No abstract

Smart home adalah suatu sistem yang menggunakan komputer dan teknologi informasi untuk mengendalikan peralatan yang terdapat di rumah seperti jendela dan lampu. Sistem dapat berupa sistem control sederhana hingga sistem yang kompleks. Komputer/mikrokontroler yang berbasis jaringan internet/ethernet dilengkapi dengan sistem cerdas dan sistem otomasi sehingga mampu membuat rumah menjadi bekerja secara otomatis. Banyak perangkat komputer/mikrokontroler yang dapat diimplementasikan sebagai pengendali dalam smart home. Sistem pengendali smart home pada penelitian ini menggunakan Xilinx xpartan-3e yang mengendalikan peralatan dalam rumah melalui jaringan LAN (Local Area Networking). Sistem pengendali ini berkomunkasi menggunakan broadcast voice pada jaringan lokal. Sistem Pengendali ini dirancang untuk dapat mengirimkan paket sinyal suara (voice) dari masukan microphone dan kemudian mengirimnya menggunakan protokol ethernet dalam jaringan lokal rumah menggunakan FPGA. FPGA ini diprogram untuk mengirimkan dan mengkodekan paket data, mengkonversi data digital menjadi data analog untuk dapat mengendalikan peralatan dalam rumah. Dari hasil pengujian simulasi menggunakan ISim, terlihat bahawa sistem bekerja secara realtime.

No abstract

Authentication is generally required on systems which need safety and privacy. In common, typed username and password are used and applied in authentication system. However, this type of authentication has been identified to have many weaknesses. In order to overcome the problem, many proposed authentication system based on voice as unique characteristics of human. We implement Dynamic Time Warping algorithm to compare human voice with reference voice as the authentication process. The testing results show that the system accuracy of the speech recognition average is 86.785%.

No abstract

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.

<p class="Abstrak">Ikan tongkol (<em>Euthynnus Affinis</em>) adalah salah satu ikan yang paling banyak diminati di Indonesia karena kandungan proteinnya hampir setara ikan tuna, namun dengan harga relatif lebih murah. Ikan termasuk komoditi pangan yang mudah rusak tanpa adanya penanganan khusus ketika ikan ditangkap. Padahal, mutu dan nilai jual ikan sangat tergantung dari parameter kesegaran ikan itu sendiri. Penelitian ini mengembangkan metode deteksi kesegaran ikan tongkol menggunakan fitur berupa citra mata ikan. Mata ikan dapat digunakan untuk mengetahui tingkat kesegarannya. Ikan segar memiliki pupil bulat berwarna hitam yang utuh dan jernih di tengahnya. Hal tersebut kemudian dijadikan <em>knowledge-based </em>dari proses deteksi kesegaran ikan. Sebelum dilakukan proses deteksi, dilakukan proses <em>pre-processing</em> untuk mendapatkan gambar kepala ikan secara otomatis. Selanjutnya dilakukan perhitungan <em>similarity</em> antara citra biner kepala ikan dengan 2 buah <em>template</em>, yakni <em>Template</em>-Mata untuk mendeteksi mata dan <em>Template</em>-Tengah untuk mendeteksi bulat hitam di tengah mata. Sebanyak 30 citra mata ikan dengan kriteria segar dan tidak segar digunakan sebagai data pengujian. Dari pengujian, kedua <em>template</em> tersebut mampu membedakan ciri morfologis dari mata ikan yang segar dengan tepat.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Tongkol fish (Euthynnus Affinis) is one of the most popular fish in Indonesia because it has more protein than tuna, but with a relatively cheaper price. Fish is a perishable food commodities if it is caught without any special handling. In fact, the quality and value of fish selling depends on the parameters of the freshness of the fish itself. This study developed a method for detecting freshness of tongkol fish using features that is extracted from the image of a fish's eye. Fish eye can be used to determine the level of freshness. Fresh fish have whole round and clear black pupils in the middle. This is then made into knowledge-base on the process of detecting the freshness. First, this fully automatic detection performed a pre-processing process to obtain automatic fish head images. It was then compared with two templates, which are eye-template and middle-template. If the fish head image has similarity below certain threshold then it is classified as fresh fish, or else it is non-fresh fish. A total of 30 images of fish with fresh and non-fresh criteria were used as test data. From the test, the two templates can classify the morphological characteristics of fresh fish eyes precisely.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Determining the quality of soil is an important task to perform especially on newly opened agricultural land since it may provide significant impact on the growth of plants. One alternative to determine physical soil quality is by visually observe the color of the soil and measure its moisture. This paper designed an embedded system classify soil condition for plants according to the dimensionality reduction of color and moisture information from the soil using k-NN algorithm. The dimension of attribute information was reduced using correlation analysis to achieve lower computational time and lower memory usage on embedded system. In this study, 39 sample of soil from various location were collected and categorized by soil expert using visual observation. In the accuracy testing on the system that used 4 attributes, 100% accuracy was given by 60:40 ratio with 7 neighbors. In contrast, the system that used only 2 attributes, 100% accuracy was given by 60:40 ratio with 5 nearest neighbors. The resource usage testing shown that by using reduced attributes dimension, the resource usage can be lowered as many as 188 bytes on program storage and 192 bytes on global variable usage. Moreover, the average of computation time performed by the system using reduced attribute dimension achieved 5.4 ms compared to the system that used all attributes which achieved 6.2 ms.