Content-based Filtering Model for Recommendation of Indonesian Legal Article Study Case of Klinik Hukumonline

Putri, Wahyuningdiah Trisari Harsanti; Prastio, Muhammad Singgih; Hendrowati, Retno; Sari, Yustiana; Achsan, Harry Tursulistyono Yani

doi:10.1109/iwbis.2019.8935726

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The importance of applying pre-processing data in machine learning-based modelling to obtain the expected performance results [20]. The pre-processing data consisted of datasets availability, tokenization, case-folding, stop word removal, stemming, and vectorization [21] [22]. a. Datasets Availability…”

Section: Selection and Pre-processingmentioning

confidence: 99%

Comparative Analysis of Multinomial Naïve Bayes and Logistic Regression Models for Prediction of SMS Spam

Raharja¹,

Sidiq²,

Fransisca³

2022

mib

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This research was conducted based on a report from the United States Federal Trade Commission regarding fraud through electronic text messages via SMS that fraudsters use to manipulate potential victims. Usually, scammers spread SMS spam as an intermediary for the crime. The development of a supervised learning algorithm is applied to predict SMS spam into three categories, such as SMS spam, SMS fraud, and promotional SMS. The prediction system is dividing into several stages in the development process, including data labelling, data preprocessing, modelling, and model validation. The known accuracy based on modelling using Logistic Regression using a test size of 15% is 99%, using a test size of 20% is 99%, and using a test size of 25% is 98%. The Multinomial Naïve Bayes algorithm's accuracy with a test size of 15%, 20%, 25% is 97%. So, the SMS spam prediction approach uses the logistic regression method, which has the highest accuracy.

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Section: Selection and Pre-processingmentioning

confidence: 99%

Comparative Analysis of Multinomial Naïve Bayes and Logistic Regression Models for Prediction of SMS Spam

Raharja¹,

Sidiq²,

Fransisca³

2022

mib

View full text Add to dashboard Cite

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Multi‐channel Convolutional Neural Network Feature Extraction for Session Based Recommendation

Feng

et al. 2021

Complexity

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A session-based recommendation system is designed to predict the user’s next click behavior based on an ongoing session. Existing session-based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. Although the performance is greatly improved, these procedures ignore the relationships between items that contain rich information. In order to obtain rich items embeddings, we propose a novel Recommendation Model based on Multi-channel Convolutional Neural Network for session-based recommendation, RMMCNN for brevity. Specifically, we capture items' internal features from three dimensions through multi-channel convolutional neural network firstly. Next, we merge the internal features with external features obtained by a GRU unit. Then, both internal features and external features are merged by an attention mechanism together as the input of the transformation function. Finally, the probability distribution is taken as the output after the softmax function. Experiments on various datasets show that our method's precision and recommendation performance are better than those of other state-of-the-art approaches.

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Content-based Filtering Model for Recommendation of Indonesian Legal Article Study Case of Klinik Hukumonline

Cited by 2 publications

References 8 publications

Comparative Analysis of Multinomial Naïve Bayes and Logistic Regression Models for Prediction of SMS Spam

Comparative Analysis of Multinomial Naïve Bayes and Logistic Regression Models for Prediction of SMS Spam

Multi‐channel Convolutional Neural Network Feature Extraction for Session Based Recommendation

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