Prediksi Klasifikasi jumlah Pembaca Sebuah Artikel pada Jurnal Biram Samtani Dengan Metode Bayesian Classification

Richasanty Septima S(1*), Ira Zulfa(2), Husna Gemasih(3), Mahmuda Saputra(4), Al Israk(5),

(1) Universitas Gajah Putih, Indonesia
(2) Universitas Gajah Putih, Indonesia
(3) Universitas Gajah Putih, Indonesia
(4) Universitas Gajah Putih, Indonesia
(5) Universitas Gajah Putih, Indonesia
(*) Corresponding Author

Abstract


One of the open access Open Journal System (OJS)-based reading media is the Biramsamtani Journal, which is the official publication media of scientific articles published by the journal manager, namely the Institute for Research and Community Service of Gajah Putih University, Central Aceh, Indonesia. Until now, the number of scientific articles that have been published is 76 articles with a total of 26,938 viewers read in 10 editions, 7 volumes, and for 5 years of publication. In the Biram Samtani Journal, an uneven and erratic number of readers was found. Where the causative factors that cannot be determined are due to too many abstracts, the position of the article in the published edition or the category of the article that needs to be adjusted to the month of publication. This problem will be studied using data mining by analyzing data using Bayesian Classification. From the test results obtained to predict the number of readers, the best obtained is model 2 with the composition of training data and 80:20 test data with an accuracy of 79.92%, accuracy of 80.15% and recall of 72.36% while for the classification of the number of readers in Biramsamtani journal articles based on the probability value of 47.7% (Low), 30.76% (High), and 21.53% (Medium).

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References


Faisal, M. R., & Nugrahadi, D. T. (2019). Belajar DATA SCIENCE Klasifikasi dengan Bahasa Pemrograman R. Banjarbaru: Scripta Cendekia.

Febriyanti dan Februariyanti (2023) “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter” Jurnal TEKNO KOMPAK, Vol. 17, No. 1, Hal: 25-38

Firmansyah dan Yulianto (2023). “Prediksi Hasil Belajar Menggunakan Naïve Bayes Classifier pada Tingkat Sekolah Dasar”. Remik: Riset dan E- urnal Manajemen Informatika Komputer Volume 7, Nomor 2, April 2023, Hal: 1174-1182

Liliana dkk, 2021. “Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes”, Jurnal Multinetics Vol. 7 No. 1 Mei 2021

Marisa, Fitri., dkk (2021). Data Mining Konsep dan Penerapnnya Deepublish, Yogyakarta.

Macfud, dkk (2023) “Analisis Algoritma Naive Bayes Classifier (NBC) Pada Klasifikasi Tingkat Minat Barang Di Toko Violet Cell”. Jurnal Mahasiswa Teknik Informatika Vol. 7 No. 1. Hal: 87-94

Nurhidayati, dkk (2023) “Implementasi Algoritma Naive Bayes Untuk Klasifikasi Penerima Beasiswa (Studi Kasus Universitas Hamzanwadi)”. Jurnal Informatika dan Teknologi Vol. 6 No. 1, Hal: 177-188

Pebdika, dkk (2023) “Klasifikasi Menggunakan Metode Naive Bayes Untuk Menentukan Calon Penerima PIP”. Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 1, Hal:452-258

Raza, Rohit., dkk, (2022). Data mining and machine learning applications. WIley & Scrivener Publishing, New Jersey U.S

Roza, dkk., (2020). Tutorial Sistem Informasi Prediksi Jumlah Pelanggan Menggunakan Metode Regresi Linier Berganda Berbasis Web Menggunakan Framework Codeigniter. Kreatif, Indonesia

Sutoyo, E., & Almaarif, A. (2020). Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm”. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 95 - 101

Wardhono, Adhitya dkk. Analisis Data Time Series Dalam Model Makroekonomi. Jawa Timur: CV. Pustaka Abadi. 2019.

Admin. Daftar Jurnal Indonesia Terindeks DOAJ. [online] (penerbitdeepublish.com). diakses 6 Mei 2023




DOI: https://doi.org/10.30645/kesatria.v5i4.478

DOI (PDF): https://doi.org/10.30645/kesatria.v5i4.478.g473

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