PREDIKSI CACAT PADA PERANGKAT LUNAK UNTUK KELAS TIDAK SEIMBANG DENGAN MENGGUNAKAN RESAMPLE ADABOOST DAN BAGGING J48

PREDIKSI CACAT PADA PERANGKAT LUNAK UNTUK KELAS TIDAK SEIMBANG DENGAN MENGGUNAKAN RESAMPLE ADABOOST DAN BAGGING J48

• By sugiono;
• Tesis
• 61

Tingkat cacat suatu software berbanding lurus dengan kualitas software. Pada proses pengembangannya, developer perlu memprediksikan tingkat kecacatan suatu software. Pada tahapan pra pemrosesan, diusulkan metode Particle Swarm optimization (PSO) untuk mengatasi masalah noise pada data training dan metode
Resampling yaitu Random Over Sampling (ROS) untuk menangani ketidak seimbangan kelas dengan cara menduplikasi kelas minoritas agar setara dengan kelas mayoritas sehingga dataset menjadi lebih seimbang. Algoritma decision tree J48 telah menunjukan kinerja perhitungan tingkat kecacatan software. Dalam penelitian ini, diusulkan metode Decision Tree J48 yang dikombinasikan dengan AdaBoost dan Bagging yang dapat mengestimasi tingkat kecacatan software melalui data training. Dataset yang digunakan pada penelitian ini menggunakan dataset PROMISE repository. Hasil penelitian menunjukan bahwa integrasi algoritma PSO+ROS+AdaBoost+J48 layak digunakan untuk memprediksi cacat software dengan rata-rata akurasi 93,507% dan nilai AUC 0,935. Penelitian ini menguji kinerja algoritma J48 yang diintegrasikan dengan seleksi fitur PSO, Pendekatan level data ROS dan pendekatan level algoritma AdaBoost dan Bagging. Hasil penelitian menunjukan model PSO+ROS+J48+AdaBoost lebih baik dari model PSO+ROS+J48+Bagging dengan nilai rata-rata akurasi 93,507% dan 92,378% serta nilai AUC sebesar 0,935 dan 0,924.

• Katakunci Tesis, Software Deffect, Adaboost, J48, Bagging
• URL http://
• Penerbit STMIK Nusa Mandiri, Jakarta DKI Jakarta

REFERENSI

Afza, a J. M. A., Farid, D., & Rahman, C. M. (2011). A Hybrid Classifier using
Boosting , Clustering , and Naïve Bayesian Classifier. 1(3), 105–109.
Alfaro, E., Gáamez, M., & García, N. (2013). Adabag: An R package for
classification with boosting and bagging. Journal of Statistical Software,
54(2). https://doi.org/10.18637/jss.v054.i02
Askari, M. M., & Bardsiri, V. K. (2014). Software Defect Prediction using a High
Performance Neural Network. 8(12), 177–188.
Batuwita, R., & Palade, V. (2010). Efficient resampling methods for training
support vector machines with imbalanced datasets. Proceedings of the
International Joint Conference on Neural Networks.
https://doi.org/10.1109/IJCNN.2010.5596787
Bhargava, N., & Sharma, G. (2013). International Journal of Advanced Research
in Decision Tree Analysis on J48 Algorithm for Data Mining. 3(6), 1114–
1119.
Bienvenido-huertas, D., Nieto-julián, J. E., Moyano, J. J., Macías-bernal, J. M.,
Castro, J., Nieto-julián, J. E., … Macías-, J. M. (2019). Implementing
Artificial Intelligence in H-BIM Using the J48 Algorithm to Manage Historic
Buildings Implementing Artificial Intelligence in H-BIM Using the J48
Algorithm to Manage. International Journal of Architectural Heritage, 0(0),
1–13. https://doi.org/10.1080/15583058.2019.1589602
Blaxter, L., Hughes, C., & Tight, M. (2010). How to Research (4th ed.). Retrieved
from
https://books.google.co.id/books/about/How_to_Research.html?id=d1VvQg
AACAAJ&redir_esc=y
Cao, Y., Miao, Q., Liu, J., & Gao, L. (n.d.). Advance and Prospects of AdaBoost
Algorithm. Acta Automatica Sinica, 39(6), 745–758.
https://doi.org/10.1016/S1874-1029(13)60052-X

