Pariwisata merupakan sektor yang sangat penting bagi pemerintah Indonesia, Salah satu bagian dari industri kepariwisataan adalah kebun binatang. Namun dengan semakin banyaknya jumlah kebun binatang di Indonesia pemerintah mengalami kesulitan dalam mengklasifikasikan jenis binatang terutama saat terjadi penambahan hewan baru. Berbagai maca teknologi informasi telah membantu manusia dalam menyelesaikan pekerjaan. Saat ini Machine Learning yang merupakan pendekatan dalam melakukan analisis prediksi terhadap berbagai permasalahan yang dihadapi manusia berkembang dengan pesat. Neural Network merupakan suatu algoritma machine learning yang terbukti tepat dalam memprediksi dan banyak dipergunakan peneliti. Penelitian ini menggunakan Neural Network dalam memprediksi jenis hewan yang ada di kebun binatang sebagai salah satu upaya membantu pemerintah dalam menyediakan informasi yang dibutuhkan. Menggunakan Neural Network diperoleh hasil akurasi sebesar 98% dengan tingkat presisi sebesar 98.1% dan Recall sebesar 98 %.

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Mobile learning has been used in the learning process in several tertiary institutions in Indonesia. However, several universities have not been able to implement mobile learning due to the limitations of computer and network infrastructure. Cloud computing is a solution for agencies that experience limitations in computer infrastructure in the form of internet-based services for their customers. This paper discusses the implementation of mobile cloud learning which is a combination of mobile learning technology and cloud computing using the renewal DeLone and McLean Model which is a successful model to measure how important the implementation of a mobile cloud learning system is. The results showed that from the F test results obtained Fcount of 13,222, then Fcount> Ftable (13,222> 3.01), then Ho was rejected and H1 was accepted. So it can be concluded Information Quality, System Quality, and Service Quality together affect the Intensity of Use.

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Quality research will not be separated from controlling systems that require a review mechanism. This demand considers it necessary to form an assessment committee or reviewer that ensures that all processes proceed towards the target target. The internal reviewer selection process is carried out by looking at several requirements of each prospective reviewer. The selection process is carried out by looking at the requirements files one by one. For this reason, it is necessary to optimize the method that is able to manage the assessment data of prospective reviewers who have the highest rating value from the results of weight calculations. Decision making in determining internal reviewers requires a method that can provide optimal decision results in terms of relatively fast processing time. The decision support method applied in determining internal reviewers is Simple Additive Weighting (SAW). The reason for choosing the SAW method in this study, the method has a basic concept that is used to find weight values on the performance rating of each alternative on all attributes. The SAW method is commonly known as the weighted summation method. There are six criteria used and fifty-five records for alternatives used. The results of the SAW method ranking obtained by A20 have the highest preference value of 0.77. This study shows the optimality of the SAW method in providing decision results based on an accuracy test value of 80%.