Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category

Parlina, Iin; Arnol, M. Yusuf; Febriati, Nur Ahlina; Dewi, Rafiqa; Wanto, Anjar; Lubis, Muhammad Ridwan; Susiani, _

doi:10.1088/1742-6596/1255/1/012031

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…After the results described above, the discussion of this study is that by using a dataset on the level of the educational population predicted using the Naive Bayes algorithm, an accuracy value of 98.4848% is obtained. With the acquisition of an a ccuracy value that is classified as very good, in line with several studies that the acquisition of accuracy results using the Naive Bayes algorithm is classified as good [19][20] [21] and Naive Bayes is an algorithm or method that is most effective and efficient for design machine learning and data mining [22].…”

Section: Use Of Naive Bayes Algorithmmentioning

confidence: 54%

Prediction of Education Level in Population Data Using Naïve Bayes Algorithm

Subarkah

Solikhatin

Darmayanti

et al. 2022

TIERS

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Education is the key to improving human resources. The Ministry of National Education is implementing a curriculum that requires students to study it (MoNE) and as part of this program, all Indonesian citizens are required to attend three years of primary education, which includes SD, MI/Equivalent, three years for SMP/Equivalent, and three years for high school/equivalent level. This study aims to determine whether or not a government program that requires Indonesian citizens to study for 12 years is required, therefore it is necessary to test predictions of data on the level of education in Blitar Regency. This study conducted a prediction test by implementing the Naive Bayes algorithm on education-level data in Blitar Regency as of 2020 which was taken from the satudata.go.id website. In the data processing, there are values of precision, recall, f-measure, Weighted Avg, and also Confusion Matrix. The accuracy results of the Naive Bayes Algorithm on education level data in Blitar district show that the district has implemented government policies regarding the 12-year compulsory education program, this is based on the results of data processing which shows an accuracy value of 98.4848% and category good classification.

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Section: Use Of Naive Bayes Algorithmmentioning

confidence: 54%

Prediction of Education Level in Population Data Using Naïve Bayes Algorithm

Subarkah

Solikhatin

Darmayanti

et al. 2022

TIERS

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“…Selain itu, metode untuk memproteksi dalam sistem berbagi melibatkan pengguna diperlukan dan kontrol akses yang mutlak (Degirmenci, 2019). Tahap selanjutnya, pengguna menggunakan hak izin yang bersifat relatif untuk mengakses sumber daya sesuai dengan identitas secara personal, disertai hak yang bersangkutan, dan wewenang pengguna (Parlina, 2019).…”

Section: Pendahuluanunclassified

Prediksi Otorisasi Pengguna Sistem Berkas pada Algoritma Klasifikasi dengan Teknik Naïve Bayes

Pamuji¹

2022

Jurnal Informatika, Manajemen dan Teknologi

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Keamanan teknologi informasi saat ini menjadi tuntutan bagi setiap institusi. Salah satu bagian terpenting dalam keamanan IT adalah ketika melindungi sumber daya yang di-akses oleh beberapa pengguna. Beberapa kelemahan yang ditemukan adanya sistem yang mengizinkan beberapa pengguna untuk berbagi data sehingga berpotensi adanya potensi otorisasi pengguna yang berlebihan. Dalam penelitian ini akan dilakukan investi-gasi terhadap 162 log data aktifitas yang memuat data training dan data testing. Adapun dalam konsep analisisnya adalah menggunakan data mining dengan metode klasifikasi dan teknik Naïve Bayes. Hasil menunjukan metode klasifikasi dengan teknik Naïve Bayes cukup efektif untuk memprediksi adanya anomali otorisasi pengguna file. Selan-jutnya sudah dibuktikan melalui kinerjanya dengan menggunakan confusion matrix yang mencapai 89% dalam nilai akurasinya. Dengan demikian, data mining akan menjadi se-buah konsep dalam pengembangan dan analisis terhadap sistem keamanan terutama sistem berkas yang mengakomodasi manajemen pengguna untuk mengizinkan akses sumber daya.

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Decision Support System in Recommending Climbing Tourism Destinations with Profile Matching Method

Ginantra

Bhawika

Zamsuri

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Climbing is one of the extreme sports categories and recreational activities that are in great demand in the last decade, especially teenagers. Climbing offers a different sensation and challenge from other sports, besides this sport also offers a beautiful view when the climbers have reached the top of the mountain that is intended, of course, also improves fitness and health. The purpose of this study is to provide climbing tourism destination recommendations to groups of organizations and individuals who have a hobby or are the first to want to do this sport. Data collection in this study was carried out by giving questionnaires to 3 groups of nature lovers organizations, each organization was given 10 questionnaires. In this study, there are 5 alternatives used, including Mount Jaya Wijaya, Mount Ijen, Mount Kerinci, Mount Rinjani and Mount Bromo with 6 Criteria, including Beauty, Altitude, Location, Cost and Pathway. Based on calculations using the Profile Matching method, the results are obtained that Mount Rinjani is a recommended climbing tourism destination.

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Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category

Cited by 10 publications

References 4 publications

Prediction of Education Level in Population Data Using Naïve Bayes Algorithm

Prediction of Education Level in Population Data Using Naïve Bayes Algorithm

Prediksi Otorisasi Pengguna Sistem Berkas pada Algoritma Klasifikasi dengan Teknik Naïve Bayes

Decision Support System in Recommending Climbing Tourism Destinations with Profile Matching Method

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