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  Subjects -> ELECTRONICS (Total: 207 journals)
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Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Number of Followers: 5  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2503-2259 - ISSN (Online) 2503-2267
Published by Universitas Muhammadiyah Malang Homepage  [23 journals]
  • Performance Evaluation of 198 Village Governments using Fuzzy TOPSIS and
           Intuitionistic Fuzzy TOPSIS

    • Authors: Wridhasari Hayuningtyas; Mauridhi Hery Purnomo, Adhi Dharma Wibawa, Muslichah Erma Widiana
      Abstract: Currently, volatility, uncertainty, complexity, and ambiguity (VUCA) have become unavoidable problems. In addition, knowledge or information that is not managed properly can result in inappropriate decision-making processes within an organization. Business Intelligence conception is then becoming an essential view for converting unstructured data and information into a more actionable strategic plan that allows organizations to make competitive decisions. Village Government (VG) is the smallest organization in the Indonesian government system because VG implemented regulation and development programs in all areas of a national government. VG executes a series of tasks every year starting from planning, budgeting, administrating, executing, and reporting. However, the important role of VG in the development of a country brings also some drawbacks such as corruption and other domino effects. Several factors have been identified that cause those problems such as lack of capabilities in managing village organization and human resources quality. Monitoring and evaluation regarding those VG performances normally have been done each year. However, measurable evaluation standard for VG performance until recently has not been determined nationally. This study is intended to make a comprehensive standard of village government performance assessment through a Good Governance Framework approach. This study involved 198 villages from Madiun Regency as a case study. Seventy-four measured parameters were proposed to evaluate VG performance mapping. Fuzzy TOPSIS is implemented to rank those 198 villages into 4 groups of VG performance levels. The fuzzy TOPSIS classification result has been validated by using manual scoring and the accuracy reached 86,4%.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature
           Importance Logistic Regression Coefficient

    • Authors: Mirza Maulana Azmi; Fauzi Dwi Setiawan Sumadi
      Abstract: The evolution of computer network technology is now experiencing substantial changes, particularly with the introduction of a new paradigm, Software Defined Networking (SDN). The SDN architecture has been applied in a variety of networks, including the Internet of Things (IoT), which is known as SD-IoT. IoT is made up of billions of networking devices that are interconnected and linked to the Internet. Since the SD-IoT was considered as a complex entity, several types of attack on vulnerabilities vary greatly and can be exploited by careless individuals. Low-Rate Distributed Denial of Service (LRDDoS) is one of the availability-based attack that may affect the SD-IoT integration paradigm. Therefore, it is necessary to have an Intrusion Detection System (IDS) to overcome the security hole caused by LRDDoS. The main objective of this research was the establishment of an IDS application for resolving LRDDoS attack using the SVM algorithm combined with the Feature Importance method, namely the Logistic Regression Coefficient. The implemented approach was developed to reduce the complexity or resource’s consumption during the classification process as well as increasing the accuracy. It could be concluded that the Linear kernel SVM algorithm acquired the highest results on the test schemes at 100% accuracy, but the training time required for this model was longer, about 23.6 seconds compared to the Radial Basis Function model which only takes about 1.5 seconds.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • Hybrid Frequency and Period Based for Angular Speed Measurement of DC
           Motor Using Kalman Filter

    • Authors: Novendra Setyawan; Basri Noor Cahyadi, Ermanu Azizul Hakim, Mas Nurul Achmadiah
      Abstract: The Incremental Rotary Encoder have been widely used to measure the angular speed of electrical drive such as Permanent Magnet Direct Current Motor (PMDCM). Nevertheless, speed measurement of PMDCM from the encoder signals can be subject to errors in some special condition such as in low resolution encoder. There are two main methods to measure the angular speed of PMDCM through encoder signal such as frequency-based and period-based wich has its own properties. Hence in this reseach aimed to improve the angular speed measurement with hybridization of frequency and period-based measurement. The Hybrid method is defined as paralleling the period and frequency then estimated the angular speed using sensor fusion with Kalman Filter. The experiment is doing by comparing of all method to get the best way in measuring. From the experimental showed that the Kalman filter parameter was fine tuned that resulting the sensor fusion or the mixed measurement between the frequency-based and the period based measure the angular speed accurately.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • XGB-Hybrid Fingerprint Classification Model for Virtual Screening of
           Meningitis Drug Compounds Candidate

