Journal of Theoretical and Applied Information Technology 31 st December Vol. 58 No JATIT & LLS. All rights reserved.

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1 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 INTEIGENT IRRIGATION WATER REQUIREMENT SYSTEM BASED ON ARTIFICIA NEURA NETWORKS AND PROFIT OPTIMIZATION FOR PANTING TIME DECISION MAKING OF CROPS IN OMBOK ISAND MOHAMMAD ISA IRAWAN, SYAHARUDDIN, DARYONO BUDI UTOMO, AVIDA MUSTIKA RUKMI Isttut Tekolog Sepuluh Nopember, Faculty of Mathematcs ad Natural Sceces Departmet of Mathematcs, Surabaya E-mal: m@ts.ac.d, syaharudd@mhs.matematka.ts.ac.d daryoo@matematka.ts.ac.d, alvda@matematka.ts.ac.d ABSTRACT Croppg patter s a schedulg for farmg tme o a certa lad a defte perod (e.g. year, cludg uflled area. I arragg crop platg patters, hydrologcal (rafall, clmatologcal (temperature, humdty, wd speed, ad sushe, crop (crop coeffcet value, productvty ad prce ad lad area data are requred. Therefore, a method that ca be appled to predct the hydro clmatologcal data s eeded. The approprate method for such predcto s Back Propagato Neural Network (BPNN. Predcto result of BPNN wll be used to determe mmum crop water requremets, ad t wll be assocated wth platg tme (age of each crop for makg croppg patter. The desg of most favorable croppg patter wll obta the maxmum proft ad reduce fal harvest problem, whch turs t ca cotrbute to atoal food reslece. Based o the smulato result, t was kow that the BPNN wth two hdde layers s able to predct hydro clmatologcal data such as of rafall, temperature, humdty, wd speed, ad sushe data wth a average accuracy rate of 9.% - 9.%. Meawhle, valdato of predctos obtaed a average percetage error of.% wth a accuracy of 99.%. The results of the optmzato of the croppg patter ombok March 0-February 0 revealed a accurateess of proft each dstrct/cty East ombok, Cetral ombok, West ombok, North ombok, ad Mataram creased.0%,.88%, 0, %,.89%, ad.8%, respectvely. Over all, the creasg average was foud to be.% from the prevous year. Keywords: Crop, Rafall, Back Propagato Neural Network (BPNN, Optmzato.. INTRODUCTION Croppg patter s defed as makg tme maagemet for platg o a certa area a defte perod (as a example, year perod of tme cludg flled area [9]. The data requred croppg patters plag are hydrologcal (rafall, clmatologcal (temperature, humdty, wd speed, ad sushe, raw crop (as well as crop coeffcet, productvty ad sellg prce, ad lad area data. The data are used to calculate water requremet ad alteratve platg tme of crop []. A croppg patter plag s eeded to obta the maxmum product, qualty ad grade of farmg. Besdes that, the croppg patter plag s also amed to determe the amout of fertlzer ad seeds whch should be dstrbuted to farmer groups certa areas durg the platg seaso each year. Accordg to the data report from Cetral Bureau of Statstcs (CBS of Nusa Teggara Barat (NTB provce, for the last e years (00-0, t revealed that the crease the productvty of crop s ot qute sgfcat, but decreased to the lad harvest area (ha ad

