Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction

Amalia, Safira; Deborah, Irene; Yulita, Intan Nurma

doi:10.22441/sinergi.2022.2.010

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Furthermore, K-fold cross validation is utilized to reduce bias in the data [22]. The technique includes only one parameter called k, which specifies the number of groups into which a given data sample should be divided [23]. When a precise value for k is specified, it can be substituted for k in the model reference.…”

Section: Evaluation and Validationmentioning

confidence: 99%

Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm

Irwanto

Goeirmanto

2023

Sinergi

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The pandemic that hit the world has greatly impacted our life. But after some time, it seems that it will be going to end because the vaccine has already been made. In response to this, some people expressed their opinions about this vaccination on social media, for example, in the form of tweets on Twitter. The authors use those opinions or tweets as sentiment analysis material to determine the assessment of this vaccination. The tweet data in this study was obtained through data crawling using the Twitter API with the Python programming language. The variables used in this case are public tweets and their sentiments. This sentiment analysis process uses the Classification method with the Naive Bayes Classifier and will be compared with the XGBoost Classifier algorithm. The results of this study indicate that people are more likely to respond positively to this vaccination. In this case, the Naive Bayes Classifier got better performance with 0.95 from ROC - AUC Score and 134 ms in runtime compared to the XGBoost Classifier algorithm with 0.882 in ROC - AUC Score and 1 minute and 59 seconds in runtime.

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Section: Evaluation and Validationmentioning

confidence: 99%

Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm

Irwanto

Goeirmanto

2023

Sinergi

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“…Random Forest in Figure 1 is a machine learning algorithm with an ensemble method that can be used for classification and regression [9][10][11][12]. A Random Forest consists of a collection of decision trees associated with a bootstrap sample from a dataset [4]. This method creates a decision tree consisting of internal, root, and leaf nodes by randomly taking some attributes and data according to the provisions in force.…”

Section: Random Forestmentioning

confidence: 99%

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Akbar,

Hariyadi,

Hanani

2023

IOTA

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Bruteforce is a hacking technique that launches an attack by guessing the username and password of the system that is the target of the attack. The Bruteforce attack on the MQTT protocol is an attack that often occurs on the IoT, so it is necessary to detect attacks on the MQTT protocol to find out normal traffic and brute force traffic. Random Forest was chosen because this method can classify a lot of data in a relatively short time, and the results from Random Forest can improve accuracy and prevent overfitting in the data classification process. This study uses two types of data: primary data from the hacking environment lab and secondary data from the IEEE Data Port MQTT-IOT-IDS2020 dataset. Trials on primary data and the results obtained are accuracy of 99.55%, precision of 100%, recall of 99.54%, and f-measure of 99.77%, the duration needed to get these results with 1796 data lines, i.e., for 0 seconds. As for the secondary data, the researcher obtained an accuracy of 99.77%, a precision of 100%, a recall of 99.43%, and an f-measure of 98.71%, the duration required to obtain these results with 85002 data lines, i.e., for 62 seconds.

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“…Random operation in random forests significantly improves classification performance. Given that the process of each Decision Tree is very fast, parallelization in creating a random forest can be made, which improves classification speed [39] [40]. Hoang and Nguyen [41] detected botnets using a machine learning technique, i.e.…”

Section: Random Forestmentioning

confidence: 99%

Effective and efficient approach in IoT Botnet detection

Susanto,

Stiawan,

Arifin

et al. 2023

Sinergi

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Internet of Things (IoT) technology presents an advantage to daily life, but this advantage is not a guarantee of security. This is because cyber-attacks, such as botnets, remain a threat to the user. Detection systems are one of the alternatives to maintain the security of IoT network. A reliable detection system should effectively detect botnets with high accuracy levels and low positive rate. It should be efficient to perform detection quickly. However, data generated by IoT networks have high dimensions and high scalability, so they need to be minimized. In network security analysis process, high-dimension data pose challenges, such as the dimension curse correlation between different dimensions, which causes features that are hard to define, datasets that are mostly unordered, cluster combination, and exponential growth. In this study, we applied feature reduction using the Linear Discriminant Analysis (LDA) method to minimize features on the IoT network to detect botnet. The reduction process is carried out on the N-BaIoT dataset which has 115 features reduced to 2 features. Performing feature reduction with detection systems has become more effective and efficient. Experimental result showed that the application of LDA combined with machine learning on the classification Decision Tree method was able to detect with accuracy that reached 100% in 98.58s with only two features.

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Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction

Cited by 4 publications

References 21 publications

Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm

Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Effective and efficient approach in IoT Botnet detection

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