    • Authors: Mohammad Hamim Zajuli Al Faroby; Helisyah Nur Fadhilah, Siti Amiroch; Rahmat Sigit Hidayat
      Abstract: Meningitis is an infection of the lining of the brain caused by diffuse inflammation, and this condition is caused by viruses or bacteria that cause Meningitis. Prevention for this disease is still in the form of strengthening antibodies with vaccines. There is no significant compound to relieve or treat Meningitis patients. In previous studies, they got seven proteins vital to Meningitis. We continued to investigate the compounds associated with the seven proteins. We chose the in-silico process by utilizing data in an open database. We use several databases for the data collection process. After that, the compound data were extracted for bonding features and chemical elements using molecular fingerprints. We use two fingerprint methods, where both we combine with three types of combinations. The combined results produce three types of datasets with different matrix sizes. We establish the Extreme Gradient Boosting (XGB) method to form the classification model for the three datasets, select the best classification model, and compare it with other classification algorithms. The XGB model has better quality than the classification model of other algorithms. We used this model to predict and quantify compounds that strongly bind to seven vital meningitis proteins. The compound with the highest predictive score (we found more than 0.99) became a drug candidate to inhibit or neutralize Meningitis.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • QSAR Study on Aromatic Disulfide Compounds as SARS-CoV Mpro Inhibitor
           Using Genetic Algorithm-Support Vector Machine

    • Authors: Rizki Amanullah Hakim; Annisa Aditsania, Isman Kurniawan
      Abstract: COVID-19 is a type of pneumonia caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus causes severe acute respiratory syndrome and 2 million active cases of COVID-19 have been found worldwide. A new strain of the SARS-CoV-2 virus emerged that proved to be more virulent than its predecessor. Regarding the design of a new inhibitor for this strain, SARS-CoV Main Protease (Mpro) was used as the target inhibitor. In the in silico development, the Quantitative Structure-Activity Relationship (QSAR) method is commonly used to predict the biological activity of unknown compounds to improve the process of drug design of a disease, including COVID-19. In this study, we aim to develop a QSAR model to predict the activity of aromatic disulfide compounds as SARS-CoV Mpro inhibitors using Genetic Algorithm (GA) – Support Vector Machine (SVM). GA was used for feature selection, while SVM was used for model prediction. The used dataset is set of features of aromatic disulfide compounds, along with information on the toxicity activity. We found that the best SVM model was obtained through the implementation of the polynomial kernel with the value of R2­­train and R2test­ scores are 0.952 and 0.676, respectively.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • Sentiment Analysis of Community Response Indonesia Against Covid-19 on
           Twitter Based on Negation Handling

    • Authors: Viry Puspaning Ramadhan; Purwanto Purwanto, Farrikh Alzami
      Abstract: The use of the internet globally, especially on the use of social media, includes Indonesia as one of the most active users in the world. The amount of information that can be obtained can be used to be processed into useful information, for example, information about the public sentiment on a particular topic. Tracking and analyzing tweets can be a method to find out people's thoughts, behavior, and reactions regarding the impact of Covid-19. The key to sentiment analysis is the determination of polarity, which determines whether the sentiment is positive or negative. The word negation in a sentence can change the polarity of the sentence so that if it is not handled properly it will affect the performance of the sentiment classification. In this study, the implementation of negation handling on sentiment analysis of Indonesian people's opinions regarding COVID-19 on Twitter has proven to be good enough to improve the performance of the classifier. Accuracy results obtained are 59.6% compared to adding negation handling accuracy obtained is 59.1%. Although the percentage result is not high, documents that include negative sentences have more meaning than negative sentences. However, for the evaluation using the MCC evaluation matrix, the results were quite good for the testing data. For the results of the proposed method whether it is suitable for data that has two classes or three classes when viewed from the results of the evaluation matrix, the proposed method is more suitable for binary data or data that has only two classes.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • Color Based Feature Extraction and Backpropagation Neural Network in
           Tamarind Turmeric Herb Recognition

    • Authors: Mila Fauziyah; Supriatna Adhisuwignjo, Bagus Fajar Afandi Afandi, Lathifatun Nazhiroh
      Abstract: The aim of this paper is finding the optimum image pattern of the tamarind turmeric herb. So far, in the process of producing tamarind turmeric herb, it is not constant in terms of taste and color, which is influenced by maturity and the amount of turmeric. Image pattern recognition will use Backpropagation algorithm applied to typical Content-based image retrieval systems. The main purpose is to apprehend various parts of tamarind turmeric herb in the retrieving processing. The camera is applied to classify the tamarind turmeric herb product, process into 5x5 pixels, and take an average of the RGB value so the stable RGB values will be obtained in each category and used as input for Backpropagation algorithm. The most suitable and the fastest process from the Backpropagation algorithm will be searched and applied in a real-time machine. In this paper will be using two methods, first, train the algorithm using ten data by change neuron, layer, momentum, and learning rate, and the last is testing with ten data. The results obtained from the training and testing algorithm that the two hidden layers can recognize 100% inputs, with three input layers used for R, G, and B value, ten neurons in the first hidden layers and the second hidden layers, one output layer with a parameter used is Learning rate 0.5 and Momentum 0.6. The best image pattern standard for tamarind turmeric herb is dark yellow with RGB values of 255, 102, 32 up to 255, 128, 48.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • A Wearable Device for Enhancing Basketball Shooting Correctness with
           MPU6050 Sensors and Support Vector Machine Classification