2 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 producto quattes (to. It s due to chages ad shftg seasos []. Oe of the ma dcators of shftg seasos s the rregular rafall patter. Ths pheomea makes the farmers NTB, especally ombok, fd some dffcultes determg croppg patters of crop or hortculture. For that reaso, a study about crop platg patters s the requred, partcularly for geeratg a method atcpatg clmate chages order to keep the products ad sustaable food eve better. Such method s also relable to predct the hydro clmatologcal data the future. Oe of the relable methods to predct ay tme seres the future s Back Propagato Neural Network (BPNN []. BPNN s a well-kow popular eural etwork algorthm for whch t ca solve a specfc problem lke patter recogto or classfcato through the process of learg []. It poses excellet ad hgh accuracy to predct tme seres data []. A BPNN wth two hdde layers scree s able to provde better ad faster predcto results compared to a BPNN wth oly oe hdde layer []. The Nastos [], has coducted a study to buld a predcto model to predct the testy of rafall data (mm/day Athes, Greece. The results showed that the BPNN s trustworthy makg predctos of future rafall data, t was clamed as a satsfactory method by evaluators of the etwork usg the Mea Absolute Error (MAE, Mea Bas Error (MBE, Root Mea Square Error (RMSE, coeffcet of Determato (R, ad the Idex of Agreemet (IA. The predcto results whch were obtaed by meas of BPNN were used to determe the effectve rafall, evapotrasprato, ad mmum water requremets for crop. Afterward, optmzato of producto proft was to ga maxmum proft wth low cost. The optmzato results wth hghest proft (come ad mmum water requremets wll be recommeded as croppg patters to be adopted by farmers for the ext platg seaso. Ths platg patter s expected as a cosderato makg decso of good crop, approprate, ad optmal plats. Geerally, t ca be proposed as referece materal the dstrbuto of fertlzers ad crop seeds to farmer s groups each rego ombok (NTB.. RESEARCH METHODS comprsg posts of clmate ombok Islad area durg 0 years (98 to 0 of the Water Resources Iformato Ceter (WRIC NTB, ad ( data o agrculture (crop, come, profts, ad farmg producto costs. The stages ths research we dvde to ma sectos amely, a. Predctg the hydrologcal data ad clmatologcal data, ths step ams to determe the value from evaporato, evapotrasprato, effectve rafall, rafall mastay, ad the eed for water treatmet b. Optmzg the profts of farm produce, ths step ams to determe the amout of beefts based o come ad cost of producto usg crop lad costrat fuctos. c. Calculatg the crop water requremets mmum, ths step ams to determe whe platg food based evapotrasprato, effectve rafall, ad decso makg. The flow chart of research procedure s preseted Fgure.. Crop Crop s defed as ay kd of plats that ca produce carbohydrates ad prote. The crop were dvded to three ( major groups, amely rce, pulse ad ut [9]. Food crop geeral are seasoal crop such as rce, cor, soybea, gree bea, peaut, cassava, ad sweet potato, but there are some crop that are pereal crop such as breadfrut ad sago.. Rafall The ature of ra s dvded to three crtera: ( Above Normal (AN f the value of the rato of rafall durg moth o average (0 years> %, ( Normal (N f the value of the comparso amout of rafall for moth o average (0 years betwee 8-%, ad ( Uder Normal (UN f the value of the rato of rafall durg moth o average (0 years <8% [0]. Besdes that, accordg to volume of mothly of rafall, moo categorzed to types that are ( Wet Moth (WM, f for (oe moth of rafall> 00 mm, ( Most Moth (MM, f for (oe moth of rafall betwee 0-00 mm, ad ( Dry Moth (DM, f for (oe moth rafall <0 mm. Of these categores, t s classfed by the type of clmate adjusted Q value obtaed wth equato below [0], The data cossts of ( hydrologcal data ( posts of rafall, ( clmatologcal data (humdty, temperature, sushe, ad wd speed 8

