No abstract

The use of internet facilities to support mobility needs in the campus environment such as browsing, downloading and uploading data is an unavoidable condition. Implementation of bandwidth management on campus networks which acts as a bandwidth divider so that it is evenly distributed by adapting the PCQ method to the proxy router device and Wireless Distribution System (WDS) technique so that the signal coverage becomes wider. Analysis conducted on Quality of Service (QoS) in the form of measurements of the quality of internet bandwidth through testing parameters download, upload, throughput, delay, jitter, and packet loss. The test was conducted at the hotspot which is located at several points of the STTM campus social facilities. Based on test results, obtained data before the application of the PCQ method in the form of throughput of 32.76%, jitter of 94.36 ms, delay of 46.83 ms, and packet loss of 20.74%. After the PCQ method is applied there is a change in the results of the parameter test value, the throughput is 41.53%, jitter is 52.48 ms, the delay is 33.62 ms, and the packet loss is 12.57%.

A quest of vacant parking space in the public area can indirectly lead to traffic congestion which can be troublesome for drivers in terms of time efficiency. This study is expected to assist drivers to get the available parking slots information in a real-time manner and support the parking control systems by constantly updating the information of vacant parking slots positions in public areas. A vision-based parking slots recognition method is proposed to identify occupied areas by vehicle which is divided into two main parts: setup configuration and object detection. Canny Edge and Hough Line Transform are used to achieve line detection for parallel parking slot marking; contour extraction and bounding rectangular are then applied for an initial parameter to form a reference area as a region of interest (ROI). Moreover, Structural Similarity Index Measurement (SSIM) exploits the reference image and target image to identify whether the area is empty or occupied by vehicle depending on structure comparison. Experimental result shows, from 50 sample images of parking slots attained by surveillance camera, the detection accuracy of 92% and precision of 89% are obtained using selected features with tuning SSIM threshold level of 0.4.

Gedung Fakultas Psikologi Universitas Jenderal Achmad Yani (Unjani) merupakan gedung perkuliahan yang berada di lingkungan kampus Unjani dan belum memiliki instalasi sistem jaringan indoor. Kondisi tersebut menyebabkan level sinyal yang terukur di dalam gedung memiliki nilai RSRP lebih kecil dari -110 dBm. Level sinyal tersebut termasuk dalam kategori kualitas sinyal 4G yang kurang baik. Selain itu faktor lain yang menyebabkan pelemahan sinyal ialah material dan konstruksi bangunan tersebut. Oleh karena itu, agar user pada gedung tersebut dapat terlayani dengan baik, solusi yang dilakukan adalah melakukan perencanaan jaringan Indoor Building Coverage (IBC) 4G. Berdasarkan perhitungan coverage planning jumlah antena yang diperlukan dalam perencanaan sebanyak 14 antena yaitu lantai 1 dan 4 masing-masing sebanyak 3 antena, lantai 2 dan 3 masing-masing sebanyak 4 antena. Berdasarkan hasil simulasi pada software RPS diperoleh persentase nilai rata-rata nilai RSRP >-90 dBm sebesar 89,93 % dan persentase rata-rata nilai SINR >3 dB sebesar 87,95 %. Berdasarkan hasil simulasi tersebut didapatkan bahwa hasil perencanaan yang telah dibuat telah memenuhi standar operator dan dapat diterapkan untuk memperbaiki coverage dan meningkatkan level sinyal di Gedung Fakultas Psikologi Unjani.

Sistem keamanan rumah merupakan implementasi yang harus dilakukan untuk meningkatkan keamanan dari kejadian yang tidak diinginkan. Beberapa implementasi hanya memberikan notifikasi sederhana berupa alarm dan tidak menjadi bukti yang kuat apabila terjadi pencurian. Salah satu solusi yang dilakukan adalah penempatan kamera untuk memantau keamanan rumah secara real time diintegrasikan dengan penyimpanan cloud. Bluemix merupakan salah satu provider untuk aplikasi cloud yang memiliki layanan pengolahan dan penyimpanan data, akses aplikasi mobile, pengawasan serta Internet of Things (IoT). Sistem yang diimplementasikan adalah integrasi Raspberry Pi dengan layanan Bluemix untuk melakukan pengawasan keamanan rumah dan memberikan notifikasi kepada pengguna. Sistem mendeteksi jarak menggunakan sensor HC-SR04 terhadap objek dan apabila jarak melewati acuan, hal tersebut adalah indikasi terjadinya pencurian. Berikutnya sistem akan menyalakan buzzer sebagai keluaran suara dan mengaktifkan kamera untuk mengambil gambar lalu diunggah ke object storage Bluemix. Langkah berikutnya yaitu layanan IBM push notification memberikan notifikasi ke perangkat Android pengguna. Pengujian dilakukan dengan menghalangi pembacaan sensor sehingga terjadi indikasi pencurian. Hasilnya adalah sistem berhasil menyalakan buzzer, mengambil gambar lalu diunggah ke Bluemix, dan notifikasi berhasil masuk pada Android. Notifikasi diterima oleh file browser pada perangkat Android dan dilakukan sinkronisasi dengan object storage untuk melakukan pengunduhan berkas gambar yang telah diunggah sebelumnya.Kata kunci: Bluemix, Raspberry Pi, object sorage, IBM push notification Home security system is an implementation that needs to be done to improve the security of unwanted events. Some implementations only provide a simple notification such as alarm and cannot become strong evidence in case of theft. One of the solutions is camera placement to monitor home security in real time integrated with cloud storage. Bluemix is a provider for cloud applications that have data processing and storage services, mobile application access, monitoring and Internet of Things (IoT). System implemented was integration of Raspberry Pi with Bluemix services to conduct home security surveillance and provide notification to user. System detected distance using HC-SR04 sensor to object and if distance passes the reference, it was an indication of theft. Next, system will turned on buzzer as a sound output and activating the camera to take picture and uploaded to Bluemix Object Storage. Next step was IBM push notification service giving notification to user's Android device. The testing was done by blocking the sensor readings so that there was an indication of theft. The result was system succeeded in turning on the buzzer, taking pictures, uploading pictures to Bluemix, and notification successfully logged on Android. Notifications are received by the file browser on Android device and synchronized with object storage to download image files that have been uploaded previously.Keywords: Bluemix, Raspberry Pi, object storage, IBM push notification