Daud, A., Mara, U. T., Mara, U. T., Mara, U. T., Mara, U. T., & Mara, U. T.
(2018). Predictive Analytics : The Application of J48 Algorithm on Grocery
Data to Predict Obesity. 1–6.
Dawson, C. W. (2009). Projects in Computing and Information Systems. In
Information Systems Journal (Vol. 2). Retrieved from
http://www.sentimentaltoday.net/National_Academy_Press/0321263553.Add
ison.Wesley.Publishing.Company.Projects.in.Computing.and.Information.Sy
stems.A.Students.Guide.Jun.2005.pdf
Diwandari1, S., & Setiawan, N. A. (2015). PERBANDINGAN ALGORITME J48
DAN NBTREE UNTUK KLASIFIKASI. 2015(Sentika), 205–212.
Faruk, Ö. (2015). Software defect prediction using cost-sensitive neural network.
Elsevier, 33, 263–277. https://doi.org/10.1016/j.asoc.2015.04.045
Fitriani, & Wahono, R. S. (2015). Integrasi Bagging dan Greedy Forward
Selection pada Prediksi Cacat Software dengan Menggunakan Naïve Bayes.
Journal of Software Engineering, 1(2), 101–108.
García, V., Sánchez, J. S., Martín-Félez, R., & Mollineda, R. A. (2012).
Surrounding neighborhood-based SMOTE for learning from imbalanced data
sets. Progress in Artificial Intelligence, 1(4), 347–362.
https://doi.org/10.1007/s13748-012-0027-5
Gray, D., Bowes, D., Davey, N., Sun, Y., & Christianson, B. (2011). The Misuse
of the NASA Metrics Data Program Data Sets for Automated Software
Defect Prediction. 96–103.
Jia, J., Xiao, X., Liu, B., & Jiao, L. (2011). Bagging-based spectral clustering
ensemble selection. Pattern Recognition Letters, 32(10), 1456–1467.
https://doi.org/10.1016/j.patrec.2011.04.008
Jiawei Han, M. K. and J. P. (2012). Data Mining: Concepts and Techniques, Third
Edition - Books24x7. Morgan Kaufmann Publishers, 745. Retrieved from
http://library.books24x7.com/toc.aspx?bkid=44712
Khoshgoftaar, T. M. (2010). Attribute Selection and Imbalanced Data : Problemsin Software Defect Prediction. https://doi.org/10.1109/ICTAI.2010.27
Kim, J. J., Ja, W. K., Lee, H. J., & Han, J. S. (2005). Amygdalar inactivation
blocks stress-induced impairments in hippocampal long-term potentiation
and spatial memory. Journal of Neuroscience, 25(6), 1532–1539.
https://doi.org/10.1523/JNEUROSCI.4623-04.2005
Korb, K. B., & Nicholson, A. E. (2005). Bayesian Artificial Intelligence. In
Technometrics (Vol. 47). https://doi.org/10.1198/tech.2005.s836
Kunang, Y. N., Darma, U. B., Andri, A., & Darma, U. B. (2013).
IMPLEMENTASI TEKNIK DATA MINING UNTUK MEMPREDIKSI
TINGKAT. (June 2016). https://doi.org/10.13140/RG.2.1.4212.1845
Laradji, I. H., Alshayeb, M., & Ghouti, L. (2014). Software defect prediction
using ensemble learning on selected features. INFORMATION AND
SOFTWARE TECHNOLOGY. https://doi.org/10.1016/j.infsof.2014.07.005
Lee, J. J., Lee, P. H., Lee, S. W., Yuille, A., & Koch, C. (2011). AdaBoost for text
detection in natural scene. Proceedings of the International Conference on
Document Analysis and Recognition, ICDAR, 429–434.
https://doi.org/10.1109/ICDAR.2011.93
López, V., Fernández, A., & Herrera, F. (2014). On the importance of the
validation technique for classification with imbalanced datasets: Addressing
covariate shift when data is skewed. Information Sciences, 257, 1–13.
https://doi.org/10.1016/j.ins.2013.09.038
Ma, B., Dejaeger, K., Vanthienen, J., & Baesens, B. (n.d.). Software Defect
Prediction Based on Association Rule Classification (2011). Available at
SSRN: https://ssrn.com/abstract=1785381 or
http://dx.doi.org/10.2139/ssrn.1785381. 0–7.
Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. (2011). A novel particle
swarm optimization algorithm with adaptive inertia weight. Applied Soft
Computing Journal, 11(4), 3658–3670.
https://doi.org/10.1016/j.asoc.2011.01.037