3 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 DM Q = 00% ( WM Table. Classfcato of Clmate Types Based o Mothly Rafall [0]. Clmate Area Values Q (% Type Category A <. Very wet B.. Wet C. 0.0 Dampy D Medum E A lttle dry F Dry G Very dry H > 00.0 Exceptoal dry Start Data Collecto (Feld, Departmet of Agrculture Crops Data Clmatology Hydrology The Frst Platg Patter Sushe Data Humdty Data Wd Speed Data Temperature Data Rafall Data No Valdato Ye s Prerprocessg Archtecture BPNN Algorthm BPNN Post-processg Predcto Error Evaporato Average Rafall Rafall Mastay Effectve Rafall Evapotrasprato Water Processg of ad Desg of Platg Patter Fsh Optmal Croppg Patter Water Requremet Irrgato Optmzato of Profts wth QM for Wdows Fgure : Flow Chart of Research Procedure. Crop Water Requremet Calculato of rrgato the water s obtaed from the locato rate of the area the water (P, evaporato (Ea, Ep ad Eo, evapotrasprato (Eto ad Etc, the lad preparato the water requremet (IR-00 ad IR- 00, the effectve rafall (Reff, the water turover (WR, ad rrgato effcecy []. Where all varables calculato usg the data obtaed from the results predcted of rafall, temperature, humdty, wd speed, ad sushe... Evaporato I determg the magtude of evaporato used by Pema method (98 [], explctely, ( e e ( u Ea = 0. s a + ( 9

4 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 where e s value s depet o the temperature of a area. The e a s obtaed by multplyg the e = h. The relatve humdty (h wth e s ( u value s the wd speed (m/s. a e s.. Evapotrasprato The magtude of evapotrasprato s affected by the radato, the slope of the saturated vapor pressure curve, ad evaporato. Radato (R, R a, R b, R c, R R b ( ( =. 0 9 Ta ( Ta ( e R =. 0 ( b a 00 where Ta =. + T R c s the m R = R a + b ( c a 00 where a = 0.8, b = 0.8, Ra = 99 R c values equato s used to determe the value of R, thus, R = Rc ( r ( where r = 0,. The the value of Rb equato ad R equato s used to determe the value of R, or, 9 m R = R R b ( The slope of the curve Saturated Vapor Pressure.T e T = ( T +. ( Evapotrasprato (E p, Et o Usg equato,, ad we get value of E p, R + Ea 0 E = p + γ (8 where γ = From equato we get value of Et o, Et = K. E o where K = 0. 8 p p p (9 Exhaust Water Evaporato (E o Usg equato (9we get, E =. (0 o Et o Furthermore, usg equato 0, we obta the magtude of evapotrasprato formula of Pema (98 She et al [], as gve below, Et = K. Et ( c c o.. Water Requremet Processg The magtude of water requremets at the tme of lad preparato ca be calculated usg the methods developed by Va de Goorda Zlstra (98 Alle et al [] IR values determed usg equato 0, thus obtaed, k Me IR = ( e k where k = MT / S, M = E + o P, e =. 8, S = 0 or S = 00 ad P =... Effectve Rafall Effectve rafall s the rafall that falls o a area of agrculture whch ca oly partally be used or absorbed by plats to meet requremet as log of growth. The magtude of effectve rafall s determed by 0% of the of mastay rafall (R 80 for rce ad (R 0 for a o-rce food crops (palawja. The steps determg effectve rafall followg. Determg Average Rafall R + R + R R R = ( Determg Mastay Rafall a For Rce : R 80 = N + ( b For Palawja: R 0 = N + ( Determg Effectve Rafall (R eff a For Rce : R 80 R eff = 0. ( b For Palawja: R eff = 0. ( Based o equatos ad, the get the equato for determg the water requremet for rrgato as gve below, Water Requremet for Rce NFR = Et + P R WR (8 c eff + R 0 Water Requremet for Palawja NFR = Et + P (9 c R eff The decso makg crop water requremet take from the reservor or the dam s a comparso betwee water requremets (NFR 0

