Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass
Abstrak– Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4...
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| Format: | Article |
| Language: | English |
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Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya
2020-04-01
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| Series: | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
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| Online Access: | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/625 |
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| author | M Aldiki Febriantono Sholeh Hadi Pramono Rahmadwati Rahmadwati |
| author_facet | M Aldiki Febriantono Sholeh Hadi Pramono Rahmadwati Rahmadwati |
| author_sort | M Aldiki Febriantono |
| collection | DOAJ |
| description | Abstrak– Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collection. Besides, the cost-sensitive decision tree C4.5 and the cost-sensitive naive Bayes method were used in the research. On the glass, lympografi, vehicle, thyroid, and wine datasets, the accuracy of the test results was 72.34 %, 68.22 %, 75.68 %, 93.82 %, and 93.95 %, respectively, using the cost-sensitive decision tree C4.5. While the cost-sensitive naive Bayes method outperforms the others by 32.24 %, 82.61 %, 25.53 %, 97.67 %, and 94.94 % on the dataset, respectively. |
| format | Article |
| id | doaj-art-e8d65b90967d46f09ffd2b527cb36ac6 |
| institution | Kabale University |
| issn | 2460-8122 |
| language | English |
| publishDate | 2020-04-01 |
| publisher | Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya |
| record_format | Article |
| series | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
| spelling | doaj-art-e8d65b90967d46f09ffd2b527cb36ac62024-12-14T10:55:12ZengDepartement of Electrical Engineering, Faculty of Engineering, Universitas BrawijayaJurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems)2460-81222020-04-01141212610.21776/jeeccis.v14i1.625419Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data MulticlassM Aldiki Febriantono0Sholeh Hadi Pramono1Rahmadwati Rahmadwati2Universitas Brawijaya MalangUniversitas BrawijayaUniversitas BrawijayaAbstrak– Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collection. Besides, the cost-sensitive decision tree C4.5 and the cost-sensitive naive Bayes method were used in the research. On the glass, lympografi, vehicle, thyroid, and wine datasets, the accuracy of the test results was 72.34 %, 68.22 %, 75.68 %, 93.82 %, and 93.95 %, respectively, using the cost-sensitive decision tree C4.5. While the cost-sensitive naive Bayes method outperforms the others by 32.24 %, 82.61 %, 25.53 %, 97.67 %, and 94.94 % on the dataset, respectively.https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/625cost sensitivedecision treemulticlassnaã¯ve bayes. |
| spellingShingle | M Aldiki Febriantono Sholeh Hadi Pramono Rahmadwati Rahmadwati Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) cost sensitive decision tree multiclass naã¯ve bayes. |
| title | Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass |
| title_full | Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass |
| title_fullStr | Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass |
| title_full_unstemmed | Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass |
| title_short | Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass |
| title_sort | perbandingan metode cost sensitive pada decision tree dan naa¯ve bayes untuk klasifikasi data multiclass |
| topic | cost sensitive decision tree multiclass naã¯ve bayes. |
| url | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/625 |
| work_keys_str_mv | AT maldikifebriantono perbandinganmetodecostsensitivepadadecisiontreedannaavebayesuntukklasifikasidatamulticlass AT sholehhadipramono perbandinganmetodecostsensitivepadadecisiontreedannaavebayesuntukklasifikasidatamulticlass AT rahmadwatirahmadwati perbandinganmetodecostsensitivepadadecisiontreedannaavebayesuntukklasifikasidatamulticlass |