Preprocessing Using Correlation Based Features Selection on Naive Bayes Classification

Kurniawan, Yogiek Indra; Cahyono, Teguh; Nofiyati,; Maryanto, Eddy; Fadli, Ari; Indraswari, Naisha Rahma

doi:10.1088/1757-899x/982/1/012012

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…Algoritma ini bekerja dengan mengasumsikan bahwa semua atribut bersifat independen atau tidak saling tergantung berdasarkan nilai-nilai yang diberikan dalam variabel kelas (Yudana, Suyanto, & Nasiri, 2023). Umumnya Naïve Bayes menggunakan Teorema Bayes dengan rumus ditunjukkan pada Persamaan 2 (Kurniawan, Cahyono, Nofiyati, Maryanto, Fadli, & Indraswari, 2020).…”

Section: Model Prediksiunclassified

Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa

Gori,

Sunyoto,

Al Fatta

2024

JTIIK

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Pendidikan merupakan aspek penting dalam kehidupan masyarakat dan memiliki peran yang sangat vital untuk menciptakan sumber daya manusia yang handal dan berkualitas dalam menghadapi berbagai tantangan pada era modernisasi. Namun, putus sekolah dan retensi siswa menjadi tantangan serius bagi perkembangan pendidikan saat ini. Salah satu faktor pemicu putus sekolah adalah kinerja akademik siswa yang rendah, mendorong perlunya tindakan pencegahan yang efektif untuk mengurangi tingkat kegagalan pendidikan. Penelitian ini bertujuan untuk memprediksi kinerja akademik siswa dengan mengintegrasikan metode Correlation-Based Feature Selection (CFS) dan Algoritma Naïve Nayes pada gabungan dataset pelajaran Matematika dan Bahasa Portugis dua sekolah menengah di Portugal. Proses preprocessing data melibatkan integrasi data, pelabelan data, transformasi data, dan pembersihan data diterapkan pada tahap awal penelitian. Hasil penelitian menunjukkan bahwa atribut signifikan yang mempengaruhi kinerja akademik siswa meliputi G2, G1, Higher, Medu, Studytime, goout, Absences, dan Failures. Melalui pemodelan algoritma Naïve Bayes, metode CFS terbukti meningkatkan nilai accuracy, recall, precision, dan f1-score dalam memprediksi kinerja akademik siswa. Sebelum CFS, model Naïve Bayes menunjukkan accuracy sebesar 89.27%, dengan recall, precision, danf1-score masing-masing sebesar 89.27%, 89.86%, dan 89.47%. Setelah implementasi CFS, evaluasi model prediksi mengalami peningkatan signifikan menjadi 91.22%, 91.22%, 92.24%, dan 91.48%.

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Section: Model Prediksiunclassified

Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa

Gori,

Sunyoto,

Al Fatta

2024

JTIIK

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“…8, KNN, DT, NB, and SVM are used to construct low-delay prediction models, and the classification effects of these algorithms are compared. Among them, the NB classifier uses three different models [23] , and Fig. 8c shows that the NB classification with a Gaussian distribution as the prior has the best effect.…”

Section: Latency Modelmentioning

confidence: 99%

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

Wang

Cai

et al. 2023

Tsinghua Sci. Technol.

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A trusted execution environment (TEE) is a system-on-chip and CPU system with a wide security solution available on today's Arm application (APP) processors, which dominate the smartphone market. Generally, mobile APPs create a trusted application (TA) in the TEE to process sensitive information, such as payment or message encryption, which is transparent to the APPs running in the rich execution environments (REEs). In detail, the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs. In this paper, we first present a comprehensive analysis of the performance of open-source TEE encrypted text. We then propose a high energy-efficient task scheduling strategy (ETS-TEE). By leveraging the deep learning algorithm, our policy considers the complexity of TA tasks, which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server. The results show that compared with the default scheduling strategy on the local device, our approach achieves an average of 38.0% energy reduction and 1.6×speedup. This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications, so that the trusted execution environment has both security and high performance.

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“…In this stage, several processes are carried out to process the data before classification is performed using the Naïve Bayes algorithm. This stage is implemented because preprocessing can improve the accuracy of Naive Bayes [24]. The process includes:…”

Section: Preprocessingmentioning

confidence: 99%

“…Naïve Bayes is a simple probability classification based on Bayes' Theorem where each feature/variable is assumed to be independent of each other. Bayes' theorem was put forward by a British scientist named Thomas Bayes as a theorem for predicting future opportunities based on experience [24]. The Bayes theorem equation can be seen in ( 1…”

Section: Naive Bayesmentioning

confidence: 99%

Naive Bayes modification for intrusion detection system classification with zero probability

et al. 2021

Self Cite

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One of the methods used in detecting the intrusion detection system is by implementing Naïve Bayes algorithm. However, Naïve Bayes has a problem when one of the probabilities is 0, it will cause inaccurate prediction, or even no prediction was found. This paper proposed two modifications for Naïve Bayes algorithm. The first modification eliminated the variable that has 0 probability and the second modification changed the multiplication operations to addition operations. This modification is only applied when the Naïve Bayes algorithm does not find any prediction results caused by zero probabilities. The results of this research show that the value of precision, recall, and accuracy in the modification made tends to increase and better than the original Naïve Bayes algorithm. The highest precision, recall, and accuracy are obtained from modification by changing the multiplication operation to the addition. Increasing precision can reach 4%, increasing recall reaches 2% and increasing accuracy reaches 2%.

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Preprocessing Using Correlation Based Features Selection on Naive Bayes Classification

Cited by 10 publications

References 10 publications

Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa

Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

Naive Bayes modification for intrusion detection system classification with zero probability

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