5 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 wth the effcecy of crop water requremets. Therefore, by usg equato 8 (for rce the equato below s the acqured, NFR Etc + P Reff + WR DR = = (0 E FORCASTING WITH BACK PROPAGATION NEURA NETWORK.. Types Of Forecastg Usg Bp The tme seres data forecastg wth ANN cossts of two types, amely: Short term predcto: y k = NN( yk, yk, yk,... ( og term predcto: y NN y y, y, y,... ( ( k, k k y k+ = k I ths research, k s the predcto result 0 usg data for 98 to 0, whle y s the predcto results 0 usg data for k+ 98 to 0 ad predcto result 0... Evaluato Of Predcto Result For testg the valdty of forecast or evaluato of predcto results these used forecast accuracy. Wlmott (98 Nastos [], gave some of the crtera to evaluate that predcto results,.e. Mea Absolute Error (MAE, Mea Bas Error (MBE, Mea Square Error (MSE, Root Mea Square Error (RMSE, coeffcet of Determato (R, ad the Idex of Agreemet (IA. Mea Absolute Error (MAE P O = MAE = ( Mea Bas Error (MBE ( P O = MBE = ( Mea Square Error (MSE ( P O = MSE = Root Mea Square Error (RMSE RMSE = ( P O = Coeffcet of Determato (R ( ( R = = ( O P ( ( O O ave = Idex of Agreemet (IA IA = (8 ( O P ( P O ave + O O ave = =. Optmzato Of Crop Optmzato of the platg patter s desged order to obta the maxmum proft. A frequetly appled method farmg aalyss s ear Programmg (P regardg to the use or the effcet allocato of lmted resources to acheve desred goals [8]. Jasbr [] troduced the followg defto of a stadard P, a. Objectve Fucto Zj = c x + cx + cx cx = cjx (9 j j= b. Fucto Costrats (mtg Factor Maxmze: ax + a x + a x a x b a x a x ax... a x b aj x j b M j= a mx am x amx... am x bm Mmze: ax + a x + a x a x b a x a x ax... a x b aj x j b M j= a mx am x amx... am x bm c. Codtos (Assumpto x, x b, b, x, x, b, b m From the above defto, we ca detfy dcators of crop platg patter optmzato (farmg as follows, a. Determat Varable (related The determat varables the optmzato s dvded to two varables, amely: The depedet varable, cotag come (Z P, producto costs (Z B, ad proft (Z. The depedet varable, cotag plat speces,... successve rce, cor, soybea, peauts, gree beas, sweet potato, ad

6 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 cassava, the producto quatty (J, J,..., J, prce sales by kg (H, H,..., H, producto costs (B, B,..., B, whch cossts of the cost of seeds, rrgato, fertlzers, plows (tractor ege, pestcdes (sectcdes ad fugcdes, labor (platg, care of, harvest, ad so o, the area of each plat (,,...,, ad the maxmum area (. b. Objectve Fucto (maxmze of proft Icome (Z P. The come (Z P s obtaed from the multplcato of the quatty of producto, sellg prce of crop, ad the lad area of each crop type, order to acqure, Z P = ( J H + ( J H ( J H (0 Producto Costs (Z B. The producto costs (Z B s obtaed from the multplcato of the producto costs of crop ad lad area of each type of crop, order to atta, Z B = B + ( + B + B +... B Profts (Z. The proft (Z s obtaed from the dfferece betwee come ad the producto costs, thus Z = Z Z ( P B Hece, the objectve fucto ca be expressed as follows Z = = [( J H B ] [( J H B ] = c. Fucto Costrats ( J H ( By usg lad area plated for each crop ( T ad the maxmum area (, the costrat fuctos ca be obtaed below, M , ,...,..,.... whle the ear Programmg (Optmzato performed usg QM for Wdows.. RESUTS AND DISCUSSION. Implemetato Ad Valdato Archtecture Of Bp The put ths research s half of the mothly ( days for 0 years. Each of the data cotaed data, so that the total data s 0 data ( x 0 years. The Neural Network Back Propagato (BPNN archtecture desg was executed to determe the best archtecture wth certa parameter settgs through trag ad testg data whch had bee dvded before. The archtectural parameters are gve as followg:. Neuro Numbers : a. Iput layer : b. Hdde layer : 00 c. Hdde layer : 0 d. Output layer :. Actvato Fucto: Sgmod Ber. Trag Algorthm: trarp. Settg Parameter: a. Max. Epoch : 0000 b. Error (Goal : c. earg Rate (R : 0.0 d. Mometum : 0.9 e. Decreased rato of R : 0. f. Icreased rato of R :.0 The archtecture used ths research for the predcto s show Fgure.

