therefore, UTS data still has a chance to be developed because it is actually simpler than MTS data. UTS prediction treats forecasts as a single variable problem, whereas MTS may employ a large number of time-concurred series to make predictions. Neural Network (NN) model could be built to predict the target variable given the other (predictor) variables. In this study, we used Particle Swarm Optimization (PSO) algorithm to optimize performance of NN on a UTS dataset. Our proposed model is validated using x-validation and and use RMSE to measure its performance. The experimental results show that NN performance after optimization using PSO produces good results compared to classical NN performance. This is evidenced by the value of RMSE = 0.410 which is the smallest RMSE value produced. The smaller the RMSE value, the better the model performance. It can be concluded that the proposed method can improve NN performance on UTS data.]]>
<![CDATA[Study of Neuromarketing: Visual Influence with Decision Making on Impulse Buying ]]>
<![CDATA[Januar, Rifat]]>;
<![CDATA[Fauzi, Hilman]]>;
<![CDATA[Ariyanti, Maya]]>;
<![CDATA[Heris, Faradisya]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1334
<![CDATA[Marketing trends have been increasing in the last few decades. Products need good branding and the right marketing strategy. Various marketing methods have been widely done, and one of them is with the study of neuroscience, especially neuromarketing. Neuromarketing is used to seek the influence of marketing stimuli on consumers and objective data through advances in neurology by utilizing human senses such as restraint, smell, taste, and touch. Measurements of neuromarketing responses to the brain can use electroencephalography signals (EEG). Measurement is done with the visual stimulus of consumers when making decisions. To analyze consumer interests, the majority still using qualitative methods, but it is still considered less effective due to many uncertain factors. In this study, neuromarketing responses were measured to the human brain using (EEG) signal analysis. Data collection was conducted on 11 respondents with a stimulus in the form of different product colors and was affected by changes in light intensity. For pre-processing used bandpass filters to get beta signals in the absence of noise. Then the data will be processed using Fast Fourier Transform (FFT) and energy extraction as characteristic extraction and classification of Support Vector Machines (SVM) in the signal pattern recognition process. The results of testing the best feature combination parameters showed an accuracy value of 72% with a combination of magnitude and phase features. By using the range of phase feature values obtained an accuracy of 67% for signal pattern recognition respondents.]]>
<![CDATA[Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification ]]>
<![CDATA[Wahyudi Setiawan]]>;
<![CDATA[Abdul Ghofur]]>;
<![CDATA[Fika Hastarita Rachman]]>;
<![CDATA[Riries Rulaningtyas]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1335
<![CDATA[Maize productivity growth is expected to increase by the year. However, there are obstacles to achieving it. One of the causes is diseases attack. Generally, maize plant diseases are easily detected through the leaves. This article discusses maize leaf disease classification using computer vision with a convolutional neural network (CNN). It aims to compare the deep convolutional neural network (CNN) AlexNet and Squeezenet. The network also used optimization, stochastic gradient descent with momentum (SGDM). The dataset for this experiment was taken from PlantVillage with 3852 images with 4 classes i.e healthy, blight, spot, and rust. The data is divided into 3 parts: training, validation, and testing. Training and validation are 80%, the rest for testing. The results of training with cross-validation produce the best accuracy of 100% for AlexNet and Squeezenet. Furthermore, the best weights and biases are stored in the model for testing data classification. The recognition results using AlexNet showed 97.69% accuracy. While the results of Squeezenet 44.49% accuracy. From this experiment environment, it can be concluded that AlexNet is better than Squeezenet for maize leaf diseases classification.]]>
<![CDATA[Firefly Algorithm For Optimizing Single Axis Solar Tracker ]]>
<![CDATA[Melfazen, Oktriza]]>;
<![CDATA[Alawity, M. Taqijuddin]]>;
<![CDATA[Dewatama, Denda]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1338
