Abstract. Cloud Computing is quickly emerging as a promising paradigm in the recent years especially for the business sector. In addition, through cloud service providers, cloud computing is widely used by Information Technology (IT) based startup company to grow their business. However, the level of most businesses awareness on data security issues is low, since some Cloud Service Provider (CSP) could decrypt their data. Hybrid Cloud Deployment Model (HCDM) has characteristic as open source, which is one of secure cloud computing model, thus HCDM may solve data security issues. The objective of this study is to design, deploy and evaluate a HCDM as Infrastructure as a Service (IaaS). In the implementation process, Metal as a Service (MAAS) engine was used as a base to build an actual server and node. Followed by installing the vsftpd application, which serves as FTP server. In comparison with HCDM, public cloud was adopted through public cloud interface. As a result, the design and deployment of HCDM was conducted successfully, instead of having good security, HCDM able to transfer data faster than public cloud significantly. To the best of our knowledge, Hybrid Cloud Deployment model is one of secure cloud computing model due to its characteristic as open source. Furthermore, this study will serve as a base for future studies about Hybrid Cloud Deployment model which may relevant for solving big security issues of IT-based startup companies especially in Indonesia.
Theft and infiltration as well as robbery are an incident that should be anticipated. The security of the room is a main priority to prevent such occurrences. This paper will be explain how to the rule based reasoning method is part of the expert system that can be used in a security room systems by monitoring the temperature change in the room as well as detecting movement in the room by using the temperature sensor and motion detector. The output indicators is the buzzer sounds that will be activated when there is an increase in the temperature and/or there is a movement in the room.
Data is something that can be manipulated by irresponsible people and causing fraud. The use of log files, data theft, unauthorized person, and infiltration are things that should be prevented before the problem occurs. The authenticity of application data that evaluates the performance of company employees is very crucial. This paper will explain how to maintain the company’s big data as a valid standard service to assess employee performance database, based on employee performance of MariaDB or MySQL 5.0.11 with InnoDB storage engine. The authenticity of data is required for decent digital evidence when sabotage occurs. Digital forensic analysis carried out serves to reveal past activities, record time and be able to recover deleted data in the InnoDB storage engine table. A comprehensive examination is carried out by looking at the internal and external aspects of the Relational Database Management System (RDBMS). The result of this research is in the form of forensic tables in the InnoDB engine before and after sabotage occurs.
E-Learning merupakan salah satu produk layanan berbasis teknologi informasi yang dikembangkan dengan tujuan untuk meningkatkan kualitas pembelajaran pada perguruan tinggi. Kesuksesan implementasi sistem e-learning tidak lepas dari peran aktif dan kesetiaan pengguna (customer loyalty) untuk memberikan penilaian maupun feedback untuk peningkatan kualitas layanan yang meliputi efektivitas, efisiensi dan kepuasan dari kegunaan e-learning secara terus menerus. Kepuasan pelanggan berdampak positif terhadap retensi pelanggan, hingga pembelian produk atau jasa lanjutan pelanggan dan kepuasan pelanggan dianggap sebagai faktor utama loyalitas pelanggan. Kegunaan e-learning dapat diukur menggunakan kerangka kerja System Usability Scale (SUS). Sedangkan untuk mengetahui tingkat loyalitas pengguna e-learning dapat menggunakan pendekatan Net Promoter Scale (NPS). Penelitian ini bertujuan untuk membandingkan algoritma Decision Trees, Naïve Bayes, dan K-Nearest Neighbor (KNN) untuk klasifikasi tingkat loyalitas pengguna e-learning dengan pendekatan kategori berdasarkan NPS. Dataset terdiri atas 100 data yang berasal dari penilaian kepuasan pengguna dari dosen dan mahasiswa sebagai pengguna e-learning. Dataset dibagi menjadi 80:20 untuk data training dan data testing. Penerapan metode 10-fold cross validation pada pengujian ketiga model algoritma berhasil menghindarkan model dari kondisi underfitting maupun overfitting. Pengujian kinerja dari tiap – tiap model algoritma machine learning menggunakan confusion matrix yang meliputi parameter accuracy, sensitivity, dan precision. Hasil pengujian menunjukkan bahwa algoritma Decision Trees memiliki tingkat akurasi terbaik yaitu sebesar 95%, diikuti dengan Naïve Bayes dengan tingkat akurasi sebesar 90% dan KNN dengan tingkat akurasi sebesar 85%.
Integrated Library System (Inlis Lite) is used to facilitate library performance in maximizing service and assist the Library in finding information. The problem on the Inlis Lite website is difficult to access. This study uses the Webqual 4.0 method which focuses on 3 variables namely (usability, information qualit and service interaction) and Importance Performance Analysis (IPA) method. The results of teh Inlis Lite website quality analysis have a conformity rate 88,71% which means that the service is still not satisactory. The average GAP of -0,46 means that the quality of the Inlis Lite website still does not meet user expections. There are 4 indicators that need to be improved quality, namely US5, US7, SIQ5, and SIQ6 which are in the first quadrant
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