Niranjan, A., Prakash, A., Veena, N., Geetha, M., Shenoy, P. D., & Venugopal, K.
R. (2017). EBJRV : An Ensemble of Bagging , J48 and Random Committee
by Voting for Efficient Classification of Intrusions. 2017 IEEE International
WIE Conference on Electrical and Computer Engineering (WIECON-ECE),
(December), 51–54.
Pandey, P., & Prabhakar, R. (2016). An Analysis of Machine Learning Techniques
( J48 & AdaBoost ) -for Classification. 0–5.
Philip, D., & Gray, H. (2012). Software Defect Prediction Using Static Code
Metrics : Formulating a Methodology. (December).
Putri, S. A., & Wahono, R. S. (2015). Integrasi SMOTE dan Information Gain
pada Naive Bayes untuk Prediksi Cacat Software. Journal of Software
Engineering, 1(2), 86–91.
Rini, D. P. (2011). Particle Swarm Optimization : Technique , System and
Challenges Particle Swarm Optimization : Technique , System and
Challenges. (May 2014). https://doi.org/10.5120/ijais-3651
Saifudin, A., & Wahono, R. S. (2015). Penerapan Teknik Ensemble untuk
Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. 1(1).
Sathyaraj, R., & Prabu, S. (2015). An Approach for Software Fault Prediction to
Measure the Quality of Diferent Prediction Methodologies using Software
Metrics. 8(December). https://doi.org/10.17485/ijst/2015/v8i35/73717
Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software
Defect-Proneness Prediction Framework. 37(3), 356–370.
Sumadhi, T., & Hemalatha, M. (2013). An Enhanced Approach for Solving Class
Imbalance Problem in Automatic Image Annotation. International Journal of
Image, Graphics and Signal Processing, 5(2), 9–16.
https://doi.org/10.5815/ijigsp.2013.02.02
Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for
Decision Making. In Business Intelligence: Data Mining and Optimization
for Decision Making (1st ed.). https://doi.org/10.1002/9780470753866

Wahono, R. S., Dian, U., Semarang, N., & Suryana, N. (2013). Combining
Particle Swarm Optimization based Feature Selection and Bagging
Technique for Software Defect Prediction Combining Particle Swarm
Optimization based Feature Selection and Bagging Technique for Software
Defect Prediction. (September). https://doi.org/10.14257/ijseia.2013.7.5.16
Wahono, R. S., Suryana, N., & Ahmad, S. (2014). Metaheuristic Optimization
based Feature Selection for Software Defect Prediction. 9(5), 1324–1333.
https://doi.org/10.4304/jsw.9.5.1324-1333
Wang, S., & Yao, X. (2013). Using class imbalance learning for software defect
prediction. IEEE Transactions on Reliability, 62(2), 434–443.
https://doi.org/10.1109/TR.2013.2259203
Witten, I. H., Frank, E., & Hall, M. a. (2011). Data Mining: Practical Machine
Learning Tools and Techniques (Google eBook). In Complementary
literature None. Retrieved from
http://books.google.com/books?id=bDtLM8CODsQC&pgis=1
Yap, B. W., Rani, K. A., Aryani, H., Rahman, A., Fong, S., Khairudin, Z., &
Abdullah, N. N. (2014). An Application of Oversampling , Undersampling ,
Bagging and Boosting in Handling Imbalanced Datasets. 13–22.
https://doi.org/10.1007/978-981-4585-18-7
Zheng, J. (2010). Expert Systems with Applications Cost-sensitive boosting
neural networks for software defect prediction. Expert Systems With
Applications, 37(6), 4537–4543. https://doi.org/10.1016/j.eswa.2009.12.056