Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung  using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods

Inayah, Fatri Nurul; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant

doi:10.21108/ijoict.v7i1.562

Cited by 6 publications

(4 citation statements)

References 3 publications

(4 reference statements)

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“…f. Model Hybrid Learning At this stage doing learning using the Hybrid Learning Method, Hybrid is a combination of 2 or more in a function [18]. Hybrid learning works by combining more than 1 model, in this study combining MLP, KNN, NB to be trained using the Vote Classifier.…”

Section: Count Vectorizermentioning

confidence: 99%

Hate Speech Hashtag Classification on Twitter Using the Hybrid Classifier Method

Syaifullah

Sibaroni

2022

Jur. Ris. Kom.

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Hate speech on social media, especially Twitter, often takes the form of racism, sexism, or political interests aimed at certain individuals or groups. These actions can trigger crime, riots, violence and even resistance to individuals or groups. Therefore, we need a process of classifying a tweet whether it is hate speech or not to reduce the abuse that occurs on Twitter. The technology used in the classification of hate speech that is most commonly used is neural networks that require user data and meta data. In previous studies, the Naïve Bayes (NB) method has been used using the bigram, unigram and feature selection features with an accuracy of 80-85%. The k-Nearest Neighbor (kNN) method has also been used which has an accuracy of 70-85% on the clarification of hate speech by political figures. Meanwhile, the most widely used method is the Support Vector Machine (SVM) method with an accuracy of 70 to the highest 95%. To get a higher accuracy in the classification of hate speech, this study will perform a Hybrid Classifier on the Hate Speech Hashtag Classification process using a combination method of MLP, kNN, NB. The data used in this study are Twitter Tweets from November 2021 to June 2022 regarding trending hashtags. The average accuracy performance results obtained using MLP, kNN, NB were 72%, 63%,73% respectively. To improve the accuracy of the classification results of the three methods, a combination of methods using the Hybrid Classifier is carried out. Experimental results show Hybrid Classifier with voting method can increase accuracy up to 74%. It was found that the use of a hybrid can provide a better system performance than the 3 classifiers in its composition, namely kNN, NB and MLP

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Section: Count Vectorizermentioning

confidence: 99%

Hate Speech Hashtag Classification on Twitter Using the Hybrid Classifier Method

Syaifullah

Sibaroni

2022

Jur. Ris. Kom.

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“…f. Model Hybrid ANN Learning Hybrid is a combination of two or more systems in one function [18]. Hybrid Model classification is a method that works by combining more than 1 model in this case using voting method and combines Multi-Layer Perceptron, Support Vector Machines and Naive Bayes Classifier to create prediction.…”

Section: E K-fold Cross Validationmentioning

confidence: 99%

Hate Speech Hashtag Classification Using Hybrid Artificial Neural Network (ANN) Method

Aryasatya

Sibaroni

2022

Jur. Ris. Kom.

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Social networking sites Twitter is frequently used as a platform for information gathering various communities/forums as well as individuals to discuss certain things. Dissemination of information on Twitter can be in the form of positive information and negative information. One of the negative information is hate speech contained in the form of hashtags on twitter. Hate Speech Hashtag Classification was be carried out using the Hybrid Artificial Neural Network (ANN) method to produce satisfactory results compared to previous methods such as KNN and so on because the large amount of data in Twitter will be very profitable and produce good accuracy when using Hybrid Learning, Hybrid Learning with 5 Cross Validation the highest accuracy is 79% , the lowest is 73%, the average accuracy is 76%.

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“…There are several studies that discuss the prediction and classification of the spread of DHF conducted by previous researchers. Research (Inayah, Prasetyowati, & Sibaroni, 2020), used the Hybrid Classification method, namely Naïve Bayes, K-Nearest Neighbor and Artificial Neural Networks to produce an accuracy value of up to 90%. This shows that the hybrid method produces higher accuracy in predicting the spread of the disease.…”

Section: Introductionmentioning

confidence: 99%

Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate

Suhendar,

Rohmawati,

Prasetyowati

2024

SinkrOn

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This study proposes utilizing the machine learning technique CART to classify the spread of dengue hemorrhagic fever (DHF). To expand the features used, the CART classification model was developed based on data collected over the previous 2 to 4 years. The data sources included the Bandung City Health Office for the cases of DHF, the Bandung Meteorology, Climatology and Geophysics Agency for the climate data, the Bandung City Central Statistics Agency for population and educational history data. The top-performing CART classification model over the past 2, 3, and 4 years achieved accuracies of 93%, 93%, and 90%, respectively. The models that exhibited the highest accuracy values and optimal number of feature extensions were chosen as the best ones. CART is among several machine learning techniques that can effectively measure the most impactful features during the classification process. The meteorological parameters were found to be irrelevant in the classification process. This study reveals that the population size, male population proportion, and educational attainment levels are the most impactful features in the classification of DHF spread in Bandung City. The research provides valuable insights into the classification of DHF spread in Bandung City through feature expansion.

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Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods

Cited by 6 publications

References 3 publications

Hate Speech Hashtag Classification on Twitter Using the Hybrid Classifier Method

Hate Speech Hashtag Classification on Twitter Using the Hybrid Classifier Method

Hate Speech Hashtag Classification Using Hybrid Artificial Neural Network (ANN) Method

Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate

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