Prediksi Cacat Perangkat Lunak Kelas Tidak Seimbang Menggunakan Resample J48 dan J48 Consolidated

• ILHAM NURJABAR
• 14002451

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ABSTRAK

ABSTRAK

Nama              : Ilham Nurjabar

NIM                 : 14002451

Program Studi :Ilmu Komputer (S2)

Jenjang            : Strata Dua (S2)

Konsentrasi      : Software Engineering

Judul                 : “PREDIKSI CACAT PERANGKAT LUNAK KELAS TIDAK SEIMBANG MENGGUNAKAN RESAMPLE J48 DAN J48 CONSOLIDATED"

Prediksi cacat perangkat lunak merupakan aspek penting dalam jaminan kualitas perangkat lunak, dengan tujuan mengidentifikasi dan mengatasi potensi cacat sebelum mereka muncul dalam lingkungan produksi. Penelitian ini menyajikan pendekatan inovatif untuk mengatasi masalah distribusi kelas yang tidak seimbang dalam prediksi cacat perangkat lunak menggunakan teknik resampling dan algoritma J48 dan J48 Consolidated. Selain itu, penelitian ini memperkenalkan varian baru dari J48, yang disebut sebagai J48 Consolidated, yang menggabungkan beberapa pohon keputusan menjadi satu model ensemble tunggal untuk meningkatkan kinerja prediksi. Model J48 Consolidated dibandingkan dengan algoritma J48 tradisional dalam konteks prediksi cacat perangkat lunak dengan distribusi kelas yang tidak seimbang. Dataset yang yang digunakan pada penelitian ini menggunakan dataset PROMISE repository. Hasil penelitian menunjukan bahwa integrase Algoritma RUS + J48 Consolidated layak digunakan untuk memprediksi cacat software dengan rata-rata akurasi 78% dengan nilai AUC 0.783. Penelitian ini menguji kinerja J48 dengan pendekata ROS dan RUS menggunakan Algoritma J48 dan J48 Consolidated. Hasil penelitian menunjukan model RUS+J48 Consolidated lebih baik dari model RUS+J48 dengan nilai rata-rata akurasi 78% dan 77% serta nilai AUC 0.783 dan 0.766. 

KATA KUNCI

Resample,J48 Consolidated,Cacat software,Imbalanced Class,Random over sampling

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

[1] R. S. Wahono and N. Suryana, “Combining particle swarm optimization based feature selection and bagging technique for software defect prediction,” Int. J. Softw. Eng. its Appl., vol. 7, no. 5, pp. 153–166, 2013, doi: 10.14257/ijseia.2013.7.5.16.

[2] Fitriyani and R. S. Wahono, “90139-ID-integrasi-bagging-dan-greedy-forwardsel,” J. Softw. Eng., vol. 1, no. 2, 2015.

[3] R. S. Wahono, N. Suryana, and S. Ahmad, “Metaheuristic Optimization based Feature Selection for Software Defect Prediction,” J. Softw., vol. 9, no. 5, 2014, doi: 10.4304/jsw.9.5.1324-1333.

[4] R. Sathyaraj and S. Prabu, “An approach for software fault prediction to measure the quality of different prediction methodologies using software metrics,” Indian J. Sci. Technol., vol. 8, no. 35, 2015, doi: 10.17485/ijst/2015/v8i35/73717.

[5] B. Ma, K. Dejaeger, J. Vanthienen, and B. Baesens, “Faculty of Business and Economics 6RIWZDUH GHIHFW SUHGLFWLRQ EDVHG RQ % DRMXQ 0D . DUHO ’ HMDHJHU -DQ 9DQWKLHQHQ DQG % DUW % DHVHQV DEPARTMENT OF DECISION SCIENCES AND INFORMATION MANAGEMENT (KBI) Software defect prediction based on asso- ciation rule c,” pp. 0–7.

