Aplikasi Prediksi Usia Kelahiran Dengan Metode Naive Bayes

Indraswari, Naisha Rahma; Kurniawan, Yogiek Indra

doi:10.24176/simet.v9i1.1827

Cited by 15 publications

(15 citation statements)

References 2 publications

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“…On the other tests from 1 to 40, it is obtained k=3 with accuracy of 93% [19]. For the value of Naïve Bayes accuracy of 78.69%, it depends on the number of training data [6]. The similar thing is also concluded by [20] that the number of test data and training data can affect the accuracy of Naïve Bayes, in this case the accuracy is 80%.…”

Section: Discussionmentioning

confidence: 65%

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Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students

Tapidingan¹,

Paseru²

2020

Indonesian J. of Inf. Syst.

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Abstrak. Stres pada umumnya didefinisikan sebagai keadaan dimana seseorang mengalami gangguan mental, ini merupakan respon terhadap kesulitan yang dialami. Siswa SMP cenderung tidak menyadari stres yang mereka hadapi. Penelitian ini bertujuan untuk membandingkan dua metode klasifikasi KNN dan Naïve Bayes untuk menentukan tingkat stres. Data penelitian ini dikumpulkan dari 254 responden dari SMP Katolik Don Bosco Bitung. Tes validasi k-cross dan pemisahan persentase dari data menunjukkan bahwa metode Naïve Bayes lebih baik dari metode KNN. Dengan k = 3, akurasi KNN mencapai 86,61% pada tertinggi dan Naïve Bayes mencapai 87,40%. Sementara itu, berdasarkan hasil uji persentase split, rata-rata akurasi Naïve Bayes lebih tinggi dari KNN dengan persentase 88,31%. Selain itu, untuk presisi dan daya ingat, Naïve Bayes lebih tinggi dari KNN dengan 88,30% dan 87,40% dilihat dari validasi k-cross.Abstract. Stress is generally defined as a state where someone is mentally disturbed as the response to the adversity that he/she experiences. Junior High School students usually are not aware of the stress that they encounter. This research aims to compare two classification methods of KNN and Naïve Bayes to determine stress level. The data of this research were gathered from 254 respondents from Catholic Junior High School of Don Bosco Bitung. The tests of k-cross validation and percentage split from the data showed that Naïve Bayes method excelled KNN method. With k=3, KNN accuracy reached 86.61% at the highest and Naïve Bayes reached 87.40%. Meanwhile, based on percentage split test, the average of Naïve Bayes accuracy was higher than KNN with percentage of 88.31%. Moreover, for the precision and recall, Naïve Bayes was higher than KNN with 88.30% and 87.40% seen from the k-cross validation.

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Section: Discussionmentioning

confidence: 65%

“…Besides that, Naïve Bayes process is faster when applied to a lot of data set and it is easier to understand [3]. [6] adds that increasing number of Naïve Bayes data can increase the accuracy of method. These two methods return sufficient accuracy score [7].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students

Tapidingan¹,

Paseru²

2020

Indonesian J. of Inf. Syst.

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“…For application in this case we will using Naive Bayes algorithm and K-Nearest Neighbor first [11]. Next we calculate using the Naive Bayes algorithm to get its accuracy, then we continue using the K-Nearest Neighbor algorithm to get better accuracy than the previous algorithm.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors

Hamonangan¹,

Saputro²,

Atmaja³

2021

joscex

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Mushrooms are plants that are widely consumed by the general public, but not all mushrooms can be consumed directly, because the types of mushrooms are feasible and it is still too difficult to distinguish, then there are several ways to identify fungi, namely by means of morphology. The morphology referred to in this paper is the morphology of fungi which includes color, habitat, class, and others. We got the morphology of this mushroom from a datasets we get from UCI Machine Learning with the 23 atribut that we use in the program. In determining the classification of this fungus we use the Naive Bayes algorithm which produces an accuracy of around 90,2% which we then improve again so that it reaches 100% accuracy using the K-Nearest Neighbors algorithm. Furthermore, in this case to prove accuracy that we had before, we use calculation accuracy with confusion matrix to show it the accuracy of classification poisonous or edible mushroom.

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“…Penggunaan data masa lampau untuk menganalisis pola yang terjadi pada masa depan Merupakan konsep dari aplikasi prediksi maupun aplikasi rekomendasi [3]. Aplikasi untuk memprediksi suatu kejadian telah banyak diimplementasikan pada banyak kasus dengan menggunakan konsep klasifikasi pada data mining, dengan berbagai macam metode maupun algoritma, seperti algoritma Naïve Bayes [4]- [6], ID3 [7], Rotation Forest [8], Neural Network [9], [10], maupun algoritma yang lain [8], [11], [12].…”

Section: Pendahuluanunclassified

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Kurniawan

Fatikasari

Hidayat

et al. 2021

J. Tek. Inform. (JUTIF)

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BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

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Aplikasi Prediksi Usia Kelahiran Dengan Metode Naive Bayes

Cited by 15 publications

References 2 publications

Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students

Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students

Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

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