Mining the crime data using naïve Bayes model

Padirayon, Lourdes M.; Atayan, Melvin S.; Panelo, Jose Sherief; Fagela, Carlito R.

doi:10.11591/ijeecs.v23.i2.pp1084-1092

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The NB Classifier is a classification approach that utilizes the Bayes theorem [23]- [26], and it has gained popularity due to its straightforward and uncomplicated nature [11], [27]- [29]. The application where NB algorithms are frequently utilized is sentiment analysis [28].…”

Section: Literature Review 21 Naïve Bayesmentioning

confidence: 99%

Factor analysis influencing Mobile JKN user experience using sentiment analysis

Al Qahar,

Ruldeviyani,

Mukharomah

et al. 2024

IJ-AI

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Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.

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Section: Literature Review 21 Naïve Bayesmentioning

confidence: 99%

Factor analysis influencing Mobile JKN user experience using sentiment analysis

Al Qahar,

Ruldeviyani,

Mukharomah

et al. 2024

IJ-AI

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“…RapidMiner Radoop is an extension of the in-memory functionality of RapidMiner that allows for the provision of sophisticated operators that are implementable for in-Hadoop execution [61]- [66]. It was developed as an extension of the in-memory functionality of RapidMiner for the provision of sophisticated operators that are implementable for in-Hadoop execution [67]- [73].…”

Section: Apache Spark Radoopmentioning

confidence: 99%

An effective classification approach for big data with parallel generalized Hebbian algorithm

et al. 2021

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Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.

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Mining the crime data using naïve Bayes model

Cited by 2 publications

References 16 publications

Factor analysis influencing Mobile JKN user experience using sentiment analysis

Factor analysis influencing Mobile JKN user experience using sentiment analysis

An effective classification approach for big data with parallel generalized Hebbian algorithm

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