7 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Fgure : Back Propagato Neural Networks wth Two Hdde ayers for Predcto The desged archtecture ca be treated to calculate the percetage of accuracy evaluated (P by comparg the same patter (Q for all patters (R. Q P = 00% ( R The smulato results usg equatos,,,,, 8, ad for trag hydrologcal data ad clmatologcal data are preseted Table ad Table, the for testg hydrologcal data ad clmatologcal data are preseted Table ad Table the that s show o Table ad Table, Table : Result Evaluato of Archtecture of Trag Hydrologcal Data earg Rate Evaluato Parameters MAE MBE MSE RMSE R IA Accurato % % % % % % % Average %

8 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Table : Result Evaluato of Archtecture of Trag Clmatologcal Data earg Rate Evaluato Parameters MAE MBE MSE RMSE R IA Accurate % % % % % % % Average % Based o Table ad Table, t s clearly kow that the smulato results for the trag hydrologcal data showed average accuracy rate of 9.%. Whle the clmatologcal data for trag revealed average accuracy rate of 9.%. Table : Result Evaluato of Archtecture of Testg Hydrologcal Data earg Rate Evaluato Parameters MAE MBE MSE RMSE R IA Accurate % % % % % % % Average % Table : Result Evaluato of Archtecture of Testg Hydrologcal Data earg Rate Evaluato Parameters MAE MBE MSE RMSE R IA Accurate % % % % % % % Average % Based o Table ad Table, t s evdetly kow that the smulato results for testg hydrologcal data showed average accuracy rate of 9.%. Whle the clmatologcal data for testg dcated average accuracy rate of 9.9%. Above all, the data 0 ca be well predcted by usg the 98 to 0 data. The predcto results was compared wth the actual data 0 to kow the percetage of error of predcto results, whch was calculated by the equato below []. where G = = P O = O 00% P result predcto data 0 ad ( O actual data 0. Based o the smulato results obtaed the archtecture, the percetage error of each data type usg equato s preseted Table.

9 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Table : Error Value of Predcto Archtecture of Hydroclmatologcal data Data Type Acurato Error Percetage Rafall 99. %.8 % Temperature 99. % 0.0 % Humdty 99.8 % 0. % Wd Speed %. % Sushe 99. % 0. % Average 99. %. % Accordg to Table, t s kow that the average error of the predctos usg the archtecture that has bee desged s.%. The archtecture whch used to predct s farly good wth average accuracy rate of 99.%.. Predcto Result Of Hydrologcal Data From the results of the predcto (usg equato, t s kow that the average rafall ombok Islad 0 s.9 mm. Whle 0, the average of rafall s. mm ombok, as Fgure ad Fgure. ra fall / Mothly Fgure : Predcto Result of Rafall of ½ Mothly of ombok 0 ra fall / Mothly Fgure : Predcto Result of Rafall of ½ Mothly of ombok 0. Predcto Result Of Clmatologycal Data From the predcto results (usg equato, t s revealed that durg 0, the average temperature, humdty, wd speed, ad sushe, are.8 C, 80.%, 9.0 m/s, ad.8%, respectvely. The average for each dstrct s descrbed Table. Table : Average Clmatologcal Data ombok Islad 0

