Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor
No full textAsthma disease is a major global health issue that affects at least 300 million people worldwide. Even for clinicians working in emergency rooms, predicting the severity of asthma is difficult. Predicting the intensity of an asthma attack is much more challenging because it is dependent on several factors, including the person's illness's features and severity. Forward Chaining and Certainty Factor algorithms can be implemented to diagnose the degree of asthma control, so the consultation process through the system becomes more detailed. The expert system can be used as an initial reference for the diagnosis process. The forward Chaining algorithm is useful for reasoning, starting from a fact to a solution. On the other hand, the Certainty Factor algorithm is used to provide a level of confidence in the conclusions by generating from the Forward Chaining algorithm. The research implemented several phases as follows analysis, data preparation, modeling, and evaluation. On evaluation, this research conduct three stages and tested using 80 medical record data. The result of the study has produced an expert system and generated an accuracy level of 65%, a precision value of 58.3%, and a recall also produced 57.13%. Therefore, the Chaining and Certainty Factor performs reasonably well in the diagnosis of asthma disease.Asthma disease is a major global health issue that affects at least 300 million people worldwide. Even for clinicians working in emergency rooms, predicting the severity of asthma is difficult. Predicting the intensity of an asthma attack is much more challenging because it is dependent on a number of factors, including the person's illness's features and severity. Forward Chaining and Certainty Factor algorithms can be implemented to diagnose the degree of asthma control, so the consultation process through the system becomes more detailed. The expert system can be used as an initial reference for the diagnosis process. Forward Chaining algorithm is useful for reasoning, starting from a fact to a solution. On the other hand, Certainty Factor algorithm is used to provide a level of confidence from the conclusions by generating from Forward Chaining algorithm. The research implemented several phase as follow analysis, data preparation, modeling, and evaluation. On evaluation, this research conduct three stages and tested using 80 medical record data. The result of the study has produced an expert system and generated an accuracy level of 65%, the precision value of 58.3%, and recall also produced of 57.13%. Therefore, Chaining and Certainty Factor performs reasonably well in the diagnosis of asthma disease
Time Series Forecasting of Significant Wave Height using GRU, CNN-GRU, and LSTM
Get PDFPredicting wave height is essential to reduce significant risks for shipping or activities carried out at sea. Waves inherit a stochastic nature, mainly generated by wind and propagated through the ocean, making them challenging to forecast. In this paper, we design time series wave forecasting using a deep learning model, which is a hybrid Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU) or CNN-GRU. We use two time series of wave data sets, i.e., reanalysis data from ERA5 by ECMWF and GFS from NOAA. As a study area, we choose Pelabuhan Ratu, located in the south of West Java which is connected to the open Indian Ocean. Moreover, we also compare the results by using other deep learning models, i.e., the Long Short-Term Memory (LSTM) and GRU. We evaluated these models to forecast 7, 14, and 30 days. Models' performance is assessed using RMSE, MAPE, and Correlation Coefficient (CC). For predicting 30 days, using the ERA5 data, the CNN-GRU model produces relatively accurate results with an RMSE value of 1.8844 and CC of 0.9938, whereas for the GFS data, results in RMSE value of 1.8852 and CC of 0.9915.Predicting wave height is essential to reduce significant risks for shipping or activities carried out at sea. Waves inherit a stochastic nature, mainly generated by wind and propagated through the ocean, making them challenging to forecast. In this paper, we design time series wave forecasting using a deep learning model, which is a hybrid Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU) or CNN-GRU. We use two time series of wave data sets, i.e., reanalysis data from ERA5 by ECMWF and GFS from NOAA. As a study area, we choose Pelabuhan Ratu, located in the south of West Java which is connected to the open Indian Ocean. Moreover, we also compare the results by using other deep learning models, i.e., the Long Short-Term Memory (LSTM) and GRU. We evaluated these models to forecast 7, 14, and 30 days. Models' performance is assessed using RMSE, MAPE, and Correlation Coefficient (CC). For predicting 30 days, using the ERA5 data, the CNN-GRU model produces relatively accurate results with an RMSE value of 1.8844 and CC of 0.9938, whereas for the GFS data, results in RMSE value of 1.8852 and CC of 0.9915
Network Security Analysis Simulation at the GCS in the UCAV to support the Indonesian Defense Area
No full textAn unmanned Combat Aerial Vehicle (UCAV) is an unmanned aircraft that has a serial control communication device that can be seen directly in real-time. In carrying out UCAV flights, it requires good and stable data transmission security so that signal loss does not occur during the communication process. Researchers create a concept of a security scheme in communication at the Ground Control Station (GCS) that can be used for the use of UCAV communication at long distances, using the Quality of Service (QoS) method from OPEN VPN with parameters Throughput, Packet Loss, Delay (Latency) and Jitter can determine the reliability of a UCAV communication network. Based on the results of Quality of Service (QoS) testing with OpenVPN Autopilot UCAV objects on the ICMP protocol have the smallest packet loss value of 0%, the delay parameter is 5.2ms, the jitter parameter gets a high value of 4.68 ms higher than the TCP protocol and UDP. The TCP protocol has a relatively small packet loss value of 0.3% and ranks second to the ICMP protocol, then the delay value is 8.48 ms greater than the ICMP and UDP protocols, and the jitter parameter value is 0.0013 ms smaller than the ICMP and UDP protocols. The use of VPN OVPN is a good recommendation. Still, researchers suggest that should use not only OPEN VPN but also L2TP VPN and PPTP VPN for security at the Ground Control Station at UCAV as a comparison.
