Alphanumeric writing ability is the main provision for children to be able to understand the subjects given in school. One alternative to help increase children's interest and ability in learning to write Alphanumeric is to use learning media in the form of software. This study is to create an application that presents Alphanumeric writing learning that is intentionally packaged in the form of a game in order to increase the learning interest of preschool age children. In this study used the method of recognition of character letters made by the player. In character recognition, the application will record the handwriting that the user has created on the board provided. The handwriting will then be used at the image processing stage that uses Tesseract as the recognition engine. And from the results of the trials, preschool children easily manage it.

AbstrakPerkembangan Teknologi Informasi dan Komunikasi (TIK) semakin meningkat dan telah merubah wajah baru pendidikan. Implementasi Teknologi Informasi dan Komunikasi (TIK) pada lembaga pendidikan yang saat ini perlu ditingkatkan dan sudah menjadi sebuah keharusan pada institusi pendidikan. Tidak menutup mata bahwa perguruan tinggi di Indonesia sudah sewajarnya tanggap terhadap perkembangan teknologi. Penggunaan Mobile Learning (M-Learning) sebagai penunjang proses belajar mengajar dirasa dapat menambah fleksibilitas dalam kegiatan belajar mengajar apalagi untuk anak usia prasekolah. Anak-anak pada usia ini harus diperkenalkan pada manfaat keberadaan teknologi. Agar dimasa depan, anak-anak pada usia ini tidak salah dalam memanfaatkan teknologi. Aplikasi M-Learning memberikan kemudahan bagi siswa didik dalam melakukan pembelajaran dimanapun dan kapanpun dengan menggunakan perangkat mobile. Kemampuan pembelajaran di fokuskan pada pembuatan aplikasi pembelajaran alphanumerik dan pengenalan hewan pada anak usia pra-sekolah. Karena dasar dari pendidikan adalah pembelajaran alphanumeric dan pengetahuan terkait hewan. Alphanumerik merupakan salah satu syarat utama bagi anak usia pra-sekolah untuk meningkatkan pemahaman dalam mata pelajaran yang diberikan di sekolah. Aplikasi M-Learning dibuat dengan memanfaatkan mobile android dan Augmented Reality.

The development of Artificial Intelligence or commonly abbreviated (AI) is defined as the intelligence of scientific entities. Artificial Intelligence is one method on a computer controller that is used to take a decision and perform one of the actions. But not all controllers on the computer can see the entire location of the target, even in some games still using the location matrix to find the location of the nearest enemy, so that if there are so many targets it can make a miscalculation on the computer controller. Researchers try to use the A-Start algorithm (A *) as Artificial Intelligence to see the development of the game if there are more than one target that must be killed by the enemy. So that in the future in a Pac-Man game can be given several targets (multiplayer).

Rhizomes, also called rootstalks, are stems that help plants to reproduce asexually, survive in winter, store food, and make stem tubers. They possess many functions and merits. Some of the commonly found rhizomes are ginger, turmeric, and galangal, yet still a lot of people still find it difficult to distinguish those three rhizomes. That's because those mentioned rhizomes do share several similarities in their shape and texture. This research submits a rhizome identification system with SVM (Support Vector Machine) classification method. Based on the experiments done, this particular method is chosen because it showed great results, quite high-valued accuracy level for data classification, and has minimum error rate. The types of rhizomes used in this research's dataset are those three varieties mentiones above, while the amount of images in this experiment consists of 150 training images and 30 testing images. The experiment is done by calculating the accuracy value from data testing classification of three classes, which are ginger class, turmeric class, and galangal class utilizing the mentioned method. This rhizome identification system that uses the SVM classificafion method gets 78% accuracy value.

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