10 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Dstrct Temperat ure ( 0 C Average Humd ty (% Wd Spee d (m/s Sush e (% East Cetral West North Averag e From the predcto result 0, the average temperature, humdty, wd speed, ad sushe are.0 C, 8.%, 9. m/s, ad.%, respectvely. The average for each dstrct s descrbed Table 8. Table 8: Average Clmatologcal Data ombok Islad 0 Dstrct East Temperat ure ( 0 C Average Humd ty (% Wd Speed (m/s Sush e (% Cetral West North Averag e Water Requremet Processg From the aalyss result (usg equatos,,,,,, 8, 9, 0,, ad about the temperature data, humdty data, sushe data, ad wd speed data obtaed average water requremets processg (early platg for rce plats as descrbed Table 9 ad Table 0. Table 9: Average Water Requremet Processg for Rce ombok 0 E a Et o IR (mm/day Dstrct (mm/da (mm/da y y S = 00 S = 0 East Cetral.8... West.8... North Averag e Based o Table 9, t s kow that average evaporato ad evapotrasprato of rce 0 the ombok Islad are.80 mm/day ad. mm/day, respectvely. The water requremets processg for rce are.9 mm/day (S = 00, ad. mm/day (S = 0. Table 0: Average Water Requremet Processg for Rce ombok Islad 0 E a Et o IR (mm/day Dstrct (mm/da (mm/da S = 00 S = 0 y y East Cetral West North.8... Averag e Based o Table 0,t s revealed that the average evaporato ad evapotrasprato of rce 0 are.8 mm/day ad. mm/day, respectvely. The water requremets processg for rce are.8 mm/day (S = 00, ad.8 mm/day (S = 0.. Proft Optmzato Aalyss of sequece s dvded to three alteratves approprate aalyss Table. Table : Optmzato Aalyss Method of Crop Platg Patter Aalyss The Order Aalyss Method Aalyss I Aalyss II Aalyss III Platg Seaso I Platg Seaso II Platg Seaso III Platg Seaso II Platg Seaso III Platg Seaso I Platg Seaso III Platg Seaso I Platg Seaso II The objectve fucto ad costrat fucto the proft optmzato: Objectve Fucto: Maxmze (Proft: Z = [( J H B ] = ( =

11 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Costrat fucto (East ombok 0: Aalyss I: Aalyss II: Aalyss III: Wth the same method obtaed costrat fuctos for Cetral ombok, West ombok, North ombok ad Mataram. The optmzato results s preseted Table. Table : Recommedatos of Aalyss Method of Crop Platg Patter 0 Dstrct/cty Proft (IDR Aalyss I II III Recomedato East ombok.08 x x x 0 0 Aalyss III Cetral ombok.8 x x x 0 9 Aalyss III West ombok.9 x 0 9. x 0 9. x 0 9 Aalyss III Mataram. x 0 9. x x 0 9 Aalyss II North ombok. x x x 0 9 Aalyss III Based o Table, t ca be see the comparso ad the percetage of proft before ad after optmzato s preseted Table. Dstrct/cty Table : Comparso ad Percetage of Proft before ad after Optmzato 0 After Before Dfferece Optmzato Optmzato (IDR (IDR (IDR Percetage of Proft (% East ombok. x 0. x 0.9 x Cetral ombok. x 0. x 0.0 x 0.80 West ombok. x 0. x 0.0 x 0.9 Mataram.98 x 0.89 x 0. x North ombok.9 x 0.08 x x 0 0. Average.80 x 0. x 0.89 x 0. Based o Table, t s show that there exst creasg the percetage proft from 0 to 0, dcatg the optmzg the results of farmg wth croppg patters. Explctly, the creasg the percetage proft East ombok, Cetral ombok, West ombok, North ombok, ad Mataram creased.0%,.80%,,9%,.%, ad 8.9%, respectvely. Hece, the average percetage creased.0% from the prevous year. Based o the above results, the optmzato proft 0 ca be calculated by meas the followg steps.. The objectve fucto s the same as the objectve fucto 0 (equato.. Costrat Fucto (East ombok 0 Aalyss I: Aalyss II: Aalyss III:

