Disease is an abnormal condition in the body that causes body misalignment. There are various types of diseases that threaten humans, both parents and children. This disease can be caused by germs, bacteria, viruses, toxins, organ failure to function, and also by inherited/hereditary diseases. The difficulty of parents to find out the disease suffered by their children is one of the problems of parents today. So, we need a system to help with this predicament. The purpose of making this application is to provide information quickly and accurately in solving problems to help consult about diseases in toddlers aged 0-5 years. In addition, to find out ways to make programs that are expert systems using programming languages for artificial intelligence applications, namely PHP and Mysql, the certainty factor method is applied in web form. Using the certainty factor method is a decision-making strategy that starts from the section premise to conclusion. The result of system implementation is that the user chooses from the symptoms that already exist in the system based on the existing symptoms then processed, from the process the system provides information on diseases in children suffered by toddlers. From the results of testing this expert system has been able to diagnose diseases in children. After the diagnosis, the types of diseases and solutions will appear. Diagnosing disease in children by using this certainty factor is expected to make it easier to diagnose disease in children

<p>Telkomsel sebagai salah satu operator selular terbesar di Indonesia yang menyediakan layanan data / internet tentunya harus selalu menjamin kualitas layanan yang terbaik bagi pelanggannya, untuk itu Telkomsel harus memastikan availability perangkat produksinya agar selalu prima, khususnya perangkat core. SGSN (Serving GPRS Support Node) dan GGSN (Gateway GPRS Support Node) sebagai komponen penting dalam core network Telkomsel harus dipastikan dalam kondisi optimal, untuk itu diperlukan pengecekan / health check secara rutin setiap hari. Adapun proses pengecekan / health check terhadap perangkat SGSN dan GGSN di Telkomsel Regional Sumbagteng saat ini masih dilakukan secara manual, dimana petugas melakukan remote connection ke masing-masing perangkat satu persatu melalui OSS Server dan menjalankan prosedur / command untuk melihat parameter-parameter kondisi SGSN dan GGSN tersebut, dimana output hasil print command health check sangat banyak sekali, sehingga proses pengecekan (health check) cenderung lama dan memunculkan potensi human error. Oleh karena itu maka penulis tertarik melakukan penelitian dengan judul “Otomatisasi Proses Health Check Perangkat SGSN dan GGSN Telkomsel Sumbagteng Berbasis Web”. Otomasisai proses ini menggunakan bahasa pemrograman PHP dengan Apache Web Server dan database MySQL. Proses pengambilan data dilakukan secara otomatis oleh sistem dan interface web sudah menampilkan parameter-parameter dalam bentuk resume, sehingga mempermudah dan mempercepat proses. Hasil dari pengujian adalah pada otomatisasi Proses Health Check Perangkat SGSN dan GGSN Telkomsel Sumbagteng Berbasis Web, proses dapat dilakukan secara rutin terjadwal dan otomatis, sehingga tidak perlu mengkoneksikan ke perangkat satu persatu, sehingga mempermudah dan mempercepat proses.</p>

The Faculty of Computer Science, Lancang Kuning University, as a private university in the city of Pekanbaru, uses the Sevima Edlink platform as a media for academic information systems and online learning. According to some students, there are still some obstacles encountered in understanding, using and functioning this edlink application. The purpose of this study was to measure the level of satisfaction of students of the Faculty of Computer Science in using Edlink using the C4.5 and Naïve Bayes algorithms. To measure the level of accuracy of the C4.5 and Naïve Bayes algorithms in order to measure the level of student satisfaction, the indicators used are the Servqual testing model, namely Tangible, Reability, Responsiveness, Assurance, and Empathy. Based on the level of accuracy of the two methods. In the dataset used there were 91 student respondents who had filled out the questionnaire. From the questionnaire data, it was then processed using both methods and 9 comparisons of the different Training Data and Testing Data were carried out. In general, students are satisfied and understand the use of the edlink application. This satisfaction was tested using the C4.5 Decision Tree Algorithm and the Naïve Bayes Classifier. Based on the comparison that has been carried out using the C4.5 Decision Tree Algorithm, it produces an average accuracy value of 77.78%, which is slightly more accurate than the Naïve Bayes Classifier which produces an average accuracy value of 71.11%.