Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification

Zain, Fadli Fauzi; Sibaroni, Yuliant

doi:10.23917/khif.v5i2.7770

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…The data in the confusion matrix shows the number of class predictions that correspond to the actual class. F1 score is used to measure classification performance by applying Equation 2 [21], where TP, TN, FP, and FN are the number of true -positives, true-negatives, false-positives, and falsenegatives, respectively [1]. The classification result which is shown in Figure 10 only uses the eccentricity properties of the corn seed with the accuracy is 64%.…”

Section: Resultsmentioning

confidence: 99%

Corn Seeds Identification Based on Shape and Colour Features

Yafie¹,

Rachmawati²,

Prakasa³

et al. 2020

khif

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Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and color features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%.

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Section: Resultsmentioning

confidence: 99%

Corn Seeds Identification Based on Shape and Colour Features

Yafie¹,

Rachmawati²,

Prakasa³

et al. 2020

khif

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“…Naïve bayes belongs to supervised learning algorithms that can be used in making predictions using probability theory with Bayes Theorem and statistical methods [17], [18]. This method uses probability classification with the Bayes Theorem which is based on strong conditionally independent assumptions between all dataset variables, with class y labels with attributes X={X1, X2, X3, Xd} [12], [19].…”

Section: Naïve Bayesmentioning

confidence: 99%

Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

Arumi

Subrata

Rahmawati

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.

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“…Naïve Bayes is an easy algorithm to implement for classification because it has low complexity, which means that the training process, Naïve Bayes doesn't need too much data train [9]. Support Vector Machine or SVM is a method using hyperplanes that group data with maximum margins [18].…”

Section: Naïve Bayes Support Vector Machine (Nbsvm)mentioning

confidence: 99%

“…The results of this study resulted in a baseline accuracy of 74.44% and when the Hyperparameter Tuning is used to baseline, it gains accuracy to 84.22%. The weakness of this research is the small dataset used by the author, which is 287 Twitter user data.Research conducted by Zain et al[9]. regarding the effectiveness of Naïve Bayes SVM weighting in classifying film reviews resulted in an accuracy of 88.8%.…”

mentioning

confidence: 99%

Naïve Bayes-Support Vector Machine Combined BERT to Classified Big Five Personality on Twitter

Martani

Setiawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Twitter is one of the most popular social media used to interact online. Through Twitter, a person's personality can be determined based on that person's thoughts, feelings, and behavior patterns. A person has five main personalities likes Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. This study will make five personality predictions using the Naïve Bayes method – Support Vector Machine, Synthetic Minority Over Sampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder from Transformers Representations (BERT). A questionnaire was distributed to people who used Twitter to collect and become a dataset in this research. The dataset obtained will be processed into SMOTE to balance the data. Linguistic Inquiry Word Count is used as a linguistic feature and BERT will be used as a semantic approach. The Naïve Bayes method is used to perform the weighting and the Support Vector Machine is used to classify Big Five Personalities. To help improve accuracy, the Optuna Hyperparameter Tuning method will be added to the Naïve Bayes Support Vector Machine model. This study has an accuracy of 87.82% from the results of combining SMOTE, BERT, LIWC, and Tuning where the accuracy increases from the baseline.

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Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification

Cited by 6 publications

References 8 publications

Corn Seeds Identification Based on Shape and Colour Features

Corn Seeds Identification Based on Shape and Colour Features

Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

Naïve Bayes-Support Vector Machine Combined BERT to Classified Big Five Personality on Twitter

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