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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Suprapto Suprapto
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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An Expert System of Chicken Disease Diagnosis by Using Dempster Shafer Method Yaqutina Marjani Santosa; Suprapto Suprapto; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 3 (2020): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.55632

Abstract

 Chicken is an animal that can provide many benefits for human life, meat and eggs can be used as food to fulfill the needs of human food, the excrement can be made fertilizer, and frequently its be used as a farm animal. Although it can provide many benefits, but for chicken farmers, the maintenance of chicken meet some obstacles that must be faced such as disease, poor environmental sanitation, and the production of eggs are declining. From some of the obstacles that have been mentioned, the most frequently encountered are animals infected with the disease. Based on the results of interviews that have been done to some chicken farmers, it can be said that the knowledge of chicken farmers against chicken disease and its handling is still very lacking. But the number of experts who understand and know about the type of chicken disease and the way of handling is limited, then it takes an expert system that can simulate knowledge and understanding of experts to overcome the problem. Based on the study of the libraries, the method suitable for use in the expert system is the Dempster shafer method by processing the value of belief in a disease. Dempster shafer method is a method used to calculate uncertainty due to the addition or reduction of new facts that will change the existing rules. Based on tests in 40 cases using an expert system applying the Dempster Shafer method, obtained the percentage of diagnostic compatibility result given by experts and system is 95%.
Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method Agung Pambudi; Suprapto Suprapto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 1 (2021): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.61627

Abstract

Based on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performance is assessed through students' learning evaluation system by giving comments to the instructions. These comments contain positive and negative sentiments that can be reviewed by conducting sentiment analysis. Research related to sentiment analysis in recent years has been widely done, but researchers rarely pay attention to the effect of sentence length from the dataset on the method's performance. This study tried to analyze sentiment related to sentence length effect on ICT training student comments using Support Vector Machine and Convolutional Neural Network methods. This study concluded that the sentence length on the dataset would affect the SVM and CNN methods' performance when combined with Word2vec. While the SVM+TFIDF method performance is not affected by sentence length, this method has the fastest process time than other methods. The CNN+Word2vec method produced the best performance in this study with a value of 0.94% accuracy, 0.95% precision, 0.96% recall, and 0.95% f1-score.
Co-Authors Agung Pambudi Wahyono Wahyono Yaqutina Marjani Santosa