Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization
(1) Universitas Islam Negeri Sultan Syarif Kasim Riau
(2) Universitas Islam Negeri Sultan Syarif Kasim Riau
(3) Universitas Islam Negeri Sultan Syarif Kasim Riau
(4) Universitas Islam Negeri Sultan Syarif Kasim Riau
Abstract
Abstract.
Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.
Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.
Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).
Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).Keywords
Full Text:
PDFReferences
Kementerian Kesehatan Republik Indonesia, “Bahan Ajar Gizi : Penilaian Status Gizi,” 2017. http://bppsdmk.kemkes.go.id/pusdiksdmk/wp-content/uploads/2017/11/PENILAIAN-STATUS-GIZI-FINAL-SC.pdf (accessed Apr. 13, 2018).
O. Dwienda, L. Maita, E. M. Saputri, and R. Yulviana, Buku Ajar Asuhan Kebidanan Neonatus, Bayi/ Balita dan Anak Prasekolah untuk Para Bidan. Yogyakarta: Deepublish, 2015.
S. Kustiyati and L. Firrahmawati, “Optimalisasi tumbuh kembang balita dengan peningkatan pengetahuan ibu tentang gizi balita,” GASTER, vol. 12, no. 2, pp. 86–98, 2015.
Kementerian Kesehatan Republik Indonesia, Buku Saku Pemantauan Status Gizi Tahun 2017. Jakarta Selatan: Direktorat Gizi Masyarakat, 2017.
Menteri Kesehatan Republik Indonesia, “Keputusan Menteri Kesehatan Republik Indonesia Nomor 1995/MENKES/SK/XII/2010 tentang Standar Antropometri Penilaian Status Gizi Anak,” 2010. http://gizi.depkes.go.id/wp-content/uploads/2011/11/buku-sk-antropometri-2010.pdf (accessed Feb. 27, 2018).
World Health Organization, “WHA Global Nutrition Targets 2025 : Stunting Policy Brief,” 2014. https://www.who.int/nutrition/topics/globaltargets_stunting_policybrief.pdf (accessed Feb. 28, 2018).
Sekretariat Wakil Presiden Republik Indonesia, “100 Kabupaten/Kota Prioritas untuk Intervensi Anak Kerdil (Stunting),” 2017. https://www.tnp2k.go.id/images/uploads/downloads/Buku Ringkasan Stunting.pdf (accessed Feb. 06, 2018).
World Health Organization, “STUNTING,” 2014. http://www.who.int/nutrition/global-target-2025/infographic_stunting.pdf (accessed Mar. 01, 2018).
J. R. Quinlan, “Improved use of continuous attributes in C4.5,” J. Artiicial Intell. Res., vol. 4, pp. 77–90, 1996.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, 1995.
M. Fakhrurrifqi and R. Wardoyo, “Perbandingan algoritma nearest neighbour, C4.5 dan LVQ untuk klasifikasi kemampuan mahasiswa,” Indones. J. Comput. Cybern. Syst., vol. 7, no. 2, pp. 145–154, 2013.
B. Hermanto and A. SN, “Klasifikasi nilai kelayakan calon debitur baru menggunakan decision tree C4.5,” Indones. J. Comput. Cybern. Syst., vol. 11, no. 1, pp. 43–54, 2017.
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, pp. 4763–4771, 2018.
M. A. Muslim, S. H. Rukmana, E. Sugiharti, B. Prasetiyo, and S. Alimah, “Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis,” J. Phys. Conf. Ser., vol. 983, no. 1, 2018.
R. S. Rohman, R. A. Saputra, and D. A. Firmansaha, “Komparasi algoritma C4.5 berbasis pso dan ga untuk diagnosa penyakit stroke,” J. Comput. Eng. Syst. Sci., vol. 5, no. 1, pp. 155–161, 2020.
A. C. Pradana, Adiwijaya, and A. Aditsania, “Implementing binary particle swarm optimization and C4.5 decision tree for cancer detection based on microarray data classification,” J. Phys. Conf. Ser., vol. 1192, no. 1, p. 012014, 2019.
D. M. B. Tarigan, D. Palupi Rini, and Sukemi, “Particle swarm optimization – based on decision tree of C4.5 algorithm for Upper Respiratory Tract Infections (URTI) prediction,” J. Phys. Conf. Ser., vol. 1196, no. 1, p. 012077, 2019.
I. Ariyati, S. Rosyida, K. Ramanda, V. Riyanto, S. Faizah, and Ridwansyah, “Optimization of the decision tree algorithm used particle swarm optimization in the selection of digital payments,” J. Phys. Conf. Ser., vol. 1641, no. 1, p. 012090, 2020.
K. Chen, K. Wang, K. Wang, and M. Angelia, “Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data,” Appl. Soft Comput. J., vol. 24, pp. 773–780, 2014.
M. Tsai, K. Chen, C. Su, and H. Lin, “An application of PSO algorithm and decision tree for medical problem,” in 2nd International Conference on Intelligent Computational Systems, pp. 124–126, 2012.
E. Budianita and Novriyanto, “Klasifikasi status gizi balita berdasarkan indikator antropometri berat badan menurut umur menggunakan learning vector quantization,” Semin. Nas. Teknol. Informasi, Komun. dan Ind., vol. 7, pp. 213–220, 2015.
S. Kusumadewi, “Klasifikasi status gizi menggunakan naive bayesian classification,” CommIT (Communication Inf. Technol. J., vol. 3, no. 1, pp. 6–11, 2009.
U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI Magazine, vol. 17, no. 3, pp. 37–54, 1996.
E. Prasetyo, Data Mining - Mengolah Data menjadi Informasi Menggunakan Matlab. Yogyakarta: ANDI, 2014.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.