Penerapan Metode Naïve Bayes Dalam Memprediksi Prestasi Siswa Di SMA Negeri 1 Panombeian Panei

Marpaung, Sarina; Solikhun,; Irawan, Irawan

doi:10.34012/jurnalsisteminformasidanilmukomputer.v4i2.1522

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Elemen ini saling terkait dalam menghasilkan lulusan SMK berpretasi. Prestasi adalah sesuatu yang didapat melalui kegiatan belajar di sekolah yang sifatnya kognitif dan mampu diukur menggunakan rentang nilai [4]. Tidak meratanya sebaran prestasi atau keberhasilan siswa dalam belajar menjadi masalah dalam upaya perwujudan tujuan dan misi SMK Negeri 5 Pekanbaru dalam menghasilkan lulusan siap terjun ke dunia kerja.…”

Section: Pendahuluanunclassified

Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine

Suryani,

Mustakim

2022

bids

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SMK Negeri 5 Pekanbaru aims to prepare competent graduates who can compete in the global market. The realization of these goals is influenced by student achievement at school. Student achievements determine the ability of students to work in certain fields. Based on observations, it is known that student achievement at SMK Negeri 5 Pekanbaru tend to be low. This is also shown by the data that has been collected through the Curriculum section. Based on the data, there can be extraction using the supervised learning method to make a classification model of student achievements. The supervised learning algorithm used in this research is a Support Vector Machine (SVM). The data used in this study are student's data grade X SMK Negeri 5 Pekanbaru in 2020 totaling 160 data. The classification process is carried out by applying the GridSearch method to find the best kernel to be implemented. Based on the implementation of GridSearch, the kernel to be used is Radial Basis Function (RBF) with Cost (C) and Gamma (?) parameters. Based on 16 experiments with different parameter values, the best classification results are obtained using the value of Cost (C) = 0.1 and the value of Gamma (?) = 0.01, with accuracy values of 94%.

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Section: Pendahuluanunclassified

Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine

Suryani,

Mustakim

2022

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“…Learning outcomes can be seen from the ability of students to understand the material, which can determine student performance [3]. In the teaching and learning process, many students are dissatisfied with the lesson, do not pay attention to what has been learned, homework becomes a burden, and learning outcomes are only aimed at advancing to grades or graduation, thus affecting student success in learning [4]. The learning system can also affect learning outcomes, especially in recent years, due to the Covid-19 pandemic, the conventional learning system has changed to a distance learning system, so this is also the cause of the decline in learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining using Naive Bayes for Student Success Rates in Learning

Wijaya

Kumar²,

Wau³

et al. 2022

mib

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Education is a very important part of human life because through education quality human resources will be formed. Quality education can be read and measured by the achievement of various indicators. However, achieving these indicators is not easy, because learning success is influenced by several factors. One of the factors that can affect the success of learning is the learning system. To understand the level of student success in learning, a data mining processing technique is needed. The algorithm that will be used in this research is the naive Bayes algorithm. This study uses 601 datasets per year from Academic Year 2019/2020 to Academic Year 2021/2022, the data used are attendance score data, assignment scores, mid-exam scores, semester exam scores, and averages. The test is divided into 3, namely testing for the Academic Year 2019/2020 dataset, testing for the Academic Year 2020/2021 dataset, and testing for Academic Year 2021/2022 using the split validation operator. The test results using the Academic Year 2019/2020 – Academic Year 2020/2021 student score dataset have an accuracy value of 95.01% while the Academic Year 2021/2022 student score dataset has an accuracy value of 97.79%.

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“…2 )Rapid Miner merupakan sebuah tools yang digunakan untuk pengolahan data mining [15]. RapidMiner digunakan untuk melakukan pengujian terhadap data yang telah didapatkan dari hasil penjualan sparepart dari tahun 2019 dan 2020 [16]. …”

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Prediksi Penjualan Sparepart Mobil Terlaris Menggunakan Metode K-Nearest Neighbor

Abdy¹,

Gultom²,

Ramadhany³

et al. 2022

Jur. Ris. Kom.

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PT. GAYA MAKMUR MULIA MEDAN is a company engaged in selling spare parts and spare parts in North Sumatra. Along with the development of technology, the increasing business competition, especially regarding the sale of spare parts. This also affects the competition between brands of goods with the quality they produce. Products offered by various brands, brands influence people to buy these products. Judging from the large number of consumer requests for spare parts products based on sales over the last 2 years, predictions are needed for sales of the best-selling spare parts products, in order to make it easier for companies to provide stock. To find out the sales of best-selling spare part product, data mining techniques and K-Nearest neighbor algorithm are used to produce predictions of the best-selling sales in the Mitsubishhi division with an accuracy of 80,00% and Isuzu accuracy of 66,67% so that acquisition targets are obtained to increase the accuracy value for the classification of best-selling product sales.PT. GAYA MAKMUR MULIA MEDAN is a company engaged in selling spare parts and spare parts in North Sumatra. Along with the development of technology, the increasing business competition, especially regarding the sale of spare parts. This also affects the competition between brands of goods with the quality they produce. Products offered by various brands, brands influence people to buy these products. Judging from the large number of consumer requests for spare parts products based on sales over the last 2 years, predictions are needed for sales of the best-selling spare parts products, in order to make it easier for companies to provide stock. To find out the sales of best-selling spare part product, data mining techniques and K-Nearest neighbor algorithm are used to produce predictions of the best-selling sales in the Mitsubishhi division with an accuracy of 80,00% and Isuzu accuracy of 66,67% so that acquisition targets are obtained to increase the accuracy value for the classification of best-selling product sales.

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Penerapan Metode Naïve Bayes Dalam Memprediksi Prestasi Siswa Di SMA Negeri 1 Panombeian Panei

Cited by 3 publications

References 8 publications

Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine

Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine

Application of Data Mining using Naive Bayes for Student Success Rates in Learning

Prediksi Penjualan Sparepart Mobil Terlaris Menggunakan Metode K-Nearest Neighbor

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