12 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Wth the same method, costrat fuctos ca be obtaed for Cetral ombok, West ombok, North ombok ad Mataram. The optmzato results s preseted Table. Table : Recommedatos of Aalyss Method of Crop Platg Patter 0 Dstrct/cty Proft (IDR Aalyss I II III Recommedato East ombok.0 x x x 0 9 Aalyss III Cetral ombok. x x x 0 9 Aalyss III West ombok. x 0 9. x x 0 9 Aalyss III Mataram.8 x x x 0 9 Aalyss II North ombok. x 0 9. x x 0 9 Aalyss III Based o Table, the comparso ad the percetage of proft before ad after optmzato ca be the calculated as t s preseted Table. Dstrct/cty Table : Comparso ad Percetage of Proft Before ad After Optmzato 0 After Before Dfferece Optmzato Optmzato (IDR (IDR (IDR Percetage of Proft (% East ombok.8 x 0.8 x 0. x Cetral ombok. x 0. x 0. x 0.89 West ombok. x 0. x 0.8 x 0 0. Mataram.8 x 0. x 0 9. x North ombok.9 x 0.08 x 0. x 0.89 Average.8 x 0. x 0.0 x 0.0 Based o Table, t s kow that the cremet of the percetage proft from 0 to 0 was also foud. The creasg of the percetage proft East ombok, Cetral ombok, West ombok, North ombok, ad Mataram are.0%,.89%, 0, %,.89%, ad.8%. The average percetage crease.0% from the prevous year. alteratve recommedato to platg tme wth mmum water requremets each dstrct/cty, as show Table,. Plag Croppg Patters From the aalyss (usg equatos 8, 9, 0 by computg crop water requremets alteratve platg tme, the we ca obta a Table : Results Optmzato of Crop Water Requremet Dstrct Recommedato NFR (mm/day Total Average DR (mm/day East ombok Alteratve of..8. Cetral ombok Alteratve of West ombok Alteratve of Mataram Alteratve of North ombok Alteratve of Based o Table, t s obtaed a croppg patter each dstrct/cty o the ombok Islad 0, s preseted Table, Table 8, Table 9, Table 0, ad Table. 8

13 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Table : Plag Croppg Patters of East ombok Remarks: Rce Soybea Sweet Potato Cor Peauts Cassava Table 8: Plag Croppg Patters of Cetral ombok Table 9: Plag Croppg Patters of West ombok Table 0: Plag Croppg Patters of North ombok Table : Plag Croppg Patters of Mataram 9 Gree Bes

