PEMBANGKIT PARAFRASA BAHASA INDONESIA BERBASIS ATURAN

Parafrasa adalah pengungkapan kembali suatu tuturan dari sebuah tingkatan atau macam bahasa menjadi tuturan yang lain tanpa mengubah pengertian. Banyak kasus plagirisme yang terjadi terhadap karya tulis orang lain, yakni salah satunya pada sebuah karya tulis ilmiah berupa skripsi. Hal tersebut dibuktikan bahwa dari 282 dokumen proposal TA 142 dokumen proposal TA yang diterima dan 140 dokumen proposal TA yang ditolak, 94 dokumen terdeteksi plagiat. Setelah dilakukan pencarian, saat ini di Indonesia tidak ada penelitian orang Indonesia tentang bagaimana membangkitkan parafrasa. Hanya saja terdapat penelitian orang Indonesia oleh Julianto dkk (2017) tentang bagaimana mengidentifikasi parafrasa. Penelitian ini bertujuan untuk membuat aplikasi yang dapat membangkitkan parafrasa bahasa Indonesia menggunakan metode Rule-Based. Proses membangkitkan parafrasa yakni mengubah kosakata dengan menggunakan sinonim, mengubah kalimat aktif menjadi kalimat pasif, dan mengubah struktur kalimat. Membangkitkan parafrasa mengikuti aturan Tata Bahasa Indonesia yang ditulis oleh Abdul Chaer. Berdasarkan hasil pengujian yang telah dilakukan oleh Plagiarism Checker bahwa teks asli sebelum di parafrasa terdeteksi memiliki Similiarity sebesar 2% sedangkan teks hasil parafrasa oleh algoritma terdeteksi tanpa memiliki Similiarity sebesar 0% dan berdasarkan hasil pengujian sebanyak 50 teks, diperoleh 30 teks yang hasil parafrasanya tidak merubah maknanya dan telah dievaluasi oleh pakar Bahasa Indonesia sehingga didapatkan akurasi sebesar 60%

Using metarules to integrate knowledge in knowledge based systems. An application in the woodworking industry

The current study addresses the integration of knowledge obtained from Data Mining structures and models into existing Knowledge Based solutions. It presents a technique adapted from commonKADS and spiral methodology to develop an initial knowledge solution using a traditional approach for requirement analysis, knowledge acquisition, and implementation. After an initial prototype is created and verified, the solution is enhanced incorporating new knowledge obtained from Online Analytical Processing, specifically from Data Mining models and structures using meta rules. Every meta rule is also verified prior to being included in the selection and translation of rules into the Expert System notation. Once an initial iteration was completed, responses from test cases were compared using an agreement index and kappa index. The problem domain was restricted to remake and rework operations in a cabinet making company. For Data Mining models, 8,674 cases of Price of Non Conformance (PONC) were used for a period of time of 3 months. Initial results indicated that the technique presented sufficient formalism to be used in the development of new systems, using Trillium scale. The use of 50 additional cases randomly selected from different departments indicated that responses from the original system and the solution that incorporated new knowledge from Data Mining differed significantly. Further inspection of responses indicated that the new solution with additional 68 rules was able to answer, although with an incorrect alternative in 28 additional cases that the initial solution was not able to provide a conclusion