APPLICATION OF CASE BASED REASONING AND NEAREST NEIGHBOR ALGORITHM FOR POSITIONING FOOTBALL PLAYERS

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1 Internatonal Journal of Mechancal Engneerng and Technology (IJMET) Volume 9, Issue 13, December 2018, pp , Artcle ID: IJMET_09_13_028 Avalable onlne at ISSN Prnt: and ISSN Onlne: IAEME Publcaton Scopus Indexed APPLICATION OF CASE BASED REASONING AND NEAREST NEIGHBOR ALGORITHM FOR POSITIONING FOOTBALL PLAYERS Lusa Lamalewa Informatcs Engneerng Department, Faculty of Engneerng, Unverstas Musamus, Merauke, Indonesa Gerzon J. Maulany Department of Informaton Systems, Faculty of Engneerng, Unverstas Musamus, Merauke, Indonesa ABSTRACT Sport s an actvty needed by humans for physcal development and growth. One of the most popular and most popular sports n the world today ncludng n Indonesa s soccer. The popularty of football s nseparable from the presence of supporters. The hgh expectatons of supporters at a club, requres the coach and management team to develop the best strateges for each match. The potental of every soccer player must be managed well n order to optmze performance n the feld n accordance wth the competences they have towards the poston of players n the feld. Players who have good abltes wll beneft one team. The ext of the best ablty of a player s nseparable from the placement of the deal poston for these players accordng to ther potental. Ths study apples case-based reasonng usng the nearest neghbor algorthm to buld a system that can help the work of football club coaches and management teams n determnng player postons accordng to the abltes and potental of football players based on smlartes n prevous cases. If there are smlartes or smlartes, experence from old problems wll be used to solve new problems. Testng the system of determnng the poston of football players based on the potental of players usng the nearest neghbor method produces an accuracy rate of 97%. Key words: Case based reasonng, nearest neghbor, poston, soccer players. Cte ths Artcle: Lusa Lamalewa, Gerzon J. Maulany, Applcaton of Case Based Reasonng and Nearest Neghbor Algorthm for Postonng Football Players, Internatonal Journal of Mechancal Engneerng and Technology 9(13), 2018, pp edtor@aeme.com

2 Applcaton of Case Based Reasonng and Nearest Neghbor Algorthm for Postonng Football Players 1. INTRODUCTION Sport s an actvty needed by humans for physcal development and growth. One of the most popular and most popular sports n the world today ncludng n Indonesa s football games (Suganda, 2017). The popularty of football s nseparable from the presence of supporters. The hgh expectatons of supporters at a club, requres the coach and management team to develop the best strateges for each match. There are many coaches who are replaced because they cannot mprove the performance of the team they care for. From several facts about the dsmssal of traners, most of them are caused by three causes, namely the problem of strategy formulaton, the coach s not objectve n selectng players and the placement of players' postons (Kuper, 2009). The poston of football players n each team s 11 people. A team conssts of one goalkeeper and ten players who fll the poston of defender, mdfelder and forward as well as a substtute player who s desgnated as a substtute n the feld. Soccer player selecton s a process of fndng the rght player by consderng the competences possessed by the player towards the placement of the player's poston (Salm, 2008). The potental of every soccer player must be managed well n order to optmze performance n the feld n accordance wth the competences possessed by the player's poston n the feld. Players who have good abltes wll beneft one team. The ext of the best ablty of a player s nseparable from the placement of the deal poston for these players accordng to ther potental. In general, the selecton or selecton of player postons often occurs n polemcs, the decson-makng process n determnng the poston of players stll reles on coach nstncts, the proxmty between players and coaches, the desre of players to place themselves n a certan poston and also the nfluence of management n electons player. So that the process of placng the player's poston does not work based on predetermned crtera. When the placement of a player's poston s not n accordance wth hs potental, ths can result n teamwork and strength not beng optmal. So the results acheved are not n accordance wth the target (Lamalewa and Wardoyo, 2017). Ths study apples case-based reasonng to buld a system to assst the work of football club coaches and management teams n determnng the poston of players accordng to the abltes and potental of football players based on smlartes n prevous cases. Research (Watson, 1997) suggests that CBR has several advantages, namely CBR s more effcent because t uses old knowledge and s able to adapt new knowledge. CBR uses past experence to solve current problems, f there are smlartes or smlartes to current problems wth old problems, then experence from old problems wll be used to solve new problems wth lttle adaptaton that fts the new problem condtons (Montan and Jan, 2010). In the CBR there are several processes, namely retreval, reuse, revse and retan. In the retreve phase there are many methods used to retreve old cases that are relevant to new cases. The retreval phase n ths study used the nearest neghbor method. Nearest Neghbor s an approach to look for cases by calculatng the closeness between new cases and old cases, whch s based on matchng weghts of a number of exstng features (Kusrn, 2009). CBR usng the nearest neghbor method has been used by research (Montan and Jan, 2010) to dagnose dengue hemorrhagc fever resultng n an accuracy of 99.25%. In addton researchers made a CBR decson support system for the dagnoss of occupatonal lung dsease wth a postve predcton of 98.6%. Bascally nearest neghbors are approaches to fndng cases by calculatng the closeness between old cases that are stored as a bass for new cases and problems. The way to calculate the closeness between old cases and new problems s to use the smlarty functon. And the smlarty threshold set n ths study s edtor@aeme.com

