Abstract.A traditional network consists of gateway sensors which transmit data to the base stations. These nodes are considered bottlenecks in multihopnetworks as they transmit their data as well as data from other nodes and hence they deplete faster in energy. One way to optimize energy efficiency in a WSN is to deploy a mobile base station which could collect data without a need for gateway nodes, and hence the multihop bottleneck would be minimized. We compare these two variations of WSN, one consisting of the multihop approach with gateway nodes, and we propose the other network structure, whereby a mobile base station collects data individually from each node using double Fermat's spiral model. Keywords: energy efficiency, mobile base station, 3-level WSN structure, spiral pattern, pattern routing. IntroductionWireless sensor networks, due to their manner of operation, act as a bridge to the physical world. They have captured the attention and imagination of many researchers, leading to a broad spectrum of ideas, ranging from environmental protection and military applications. A wireless sensor network is made up of spatially distributed sensors which are deployed on a wide range of area, and these sensors are used to monitor physical or environmental conditions such as temperature, pollution, pressure and motion. Sensor networks are keys in gathering information needed by smart environments. Each node in a sensor network is equipped with a radio transceiver, a small microcontroller and an energy source, mostly a battery. One of the most distinguished components of wireless sensors networks are the base stations. They have increased computational energy and communication resources. They act as gateways between sensor nodes and the end user.
Abstrak - Kondisi geografis Indonesia dengan dua per tiganya perairan dapat memberikan keuntungan tersendiri bagi masyarakat Indonesia yang berprofesi sebagai nelayan. Salah satu komoditas budidaya air laut yang memiliki nilai ekonomi dan permintaan ekspor yang tinggi salah satunya adalah udang vaname (Liopenaeus Vannamei), yang sering disebut sebagai udang putih. Salah satu metode klasifikasi adalah algoritma C4.5, metode algoritma C4.5 dalam pemilihan atribut dapat dilakukan dengan cara menggunakan Gain dengan tujuan mencari nilai Entropy. Algoritma C4.5 dapat membagi-bagi data berdasarkan kriteria yang dipilih agar terbentuk sebuah pohon keputusan (Decision Tree) yang menggunakan pendekatan secara top down. Dari data-data yang diperoleh maka bisa dihitung dari hasil perhitungan pada luas tambak, bibit, pakan, dan pupuk akan didapatkan hasil optimasinya. Hasil dari program yang dibuat berdasarkan data-data kualitas air yang dianalisa dengan teknik data mining dapat dimanfaatkan untuk mendapatkan informasi yang berguna untuk pihak perusahaan dan dari pemodelan pohon keputusan dengan metode Algoritma C4.5 didapatkan hasil accuracy sebesar 76,47%, precision sebesar 72,72% dan recall sebesar 88,88%. Dari hasil pengujian tersebut diketahui bahwa klasifikasi termasuk dalam kategori Fair Classification. Abstract - Indonesia's geographical condition, with two-by-one of its waters, can provide its own benefits for Indonesians who are fishermen. One of the sea water cultivation commodities which has economic value and high export demand one of them is shrimp Vaname (Liopenaeus Vannamei), which is often referred to as white shrimp. One method of classification is algorithm C 4.5, algorithm method C 4.5 in attribute selection can be done by using Gain with the purpose of finding the value of Entropy. The C 4.5 algorithm can divide data based on criteria chosen to form a decision Tree using a top down approach. From the data obtained, it can be calculated from the results of calculations on the area of pond, seedlings, feed, and fertilizer will be obtained the results of its optimization. The results of the programs created based on water quality data analyzed by data mining techniques can be utilized to obtain useful information for the company and from the modeling of decision tree with algorithm method C 4.5 Obtained the accuracy of 76.47%, precision at 72.72% and recall by 88.88%. From the test results it is known that the classification belongs to the Fair Classification category.
A two-year study by the Ministry of Research, Technology and Education in Indonesia presented the evaluation of most universities in Indonesia. The findings of the evaluation are the peculiarities of various dissertation softcopies of doctoral students which are similar to any texts available on internet. The suspected plagiarism behavior has a negative effect on both students and faculty members. The main reason behind this behavior is the lack of standardized awareness among faculty members with regard to plagiarism. Therefore, this study proposes a computerized system that is able to detect plagiarism information by using K-means and cosine distance algorithm. The process starts from preprocessing process that includes a novel step of checking Indonesian big dictionary, vector space model design, and the combined calculation of K-means and cosine distance from 17 documents as test data. The result of this study generally shows that the documents have detection accuracy of 93.33%.
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