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"ABSTRAKPerkembangan teknologi dan transformasi sosial-ekonomi telah menyebabkan bisnis jaringan seluler 5G mengalami perubahan, sehingga jaringan seluler 5G diharapkan dapat mengirim informasi dengan cepat dan mendukung kasus penggunaan yang banyak bermunculan dari berbagai aplikasi. Salah satu kasus penggunaan pada jaringan 5G adalah massive Machine Type Communication (MTC). Salah satu aplikasi massive MTC adalah jaringan sensor nirkabel (JSN). Tantangan bagi jaringan seluler 5G ini adalah bagaimana memodelkan arsitektur/topologi untuk mendukung JSN dan bagaimana mengatasi masalah efisiensi konsumsi energi di JSN. Untuk menjawab tantangan ini, maka diterapkan sistem HAP yang terintegrasi JSN dan menggunakan protokol routing Low Energy Adaptive Clustering Hierarchy. Sistem HAP dirancang untuk digunakan di ketinggian 20 km dengan topologi tanpa dan dengan clustering, menggunakan 1.000 node sensor. Sistem ini telah disimulasikan dengan menggunakan MATLAB. Simulasi dilakukan untuk melihat konsumsi energi, jumlah node yang mati dan rata-rata total paket yang dikirim ke HAP untuk topologi tanpa dan dengan clustering. Dari serangkaian simulasi, terlihat bahwa topologi dengan clustering dapat mengurangi konsumsi energi dan jumlah node yang mati, sekaligus meningkatkan total paket yang dikirimkan ke HAP."

"This article describes an empirical study of hybrid approach of quicksort algotihms."

"Site Specific Management (SSM), which also variously referred to as Variable Rate Technology (VRT), is an emerging technology that enables producers to make more precise input application decisions based on soil and field characteristics. This study analyzes factors influencing the adoption of VRT for fertilizer application for cash grain production in Ohio. Results show that producer and field characteristics might influence the adoption decision on various SSM components differently. It also provides insight as to the sequence of adoption of SSM component technologies and how this sequence might differ for producers of differing characteristics."

"ABSTRAKGenome Sequence Analysis for genetic datasets by using ORF (Open Reading Frames) techniques is an interesting area of research for bioinformatics researchers nowadays. There is a strong research focus on comparative analysis between genetic behaviors and diversity of different species. Contrary to whole genome sequence analysis, scientists are now trying to concentrate specifically on layered analysis to get a better insight of relevancy among genetic datasets. This phenomenon will help to better understand species. An ORF statistical analysis for genetic data-sets of species Chimera Monstrosa and Poly Odontidae is presented. For completion of this analysis, we use a hybrid approach that combines a generic mechanism for statistical analysis with specific approach designed for out performance. At first instance, genetic datasets are refined for better usage at next level. These sets are then passed through layers of filters that perform DNA to Protein translation. Statistical comparison is performed during this translation. This layered architecture helps in better understanding of the degree of similarity and differences in genomic sequences. "