Analisis Sentimen Pembelajaran Daring Menggunakan Metode NaÃ¯ve Bayes, KNN, dan Decision Tree

Putra, Tobby Wiratama; Triayudi, Agung; Andrianingsih, Andrianingsih

doi:10.35870/jtik.v6i1.368

Cited by 6 publications

(7 citation statements)

References 4 publications

(6 reference statements)

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“…The algorithm is an explanation of the steps for solving the problem. The following is the solving algorithm of the K-Nearest Neighbor method [13], which is as follows: The process of extracting information carried out by a user who interacts with a set of documents using Text mining analysis tools is the process of extracting patterns (useful information and knowledge) from a number of data sources through the identification of interesting patterns [14].…”

Section: Ii2 Multinomial Naive Bayesian Clasifier Methodsmentioning

confidence: 99%

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Tarigan¹,

Gunawan²,

Hayadi

2023

ICoSTEC

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Javanese script is one of Indonesia's cultural heritages that are increasingly rarely used today. The difficulty of recognizing the shapes of letters, let alone writing them, is the main obstacle in using the Hanacaraka script. This research offers an alternative to Hanacaraka script recognition using a combination of image feature extraction and machine learning, where we utilize a pre-trained SquzeeNet model and Multilayer Backpropagation algorithm. Of the 18 models built using ReLu, Sigmoid, and Tanh activation functions, we found that the Tanh activation function, using the combination of 50-50-100 neuron configuration and 25 epochs, was the most optimal function used to classify the training data with accuracy, precision, and recall values of 93.8%. Meanwhile, the Tanh activation function, using the 50-100-50 neuron configuration and 50 epochs, is the most optimal function to classify the testing data, with accuracy, precision, and recall values of 89.1%, 89.5%, and 89.5%. All built models show a training and testing performance ratio below 10%. From this result, we conclude that all models have good reliability in the training and testing classification process.

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Section: Ii2 Multinomial Naive Bayesian Clasifier Methodsmentioning

confidence: 99%

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Tarigan¹,

Gunawan²,

Hayadi

2023

ICoSTEC

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“…Then the group of words will be assessed and will produce output classification results which have certain meanings such as positive, negative, and neutral [21]. K-NN is an algorithm used as a classifier that uses training data with the closest distance to the object [35]. The K-NN classification theorem is as follows:…”

Section: Data Splittingmentioning

confidence: 99%

Analyzing Twitter Users’ Sentiments on the Surge of Fuel Oil Prices in Indonesia using the K-Nearest Neighbor Algorithm

Budianti,

Marvyna,

Sukaridhoto

2024

E3S Web of Conf.

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Sentiment analysis offers an effective solution for automating the classification of text data based on polarity, facilitating the assessment of public opinion. Among various social media platforms, Twitter stands out as a significant source of concise textual data reflecting users’ viewpoints on diverse topics. Notably, the recent surge in the price of fuel oil (BBM) in Indonesia has sparked considerable discussion and expression on Twitter. In this study, our objective was to perform a comprehensive sentiment analysis of Twitter users’ reactions to the rising fuel prices in Indonesia by employing the K-Nearest Neighbor (K-NN) algorithm. The research followed a structured approach encompassing data collection, text preprocessing, data labeling, feature extraction, data splitting, classification, and algorithm performance evaluation. The results revealed a dominance of negative sentiments among the 5,000 collected tweet data. The sentiments were categorized as 54.6% negative, 31.8% positive, and 13.6% neutral. This indicates a prevailing level of dissatisfaction and concern expressed by Twitter users regarding the fuel price increase. The K-NN algorithm’s classification performance was most promising when evaluated in an 80:20 data ratio experiment, yielding an accuracy rate of 65%, precision of 74%, recall of 45%, and an error rate of 35%. These findings suggest that the K-NN algorithm is valuable for effectively gauging public sentiment towards the escalating fuel prices in Indonesia. This research highlights the potential of sentiment analysis and the K-NN algorithm in assessing public reactions to significant events, providing valuable insights for policymakers and stakeholders in the energy sector.

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“…Evaluasi Akurasi ialah perhitungan keseluruhan data yang dikelompokkan secara negatif ataupun positif. Perhitungan dapat dilakukan menggunakan rumus berikut [18].…”

Section: Confution Matrixunclassified

Analisis Sentimen Layanan Shopeefood Pada Twitter Dengan Metode K-Nearest Neighbor, Support Vector Machine, dan Decision Tree

Farhan

2023

Jurnal.ilmiah.informatika

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Transportation is an infrastructure that connects one point to another. In an era like this, people's needs are getting wider, not just about delivering people, but more than that, for example delivering goods, groceries, or food and so on. Transportation that is supported by smartphones and the result of a combination of transportation services with communication technology is called online transportation. One of the online transportation that is on the rise is ShopeeFood. ShopeeFood is an online transportation that focuses on services that deliver food to its customers. The purpose of this study was to obtain classification results according to the level of accuracy of the Twitter community's perception of shopeeFood services. This study uses three methods, namely KNN, SVM and Decision Tree. The data used is twitter data obtained by crawling using RapidMiner software. Based on the test results, it was found that the KNN method has a higher value than the other methods with an accuracy of 71.49%, precision of 72.86% and recall of 76.94%.

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Analisis Sentimen Pembelajaran Daring Menggunakan Metode NaÃ¯ve Bayes, KNN, dan Decision Tree

Cited by 6 publications

References 4 publications

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Analyzing Twitter Users’ Sentiments on the Surge of Fuel Oil Prices in Indonesia using the K-Nearest Neighbor Algorithm

Analisis Sentimen Layanan Shopeefood Pada Twitter Dengan Metode K-Nearest Neighbor, Support Vector Machine, dan Decision Tree

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