Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python

Pinata, Ngakan Nyoman Pandika; Sukarsa, I Made; Rusjayanthi, Ni Kadek Dwi

doi:10.24843/jim.2020.v08.i03.p04

Cited by 13 publications

(14 citation statements)

References 8 publications

(10 reference statements)

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“…In this research, the evaluation phase was carried out using a confusion matrix. Confusion Matrix measure the performances by comparing the predictive with the actual data [19]. Confusion Matrix has four representations of the result.…”

Section: Discussionmentioning

confidence: 99%

“…The table above shows that the addition of parameters to the XGBoost algorithm is proven to achieve better results than without the addition of parameters. In this experiment, the max_depth parameter is used which is a parameter to limit the growth or depth of the tree [19]. To get the optimal max_depth value, experiments were carried out as shown in figure 4.…”

Section: Scenario 3: Adding Parameter Value During the Modeling Proce...mentioning

confidence: 99%

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Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection

Putri

Dwifebri

Adiwijaya

2023

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Aside from the Holy Qur'an, Hadith is indeed a life guide that every Muslims in this world must follow. The technology for classifying texts and sentences, including categorizing hadiths, is evolving in tandem with the advancement of the times. The model used to perform classification has also been developed and optimized such as the use of the XGBoost algorithm which is more optimized than the previous tree algorithm. This can also make it easier for us as Muslims to study hadiths by categorizing them according to recommendations, prohibitions, and information. This study conducted text classification of Indonesian translations of hadith texts based on recommendations, prohibitions, and information using the XGBoost algorithm, TF-IDF for its feature extraction, and Chi-Square for its feature selection. In this study, experiments were carried out by changing the order of the preprocessing process for the stopword removal and stemming parts, performing the classification process with and without using chi-square as a feature selection, and adding parameter value during the modeling process with XGBoost and the highest final results obtained were 79% for accuracy, 79% for precision, 78% for recall and 78% for F1-score.

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Section: Discussionmentioning

confidence: 99%

Section: Scenario 3: Adding Parameter Value During the Modeling Proce...mentioning

confidence: 99%

Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection

Putri

Dwifebri

Adiwijaya

2023

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“…XGBoost is Supervised Learning that can be used to make predictions and classifications. XGBoost can also be applied to various disciplines such as education, health, government and others [13]. The computational stages performed on the XGBoost Algorithm are shown in Figure 2 [14].…”

Section: Methodsmentioning

confidence: 99%

Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

Wardhani

Akbar

2022

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One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%

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“…Adapaun penelitian yang menggunakan metode XGBoost oleh Ngakan Nyoman Pandika Pinata,dkk Tahun 2020 menunjukkan model XGBoost memiliki performa yang sangat baik dalam prediksi kecelakaan lalu lintas di Bali [10]. Metode XGBoost juga diterapkan `pada penelitian Muhamad Syukron,dkk pada Tahun 2020 untuk klasifikasi tingkat penyakit Hepatitis C dan mendapatkan hasil XGBoost memiliki recall kelas sirosis yang lebih baik dibanding Random Forest [9].…”

Section: Pendahuluanunclassified

“…a. Klasifikasi dengan eXtreme Gradiant Boosting (XGBoost) Extreme Gradient Boosting atau lebih umum XGBoost adalah implementasi lanjutan dari algoritme gradien boosting yang menggunakan pohon keputusan sebagai dasar klasifikasi. Diperkenalkan pada tahun 2014, telah banyak digunakan untuk menyelesaikan berbagai masalah klasifikasi atau regresi karena kecepatan, efisiensi, dan skalabilitasnya [10]. Gradient boosting merupakan algoritma yang dapat menemukan solusi optimal untuk berbagai masalah [16].…”

Section: Processingunclassified

Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning

Maghfiroh

Findawati

Indahyanti

2023

bits

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Accounts are a collection of numbers commonly used for all transactions in the banking world, from saving, withdrawing cash, to checking account balances either directly or online using m-banking. In bank accounts and the process of opening them, there are various kinds of criminal acts committed by individuals and groups. As a bank's obligation to prevent crime so that it can provide trust to the community. In an effort to prevent criminal acts of fraud can be solved using data mining techniques, namely classification. The purpose of classification is to predict the class label of an object based on existing attributes. The classification methods used in this research are extreme gradiant boosting (XGBoost) and random forest on 22029 records. In the classification process, this study uses a percentage ratio of 90% train data and 10% test data and tuning parameters processed by randomized search cross validation. The research stages start from preprocessing to evaluation and get a train score of 99.50% and a test score of 99.59% for extreme gradiant boosting (xgboost) while random forest gets a train score of 99.46% and a test score of 99.59%. These results show that the classification results of extreme gradiant boosting (XGBoost) are better than random forest.

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Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python

Cited by 13 publications

References 8 publications

Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection

Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection

Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning

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