3 Lusa Lamalewa, Gerzon J. Maulany 2. METHODOLOGY 2.1. System Descrpton The research method used n ths research s to buld a system usng case-based reasonng (CBR) for postonng football players. Case based reasonng (CBR) s a method used to solve problems by usng old events as a soluton to a new case by lookng at the smlarty level. The smlarty value between the new case and the old case s calculated usng the smlarty functon, the hgher the smlarty value the greater the smlarty of the soluton between the new case and the old case. CBR can be orented as a process cycle, whch s dvded nto four sub-processes (Aamodt and Plaza, 1994) namely: 1) Retreve: Take the case that s most smlar / relevant (smlar) to the new case; 2) Reuse: reuse knowledge and nformaton n cases to solve problems; 3) Revse: revse the proposed soluton; and 4) Retan: a part of storng experences that mght be useful for solvng future problems. The nearest neghbor method s used n ths study to measure the smlarty of cases durng the retreval process. The process of determnng the poston of a soccer player startng wth the management team ncludes the condtons of the player ncludng the player's dentty and player's ablty. After enterng data about the condton of the player, the next step s that the system wll take the cases stored on the bass of the case to measure local and global smlarty. The case base s a place for storng football players' data, namely the player's dentty, player's ablty and prevous player's poston as knowledge for the system. Measurement of local smlarty s done to measure the smlarty between attrbutes found n new cases and old cases. Each player's ablty has a weght wth a certan value based on the player's poston. The weght value s obtaned from a soccer coach who s an expert. Measurement of local smlarty for numercal features (Pal and Shu, 2004) usng equaton (1). f ( S, T ) 1 ( S T ) f max f mn (1) where: f(s,t ) : Local smlarty to the attrbute between the source case and the target case attrbute S : Attrbute of the source case T : Attrbute of the target case fmax : The maxmum value of the attrbute used fmn : The mnmum value of the attrbute used Furthermore, the system wll perform an overall (global) calculaton to fnd out the smlarty of cases usng the nearest neghbor method by addng a factor of expert confdence level (Mancasar, 2012) as ndcated by equaton (2). where: SmNN SmNN( T, S) n 1 f ( S, T ) w J ( S, T ) * P( S)* n J ( T ) w, p( S ) 1, p( S ) : Global smlarty between case T (target case) and S (source case) (T,S) n : The number of features avalable f(s,t ) : The smlarty of the feature of the source case and the target case / local smlarty functon S : Feature of the source case T : Feature of the target case w (p(s) : The value of the feature on the crtera of the source case P(S) : Percentage of expert confdence n a case n the source case J(S,T ) : The many features contaned n the target case that appear n the source case feature J(T ) : The number of features contaned n the target case (2) edtor@aeme.com

