CASE BASED REASONING DAN RULE BASED REASONING UNTUK DIAGNOSIS PENYAKIT GIZI BURUK PADA BALITA; CASE BASED REASONING AND RULE BASED REASONING FOR DIAGNOSIS MALNUTRITION AMONG CHILDREN UNDER FIVE YEARS OLD

NURFALINDA, Sri Hartati

2016 | Disertasi | FMIPA

This research is conducted to build a diagnose system malnutrition among children under five years old. The system was developed with Case Based Reasoning (CBR) and Rule Based Reasoning (RBR). CBR is a case based reasoning system, using old knowledge to solve new problems. CBR can provide new solutions to problems by looking at most similarity case to the previous cases that have been stored in the base case. In this research the process of CBR using a bayesian model indexing to find the type of disease malnutrition among children under five years old. The nearest neighbor methode used in the process to determine the most similar of cases between new cases and the old cases that have been stored in the database as a case base. RBR is a rule based reasoning by making the rules that derived from experts and books of knowledge about the disease of malnutrition among children under five years old. The method used to RBR is the certainty factor that used to uncertainty of paramedics to the value CF obtained from experts. Tests carried out by using 70 case based were recorded in case of data based and 20 case based serve as a new case. Testing is done with four scenarios. The first scenario is to use the CBR, system able to produce accuracy 85% with threshold 0,75. The second scenario with CBR-RBR with CBR system first with accuracy of 90% and the third scenario with testing accuracy by RBR system is 90%. The fourth scenario with RBR-CBR, with RBR system first with accuracy of 100%.

Kata Kunci : Malnutrition, children under five years old, CBR, RBR, bayesian, nearest neighbor, certainty factor.