• Ilham Ilham Universitas Muhammadiyah Gresik Jl.Sumatra 101 GKB Gresik



Model, Bayessian Network, coastal, Hybrid algorithm


This study was conducted to see the trend of diseases caused by unhealthy lifestyles on disadvantaged communities and coastal villages around Gresik and Tuban using hybrid algorithms through the construction of the structure of Bayesian Network. The problem to be solved in this study is no system that can detect a relationship between unhealthy behavior that caused the disease. Model of this structure has never been applied directly in the field to detect a causal events for example, if a behavior is unhealthy will arise disease. Application of this model needs to be done with a field study to determine and prove the actual benefits of the concept of a hybrid construction of Bayesian network structure. The purpose of this research is to produce a software model capable of early detection of disease risk propensity underdeveloped rural and coastal communities who have unhealthy lifestyles in the form of construction of the structure and generates a probability value with a tendency disease. The comparison between the structure of the origin of the structure of the trial results indicate the level of suitability for complete test data difference of 10% to the original structure, and suitability for the test data is incomplete for more than 20% depending on the amount of his missing value. The validity of that smoke will have the tendency has tuberculosis disease, bronchitis or Lung Cancer through the test system is 80% to 90%.


Sitohang, B., & Saptawati, P. (2006). Improve-ment of CB & BC Algorithm (CB* Algorithm) for Learning Structure of Bayesian Networks as Classifier in Data Mining. Sekolah Teknik Elektro dan Informatika, ITB.J.ICT, Vol.1, No.1, 2007, 29-41

Sandhyaduhita, P.I. (2005). Algoritma CB: Algo-ritma yang Dibangun dengan Dua Pendekatan untuk Konstruksi Struktur Bayesian Network dalam Data Mining. Program Studi Teknik Infor¬matika, STEI, ITB.

Selvia Lorena Br Ginting (2008). Studi algo¬ritma CB Dalam Data Mining untuk Konstruksi Struktur Bayesian Network dari Basis Data Incomplete, Konferensi Nasional Sistem dan Informatika 2008; Bali, November 15, 2008, KNS,108-038

Cheng, J., Bell, D., & Liu, W. (1997). An Algorithm for Bayesian Belief Networks Con-struc¬tion from Data . Proceeding of Ai & STAT ’97 (pp.83-90). Ft. Lauderdale, Florida.

Cheng, J., Bell, D., & Liu, W. (1998). Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory. Faculty of Informatics, University of Ulster, U.K.

Sebastiani, P., & Ramona, M. (1997). Bayesian Inference with Missing Data Using Bound and Collapse. Report KMi-TR-58, Knowledge Media Institute, The Open University.

Singh, M., & Valtorta, M. (1995). Construction of Bayesian Network Structures from Data: A Brief Survey and an Efficient Algorithm. Dept. of Computer Science, University of South Caro-lina, Columbia, USA.

Simanjuntak, H. (2006). Pengembangan Algo-ritma CB untuk Konstruksi Struktur Bayesian Network dari Data Tidak Lengkap. Program Studi Teknik Informatika, STEI, ITB.

Maharani, H. (2005). Konstruksi Struktur Baye-sian Network dalam Data Mining untuk Basis Data Incomplete dengan Metode Bound and Collapse. Program Studi Teknik Informatika, STEI, ITB.

Neapolitan, R.E. (2004). Learning Bayesian Networks. USA: Pearson Pentice Hall.

Cheng, Jie, et al. (2001). Learning Bayesian Network from data: An Information-Theory Based Approach. Department of Computing Science, University of Alberta., Faculty of Infor-matics, University of Ulster, Toronto, Canada.