Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

Kurniadi, Dede; Nuraeni, Fitri; Lestari, Sri

doi:10.37396/jsc.v5i2.215

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The results of this study got a naïve Bayes algorithm of 99.22%. Research by (Kurniadi et al, 2022), researchers conducted research on predicting student data using the naïve Bayes algorithm. From this research, 3 tests were carried out, namely when using the naïve Bayes algorithm, using the forward selection feature, then SMOTE.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset

Rahman,

Fauzi Abdulloh

2023

SinkrOn

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The background is the increasing cybersecurity threats in the form of phishing attacks that can be detrimental to individuals and organizations. The purpose of this research is to compare the performance of four Naive Bayes variants in classifying phishing emails with a method that involves a data pre-processing stage, phishing emails are collected, cleaned, and converted into appropriate numerical features. Next, the GridSearch approach was used to find the best parameters. This research objective is to understand how each Naive Bayes variant works on phishing email datasets. This phishing detection task is based on the following performance evaluation criteria such as accuracy, precision, recall, and F1-score. In this study, Bernoulli got the best accuracy of 97.34% but when the results obtained a hyperparameter, the results showed an increase with the most optimal results and the best performance is Bernoulli 97.38%. The research results are to provide an in-depth insight into the effectiveness of each variant of Naive Bayes in dealing with phishing email datasets and researchers in selecting the most suitable Naive Bayes variant for phishing detection tasks. In addition, the applied GridSearch method can guide how to find the best parameters for Naive Bayes models in other contexts. In summary, this study focuses on analyzing the performance of four variants of Naive Bayes Gaussian, Multinomial, Complement, and Bernoulli with the best algorithms Bernoulli 97.38%.

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Section: Literature Reviewmentioning

confidence: 99%

Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset

Rahman,

Fauzi Abdulloh

2023

SinkrOn

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“…Naive Bayes adalah suatu metode klasifikasi probabilistik yang sederhana untuk menghitung beberapa kemungkinan dengan dijumlahkannya frekuensi dan kombinasi nilai dari suatu dataset [5]. Dengan menerapkan fitur forward selection pada algoritma Naive Bayes yang digunakan untuk mengetahui atribut yang paling mempengaruhi terhadap ketepatan masa studi mahasiswa [6]. Politeknik LP3I Kampus Padang adalah salah satu perguruan tinggi dengan jenjang pendidikan Diploma Tiga (D-III) Manajemen Informatika yang bernaung di bawah bendera LP3I group di Indonesia yang berpusat di Padang Sumatera Barat [7] [8].…”

Section: Pendahuluanunclassified

“…Kualitas pengelolaan pendidikan suatu perguruan tinggi dan program studi, dapat dilihat dari peringkat akreditasinya. Pada proses akreditasi, salah satu aspek penting yang dinilai adalah mahasiswa, mulai dari bagaimana proses mahasiswa masuk ke perguruan tinggi, memilih program studi sampai pada ketepatan masa studi mahasiswa dari program studi tersebut [6]. Mahasiswa adalah salah satu pilar penting dalam siklus hidup suatu perguruan tinggi [11].…”

Section: Pendahuluanunclassified

Implementasi Algoritma Naive Bayes Classifier Menggunakan Feature Forward Selection Dalam Memprediksi Ketepatan Masa Studi Mahasiswa

Noliza Safitri

2023

JTIF

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Students are one of the important pillars in the life cycle of a higher education institution. One indicator of the success of a study program can be seen from the accuracy of the student's study period. The accuracy of a student's study period refers to the time schedule that the student must achieve from entering the study program to graduating, according to the time span determined by the university. At the Diploma Three (D-III) level, it is said to graduate on time if you have completed the equivalent of three academic years of study and not graduate on time if you have completed more than three academic years. Study. This research aims to influence the quality of study programs, so that it is used as an assessment criterion for accreditation by (BAN-PT). The results of this research are presented in accordance with the research conducted. The data used in this research is academic data for students of the Informatics Management Study Program (D-III) Class 2020 – 2022. The categories used are gender, major, IPS 1 to IPS 4, credits, and GPA. The test results using 129 training data and 40 test data obtained an accuracy of 97.50% using the Naive Bayes method with forward selection. There were 12 students who made late predictions and 28 students made correct predictions. So it can be stated that the Naive Bayes process model is suitable for use in determining good decision results in terms of prediction and classification.

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Rekomendasi Pemilihan Program Studi Menggunakan Algoritma Naïve Bayes

Fikrillah,

Kurniadi

2023

Jurnal Algoritma

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Tujuan dari penelitian ini untuk memberikan rekomendasi pemilihan program studi kepada calon mahasiswa yang akan masuk ke Institut Teknologi Garut (ITG). Hasil dari rekomendasi ini akan memberikan informasi program studi yang sesuai dengan nilai akademik calon mahasiswa tersebut. Untuk mencapai tujuan tersebut penelitian ini menggunakan Algoritma Naïve Bayes untuk memprediksi peluang masa depan berdasarkan data pada masa lalu, selanjutnya untuk mendapatkan hasil rekomendasi yaitu dengan cara mencari nilai probabilitas terbesar pada setiap atribut. Tahapan algoritma yang dilakukan meliputi pengumpulan data, pemprosesan data, pemodelan, dan evaluasi. Data yang digunakan untuk kebutuhan analisis menggunakan data yang sesuai dengan Nilai Akhir Sekolah mahasiswa Sekolah Tinggi Teknologi Garut semasa sekolah dari angkatan tahun 2014 sampai dengan tahun 2019 dengan jumlah data sebanyak 30 pada masing masing program studi dengan total keseluruhan data sebanyak 90. Dari empat kali pemodelan data dan pengujian algoritma menghasilkan akurasi perhitungan algoritma Naïve Bayes terbaik dengan akurasi sebesar 73,4%.

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Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

Cited by 3 publications

References 10 publications

Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset

Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset

Implementasi Algoritma Naive Bayes Classifier Menggunakan Feature Forward Selection Dalam Memprediksi Ketepatan Masa Studi Mahasiswa

Rekomendasi Pemilihan Program Studi Menggunakan Algoritma Naïve Bayes

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