Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization

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).

A sentiment analysis of madura island tourism news using C4.5 algorithm

“…Decision Tree has several algorithms, namely ID3, C4.5 and CART [22], where the C4.5 flowchart is presented in Figure 2. The C4.5 algorithm is known to exceed the Learning Vector Quantization (LVQ) algorithm with average accuracy and has a fast processing time [23].…”

Algoritma C4.5 Untuk Prediksi Bimbingan Siswa Berdasarkan Tipologi Hippocrates-Galenus

“…Penelitian oleh Xuanyuan dan Yue (2020) menggunakan algoritma C4.5 untuk pelayanan ansuransi dan finansial [12], penelitian yang dilakukan oleh Dony Fahrudi mengkalisfikasikan kepribadian dengan C4.5 dan naïve bayes [13], Penelitian tentang pengoptimasian entrophy pada Algoritma C4.5 oleh Sekhar Reddy [14], prediksi penyakit diabetes dengan Algoritma C4.5 oleh Sanni Ucha [15], lalu penelitian oleh Panji Bimo mengkalsifikasikan pohon keputusan dengan algoritma C4. 5[16], dan penelitian tentang klasifiksai gizi balita dengan algoritma C4.5 dan Swarm Optimization oleh Amanhy Akhyar [17], penelitian yang dilakukan oleh Aprianto Tumangor untuk memprediksi tingkat kemampuan anak [18], lalu penelitian untuk memprediksi kerusakan mesin ATM oleh Dahri Yani Hakim [19]. Setelah mendapatkan hasil penelitian ini pihak Guru BK di SMPN 1 Tembilahan dapat melakukan proses bimbingan secepat mungkin dan memberikan bimbingan yang diperlukan oleh siswa yang membutuhkan.…”

Machine Learning Method to Predict the Toddlers’ Nutritional Status

“…Several ML methods are used to classify/predict malnutrition or nutritional status in toddlers, including the naïve Bayes (NB) method [3][4][5][6], logistic regression [7], k-nearest neighbor (kNN) [4,5,8], decision tree (DT) [6,[9][10][11], support vector machine (SVM) [12], and learning vector quantization [13]. Several studies compare several ML methods to classify malnutrition in toddlers [4,[14][15][16][17][18][19].…”