Implementasi Metode Naïve Bayes Classifier Untuk Klasifikasi Status Gizi Stunting Pada Balita

Titimeidara, Monica Yoshe; Hadikurniawati, Wiwien

doi:10.33884/jif.v9i01.3741

Cited by 13 publications

(16 citation statements)

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“…Dalam pengukuran usia dibawah 60 bulan cenderung memakai berat badan dibagi umur (BB/U) [9]. Pada penelitian sebelumnya, implementasi Naive Bayes Classifier dalam klasifikasi status gizi stunting pada balita dengan 275 data training dan 25 data testing menggunakan parameter berat badan, tinggi badan, usia, jenis kelamin dan status ekonomi memperoleh hasil akurasi 88% [10]. Penelitian lain menggunakan K-Nearest Neighbor juga pernah dilakukan dan memperoleh nilai akurasi sebesar 73.53% dengan hasil pengujian klasifikasi status gizi pada balita menggunakan jumlah data sebanyak 170 dengan 136 data sebagai data latih dan 34 data sebagai data uji dan nilai K = 5 [11].…”

Section: Pendahuluanunclassified

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Setiawan

Triayudi²

2022

mib

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One of the efforts in increasing the progress of a country is improving the quality of nutrition. Good balanced nutrition can increase the body's endurance, increase the level of intelligence and make a person develop normally. Infancy is a period of development and growth that requires more nutritional content than other age groups, making it the most vulnerable to nutritional disorders. Nutritional status under 5 years can be seen using the Anthroperti method. Calculations done manually make the process take a long time and errors often occur in entering data. Data mining can make decisions on the nutritional status of toddlers faster by looking at patterns in previous data. Naïve Bayes and K-Nearest Neigbor are one of the methods in classification. Naïve Bayes has the advantage of being able to achieve high accuracy values with minimal training data. Meanwhile, the K-Nearest Neighbor method has the advantage of being able to work optimally on data that has random errors (noise). Using 412 nutritional data for toddlers, the results obtained with Naïve Bayes getting an accuracy of 80.60%, while with K-Nearest Neighbor, an accuracy of 91.79% was obtained.

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Section: Pendahuluanunclassified

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Setiawan

Triayudi²

2022

mib

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“…Pemilihan metode Naive Bayes didasarkan pada nilai akurasi yang cukup tinggi pada berbagai data penelitian, diantaranya adalah klasifikasi terhadap data nasabah asuransi yang menghasilkan kesimpulan bahwa metode ini memiliki akurasi yang cukup baik berdasarkan percobaan yang telah dilakukan sebanyak dua kali yaitu sebesar 80% di percobaan pertama dan di percobaan kedua sebesar 74,67% [6]. Penelitian selanjutnya menggunakan metode Naive Bayes untuk mengklasifikasikan penyakit diabetes, status gizi, kinerja satpam, dan penerima kartu Indonesia sehat, dengan persentase akurasi diatas 85% [7]- [10]. Selain itu algoritma Naive Bayes Classifier mampu memiliki tingkat error minimal bila dibandingkan dengan algoritma klasifikasi lainnya [10].…”

Section: Pendahuluan 1latar Belakangunclassified

“…Penelitian selanjutnya menggunakan metode Naive Bayes untuk mengklasifikasikan penyakit diabetes, status gizi, kinerja satpam, dan penerima kartu Indonesia sehat, dengan persentase akurasi diatas 85% [7]- [10]. Selain itu algoritma Naive Bayes Classifier mampu memiliki tingkat error minimal bila dibandingkan dengan algoritma klasifikasi lainnya [10].…”

Section: Pendahuluan 1latar Belakangunclassified

Quality Classification of Palm Oil Varieties Using Naive Bayes Classifier

et al. 2022

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As one of the leading commodities of the Indonesian economy, the ever-increasing production of palm oil has created intense competition among palm oil (CPO) producers. This causes CPO producers to increase their palm oil production without compromising the quality of the palm oil produced. CPO producers are required to be able to objectively determine the quality of superior and precise oil palm varieties in order to produce high economic value palm oil. Therefore, a model is needed to determine the quality of oil palm from several existing varieties. The Naive Bayes Classifier method in this study was used to classify the quality of oil palm based on predetermined variables using a data set of 28 oil palm varieties. Method testing is done by using a confusion matrix and K-fold cross-validation scheme. This study shows a reasonably high accuracy value of 64.25% and a low error rate of 35.7%, indicating that the Naive Bayes Classifier can classify the quality of oil palm varieties quite well.

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“…Stunting is a condition disorder where toddlers have less length or height when compared to their age (Desyanti & Nindya, 2017) and lack nutritional intake in infants (Perdana et al, 2021). One of the impacts of malnutrition on babies can result in a slowing posture during growth (Titimeidara & Hadikurniawati, 2021). This condition is measured by a length or height more than minus two standard deviations of the median child growth standard from the World Health Organization (WHO) (Rachmi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

2023

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Several studies from Indonesia reveal that malnutrition and stunting are still severe concerns to be addressed in the future. The complexity of the problem of stunting or nutritional status requires the responsibility of all parties, including science and technology. The issue of monitoring and data collection related to stunting or the nutritional status of children in Indonesia, especially Medan City, North Sumatra Province, is an essential factor in determining the calculations carried out by each Community Health Center with many attributes. Currently, the Support Vector Machine method is a solution to increase government intervention's effectiveness in classifying malnutrition and stunting. However, the Support Vector Machine algorithm still needs to improve, namely the difficulty of selecting the right and optimal features for the attribute weights, causing a low prediction accuracy. Therefore, researchers aim to optimize the Support Vector Machine Algorithm with Particle Swarm Optimization using Linear, Polynomial, Sigmoid, and Radial Basic Function kernels. The results were obtained from research utilizing nutritional status data, that performance in improving the Support Vector Machine algorithm based on Particle Swarm Optimization using four kernel tests, namely Linear, Polynomial, Sigmoid, and Radial Basic Function obtained different results, not all kernels in this study can improve accuracy well. The best performance is using the Radial Basic Function kernel with an Accuracy value of 78%, Precision of 89%, Recall of 66%, and F1-Score of 72%, so it is feasible for accurate information regarding the classification of nutritional status.

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Implementasi Metode Naïve Bayes Classifier Untuk Klasifikasi Status Gizi Stunting Pada Balita

Cited by 13 publications

References 0 publications

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Quality Classification of Palm Oil Varieties Using Naive Bayes Classifier

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

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