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"Estimasi biaya merupakan kunci kesuksesan suatu proyek dari tahap konsep hingga tahap akhir. Dua komponen yang terdapat dalam estimasi biaya adalah biaya langsung dan biaya tidak langsung. Biaya langsung memegang peranan yang penting karena besarnya dominan terhadap total biaya. Efisiensi pada biaya langsung dapat berpengaruh terhadap laba kotor proyek. Dengan menggunakan data historis proyek, bobot laba kotor akan diketahui berdasarkan bobot biaya langsung yang ada. Metode yang digunakan dalam penelitian ini adalah Fuzzy Logic dan Artificial Neural Network menggunakan Matlab. Dari penelitian yang dilakukan, diketahui bahwa untuk proyek kampus, ANN memberikan hasil yang lebih akurat. Dengan menggunakan hasil ini, simulasi telah dilakukan dan diperoleh range laba kotor proyek sebesar (13,51-14,69%). Sementara itu range untuk komponen biaya langsungnya (25,93-34,13%) untuk biaya upah; (45,71-56,11%) untuk biaya bahan; (27,61-43,90%) untuk biaya subkontraktor; (5,74-14,3%) untuk biaya peralatan; dan (9,08-12,19%) untuk biaya overhead proyek. Sedangkan untuk proyek kantor, Fuzzy Logic memberikan hasil yang lebih akurat. Dengan menggunakan hasil tersebut, simulasi telah dilakukan dan diperoleh range laba kotor sebesar (13,09-16,74%). Range biaya langsungnya (23,32-31,33%) untuk biaya upah; (44,47-53,08%) untuk biaya bahan; (37,47-48,99%) untuk biaya subkontraktor; (8,21-13,52%) untuk biaya peralatan; dan (11,87-16,09%) untuk biaya overhead proyek.

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Cost estimate is the key to the success of a project from concept stage until the final stage. Two components are included in cost estimate are direct cost and indirect cost. Direct cost is important because of the large dominant to the total cost. Efficiency on direct cost can effecting gross profit project. With use historical data project, weight of gross profit will be known based on existing weight of direct cost. Methods used in this research are Fuzzy Logic and Artificial Neural Network with Matlab. From the research conducted, it is known that for college project, ANN produced more accurate output. With use this result, simulation have been done and obtained range of gross profit by (13,51-14,69%). While range components of direct cost are (25,93-34,13%) for wage cost; (45,71- 56,11%) for materials cost; (27,61-43,90%) for subcontractor cost; (5,74-14,3%) for equipment cost; and (9,08-12,19%) for overhead project cost. For office project, Fuzzy Logic gives more accurate result. With use this result, simulation have been done and obtained range of gross profit by (13,09-16,74%). Range components of direct cost are (23,32-31,33%) for wage cost; (44,47-53,08%) for materials cost; (37,47-48,99%) for subcontractor cost; (8,21-13,52%) for equipment cost, and (11,87-16,09%) for overhead project cost."

"Optical coatings, including mirrors, anti-reflection coatings, beam splitters, and filters, are an integral part of most modern optical systems. Optical thin films and coatings provides an overview of thin film materials, the properties, design and manufacture of optical coatings and their use across a variety of application areas.Part one explores the design and manufacture of optical coatings. Part two highlights unconventional features of optical thin films including scattering properties of random structures in thin films, optical properties of thin film materials at short wavelengths, thermal properties and colour effects. Part three focusses on novel materials for optical thin films and coatings and includes chapters on organic optical coatings, surface multiplasmonics and optical thin films containing quantum dots. Finally, applications of optical coatings, including laser components, solar cells, displays and lighting, and architectural and automotive glass, are reviewed in part four."