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All Journal Jurnal Mina Sains Jurnal Cahaya Mandalika
Dian Miftahul Jannah
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Languange, Linguistic, Communication & Media

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Perbaikan Daya Cerna Tepung Darah Menggunakan Teknik Silase Dan Teknik Spray Dried Pada Ikan Nila (Oreochromis niloticus) Dian Miftahul Jannah; Rosmawati Rosmawati; Reza Samsudin
JURNAL MINA SAINS Vol. 2 No. 1 (2016)
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.959 KB) | DOI: 10.30997/jms.v2i1.415

Abstract

Tujuan dari penelitian ini ialah untuk mengevaluasi penggunaan teknik silase dan spray dried terhadap daya cerna tepung darah pada ikan Nila. Penelitian ini memakain Rancangan Acak Lengkap dengan 3 perlakuan dan masing-masing 3 ulangan. Sebagai perlakuan adalah tepung darah tanpa teknik pengolahan (kontrol), tepung darah dengan teknik silase, dan tepung darah dengan teknik spray dried. Percobaan menggunakan benih Nila berbobot 7,0 g, tiap akuarium berisi 20 ekor benih ikan Nila. Nilai kecernaan protein, nilai kecernaan lemak, nilai kecernaan energi, dan nilai kecernaan total dievaluasi. Hasil penelitian memperlihatkan ada perbedaan diantara perlakuan secara nyata terhadap nilai kecernaan protein, nilai kecernaan lemak, nilai kecernaan energi, dan nilai kecernaan total (P<0,05). Nilai kecernaan protein, nilai kecernaan lemak, nilai kecernaan energi, dan nilai kecernaan total  tertinggi dari perlakuan teknik silase adalah 96,56%, 90,48%, 94,15%, dan 92,08%.Kata Kunci: Silase darah, spray dried, nilai kecernaan, ikan Nila
Peramalan Jumlah Persediaan Sparepart Carburetor Assy dengan Metode Exponential smoothing Brown dengan Genetic Algorithm (order cross over dan one point mutation) di PT Hitachi Astemo Bekasi Powertrain System Dian Miftahul Jannah; Dani Leonidas; Ekra Sanggala
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 5 No. 1 (2024)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v5i1.2378

Abstract

Abstrac: One of the business people in the automotive sector who continues to move as a manufacturing company in the production of vehicle spare parts is PT ABC which is the largest manufacturer of spare parts for several vehicle companies such as Astra Honda Motor (AHM), Yamaha and so on. One type of production from PT ABC is the carburator assy which functions to mix fuel and air to produce propulsion for vehicles. Seeing its very important function, PT ABC places the carburator assy as a type of spare part that must be produced every month In research, a method is needed to solve the problems that occur. Research methods are scientific ways to obtain data / information as it is and not as it should be, with certain purposes and uses. This research is based on descriptive research. This study used the double exponential smoothing Brown method for forecasting carburator assy 052A with Alpha assessment using Genetic Algorithm order Cross over PT ABC.Calculations carried out for forecasting the Double exponential smoothing Brown method using Alpha values of 0.36 and 0.32 obtained randomly resulted in RMSE values that were greater than Alpha values obtained using Genetic Algorithms, namely 103.7234 for Alpha values of 0.36 and 117.6129 for Alpha values of 0.32. Therefore, it can be interpreted that determining the Alpha value using the Genetic Algorithm method can be a solution to reduce the RMSE error value in Double exponential smoothing Brown forecasting. In research conducted at PT ABC by analyzing the influence of Genetic Algorithms in determining α values, several conclusions can be obtained, namely as follows: Determination of α value for the calculation of Double exponential smoothing Brown using the Genetic Algorithm obtained an Alpha value of 0.43 with the smallest error result of 95.29633. Calculations MADe on Genetic Algorithms can determine α value that can minimize errors in forecasting compared to α values without Genetic Algorithms.
Co-Authors Dani Leonidas Ekra Sanggala Reza Samsudin Rosmawati Rosmawati