Traffic accident analysis based on C4.5 algorithm in WEKA

Li, Jiajia; He, Jie; Liu, Ziyang; Zhang, Hao; Chen, Zhang

doi:10.1051/matecconf/201927201035

Cited by 8 publications

(4 citation statements)

References 7 publications

(6 reference statements)

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“…Decision tree digunakan karena proses pada pohon keputusan mirip dengan proses pengambilan keputusan yang dilakukan oleh manusia sehingga lebih mudah untuk diinterpretasikan (Patel & Prajapati, 2018). Selain itu, metode ini juga dapat menangani kumpulan data besar dengan sejumlah besar variabel secara efisien (Li et al, 2019).…”

Section: Aturan Keputusan Pola Kecelakaanunclassified

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Susanto,

Yuniarto

2023

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Jalan tol sebagai jalan bebas hambatan tidak membuat jalan tol terbebas dari kecelakaan lalu lintas. Di Provinsi Jawa Barat, terdapat total 20 ruas tol dengan panjang ruas tol mencapai 521,15 km pada 2020. Tol Cipali adalah salah satu ruas dengan fatalitas tertinggi di dunia. Penanggulangan kecelakaan lalu lintas penting dilakukan sebagai upaya untuk mengurangi kejadian kecelakaan lalu lintas. Namun, data official mengenai kecelakaan lalu lintas di jalan tol oleh lembaga resmi tidak tersedia secara rinci, sehingga digunakan sumber data alternatif seperti berita online. NER dengan Bi-LSTM-CNN dimanfaatkan untuk melakukan ekstraksi data kecelakaan. Hasil dari ekstraksi berita dianalisis dengan membuat aturan keputusan untuk mengetahui pola kecelakaan yang terjadi. Aturan keputusan ini berupa decision tree dengan dataset yang menggunakan data tiga tol dengan fatalitas tertinggi dengan fitur imputasi mode by concept sebagai metode penanganan missing value dan jalan tol sebagai atribut, menghasilkan nilai f1-score sebesar 67,76% dan nilai akurasi sebesar 75,49%.

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Section: Aturan Keputusan Pola Kecelakaanunclassified

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Susanto,

Yuniarto

2023

semnasoffstat

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“…Penerapan Metode C4.5 pada penelitian lainnya adalah sistem prediksi dan evaluasi prestasi akademik mahasiswa menggunakan Data Mining, di mana klasifikasi dilakukan untuk menemukan faktor-faktor yang mempengaruhi prestasi mahasiswa [11]. Mengidentifikasi faktor-faktor yang mempengaruhi jenis kecelakaan dengan menggunakan Algoritma C4.5 [12]. Menggabungkan Algoritma C4.5 dengan Algoritma Relief-F untuk mengatasi permasalahan status stabilitas tegangan sistem agar dapat diidentifikasi secara online [13].…”

Section: Pendahuluanunclassified

Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

2021

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Student learning in schools has changed since the Covid-19 pandemic. Student learning in normal conditions is carried out face-to-face and turns into online or online learning. The research was conducted to predict student learning outcomes during the COVID-19 pandemic so that the results of this study can be used as a reference in policymaking in schools. The C4.5 method was used in the study to classify the data for class XII of the Multimedia Department at SMKN 2 Padang Panjang and the classification results could predict student learning outcomes during the pandemic. Processed student value data were taken from 1 (one) subject as the research data sample. Analysis of the value of student learning outcomes using the C4.5 Method to obtain new knowledge from student learning outcomes data carried out during the COVID-19 pandemic. The data analyzed consisted of attributes of attendance, assignments, daily tests, and test scores which influenced the decision criteria for student learning outcomes in online learning. The learning outcome decision criteria consist of "Satisfactory" and "Not Satisfactory" which refer to the Minimum Completion Criteria. Tests conducted on the training data of learning outcomes show that the value of the Daily Test is the most influential attribute in decision making. Implementation of the results using the RapidMiner Studio 9.2.0 software and produces an accuracy of 83.33% of the test data testing with the rules of data analysis training results. The results of the C4.5 classification testing method in this study can be used to predict student learning outcomes. The test results with an accuracy of 83.33% can be recommended to help schools in making policies

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“…In addition, in the traffic accidents analysis based on the algorithm C4.5 using the WEKA software of machine learning refers to the article [26]. The study acknowledges that the data mining contributes to the prevention of accidents and the safety management of traffic.…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization

Kalathas¹,

Papoutsidakis²,

Drosos³

2020

Adv. sci. technol. eng. syst. j.

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Information is the driving force of businesses because it can ensure the ability of knowledge and prediction. The railway industry produces a huge ume of data, with the proper processing of them and the use of innovative technology, there is the possibility of beneficial information to be provided which constitute the deciding factor for the correct decision making. Safety is the railway comparative advantage that has to be reinforced by each business administration while making the optimum decisions. The main purpose of this paper is the investigation of the most important dysfunctions that arise in a train and can cause its immobilization at the main passenger rail, resulting in huge delays of conducting the routes setting the passengers at risk. Afterwards the total of malfunctions is assessed and the most important, potentially, malfunction is assessed, so as the executives of the Greek Railway company to plan and redefine the processes and the initial plan of the predictive maintenance. This paper demonstrates the effort of implementing innovative applications by making use of methods from the rapidly developed field of Data Mining to the Greek Railway Company that uses obsolete procedures for the control of the trains' functionality in order to investigate the data for the provision of specialized information which will be used as a tool for the faster, more accurate and precise decision making. This decision making approach is based on a specific algorithm's design in order to automatically detect faults and make periodic maintenance of trains easier. Holistic approach is performed in the management of real data from the Greek railway industry and a predictive model of Machine Learning is developed, for the optimization of the management's performance of the trains reinforcing the strategic target of the railway industry which is the transportation of citizens with safety and comfort.

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Traffic accident analysis based on C4.5 algorithm in WEKA

Cited by 8 publications

References 7 publications

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Decision Tree Berdasarkan Ekstraksi Informasi dari Berita Online Menggunakan Named Entity Recognition (NER)

Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization

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