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A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

Image Segmentation is a process to separate between foreground and background. Segmentation process in low contrast image such as dental panoramic radiograph image is not easily determined. Image segmentation accuracy determines the success or failure of the final analysis process. The process of segmentation can occur ambiguity. This ambiguity is due to an ambiguous area if it is not selected as a region so it may have occurred cluster errors. To solve this ambiguity, we proposed a new region merging by iterated region merging process on dental panoramic radiograph image. The proposed method starts from the user marking and works iteratively to label the surrounding regions. In each iteration, the minimal gray-levels value is merged so the unknown regions significantly reduced. This experiment shows that the proposed method is effective with an average of ME and RAE of 0.04% and 0.06%.

Algoritma Genetika (GA) adalah salah satu algoritma yang powerful untuk menyelesaikan masalah penjadwalan mata kuliah. Pada GA, terdapat operator crossover yang berperan aktif dalam pembuatan anak atau offspring. Crossover juga menjadi fondasi dalam menghasilkan solusi yang optimal. Kesalahan dalam pemilihan crossover membuat meningkatnya tingkat pelanggaran atau fitness terhadap constraint. Semakin tinggi nilai Fitness maka semakin buruk solusi yang dihasilkan. Pada penelitian ini, dilakukan analisis terdapat jenis crossover yang ada di GA yaitu One-Point Crossover, Multi-Point Crossover dan Order Crossover. Analisis yang dilakukan pada penelitian ini adalah dengan membandingkan nilai fitness dan waktu eksekusi antara jenis crossover tersebut. Hasil penelitian menunjukkan bahwa nilai fitness yang paling kecil dapat dihasilkan oleh One-Point Crossover pada 9 dataset. Untuk waktu eksekusi yang paling cepat dapat dihasilkan oleh Multi-Point Crossover pada 12 dataset.Kata kunci; Algoritma Genetika, Crossover, Penjadwalan, Pelanggaran

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