Prediction of Teachers' Lateness Factors Coming to School Using C4.5, Random Tree, Random Forest Algorithm

Gata, Windu; Grand, Grand; Fatmasari, Rhini; Baharuddin, Baharuddin; Patras, Yuyun Elizabeth; Hidayat, Rais; Tohari, Siswanto; Wardhani, Nia Kusuma

doi:10.2991/icream-18.2019.34

Cited by 8 publications

(11 citation statements)

References 3 publications

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“…Decision tree algorithms are a family of machine learning classification and regression algorithms that fits a model on a given dataset having considered the entropy of some or all attributes for making its splitting decision. Tree-based machine learning algorithms are widely used and acceptable for various research and industrial areas, even as distant as software defect prediction in the field of software engineering [24] and even for the prediction of factors in educational management [25]. Decision Tree models are known to always produce interpretable models.…”

Section: B Implemented Modelsmentioning

confidence: 99%

“…Fundamentally, all decision tree algorithm can perform both regression and classification (primarily binary classification) analyses. Decision algorithms usually fit its model through a greedy top-down method which is performed recursively on the dataset to find the most informative variable at each split decision junction [25]. Additionally, it may also include a method for producing a fine-tuned tree by the way of pruning the initial tree based on the error rate thereby removing redundant branches [26].…”

Section: B Implemented Modelsmentioning

confidence: 99%

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Detecting Generic Network Intrusion Attacks using Tree-based Machine Learning Methods

Alsariera¹

2021

IJACSA

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The development Intrusion Detection System (IDS) has a solid impact in mitigating against internal and external cyber threats among other cybersecurity methods. The machine learning-based method for IDS has proven to be an effective approach to detecting either anomaly or multiple classes of intrusion. For the detection of various types of intrusion by a single IDS model, it is discovered that the overall high accuracy of the IDS model does not translate to high accuracy for each attack type. Some intrusion attacks are seen to share similarities with other attacks thereby evading detection, one of which is the generic attack. The notoriety of the generic attack is the ability of a single generic attack to compromise a whole bunch of blockciphers. Therefore, this study proposed a machine learning framework to specifically detect generic network intrusion by implementing two (2) decision tree algorithms. The decision tree methods were developed using two distinct variants namely the J48 and Random Tree algorithms. A balanced generic network dataset was curated and used for model development. A 10-fold cross-validation technique was implemented for model development and performance evaluation, where all obtainable performance scores were extracted and presented. The performances of the decision tree methods for generic network intrusion attack detection were comparative analysis and also evaluated against existing methods. The proposed methods of this study are robust, stable and empirically seen to have outperformed existing methods.

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Section: B Implemented Modelsmentioning

confidence: 99%

Section: B Implemented Modelsmentioning

confidence: 99%

Detecting Generic Network Intrusion Attacks using Tree-based Machine Learning Methods

Alsariera¹

2021

IJACSA

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“…Bentuk kedisiplinan siswa terhadap tata tertib sekolah adalah tepat waktu datang di sekolah. Terlambat dapat diasumsikan sebagai suatu kegiatan yang tidak dapat dilakukan sebelum waktunya atau tepat pada waktunya (Gata et al, 2019). Tindakan ini merupakan bentuk ketidakmampuan seseorang untuk berada pada suatu tempat dan waktu yang telah disepakati sebelumnya.…”

Section: Pendahuluanunclassified

Teknik Self Management untuk mengurangi perilaku terlambat datang di Sekolah

Astuti

Lestari

2020

Counsellia

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Penelitian ini bertujuan untuk mengetahui keefektifan teknik self management untuk mengurangi perilaku terlambat datang di sekolah. Penelitian ini menggunakan pendekatan kualitatif dengan fokus penelitian perilaku terlambat datang di sekolah. Subjek penelitiannya adalah siswa kelas X SMK Negeri I Panjatan yang ditentukan menggunakan teknik purposive sampling. Ada 3 siswa sebagai subjek penelitian yang ditentukan berdasarkan frekuensi keterlambatan siswa datang di sekolah. Metode pengumpulan data yang digunakan adalah observasi, wawancara dan dokumentasi, sedangkan analisis datanya menggunakan tiga tahapan yaitu: (1) reduksi data, (2) penyajian data, (3) verifikasi atau penarikan kesimpulan. Berdasarkan hasil penelitian dapat disimpulkan bahwa ada perubahan perilaku yang ditandai dengan menurunnya perilaku terlambat siswa antara sebelum dan sesudah diberikan layanan teknik self management. Abstract: This research aims to find out the effectiveness of self management techniques to reduce late arrival behavior at school. This study uses a qualitative approach with a focus on late-arrival behavior research in school. The research subjects were class X student of SMK Negeri I Panjatan who were determined using purposive sampling techniques. There are 3 students as research subjects that are determined based on the frequency of late students coming to school. Data collection methods used are observation, interviews and documentation, while the data analysis use three stages, namely: (1) data reduction, (2) data presentation, (3) verification or drawing conclusions. Based on the results of the study it can be concluded that there is a change in behavior marked by a decrease in late student behavior between before and after self management techniques are provided

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“…For the processing of continuous attributes in C4.5 algorithm, the key step is to find the optimal segmentation threshold of attributes, form discrete intervals and then recursively establish branches [16,17]. Among them, the way to determine the optimal segmentation threshold is to average all the adjacent values of attributes in turn, so as to form a threshold sequence before selection [18]. Although this process does not leave out the best threshold, the selection range of sequences is larger.…”

Section: A C45 Improved Processing Of Continuous Attribute Segmentamentioning

confidence: 99%

An Improved C4.5 Algorthm in Bagging Integration Model

Song

Liu

et al. 2020

IEEE Access

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The C4.5 algorithm has three shortcomings: the wide range of candidate segmentation threshold sequences for continuous attributes, the comprehensive influence of different attributes and local subsets under the same attribute, and the inter-attribute redundancy. When dealing with continuous attributes, sampling and threshold supplement processing near the transition boundary of the attribute interval corresponding to the adjacent different categories are performed for narrowing the range of candate segmentation threshold sequences. By adding standardizing Euclidean distance of the attribute global and local factors to represent attribute weight, the calculation of C4.5 information gain is otpimized. And averaging Gini index of other attributes and adding correction factor, the influence of redundancy between attributes is greatly decreased. The overall average improvement range of the base classifier and the bagging integration classifier is 0.6% ~ 2.1% and 0.7% ~ 2.7%, respectively, which shows that this integration model can improve the classification accuracy and also validate its feasibility and reliability.

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Prediction of Teachers' Lateness Factors Coming to School Using C4.5, Random Tree, Random Forest Algorithm

Cited by 8 publications

References 3 publications

Detecting Generic Network Intrusion Attacks using Tree-based Machine Learning Methods

Detecting Generic Network Intrusion Attacks using Tree-based Machine Learning Methods

Teknik Self Management untuk mengurangi perilaku terlambat datang di Sekolah

An Improved C4.5 Algorthm in Bagging Integration Model

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