Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features
No full textThe increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. For LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, primary axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall, and accuracy, it is obtained that the precision value is 85%, recall is 82%, and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by single-layer network architecture. This makes the computational process faster and does not burden the computational process.
 
System Dynamic Modeling: A Case Study of a Hotel Food Supply Chain
Get PDFHowever, a closer examination reveals that many firms are battling inventory bottleneck as the covid pandemic spike in demand converges, which results in unpredictability and unstable sale fluctuation. This situation forced the hotel industry to find a balance between fulfilling demand and inventory turnover, which is impossible to predict accurately. Therefore, this research aims to combine system dynamics modelling with a hotel food supply chain system to solve the unpredictability of supply chain dynamics. In addition, The Causal Loop Diagram (CLD) and Stock and Flow Diagram (SFD) are used in this study to model the complexity of the case study supply chain, with an objective model to minimize the instability of inventory turnover and to stabilize the sales movement. This study proposes five policy scenario simulations where standard deviation (SD) and Standard of Error (SE) are used as the decision-making parameters. The simulation result suggests that the fifth scenario provides the highest SD (49.484) and the lowest SE (6.336). Therefore, controlling the customer response time variable to a maximum of 25 minutes and the menu unavailability variable to 15 occurrences per week will result in higher stability of the case study inventory turnover.However, a closer examination reveals that many firms are battling inventory bottleneck as the covid pandemic spike in demand converges, which results in unpredictability and unstable sale fluctuation. This situation forced the hotel industry to find a balance between fulfilling demand and inventory turnover, which is impossible to predict accurately. Therefore, this research aims to combine system dynamics modelling with a hotel food supply chain system to solve the unpredictability of supply chain dynamics. In addition, The Causal Loop Diagram (CLD) and Stock and Flow Diagram (SFD) are used in this study to model the complexity of the case study supply chain, with an objective model to minimize the instability of inventory turnover and to stabilize the sales movement. This study proposes five policy scenario simulations where standard deviation (SD) and Standard of Error (SE) are used as the decision-making parameters. The simulation result suggests that the fifth scenario provides the highest SD (49.484) and the lowest SE (6.336). Therefore, controlling the customer response time variable to a maximum of 25 minutes and the menu unavailability variable to 15 occurrences per week will result in higher stability of the case study inventory turnover
Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet
Get PDFA brain tumor is a lump caused by an imperfect cell turnover cycle in the brain and can affect all ages. Brain tumors have 4 grades, namely grades 1 to 2 are benign tumor grades, and grades 3 to 4 are malignant tumor grades. Therefore, early identification of brain tumor disease is very important in providing appropriate treatment and treatment. This study uses a dataset obtained through the Kaggle website titled Brain Tumor Classification (MRI). The number of data is 3264 images with details of Glioma tumors (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and without tumors (500 images). In this study, there are 4 scenarios with different testers. This study proposes the classification of brain tumors using Hyperparameter Tuning and EfficientNet models on MRI images. The EfficientNet model used is the EfficientNetB0 and EfficientNetB7 models with the architecture used are the input layer, GlobalAveragePooling2D layer, dropout layer, and dense layer as well as adding augmentation data to the dataset to manipulate the data in order to improve the results of the proposed model. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. Accuracy results in Scenario 1 are 91%, scenario 2 is 95% accurate, scenario 3 is 95%, and scenario 4 is 98%.
A brain tumor is a lump caused by an imperfect cell turnover cycle in the brain and can affect all ages. Brain tumors have 4 grades, namely grades 1 to 2 are benign tumor grades, and grades 3 to 4 are malignant tumor grades. Therefore, early identification of brain tumor disease is very important in providing appropriate treatment and treatment. This study uses a dataset obtained through the Kaggle website titled Brain Tumor Classification (MRI). The number of data is 3264 images with details of Glioma tumors (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and without tumors (500 images). In this study, there are 4 scenarios with different testers. This study proposes the classification of brain tumors using Hyperparameter Tuning and EfficientNet models on MRI images. The EfficientNet model used is the EfficientNetB0 and EfficientNetB7 models with the architecture used are the input layer, GlobalAveragePooling2D layer, dropout layer, and dense layer as well as adding augmentation data to the dataset to manipulate the data in order to improve the results of the proposed model. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. Accuracy results in Scenario 1 are 91%, scenario 2 is 95% accurate, scenario 3 is 95%, and scenario 4 is 98%
K Nearest Neighbor Imputation Performance on Missing Value Data Graduate User Satisfaction
Get PDFA missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing value, or replacing missing values with statistical estimates using one calculated value such as, mean, median, min, max, and most frequent methods. Maximum likelihood and expectancy maximization, and machine learning methods such as K Nearest Neighbor (KNN). This research uses KNN Imputation to predict the missing value. The data used is data from a questionnaire survey of graduate user satisfaction levels with seven assessment criteria, namely ethics, expertise in the field of science (main competence), foreign language skills, foreign language skills, use of information technology, communication skills, cooperation, and self-development. The results of testing imputation predictions using KNNI on user satisfaction level data for STMIK PPKIA Tarakanita Rahmawati graduates from 2018 to 2021. Where using the five k closest neighbors, namely 1, 5, 10, 15, and 20, the error value of the k nearest neighbors is 5 in RMSE is 0, 316 while the error value using MAPE is 3,33 %, both values are smaller than the value of k other nearest neighbors. K nearest neighbor 5 is the best imputation prediction result, both calculated by RMSE and MAPE, even in MAPE the error value is below 10%, which means it is very good.
A missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing value, or replacing missing values with statistical estimates using one calculated value such as, mean, median, min, max, and most frequent methods. Maximum likelihood and expectancy maximization, and machine learning methods such as K Nearest Neighbor (KNN). This research uses KNN Imputation to predict the missing value. The data used is data from a questionnaire survey of graduate user satisfaction levels with seven assessment criteria, namely ethics, expertise in the field of science (main competence), foreign language skills, foreign language skills, use of information technology, communication skills, cooperation, and self-development. The results of testing imputation predictions using KNNI on user satisfaction level data for STMIK PPKIA Tarakanita Rahmawati graduates from 2018 to 2021. Where using the five k closest neighbors, namely 1, 5, 10, 15, and 20, the error value of the k nearest neighbors is 5 in RMSE is 0, 316 while the error value using MAPE is 3,33 %, both values are smaller than the value of k other nearest neighbors. K nearest neighbor 5 is the best imputation prediction result, both calculated by RMSE and MAPE, even in MAPE the error value is below 10%, which means it is very good
Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia
Get PDFCOVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic
DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features
Get PDFThis study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time.
This study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time
Strategy to Improve Employee Security Awareness at Information Technology Directorate Bank XYZ
Get PDFBank handles private information like customer financial transactions and personal data. There was a 63% increase in cyberattacks attempted against Bank XYZ in 2021, and 1,323 attempted attacks on corporate email Bank XYZ. Therefore, implementing security awareness training for all employees is crucial for Bank XYZ. The information security awareness program must be assessed to determine the program's efficiency and the level of information security awareness among employees. Therefore, this study assesses the information security awareness at Bank XYZ, especially the Information Technology (IT) Directorate using the Human Aspect of Information Security Questionnaire (HAIS-Q) method. The findings of this study revealed that employees at Bank XYZ in the information security work unit had a "Good" level of awareness. In contrast, the results from other IT work units were “Medium”. Based on the assessment results, Bank XYZ's security awareness strategy recommendation is to align awareness content with information security policies and procedures, use a variety of media awareness, and focus on the "Internet Use" and "Information Handling" awareness areas. As a way of determining the achievement of information security Key Performance Indicators (KPI), security awareness measurement must be done regularly, for example, once a year.
Bank handles private information like customer financial transactions and personal data. There was a 63% increase in cyberattacks attempted against Bank XYZ in 2021, and 1,323 attempted attacks on corporate email Bank XYZ. Therefore, implementing security awareness training for all employees is crucial for Bank XYZ. The information security awareness program must be assessed to determine the program's efficiency and the level of information security awareness among employees. Therefore, this study assesses the information security awareness at Bank XYZ, especially the Information Technology (IT) Directorate using the Human Aspect of Information Security Questionnaire (HAIS-Q) method. The findings of this study revealed that employees at Bank XYZ in the information security work unit had a "Good" level of awareness. In contrast, the results from other IT work units were “Medium”. Based on the assessment results, Bank XYZ's security awareness strategy recommendation is to align awareness content with information security policies and procedures, use a variety of media awareness, and focus on the "Internet Use" and "Information Handling" awareness areas. As a way of determining the achievement of information security Key Performance Indicators (KPI), security awareness measurement must be done regularly, for example, once a year
Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis
No full textIndonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the best accuracy from the naive Bayes variation by setting the minimum and maximum document frequency parameters on the Indonesian General Analysis Dataset for sentiment analysis. The naive Bayes classifier variations used include Bernoulli naive Bayes, gaussian naive Bayes, complement naive Bayes and multinomial naive Bayes. The research stage begins with downloading the dataset. Preprocessing becomes the next stage which consists of tokenizing, stemming, converting abbreviations and eliminating conjunctions. In the preprocessed data, feature extraction is carried out by converting the dataset into vectors and applying the TF-IDF method before entering the sentiment analysis classification stage. Tests in this study were carried out by applying the minimum document frequency (min-df) and maximum document frequency (max-df) for each variation of naive Bayes to obtain the appropriate parameters. The test uses k-fold cross validation of the dataset to divide the training data and sentiment analysis test data. The next confusion matrix is made to evaluate the level of accuracy.
Indonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the best accuracy from the naive Bayes variation by setting the minimum and maximum document frequency parameters on the Indonesian General Analysis Dataset for sentiment analysis. The naive Bayes classifier variations used include Bernoulli naive Bayes, gaussian naive Bayes, complement naive Bayes and multinomial naive Bayes. The research stage begins with downloading the dataset. Preprocessing becomes the next stage which consists of tokenizing, stemming, converting abbreviations and eliminating conjunctions. In the preprocessed data, feature extraction is carried out by converting the dataset into vectors and applying the TF-IDF method before entering the sentiment analysis classification stage. Tests in this study were carried out by applying the minimum document frequency (min-df) and maximum document frequency (max-df) for each variation of naive Bayes to obtain the appropriate parameters. The test uses k-fold cross validation of the dataset to divide the training data and sentiment analysis test data. The next confusion matrix is made to evaluate the level of accuracy
Integration of AHP and TOPSIS Methods for Small and Medium Industries Development Decision Making
No full textFinancial problems are one of the reasons why small and medium-sized industries (SMIs) in West Kutai have not developed optimally. Government assistance programs are one of the solutions. This program must be appropriate, so a decision-making tool is needed to help choose the right SMIs to be assisted later. The weight of the criteria was determined using the Analytical Hierarchy Process (AHP) technique, and the priority of the SMIs as the preferred proposal for the recipients of development assistance was determined using the Technique for Other Reference by Similarly to Ideal Solution (TOPSIS) approach. Labor, investment, production capacity, production value, and raw materials were used to determine the priorities of SMIs beneficiaries. Furthermore, TOPSIS prioritizes the development of alternative small and medium-sized industries with types of handicraft commodities. Integration of AHP and TOPSIS methods has been successfully used in the IKM Development Priority Determination Application, with 83.3% precision and 96.4% accuracy achieved by using a confusion matrix so that the IKM ranking can be known. The results of the study found that integration of the two methods was successfully used for Small and Medium Industries Development Decision Making.Financial problems are one of the reasons why small and medium-sized industries (SMIs) in West Kutai have not developed optimally. Government assistance programs are one of the solutions. This program must be appropriate, so a decision-making tool is needed to help choose the right SMIs to be assisted later. The weight of the criteria was determined using the Analytical Hierarchy Process (AHP) technique, and the priority of the SMIs as the preferred proposal for the recipients of development assistance was determined using the Technique for Other Reference by Similarly to Ideal Solution (TOPSIS) approach. Labor, investment, production capacity, production value, and raw materials were used to determine the priorities of SMIs beneficiaries. Furthermore, TOPSIS prioritizes the development of alternative small and medium-sized industries with types of handicraft commodities. Integration of AHP and TOPSIS methods has been successfully used in the IKM Development Priority Determination Application, with 83.3% precision and 96.4% accuracy achieved by using a confusion matrix so that the IKM ranking can be known. The results of the study found that integration of the two methods was successfully used for Small and Medium Industries Development Decision Making.
 
Aspect-Based Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion
Get PDFSocial media has recently been widely used by users, especially Indonesians, as a place to express themselves in sentences, pictures, sounds, or videos. Twitter is one of the social media favored by people of diverse ages. Twitter is a social media that provides features like social media in general. However, Twitter has a unique feature where users can send or read text messages limited to only a few characters. Therefore, user tweets with topics related to a particular product can be utilized by companies to become input in the development of these products. This research was conducted using tweet data on the topic of Telkomsel, which is divided into two aspects, namely signal and service. Aspect-based sentiment analysis of Telkomsel was carried out using Logistic Regression with FastText feature expansion to reduce vocabulary mismatch in tweets so that the classification stage can be performed optimally. In addition, the Synthetic Minority Oversampling Technique (SMOTE) sampling method was applied to overcome data imbalance. The test results prove that feature expansion can improve F1-Score values for signal and service aspects. For the signal aspect, F1-Score increased by 3.33% from the baseline with a value of 96.48%. While for the service aspect, F1-Score increased by 12.91% from the baseline with a value of 95.57%.Social media has recently been widely used by users, especially Indonesians, as a place to express themselves in sentences, pictures, sounds, or videos. Twitter is one of the social media favored by people of diverse ages. Twitter is a social media that provides features like social media in general. However, Twitter has a unique feature where users can send or read text messages limited to only a few characters. Therefore, user tweets with topics related to a particular product can be utilized by companies to become input in the development of these products. This research was conducted using tweet data on the topic of Telkomsel, which is divided into two aspects, namely signal and service. Aspect-based sentiment analysis of Telkomsel was carried out using Logistic Regression with FastText feature expansion to reduce vocabulary mismatch in tweets so that the classification stage can be performed optimally. In addition, the Synthetic Minority Oversampling Technique (SMOTE) sampling method was applied to overcome data imbalance. The test results prove that feature expansion can improve F1-Score values for signal and service aspects. For the signal aspect, F1-Score increased by 3.33% from the baseline with a value of 96.48%. While for the service aspect, F1-Score increased by 12.91% from the baseline with a value of 95.57%