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The distribution of Birgus sp in Batu Asahan Coastal Waters, District IV Jurai Pesisir Selatan Regency, West Sumatera Province Rio Syahputra; Rifardi Rifardi; Afrizal Tanjung
Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan Vol 5 (2018): Edisi 1 Januari s/d Juni 2018
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan

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 ABSTRACT             This research was conducted in May 2017 in BatuAsahan Coastal Waters, District IV Jurai, Pesisir Selatan Regency, West Sumatera Province. This study aims to determine the distribution of Birgussp in BatuAsahan Beach, District IV Jurai. The method was used survey method with research location divided into three intertidal zone (upper, lower and middle) waters, consist of 3 station perpendicular to coastline where each station there is 1 transect. The distribution pattern of Birgussp in each station is clustered. Birgussp species found in 3 species of Birguscavipes, BirgusregosusandBirgusbrevamanus the most dominant type of Birguscavipes found in almost every station found. The population abundance of Birgussp is the most common in station III with the amount of 18.67 ind / m². The dominance index at each research station ranged from 0.4266 - 0.6504, The dominance index that has been obtained shows no more dominant type. Keywords: Birgus sp, clustered, Intertidal zone
Prakiraan Beban Listrik Jangka Pendek Kota Banda Aceh Berbasis Logika Fuzzy . Syukriyadin; Rio Syahputra
Jurnal Rekayasa Elektrika Vol 10, No 1 (2012)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (710.596 KB) | DOI: 10.17529/jre.v10i1.149


One of the technical aspects that support the optimal operation planning of a power plant when viewed in terms of system reliability and economic is about short-term load forecasting. The objective of this research is to forcasting hourly short-term electric load peak (17:30 to 22:30 GMT) at loading area of Transmission Distribution Banda Aceh Unit of PT. PLN P3B Aceh 150-20 kV by using Adaptive Neuro Fuzzy Inference System (ANFIS) method. The toolbox used to predict short-term electric load in this research is by using MATLAB software R2007b and Microsoft Excel 2007. ANFIS structure is trained using ANFIS Sugeno models, three types of membership functions with three and four fuzzy sets for each type of membership function. ANFIS structure is trained using a hybrid algorithm. From the simulation results obtained that the structure of the input membership functions of ANFIS 3 gbell with three fuzzy sets as the ideal structure. Further results of ANFIS estimation compared with the moving average method. From the simulation results is shown that ANFIS models generate MAPE 3.42%, while the forecasts using the moving average method generate MAPE 6.58%.