skip to main content


*Fatiya Nur Umma  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Budi Warsito  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Di Asih I Maruddani  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under

Citation Format:

Pemalang regency is a district which has amount of poverty around 16.04%. One of the effort that must be improved in tackling poverty is increasing the accuracy of the government program’s target. The improvement of target accuracy is expected to give the better impact on the welfare of the population. This study classified the poverty status of households in Pemalang regency using C5.0 Algorithm. The poverty status of households is divided into two classes, namely poor and non-poor. There was an imbalance of data in both classes. Data imbalances were handled by using Synthetic Minority Oversampling Technique (SMOTE). From the research that has been done, SMOTE application in classification of household poverty status affected the evaluation value of the model. Previously the model could not classify the minority class and after using SMOTE the model produced an average value of sensitivity 25.80%. SMOTE application increased the average value of specificity from 91.16% to 94.91%. However, SMOTE application decreased the average value of accuracy which originally 91.16% down to 82.2%.

Keywords : C5.0, Household poverty, Classification, SMOTE

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
Type Research Instrument
  Download (21KB)    Indexing metadata
Keywords: C5.0, Household poverty, Classification, SMOTE

Article Metrics:

  1. Arif, M. 2018. Decision Tree Algorithms C4.5 and C5.0 in Data Mining: A Review. International Journal of Databases Theory and Application. Vol.11, No.1, hal.1-8
  2. Blagus, R dan Lara, L. 2013. SMOTE for High-Dimensional Class-Imbalanced Data. BMC Bioinformatics, Vol. 14, hal.106
  3. Chawla, N. V., Bowyer, K. W., Hall, L. O., dan Kegelmeyer, W. P. 2002. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, Vol. 16, hal. 321–357
  4. Han, J dan Kamber, M. 2006. Data mining: concepts and techniques. 2nd edition. San Francisco: Morgan Kauffman
  5. Kantardzic, M. 2011, Data Mining: Concept, Models, Methods, and Algorithms. 2nd edition. New Jersey: John W & Sons, Inc
  6. Larose, D. T, 2005. Discovering Knowledge in Data: An Introduction to Data Mining. New Jersey: Jhon Wiley & Sons Inc
  7. Qiong, Gu., Wang, X.-M., Zhao, Wu., Ning, B., dan Xin, C.-S.,. 2016. An Improved SMOTE Algorithm Based On Genetic Algorithm For Imbalanced Data Classification. Journal of Digital Information Management, Vol. 14, No. 2, hal. 92–103
  8. Susanto, Sani dan Dedy Suryadi. 2010. Pengantar Data Mining Menggali Pengetahuan dari Bongkahan Data. Yogyakarta: Andi
  9. Tan, P., Steinbach, M., dan Kumar, V. 2006. Introduction to Data Mining. Boston: Pearson Education
  10. Witten, I. H dan Frank, E. 2005. Data Mining Practical Machine Learning Tools and Techniques. 2nd Edition. San Fransisco: Morgan Kaufmann

Last update:

No citation recorded.

Last update:

No citation recorded.