PREDIKSI CLUSTERING, CALCULATION DAN CLASSIFICATION FRUIT AND VEGETABLE CONSUMPTION

ADRIYENDI ADRIYENDI

doi:10.31958/js.v7i2.135

PREDIKSI CLUSTERING, CALCULATION DAN CLASSIFICATION FRUIT AND VEGETABLE CONSUMPTION

ADRIYENDI ADRIYENDI

Abstract

Prediction model using combination of K-Means Clustering, Excel Function, and Naïve Bayes Classifier. Process is dataset, clustering, calculation, classification and prediction. Dataset source on BPS 2013 about consumption of fruit and vegetable. Clustering using K-Means Clustering. Clustering by output Cluster 1, Cluster 2, and Cluster 3. Calculation using Excel. Calculation by output Priority Yes and Priority No. Classification using Naïve Bayes Classifier. Classification by output Class Good and Class Bad. All data processing for clustering, calculation, and classification using Excel. Experimental results on BPS 2013 Dataset show percentage of fruit consumption 42,42% Class Good (class above average) and percentage fruit consumption of fruit consumption 57,58% Class Bad (class below average). Percentage of vegetable consumption 45,45% Clas Good (class above average) and percentage of vegetable consumption 54,55% Class Bad (class below average). Clustering, calculation and classification can be combined becamed prediction model.

Key words: clustering, calculation, classification, fruit and vegetable consumption

Full Text:

PDF (Bahasa Indonesia)

References

S. M. Perdana, Hardinsyah, dan E. Damayanthi, (2013). Alternative of balanced diet index to assess nutritional quality of diet in indonesian adult females, Journal of Nutrient & Food, vol. 9, no. 1, pp. 43-50.

A. A. Candra, B. Setiawan, dan M. R. M. Damanik, (2013). The effect of snack feeding, nutrition education, and iron suplementation to nutritional status, nu-trition knowledge, and anemia status in elementary school students, Journal of Nutrient & Food, vol. 8, no. 2, pp. 103-108.

M. Fasitasari, (2013). Nutrition therapy in elderly with chronic obstructive pulmonary diseas, Sains Medika Journal, vol. 5, no. 1, pp. 50-61.

X. Wang, Y. Ouyang, J. Liu, M. Zhu, G. Zhao, W. Bao, dan F. B. Hu, (2014). Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies, Bio. Med. Journal, pp. 1-14.

O. Stackelberg, M. Björck, S. C. Larsson, N. Orsini, dan A. Wolk, (2013), Fruit and vegetable consumption with risk of abdominal aortic aneurysm, Circulation: Journal of the American Heart Association, vol. 128, pp. 795-802.

T. S. Conner, K. L. Brookie, A. C. Richardson, dan M. A. Polak, (2014). On carrots and curiosity: eating fruit and vegetable is associated with greather flourishing in daily life, British Journal of Health Psychology, pp. 1-31.

S. Shukla dan S. Naganna, (2014). A review on k-means data clustering approach, Int. Journal of Information & Computation Technology, vol. 4, no. 17, pp. 1847-1860.

G. Liu, S. Huang, C. Lu, dan Y. Du, (2014). An imporved k-means algorithm based on association rules, Int. Journal of Computer Theory and Engineering, vol. 6, no. 2, pp. 146-149.

Y. Kumar dan G. Sahoo, (2014). A new initialization method to originate initial cluster centers for k means algortihm, Int. Journal of Advanced Science and Technology, vol. 62, pp. 43-54.

K. K. Manjusha, K. Sankaranarayanan dan P. Seena, (2014). Prediction of different dermatological conditions using naïve bayesian classification, Int. Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 1, pp. 864-868,

Bustami, (2014). Implementing naïve bayes algorithm for classification customer data insurance, Informatic Journal, vol. 8, no. 1, pp. 1-15.

V. Shukla dan S. Vashishtha, (2014). New hybrid intrusion detection system based on data mining technique to enhanced performance, Int. Journal of Computer Science and Information Security, vol. 12, no. 6, pp. 14-19.

W. Yassin, N. I. Udzir, Z. Muda, dan M. N. Sulaiman, (2013). Anomaly-based intrusion detection through k-means clustering and naïves bayes classification, Proceedings of the 4th International Conference on Computing and Informatics, 28-30 August, Sarawak, Malaysia, pp. 298-303.

M. Banerjee dan R. Soni, (2013). Design and implementation of network intrusion detection system by using k-means clustering and naïve bayes, Int. Journal of Science, Engineering and Technology Research, vol. 2, no. 3, pp. 756-760.

Y. Emami, M. Ahmadzadeh, M. Salehi dan S. Homayoun, (2014). Efficient intrusion detection using weighted k-means clustering and naïve bayes classification, Journal of Emerging Trends in Computing and Information Sciences, vol. 5, no. 8 pp. 620-623.

N. O. F. Elssied dan O. Ibrahim, (2014). K-means clustering scheme for enhanced spam detection, Research Journal of Applied Sciences, Engineering and Technology, vol.7, no.10, pp. 940-1952.

N. Sharma dan S. Niranjan, (2013). Performance enhancement using combinatorial approach of classification and clustering in machine learning, Int. Journal of Application or Innovation in Engineering & Management, vol. 2, no. 4 pp. 71-78.

DOI: http://dx.doi.org/10.31958/js.v7i2.135