Perkembangan Teknologi Informasi dan Komunikasi atau disingkat TIK yang ada saat ini ikut memberikan kontribusi dalam pengembangan bidang pendidikan khususnya di perguruan tinggi, termasuk dalam proses belajar mengajar. Proses belajar mengajar yang bersifat konvensional, dimana dosen dan mahasiswa melakukan kegiatan tatap muka di ruang kelas, sudah mulai beralih ke proses belajar jarak jauh dengan memanfaatkan TIK dalam bentuk e-learning. Dengan memanfaatkan proses e-learning ini, masalah waktu dan tempat yang biasa kita temui dapat dipecahkan, terutama pada keterbatasan ukuran kelas terhadap jumlah murid dan keterbatasan waktu untuk dapat hadir di ruang kelas. E-Learning berkembang sejalan dengan perkembangan TIK, dimana konsep E-Learning 1.0 terus mengalami perbaikan menuju ke E-Learning 2.0. Perkembangan konsep ini terutama dijumpai dalam bentuk komunikasi pada proses yang disediakan, jenis media yang digunakan dan peran dosen (Pengajar) dan mahasiswa (murid) dalam proses belajar mengajar tersebut. Deklarasi gerakan Indonesia Go Open Source atau IGOS oleh lima menteri pada tanggal 3 juni 2004 serta keharusan bagi seluruh instansi pemerintah untuk menggunakan perangkat lunak legal paling lambat Desember 2011 ikut mendorong perkembangan e-learning dengan menggunakan teknologi open source seperti Sistem Operasi Linux, Learning Management System LMS (moodle, Sakai, Dukeos), Blog (Wordpress, Joomla, Drupal), Netmeeting, Animasi dan Simulasi (Flash dan Blender). Tulisan ini akan membahas tentang pemanfaatan teknologi open source yang tersedia saat ini dalam pengembangan proses belajar jarak jauh khususnya di perguruan tinggi.

The human face can be used for face recognition in order to increase the level of security of a safe deposit box because every person has his/her facial characteristics that have similarities with one another. One of the tasks for face recognition is to compare the face in real-time to the ones in the dataset so that the owner can be verified. This final project aims to implement face recognition based using Raspberry Pi to increase the level of security of the safe deposit box system design. This study uses the Raspberry Pi 3B+ because it has sufficient processing capabilities and has a few pre-built modules that make researching this less difficult. Raspberry Pi uses Linux as the operating system, which has access to a large number of libraries and applications compatible with it [1]. Of the many methods used for face detection, in this final project the Viola-Jones method is being used. From the result of this research, the success rate that was obtained is 60%. This number was obtained after 40 trials, the system was able to detect as much as 24 times [2]. The final results shows that the light intensity greatly affects the performance of the system. The light intensity of 8 Lux has an accuracy rate of 30%, while the 40 Lux has an accuracy rate of 90%.Wajah manusia dapat digunakan dalam pengenalan wajah untuk meningkatkan keamanan brankas karena setiap manusia memiliki fitur-fitur wajah yang berbeda-beda. Salah satu tugas dari pengenalan wajah adalah membandingkan wajah pada citra foto dengan wajah yang telah disimpan di dataset, agar identitas pemilik wajah dapat diketahui. Makalah ini mengimplementasikan pengenalan wajah berbasis Raspberry Pi pada sistem brankas untuk meningkatkan keamanan brankas. Penelitian ini menggunakan Raspberry Pi dikarenakan memiliki kemampuan pemrosesan yang cukup dan memiliki modul yang mempermudah implementasi. Raspberry Pi menggunakan Linux sebagai sistem operasi, yang memiliki akses ke sejumlah besar perpustakaan dan aplikasi yang kompatibel. Dari sekian banyak metode yang telah diaplikasikan untuk deteksi wajah, metode yang dipakai untuk penelitian ini  adalah metode Viola Jones. Dari hasil penelitian ini, diperoleh nilai keberhasilan sebesar 60%. Nilai ini diperoleh setelah melakukan percobaan sebanyak 40 kali, dengan keberhasilan deteksi oleh sistem sebanyak 24 kali. Hasil akhir menunjukkan bahwa intensitas cahaya sangat mempengaruhi peforma dari sistem. Ketika intensitas cahaya bernilai 8 Lux didapatkan tingkat akurasi sebesar 30 %, sedangkan ketika intensitas cahaya bernilai 40 Lux maka didapatkan tingkat akurasi sebesar 90%.

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Along with the increasing level of criminal data theft that occurs, a good room security system is needed to maintain data security of a particular agency or organization. A good security system is a security system that can be monitored and controlled over long distances using the internet or better known as IoT (Internet of Things). In this thesis, an IoT-based room security system will be designed using an Android application, this system works using several modules including RFID (Radio Frequency Identification) module, camera module and solenoid door lock. RFID cards in the RFID Module function as a process of identifying people who want to enter the room. In addition, this RFID module functions also as a trigger for the camera to work and taking pictures of people who want to enter the room. Data obtained from both modules will be sent to the Android application. The admin of the room can carry out a verification process to allow or reject the person who wants to enter the room. The results of the testing of this system can run well when the internet speed of the access point runs fast and stable, but it is less optimal when the internet speed of the access point runs slowly.

Network intrusion detection system is a system that can detect illegal accesses or intrusions happened in a computer network. Actually, there are many types of intrusion detection systems and the differences are based on how network administrators implement the system to secure the network. In this study, the system called Network Intrusion Detection System or NIDS in brief, is used to design and implement the intrusion detection system in the network model design. Intrusion detection system will utilize the snort application that serves as a sensor and detection server and be implemented in a network model that has been designed previously. The performance of the system is investigated through the monitoring of the use of disk space, memory usage and cpu usage of the system during intrusion detection identification process. Three different intrusion scenarios such as Portscanning, ICMP flooding and SYN flooding is performed to see the effect in the system's performance. During the test, the use of disk space still has not shown any significant use, due to the detection time limit used in this study was too short. However, the difference in memory