    • Authors: Baginda Achmad Fadillah; Aji Gautama Putrada, Maman Abdurohman
      Abstract: One of the impacts of Covid-19 is the delay of basketball sports competitions, which influences the athlete’s fitness and the athlete’s ability to play, especially for shooting techniques. Existing research in wearable devices for basketball shooting correctness classification exists. However, there is still an opportunity to increase the classification performance. This research proposes designing and building a smartwatch prototype to classify the basketball shooting technique as correct or incorrect with enhanced sensors and classification methods. The system is based on an Internet of things architecture and uses an MPU6050 sensor to take gyroscope data in the form of X, Y, and Z movements and accelerometer data to accelerate hand movements. Then the data is sent to the Internet using NodeMCU microcontrollers. Feature extraction generates 18 new features from 3 axes on each sensor data before classification. Then, the correct or incorrect classification of the shooting technique uses the Support-Vector-Machine (SVM) method. The research compares two SVM kernels, linear and 3rd-degree polynomial kernels. The results of using the max, average, and variance features in the SVM classification with the polynomial kernel produce the highest accuracy of 94.4% compared to the linear kernel. The contribution of this paper is an IoT-based basketball shooting correctness classification system with superior accuracy compared to existing research.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • Convolutional Neural Network (CNN) Models for Crop Diseases Classification

    • Authors: Deni Sutaji; Harunur Rosyid
      Abstract: Crop diseases have a significant impact on agricultural production. As a result, early diagnosis of crop diseases is critical. Deep learning approaches are now promising to improve disease detection. Convolutional Neural Network (CNN) models can detect crop disease using images with automatic feature extraction. This study proposes crop disease classification considering ten pre-trained CNN models. Fine-tuning for each model was conducted in the Plant Village dataset. The experimental results show that fine-tuning improves the model’s performance with an average accuracy of 8.85%. The best CNN model was DenseNet121, with 94.48% and 98.97% accuracy for freezing all layers and unfreezing last block convolution layers. Moreover, fine-tuning produces less time-consuming with an average of 2.20 hours. VGG19 is the less time-consuming reduction by 8 hours. On the other hand, MobileNetV2 is the second-best performance model with less time-consuming than DenseNet121, and produces fewer parameters, which is affordable for embedding it to mobile devices.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
  • Design and Simulation of Utilization of Solar Cells as Battery Chargers
           CC-CV (Constant Current-Constant Voltage) Method with Fuzzy Control

    • Authors: Indhana Sudiharto; Endro Wahjono, Lugiana Nur Fitriah Rhamadani Lugiana
      Abstract: In a country with a tropical climate, the use of the sunlight is very important. Thus, to be able to apply, a solar power conversion system is needed into a source of electrical energy. The use of electrical equipment that is quite high will increase the consumption of electrical power so that people spend more and more on electricity costs. A battery is a device consisting of electrochemical cells that can store electrical energy. Overcharging the battery causes the battery to be susceptible to damage. So that the process of charging the battery becomes important, to get maximum attention and good efficiency. In this study, the use of solar cells with battery chargers using the CC-CV (Constant Current-Constant Voltage) Fuzzy Control method uses a solar cell to convert sunlight into electrical energy. The specifications of the solar cell used are 100 WP, while the charging process uses a DC-DC Sepic Converter. DC-DC Sepic Converter can increase efficiency and output polarity that is not reversed. This system is used to charge the lead-acid battery of 12 Volt 20 Ah. The charging method used is constant current-constant voltage (CC-CV) using Fuzzy Logic Control to adjust the duty cycle so that the converter output is by the constant current - constant voltage (CC-CV) planning. The constant current - constant voltage (CC-CV) method was chosen because it can provide good efficiency in charging time and the addition of the Constant Voltage method after Constant Current is enabled to keep the voltage at the setpoint and avoid overvoltage during the charging process. Sepic Converter is used to maintain the value of the voltage set point at 14.4 Volts and 6 Ampere for battery charging current.
      PubDate: Tue, 21 Jun 2022 00:00:00 +000
       
 
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