Penjadwalan Proses Produksi Menggunakan Ant Colony Algorithm

Pada penelitian ini telah dilakukan analisa penjadwalan pada sektor industri garment, penelitian ini dilakukan dengan tujuan untuk mengoptimalkan proses produksi pada Perusahaan CV.Inda Jaya sebagai alternatif pemecahan masalah yang bisa digunakan untuk membantu mengoptimalkan waktu pemrosesan produksi dengan menggunakan ant colony algorithm, algoritma tersebut mempunyai tingkat keberhasilan yang tinggi, ant colony algorithm juga banyak digunakanuntuk menyelesaikan berbagai macam proses penjadwalan baik di bidang industri maupun bidang lainnya, ant colony algorithm sendiri banyak dikembangkan karena memang memberikan hasil yang memuaskan dalam menemukan rute terpendek untuk menyelesaikan permasalahan penjadwalan,proses yang dilakukan pada penelitian ini terbagi dua tahap dimana tahap pertama yaitu mengolah bahan mentah kemudian menjadi bahan setengah jadi yang diproses pada tahap dua yaitu proses finishing, hasil dari penelitian ini adalah generate otomatis makespan dengan menggunakan ant colony algorithm, pada tahap pertama menghasilkan jumlah makespan sebanyak 110 menit hasil tersebut menghemat waktu pemrosesan produksi sebanyak 10 menit dan pada tahap kedua menghasilkan waktu makespan sebanyak 140 menit hasil tersebut menghemat waktu sebanyak 40 menit

Average Hashing for Perceptual Image Similarity in Mobile Phone Application

Common problem occurs in almost all mobile devices was duplicated data or files. Such as duplicated images that often happen by event like capturing perceptually similar photos by the user, or images that shared several times in messaging applications chat groups. This common problem can be solved by manually search and remove the duplicated images one by one by the users, but better solutions is by building automated application that search perceptually similar images then provide the result to the users. We study and implementing Average Hashing and Hamming distance for perceptual image similarity into application under mobile phone platform to realize the solution for the problem. The result was very promising in speed and accuracy for finding perceptually similar images under limited resources device like mobile phone

Determining Study Groups Based on Student Profile Criteria Using K-Means Method

Students of PTI FT-UNP Study Program are arranged into study groups based on one criterion, namely the entry route and is carried out on new students. As a result, a study group that is formed has far differences between one group and another regarding academic ability, regional origin, school origin and other factors. This is not beneficial for the interest and motivation to learn students and teachers also have difficulty in determining learning strategies between student study groups. Improvements in the preparation of study groups can be done by determining student profiles as the basis for preparation. Through the application of the K-Means method which is implemented in an application, the student clustering which is used as the basis for the distribution of the formation of study groups is proven to be better. The resulting study group succeeded in showing a heterogeneous study group among its members and homogeneous in each study group