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Abstrak :
Tambak udang putih merupakan sebuah ekosistem buatan kompleks yang membutuhkan bantuan manusia untuk mempertahankan keseimbangan elemen-elemen pembentuknya. Permasalahan di dalamnya disebabkan oleh proses ekologis baik secara biologi, kimia, maupun fisika yang saling terkait. Sehingga untuk menghasilkan solusi optimal perlu diperhitungkan bagaimana keterhubungan antar elemen. Pengetahuan mengenai hubungan elemen-elemen tersebut umumnya dikuasai oleh pakar, tetapi tidak seluruh tambak mampu menyediakan pakar dalam budidayanya. Pengembangan sistem pakar dalam penelitian ini ditujukan untuk menjawab kebutuhan pakar di tambak udang menggunakan teknik klasifikasi. Pengetahuan pakar direpresentasikan dalam decision table dengan penggunaan multi atribut. Hasil eksperimen menunjukkan bahwa sistem mampu menghasilkan solusi dari berbagai variasi masalah yang mungkin terjadi di tambak seperti pakar dan fleksibel untuk dimodifikasi.

White shrimp embankment is a complex artificial ecosystem that requires human intervention to maintain the balance of its constituent elements. The problems inside are caused by ecological processes therein, either biology, chemistry, and physics that are interlinked so as to produce the optimal solution needs to be taken into account how the connection between elements. Knowledge about the relationships among these elements is generally dominated by experts, but not all embankments are able to provide experts in the cultivation. Development of expert systems in this study aimed to answer the needs of experts in shrimp embankments using the classification technique. Expert knowledge is represented in a decision table with the use of multi attributes. The experimental results show that the system is capable of generating solutions from a variety of problems that may occur in embankments such as expert and versatile to be modified.

Abstrak :
In most rule-based expert system, building of rules can easily be done. Knowledge engineer or expert does not have to do any work specifying rules and how they are linked to each other. Sometime the knowledge engineer or expert can reference rules or facts that have not yet been created. It seems to be a simple and an instant work. The problem due to the performance of the knowledge will not occur until the number of niles is getting higher. Some problem may appear in the form of inconsistent rules, unreachable rules, redundant rule and rotating chain of rules. In order to solve that problem and to achieve that mentioned performance, a rule-based knowledge acquisition system using Ternary Grid is developed. This system acquires knowledge from human expert using grid or matrix system. Ternary Grid represents a model of rule-based knowledge in a grid or matrix format.