[6] Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu, “A general software defectproneness prediction framework,” IEEE Trans. Softw. Eng., vol. 37, no. 3, pp. 356–370, 2011, doi: 10.1109/TSE.2010.90.

[7] T. M. Khoshgoftaar, K. Gao, and N. Seliya, “Attribute selection and imbalanced data: Problems in software defect prediction,” Proc. - Int. Conf. Tools with Artif. Intell. ICTAI, vol. 1, pp. 137–144, 2010, doi: 10.1109/ICTAI.2010.27.

[8] M. M. Dr. Bhargava N., Sharma G., Dr. Bhargava R., “International Journal of 62 Advanced Research in Decision Tree Analysis on J48 Algorithm for Data Mining,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 3, no. 6, pp. 1114–1119, 2013.

[9] N. Daud, N. L. Mohd Noor, S. A. Aljunid, N. Noordin, and N. I. M. Fahmi Teng, “Predictive Analytics: The Application of J48 Algorithm on Grocery Data to Predict Obesity,” 2018 IEEE Conf. Big Data Anal. ICBDA 2018, no. November, pp. 1–6, 2019, doi: 10.1109/ICBDAA.2018.8629623.

[10] N. Diwandri and A. Setiawan, “Perbandingan Algoritme J48 Dan Nbtree Untuk Klasifikasi,” Semin. Nas. Teknol. Inf. dan Komun. 2015 (SENTIKA 2015), vol. 2015, no. Sentika, pp. 205–212, 2015.

[11] P. Vasco, “An update of the J48Consolidated WEKA ’ s class?: CTC algorithm enhanced with the notion of coverage,” no. June, 2014.

[12] A. Saifudin and R. S. Wahono, “Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software,” J. Softw. Eng., vol. 1, no. 1, 2015.

[13] D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson, “The misuse of the NASA Metrics Data Program data sets for automated software defect prediction,” IET Semin. Dig., vol. 2011, no. 1, pp. 96–103, 2011, doi: 10.1049/ic.2011.0012.

[14] B. W. Yap, K. A. Rani, H. A. Abd Rahman, S. Fong, Z. Khairudin, and N. N. Abdullah, An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets, vol. 285 LNEE. 2014.

[15] M. McDonald, R. Musson, and R. Smith, The Practical Guide to Defect Prevention (Best Practices (Microsoft)). 2007.

[16] D. Philip and H. Gray, “Software Defect Prediction Using Static Code Metrics?: Formulating a Methodology,” Journal, no. December, 2012. 63

[17] D. Bienvenido-Huertas, J. E. Nieto-Julián, J. J. Moyano, J. M. Macías-Bernal, and J. Castro, “Implementing Artificial Intelligence in H-BIM Using the J48 Algorithm to Manage Historic Buildings,” Int. J. Archit. Herit., vol. 14, no. 8, pp. 1148–1160, 2020, doi: 10.1080/15583058.2019.1589602.

[18] S. Wang and X. Yao, “IEEE TRANSACTIONS ON RELIABILITY 1 Using Class Imbalance Learning for Software Defect Prediction,” pp. 1–11.

[19] R. Batuwita and V. Palade, “Efficient resampling methods for training support vector machines with imbalanced datasets,” Proc. Int. Jt. Conf. Neural Networks, 2010, doi: 10.1109/IJCNN.2010.5596787.

[20] F. Gorunescu, Data Mining: Concepts, models and techniques. 2011.

[21] C. Vercellis, Business Intelligence: Data Mining and Optimization for Decision Making. 2009.

[22] V. López, A. Fernández, and F. Herrera, “On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed,” Inf. Sci. (Ny)., vol. 257, pp. 1–13, 2014, doi: 10.1016/j.ins.2013.09.038.

[23] C. W. Dawson, Projects in Computing and Information Systems. 2009.

[24] L. Blaxter, C. Hughes, and M. Tight, How to Research. 2018.