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All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Algoritma
Nugroho Haryono
Fakultas Pertanian, Universitas Lampung
Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering

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ANALYSIS OF WATER BALANCE ON SOYBEAN CULTIVATION (Glycine max [L] Merr.) IN DRY LAND -, Oktaviani; Triyono, Sugeng; Haryono, Nugroho
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 2, No 1 (2013)
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (927.994 KB)

Abstract

ABSTRACTSoybean is one important food crop in Indonesia after rice and corn. In 2008 to 2010, the amount of soybean harvest has fluctuated. One effort to increase the soybean yield is by using the expansion programs to exploit dry lands. However, water scarcity is the main problem faced in dry land cultivation.  For this reason, study on optimalization of water use become very important and potential of water harvesting is needed to be explored.  This study aimed to analyze and to partition the water balance at soybean cultivation, and also to explore the potential of rainwater harvesting.  The experiment was conducted at the Integrated Field Laboratory of the College of Agriculture, University of Lampung from 15 October 2011 to 6 January 2012.  The observations were carried out by constructing 8 plots, 2x1 m2 each, and in a longitudinal direction of the 5-6% slope.  Four of the experimental plots were sealed with plastic liner and another four pots were left without liner.  Each plot was equipped with a 1x0,5x0,5 m3 water storage pond at the down end of the plot.  Physical properties were determined at the beginning experiment; while rainfall, soil moisture, and water surface of the ponds were monitored evey day.  Soybean yield was weighed at the harvest time. The results showed that water balance could be divided into input and output.  Water input was partitioned into rainfall 41,56% and irrigation 58,44%.  Water output was partitioned into runoff 6,76%, percolation 13,74%, evapotranspiration 74,35%, moisture stored in the soil 0,20%, and irrigation surcharge 5,04%.  Irrigation efficiency was found about 91%, while rainwater harvested contributed 20,74% of the irrigation total.  Potential of productivity was found to be 3,3 ton soybean yield per ha, and potential water use productivity was about 0,74 kg soybean per m3 irrigation water. Keywords: evapotranspiration, soybean, surface runoff, water balance, water harvesting.
Analisis indikator Bollinger Bands, Stochastics dan Relative-Strength Index Untuk Prediksi Pergerakan Gold Futures Berbasis Deep Learning Gabriel, Evander; Lukito, Yuan; Haryono, Nugroho
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2264

Abstract

Gold futures price predictions are challenging due to high volatility and its role as a safe-haven asset influenced by global political and economic conditions. The right trading strategy is needed to take advantage of price fluctuations, one of which is through technical, fundamental, sentiment, and machine learning analysis. This study analyzes the effectiveness of technical indicators Bollinger Bands (BB), Stochastic Oscillator (STOCH), and Relative Strength Index (RSI) in predicting Gold Futures prices using the Deep Learning Long Short-Term Memory (LSTM) model. The research data consists of ±40,000 Gold Futures prices from Yahoo Finance, which are divided into training, validation, and test data using the sliding window method (20% shift from 0%–60%). Model performance is evaluated through Return, Real, Trade, Win-rate, and Profit-factor using back testing in Metatrader 5 (100 leverage). The results show that the LSTM model with BB features (period 20, deviation 2) produced the highest average return of $100.48, a win rate of 32.53%, and a profit factor of 2.30. The second-best model used a combination of the three indicators with an average return of $98.033, a win rate of 30.96%, and a profit factor of 2.12.
Co-Authors Gabriel, Evander Oktaviani - Yuan Lukito