An unmanned Combat Aerial Vehicle (UCAV) is an unmanned aircraft that has a serial control communication device that can be seen directly in real-time. In carrying out UCAV flights, it requires good and stable data transmission security so that signal loss does not occur during the communication process. Researchers create a concept of a security scheme in communication at the Ground Control Station (GCS) that can be used for the use of UCAV communication at long distances, using the Quality of Service (QoS) method from OPEN VPN with parameters Throughput, Packet Loss, Delay (Latency) and Jitter can determine the reliability of a UCAV communication network. Based on the results of Quality of Service (QoS) testing with OpenVPN Autopilot UCAV objects on the ICMP protocol have the smallest packet loss value of 0%, the delay parameter is 5.2ms, the jitter parameter gets a high value of 4.68 ms higher than the TCP protocol and UDP. The TCP protocol has a relatively small packet loss value of 0.3% and ranks second to the ICMP protocol, then the delay value is 8.48 ms greater than the ICMP and UDP protocols, and the jitter parameter value is 0.0013 ms smaller than the ICMP and UDP protocols. The use of VPN OVPN is a good recommendation. Still, researchers suggest that should use not only OPEN VPN but also L2TP VPN and PPTP VPN for security at the Ground Control Station at UCAV as a comparison
Implementation of Open-Source ERP-Based Fleet Management System on SMEs Transportation Service Provider
Get PDFEnterprises Resource Planning (ERP) systems can be analogized as the backbone of the information system in a company. Many large-scale companies have adopted ERP systems to increase efficiency in the company's business processes. This research departs from the issue of Small and Medium Enterprises (SMEs) that are required to use ERP to compete in the global market but cannot implement an ERP system at a high cost. However, SMEs have financial limitations in adopting high-cost ERP systems. On the other hand, many open-source ERP systems can currently be used for free but with a limited number of modules. This study focuses on implementing an open-source ERP system using Odoo software version 15 on SMEs Transportation Services Providers. The goal is to develop an open-source ERP-based fleet management system for SMEs. The system developed successfully met the company's managerial expectations. All processes previously carried out manually have been carried out using the developed system. All cross-sectional data is stored in the company's master data and can be integrated to support the decision-making process, and company archives have been well documented. Based on the results of User Acceptance Testing (UAT), 98% of the system has met the needs of SMEs. It can be concluded that the implementation of an open-source ERP-based fleet management system is very helpful in managing the business processes of SMEs Transportation Services Providers more effectively and efficiently.Enterprises Resource Planning (ERP) systems can be analogized as the backbone of the information system in a company. Many large-scale companies have adopted ERP systems to increase efficiency in the company's business processes. This research departs from the issue of Small and Medium Enterprises (SMEs) that are required to use ERP to compete in the global market but cannot implement an ERP system at a high cost. However, SMEs have financial limitations in adopting high-cost ERP systems. On the other hand, many open-source ERP systems can currently be used for free but with a limited number of modules. This study focuses on implementing an open-source ERP system using Odoo software version 15 on SMEs Transportation Services Providers. The goal is to develop an open-source ERP-based fleet management system for SMEs. The system developed successfully met the company's managerial expectations. All processes previously carried out manually have been carried out using the developed system. All cross-sectional data is stored in the company's master data and can be integrated to support the decision-making process, and company archives have been well documented. Based on the results of User Acceptance Testing (UAT), 98% of the system has met the needs of SMEs. It can be concluded that the implementation of an open-source ERP-based fleet management system is very helpful in managing the business processes of SMEs Transportation Services Providers more effectively and efficiently
Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques
Get PDFFinancial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes.
Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes
Learning Management System Acceptance Analysis Using Hedonic Motivation System Adoption Model
Get PDFOnline learning using LMS (Learning Management System) results in demotivation for Lecturers and Students. This study aims to explore the relationship between the contentment of using LMS with the behavioural intentions and user focus while using the LMS. The present study employed the user's perception of using LMS with HMSAM (Hedonic Motivation System Adoption Model) as the theoretical basis. The quantitative research method employed a questionnaire as a data collection method. The collected data were analysed statistically using the PLS-SEM method with SmartPLS 3.2.9 application. The results of the study showed that of the 10 (ten) hypotheses, 9 (nine) were accepted, and 1 (one) was rejected. In particular, the hypothesis indicating excitement affects behavioural intentions using the LMS shows a t-statistic value of 1.887 (t-statistics < t-value) hence being rejected. This study also provides recommendations for LMS development based on usability, curiosity, excitement, and control factors.
Online learning using LMS (Learning Management System) results in demotivation for Lecturers and Students. This study aims to explore the relationship between the contentment of using LMS with the behavioural intentions and user focus while using the LMS. The present study employed the user's perception of using LMS with HMSAM (Hedonic Motivation System Adoption Model) as the theoretical basis. The quantitative research method employed a questionnaire as a data collection method. The collected data were analysed statistically using the PLS-SEM method with SmartPLS 3.2.9 application. The results of the study showed that of the 10 (ten) hypotheses, 9 (nine) were accepted, and 1 (one) was rejected. In particular, the hypothesis indicating excitement affects behavioural intentions using the LMS shows a t-statistic value of 1.887 (t-statistics < t-value) hence being rejected. This study also provides recommendations for LMS development based on usability, curiosity, excitement, and control factors. 
Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3
Get PDFBrain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher applied.
Brain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher applied
Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding
No full textSocial media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.
Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy
Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm
Get PDFCrude oil prices play a significant role in the global economy, therefore accurate prediction of oil prices is very important. Therefore, a forecasting model is needed to predict Crude Oil Prices. The purpose of this study is to forecast the price of crude oil from Indonesia (ICP). The data source is from a website published by the Ministry of Energy and Mineral Resources (ESDM), namely monthly crude oil price data specifically for six main types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data used is data for a period of 5 years (2018 – 2022). The data available is in the form of time series data. Dated Brent combined with the Alpha factor for each month and year is a reference in determining the ICP price. Forecasting Indonesian crude oil prices in the future is based on the historical oil price of the previous period. The Data Mining algorithm used for forecasting is Multiple Linear Regression. The dataset processed using training data is 80%, and testing data is 20%. The model produced, on average, has a good level of accuracy in calculating MAPE where for SLC = 9%, Attaka = 45%, Duri = 126%, Belida = 33%, Banyu = 150% and SC = 50%. Based on the MAPE calculation value, the Linear Regression Equation to predict Indonesian Crude Oil Prices (ICP) shows that the model produced by SLC crude oil is very good. Attaka, Belida and SC crude oil yielded fair yields and Duri and Banyu crude oil yielded poor yields.Crude oil prices play a significant role in the global economy, therefore accurate prediction of oil prices is very important. Therefore, a forecasting model is needed to predict Crude Oil Prices. The purpose of this study is to forecast the price of crude oil from Indonesia (ICP). The data source is from a website published by the Ministry of Energy and Mineral Resources (ESDM), namely monthly crude oil price data specifically for six main types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data used is data for a period of 5 years (2018 – 2022). The data available is in the form of time series data. Dated Brent combined with the Alpha factor for each month and year is a reference in determining the ICP price. Forecasting Indonesian crude oil prices in the future is based on the historical oil price of the previous period. The Data Mining algorithm used for forecasting is Multiple Linear Regression. The dataset processed using training data is 80%, and testing data is 20%. The model produced, on average, has a good level of accuracy in calculating MAPE where for SLC = 9%, Attaka = 45%, Duri = 126%, Belida = 33%, Banyu = 150% and SC = 50%. Based on the MAPE calculation value, the Linear Regression Equation to predict Indonesian Crude Oil Prices (ICP) shows that the model produced by SLC crude oil is very good. Attaka, Belida and SC crude oil yielded fair yields and Duri and Banyu crude oil yielded poor yields
Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter
Get PDFPPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel was obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the polynomial kernel model produces higher accuracy; both applied to the One Against One and One Against Rest methods. In contrast, the RBF kernel model produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus.
PPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the model that uses polynomial kernel produces higher accuracy, both applied to the One Against One and One Against Rest methods. In contrast, the model that uses the RBF kernel produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus