Naive Bayes Method and C4.5 in Classification of Birth Data

https://doi.org/10.22146/ijccs.78198

Asep Afandi(1*), Noviana Noviana(2), Deti Nurdianah(3)

(1) Information Systems, STMIK Dian Cipta Cendikia Kotabumi, Lampung
(2) Information Systems, STMIK Dian Cipta Cendikia Kotabumi, Lampung
(3) Puskesmas Candi Rejo, Lampung
(*) Corresponding Author

Abstract

Data on the birth and productive age of a mother to get pregnant in Lampung is still high. to find out the comparison of the productive age of pregnant women and whether they have met the minimum and maximum requirements for a mother to become pregnant, and the criteria for babies born. Where the results of data processing will be used as a source of data for counseling mothers, especially for residents of Banjar Kertahayu village. The data processing requires a special method so that the results become a benchmark for a decision later, such as Data Mining. The method used for data processing used is Naive Bayes and C4.5 Algorithm. The data used is birth data in 2017-2021, the source of data from the Banjar Village Midwife-Central Lampung Regency. Research Results Method C 4.5 Middle age has a dominant age category value of 0.3324138. where the highest value is in 2017, and accuracy is 100 percent from the 2017-2021 data. The baby weight criterion using the Naïve Bayes Class Method has a dominant Middle-aged category value of 0.09675, the highest value in 2017, The results of accuracy for 5 years have accuracy of 92.84% based on 2017-2021 birth data

Keywords

C4.5 Algorithm; Naïve Bayes; Python; Dominant Age Category; The Baby's Weight

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References

[1] dr. M. D. C. Pane, “Berapa Usia Terbaik untuk Hamil?,” https://www.alodokter.com/, 2022. https://www.alodokter.com/berapa-usia-terbaik-untuk-hamil#:~:text=Saat seorang wanita berada di,an dikatakan ideal untuk hamil.

[2] D. Lampung, “Profil Kesehatan Provinsi Lampung Tahun 2019,” Pemerintah Provinsi Lampung Dinkes, no. 44, p. 136, 2019.

[3] 2015-2019 Renstra dinkes lampung, “Rencana Strategis Dinkes Provinsi Lampung Tahun 2015-2019,” no. 46, p. 58 (9), 2019, [Online]. Available: https://dinkes.lampungprov.go.id/wp-content/uploads/2016/07/1.RENSTRA-DINAS-KESEHATAN-PROVINSI-LAMPUNG-2015-2016.pdf

[4] Nurhachita and E. S. Negara, “A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students,” IAES Int. J. Artif. Intell., vol. 10, no. 2, pp. 324–331, 2021, doi: 10.11591/ijai.v10.i2.pp324-331.

[5] R. Hasan, S. Palaniappan, S. Mahmood, K. U. Sarker, and A. Abbas, “Modelling and predicting student’s academic performance using classification data mining techniques,” Int. J. Bus. Inf. Syst., vol. 34, no. 3, pp. 403–422, 2020, doi: 10.1504/IJBIS.2020.108649.

[6] F. Aziz and A. Lawi, “Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees,” Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 2955–2962, 2022, doi: 10.11591/ijece.v12i3.pp2955-2962.

[7] M. Sadikin and F. Alfiandi, “Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk,” Int. J. Electr. Comput. Eng., vol. 8, no. 6, p. 4763, 2018, doi: 10.11591/ijece.v8i6.pp4763-4771.

[8] R. R. K. Al-Taie, B. J. Saleh, A. Y. F. Saedi, and L. A. Salman, “Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq,” Int. J. Electr. Comput. Eng., vol. 11, no. 6, pp. 5229–5239, 2021, doi: 10.11591/ijece.v11i6.pp5229-5239.

[9] T. R. Stella Mary and S. Sebastian, “Predicting heart ailment in patients with varying number of features using data mining techniques,” Int. J. Electr. Comput. Eng., vol. 9, no. 4, pp. 2675–2681, 2019, doi: 10.11591/ijece.v9i4.pp2675-2681.

[10] A. Saleh, “Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga,” Citec J., vol. 2, no. 2354-5771. 207–217., p. 3, 2015.

[11] A. Mailana, A. A. Putra, S. Hidayat, and A. Wibowo, “Comparison of C4.5 Algorithm and Support Vector Machine in Predicting the Student Graduation Timeliness,” J. Online Inform., vol. 6, no. 1, p. 11, 2021, doi: 10.15575/join.v6i1.608.

[12] Xiaoling Shu, Knowledge Discovery in the Social Sciences: A Data Mining Approach. okland, california: University of California Press, 2020.

[13] R. Takdirillah, “Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Sebagai Pendukung Informasi Strategi Penjualan,” Edumatic J. Pendidik. Inform., vol. 4, no. 1, pp. 37–46, 2020, doi: 10.29408/edumatic.v4i1.2081.

[14] I. I. Wiratmadja, S. Y. Salamah, and R. Govindaraju, “Healthcare data mining: Predicting hospital length of stay of dengue patients,” J. Eng. Technol. Sci., vol. 50, no. 1, pp. 110–126, 2018, doi: 10.5614/j.eng.technol.sci.2018.50.1.8.

[15] C. V. D, “Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments,” Int. J. Electr. Comput. Eng., vol. 9, no. 5, p. 4452, 2019, doi: 10.11591/ijece.v9i5.pp4452-4459.

[16] R. Amanda and E. S. Negara, “Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms,” J. Online Inform., vol. 5, no. 1, pp. 61–72, 2020, doi: 10.15575/join.v5i1.505.

[17] N. Kewsuwun and S. Kajornkasirat, “A sentiment analysis model of agritech startup on Facebook comments using naive Bayes classifier,” Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 2829–2838, 2022, doi: 10.11591/ijece.v12i3.pp2829-2838.

[18] A. R. Isnain, N. S. Marga, and D. Alita, “Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 15, no. 1, p. 55, 2021, doi: 10.22146/ijccs.60718.

[19] Y. I. Kurniawan, U. M. Surakarta, and N. Bayes, “COMPARISON OF NAIVE BAYES AND C . 45 ALGORITHM IN DATA MINING,” vol. 5, no. 4, pp. 455–464, 2018, doi: 10.25126/jtiik.

[20] D. Y. H. Tanjung, “Analisis perbandingan algoritma id3 dan c4.5 terhadap data pengisian uang atm,” CSRID J., vol. 13, no. 3A, pp. 231–242, 2021.

[21] C. Paramitha Lubis, R. Rosnelly, and Z. Situmorang, “Penerapan Metode Naïve Bayes Dan C4.5 Pada Penerimaan Pegawai Di Universitas Potensi Utama Application of Naïve Bayes and C4.5 Methods in Receiving Employees Inuniversity of Potensi Utama,” CSRID J., vol. 12, no. 1, pp. 51–63, 2020, [Online]. Available: https://www.doi.org/10.22303/csrid.12.1.2020.51-63

[22] R. Maulid, “Belajar Python Otodidak dari Fungsi dan Prosedurnya,” https://www.dqlab.id/, 2022. https://www.dqlab.id/belajar-python-otodidak-dari-fungsi-dan-prosedurnya

[23] H. Thamrin, “Analyzing and Forecasting Admission data using Time Series Model,” J. Online Inform., vol. 5, no. 1, pp. 35–44, 2020, doi: 10.15575/join.v5i1.546.

DOI: https://doi.org/10.22146/ijccs.78198

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