<![CDATA[Solar cells mounted on solar panel modules are expected to track sunlight throughout the day to produce maximum energy. The Firefly algorithm (FA) is embedded in the Arduino Mega microcontroller to control the tracking of the sun's position by the solar panel so that the absorption of solar energy can be as much as possible to get maximum electrical energy. The brightest light captured by the solar panel is represented as the light intensity of a firefly. The output of the solar tracking system is obtained by finding the best value of light intensity between fireflies. Parameter changes in FA, such as firefly population, random numbers, and number of iterations affect the results of FA. The largest population, the highest random number and iteration provide the best solution but take a long time to execute. FA can control solar panels in tracking the sun's position precisely with an average error of 1.28% and can absorb a total energy of 666.14 Watt/day. The best solution (98% of setpoint 720) was obtained when the population was set to 50, the random number to 0.8, and iteration to 50. This research can be used as a reference for later using a controller with higher specifications to speed up the FA process time in getting maximum control results.]]>
<![CDATA[A Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data ]]>
<![CDATA[Khusnul Khotimah, Bain]]>;
<![CDATA[Kustiyahningsih, Yeni]]>;
<![CDATA[Miswanto, Miswanto]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1340
<![CDATA[The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search. In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques.]]>
<![CDATA[Employee Attrition and Performance Prediction using Univariate ROC feature selection and Random Forest ]]>
<![CDATA[Aris Nurhindarto]]>;
<![CDATA[Esa Wahyu Andriansyah]]>;
<![CDATA[Farrikh Alzami]]>;
<![CDATA[Purwanto Purwanto]]>;
<![CDATA[Moch Arief Soeleman]]>;
<![CDATA[Dwi Puji Prabowo]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1345
<![CDATA[Each company applies a contract extension to assess the performance of its employees. Employees with good performance in the company are entitled to future contracts within a certain period of time. In a pandemic time, many companies have made decisions to carry out WFH (Work from Home) activities even to Termination (Attrition) of Employment. The company's performance cannot be stable if in certain fields it does not meet the criteria required by the company. Thus, due to many things to consider in contract extension, we are proposed feature selection steps such as duplicate features, correlated features and Univariate Receiver Operating Characteristics curve (ROC) to reduce features from 35 to 21 Features. Then, after we obtained the best features, we applied into Decision Trees and Random Forest. By optimizing parameter selection using parameter grid, the research concluded that Random Forest with feature selection can predict Employee Attrition and Performance by obtain accuracy 79.16%, Recall 76% and Precision 82,6%. Thus with those result, we can conclude that we can obtain better prediction using 21 features for employee attrition and performance which help the higher management in making decisions.]]>
<![CDATA[Skin Lesion Image Classification using Convolutional Neural Network ]]>
<![CDATA[Lailis Syafa'ah]]>;
<![CDATA[Ilham Hanami]]>;
<![CDATA[Inda Rusdia Sofiani]]>;
<![CDATA[Mohammad Chasrun]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1353
<![CDATA[Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.]]>
<![CDATA[Recommended Features of Learning Management System Using Feature-Oriented Method Software Development on Moodle ]]>
<![CDATA[Hari Windu Asrini]]>;
<![CDATA[Galih Wasis Wicaksono]]>;
<![CDATA[Budiono Budiono]]>;
<![CDATA[Gilang Ramadhan]]>;
<![CDATA[Evi Dwi Wahyuni]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i4.1354
<![CDATA[Learning Management System (LMS) serves as a learning media effective for online learning during Covid-19. The rising use of LMS these days indicates that LMS features need to be further measured for their relevance to the National Standards of Higher Education (SN-Dikti). This measurement is also essential to be applied for five blended learning criteria for flexible time and place to access lessons. The objective of this research is to analyze the features of the LMS implemented on Moodle version 3.10. The analysis of the features was carried out by employing the methods of Feature-Oriented Software Development (FOSD), a method that elaborates software system relating to existing features of LMS, involving: 1) Canvas; 2) Edmodo; 3) Google Classroom; 4) Moodle; 5) Zoom Meeting; 6) Google Meet; 7) efront, and 8) Microsoft Teams. In accord with the testing and validation by experts, the research revealed that there were 56 features of LMS pertinent to learning and assessment standards of SN-Dikti and E-learning self-study criteria with other blended-media or events, instructor-led program, live e-learning, on the job training, simulation, and lab centered on blended learning. Those 56 features were approved by experts and implemented by employing Moodle version 3.10 relevant to the SN-Dikti and blended learning]]>
