KLASIFIKASI PEMBAGIAN ARUS LALU LINTAS MENGGUNAKAN ALOGARITMA NAÏVE BAYES DAN MODEL LINEAR

AbstractThe density of traffic flow is a problem for every big city, especially as it is easy to have a private vehicle, causing the flow to increase every year. So to overcome traffic flow, a system that can make optimal traffic performance is needed is needed. The purpose of this study is to determine whether the road conditions are empty, smooth, dense and very congested so as to produce a prediction of road options whether to continue passing the road or find another way, as well as to test the accuracy of traffic flow using the naive bayes method and the liner model. The classification stages carried out are data input, data preprocessing, classification, and the results of accuracy, precision, and recall. And the results of this study the naive bayes method obtained higher accuracy than the linear model, namely for naive bayes accuracy 95.70%, precision 95.67%, and recall 100%, while for naive bayes accuracy 92.10%, precision 95.68%, and recall 96.20%. then the result is the naive bayes method is superior in the traffic flow data classification process. And the results of decision making obtained results from traffic flow data obtained that the road is empty so that the road can be passed without having to find another way. Keywords - Classification, Naive Bayes, Traffic, Linear Model, Flow Density AbstrakKepadatan arus lalu lintas menjadi masalah setiap kota-kota besar, apalagi seiring mudah nya dalam memiliki kendaraan pribadi sehingga menimbulkan arus yang meningkat pada setiap tahunnya. Maka untuk penanggulangan arus lalu lintas dibutuhkan sistem yang bisa membuat kinerja lalu lintas yang optimal. Tujuan penelitian ini adalah mengetahui kondisi jalan apakah lengang, lancar, padat dan sangat padat sehingga menghasilkan prediksi opsi jalan apakah tetap melewati jalan tersebut atau mencari jalan lain, serta menguji tingkat akurasi arus lalu lintas menggunakan metode naive bayes dan model liner. Dengan tahapan klasifikasi yang dilakukan yaitu input data, preprocessing data, klasifikasi, dan hasil accuracy, precision, dan recall. Dan hasil penelitian ini metode naive bayes mendapatkan accuracy lebih tinggi dari model linier yaitu untuk naive bayes accuracy 95.70%, precision 95.67%, dan recall 100%, sedangkan untuk naive bayes accuracy 92.10%, precision 95.68%, dan recall 96.20%. maka hasilnya metode naive bayes lebih unggul dalam proses klasifikasi data arus lalu lintas. Dan hasil dari pengambilan keputusan didapat hasil dari data arus lalu lintas didapatkan jalan tersebut lengang sehingga jalan tersebut dapat dilalui tanpa harus mencari jalan lain. Kata Kunci - Klasifikasi, Naive Bayes, Lalu Lintas, Model Linier, Kepadatan Arus

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Forensic Malware Identification Using Naive Bayes Method

2020 International Conference on Information Technology Systems and Innovation (ICITSI) ◽

10.1109/icitsi50517.2020.9264959 ◽

2020 ◽

Author(s):

Beno Ramadhan ◽

Yudha Purwanto ◽

Muhammad Faris Ruriawan

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Naive Bayes Method

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PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

SINTECH (Science and Information Technology) Journal ◽

10.31598/sintechjournal.v3i1.523 ◽

2020 ◽

Vol 3 (1) ◽

pp. 22-34

Author(s):

Komang Aditya Pratama ◽

Gede Aditra Pradnyana ◽

I Ketut Resika Arthana

Keyword(s):

Naive Bayes ◽

Intelligent System ◽

Naïve Bayes ◽

The State ◽

State University ◽

Bayes Method ◽

New Students ◽

Computer Based ◽

Entrance Test ◽

Naive Bayes Method

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.

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OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

Jurnal Ilmiah SINUS ◽

10.30646/sinus.v17i1.378 ◽

2019 ◽

Vol 17 (1) ◽

pp. 1

Author(s):

Muqorobin Muqorobin ◽

Kusrini Kusrini ◽

Emha Taufiq Luthfi

Keyword(s):

Feature Selection ◽

Naive Bayes ◽

Information Gain ◽

Confusion Matrix ◽

Education Institution ◽

Naïve Bayes ◽

Bayes Method ◽

Education Development ◽

Main Requirement ◽

Naive Bayes Method

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy.

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