14 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 From the Table, the croppg patter East ombok for PS I wll start o February II wth plat of rce ( 8 ha, PS II starts Jue II wth plat of rce (,0 ha, cor ( 8 ha, soybea (, ha, peauts (, ha, ad cassava (, ha, whle the PS III wll start November I wth plat of rce ( 90 ha, gree beas (,9 ha, ad sweet potato (88 ha. The from the Table 8, the croppg patter the Cetral ombok for PS I wll startg July II wth plat of rce ( ha, PS II wll start November II wth plat of rce (0,9 ha, cor (, ha, soybea (,08 ha, peauts (,9 ha, ad cassava (,0 ha, whle the PS III wll start March II wth plat of rce ( 8 ha, gree beas (80 ha, ad sweet potato ( ha. I the West ombok, for PS I wll start July II wth plat of rce (,90 ha, PS II wll start November II wth plat of rce (,9 ha, cor (, ha, soybea (,98 ha, peauts (,9 ha, ad cassava ( ha, whle the PS III wll start March II wth plat of rce (,08 ha, gree beas ( ha, ad sweet potato (9 ha. I the North ombok for PS I wll start Jue I to wth plat of rce (89 hectares, PS II wll start October I to wth plat of rce (,00 ha, cor (89 ha, peauts (,9 ha, ad cassava (,0 ha, whle the PS III wll start February I wth plat of rce (, ha, cor (,9 ha, peauts (,899, gree beas ( ha, ad sweet potato (8 ha. I the Mataram for PS I wll start August II wth plat of rce (800 ha, gree beas ( ha, sweet potato ( ha, soybea (98 ha, PS II wll start December II wth plat of rce (0 ha ad cassava ( ha, whle the PS III wll start Aprl II wth plat of rce (,0 ha, cor ( ha, soybea (98 ha, ad peauts (9 ha.. CONCUSION Artfcal eural etwork wth Back propagato method or BPNN s relable to predct hydro clmatologcal data such as rafall, temperature, humdty, wd speed, ad sushe data wth accuracy rate of 9.% - 9.% for trag ad testg data. Whle the valdato testg of the predctos obtaed the average percetage error of.% wth a average accuracy rate of 99.%. Plag croppg patters ombok Islad durg 0 creased the proft each dstrct/cty. Explctly, the creasg the percetage proft (from 0 to 0 East ombok, Cetral ombok, West ombok, North ombok, ad Mataram creased.0%,.80%,,9%,.%, ad 8.9%, respectvely. Hece, the average percetage creased.0% from the prevous year. The creasg of the percetage proft (from 0 to 0 East ombok, Cetral ombok, West ombok, North ombok, ad Mataram were.0%,.89%, 0,%,.89%, ad.8%. The average percetage creased.0% from the prevous year. REFERENCES [] Adeloye, A.J., Rustum, R., Karyama, I.D. Neural Computg Modelg of the Referece Crop Evapotrasprato. Evrometal Modellg & Software. 0. Vol. 9, pp.. [] Alle, R.A., Perera,.S., Raes, D., Smth, M. Crop Evapotrasprato. FAO, Rome [] Arora, J.S. Itroducto to Optmum Desg. odo, Elsever, Ic. 00. [] Fausett,., 99. Fudametals of Neural Network. Pretce Hall, New York. [] Huag, J., Bradley, G.R.u, C., Zhag H., ad Che, F. Croppg Patter Modfcatos Chage Water Resource Demads the Bejg Metropolta Area. Joural of Itegratve Agrculture. Vol., 0, pp [] u, H., Ta, H., Pa, D.,, Y. Forecastg Models for Wd Speed Usg Wavelet, Wavelet Packet, Tme Seres ad Artfcal Neural Networks. Appled Eergy Vol. 0, 0, pp [] Nastos, P.T. Ra Itesty Forecast Usg Artfcal Neural Networks Athes, Greece. Atmospherc Research. Vol. 9, 0, pp. 0. [8] Sadegh, S.H.R, Jall, K, ad Nkkam, D. ad Use Optmzato Watershed Scale. ad Use Polcy. Vol., 009. pp [9] Sakellarou-Makratoak, M., ad Vageas, I. Mappg Crop Evapotrasprato ad Total Crop Water Requremets Estmato Cetral Greece. Europea Water. Vol. /, 00, pp.. [0] Schmdt, F.H. ad J.H.A. Ferguso. 9. Rafall Type Based o Wet ad Dry Perod Rato fo Idoesa wth Wester New Guea 0

15 Joural of Theoretcal ad Appled Iformato Techology st December 0. Vol. 8 No JATIT & S. All rghts reserved. ISSN: E-ISSN: 8-9 Verh. No.. Bureau of Meteorology ad Geophyscs, Jakarta [] She, Y.,, S., Che, Y., Q, Y., ad Zhag, Y. Estmato of Regoal Irrgato Water Requremet ad Water Supplyrsk the Ard Rego of Northwester Cha Agrcultural Water Maagemet. Vol. 8, 0, pp.. [] Traore, S., Wag, Y., ad Kerh, T. Artfcal Neural Network for Modelg Referece Evapotrasprato Complex Process Sudao- Sahela Zoe. Agrcultural Water Maagemet. Vol. 9, 00, pp. 0. [] Zhag, G.P. Tme Seres Forecastg Usg a Hybrd ARIMA ad Neural Network Model. Neurocomputg. Vol. 0, 00, pp. 9.