4 Applcaton of Case Based Reasonng and Nearest Neghbor Algorthm for Postonng Football Players The results of the calculaton of global smlarty of new cases wth each subsequent case are then compared to get the old case wth the hghest smlarty value, the value of smlarty s between 0 and 1. The hghest value s the value most smlar to the new case. The next stage s the hghest smlarty value obtaned compared to the threshold value that has been determned, the threshold value used s 0.8. If a new case wth an old case on the base of the case has a smlarty value above the threshold or equal to 1, then the new case s carred out a reuse process so that the concluson s the poston for the player. These results are then stored n the case base as new knowledge. And f the smlarty value s lower than the threshold, t ndcates that the new case s ncreasngly not smlar to the old case stored on the bass of the case, then the case s submtted to the coach to be revsed, and then stored nto the base case as a new case (retan) Case Acquston Case acquston s the process of collectng cases, then used as nformaton to be studed, processed and organzed n a structured manner so that t can be used as a knowledge base. The source of knowledge n ths study was obtaned from data held by soccer coaches who were used as experts Case Representaton The case data of soccer players obtaned n ths study comes from the evaluaton data of the PSS Sleman team n the last fve years. The data obtaned s then consulted wth the traner to determne whch data can be used as a case feature. The selecton of features performed by the traner s based on two consderatons, namely the level of mportance of the feature on the player's poston and the avalablty / completeness of these features n the evaluaton data of the PSS Sleman team. Accordng to the coach, there are several features that are consdered mportant n football games that must be possessed by players to determne the poston of a player, as follows: strength, speed, stamna, acceleraton, aerobc endurance, aglty, flexblty, coordnaton, reacton, mult poston, use both legs n kckng, movement wthout balls, accuracy (accuracy), decson makng, drbble, take advantage of shootng opportuntes, ablty to grab the ball, the ablty to control the opponent's moton, change from defense to attack, change from attack to defense and headng ablty. Each attrbute n complng a case has ts own value, n ths study each attrbute has a value that s gven by the traner. There are several attrbutes that are assessed by dong a physcal test and some other attrbutes are assessed when the players practce sparrng n teams. Each feature s calculated wth local smlarty usng a numerc type System Archtecture The system s made to be able to select the poston of football players based on the data entered by the user. System users are dvded nto two groups, namely the management team and the coach. System archtecture desgn conssts of: 1. Input module The nput module s the ntal stage, namely the management team enters the player's new data whch s the new problem nto the system usng a GUI-based data nput module (graphcal user nterface). The new data of the player entered ncludes the player's name and the physcal crtera and ablty of the player. 2. CBR Cycle The next stage s processng the nputted data n the CBR Cyrcle envronment ncludng retreval, reuse, revson, and retan processes. In the retreval process, the process of calculatng the smlarty value wll be carred out between the new problem and the old case edtor@aeme.com

5 Lusa Lamalewa, Gerzon J. Maulany on the bass of the case, the smlarty value s calculated based on the smlarty of features usng equaton (1) and equaton (2). After the new case and the old case have found the hghest smlarty value and above the threshold, then the old case s used to solve the problem of the new problem wth a soluton that s n the old case s used as a soluton to the new problem. But f the smlarty value s below the threshold whch s below 0.8, the new problem wll be handled by the coach to be gven the rght soluton. 3. Output Modules In the output module a soluton s obtaned n the form of a player poston. Data from the selecton can be saved nto the case base (retan) as a learnng process so that the number of cases on a case bass ncreases System Testng and System Accuracy Testng the system for determnng the poston of soccer players s done wth 2 scenaros, namely testng by enterng the test data one by one and usng k-fold cross valdaton. And each scenaro s then calculated for accuracy. The accuracy of the system test s calculated by comparng the number of correct decsons wth the number of test data (Tempola and Abdullah, 2018). The comparson can be wrtten n the form of equaton 3 below: Akuras = x100% (3) Other useful methods were provded n prevous researchers (Latuheru and Sahupala, 2018; Maulany et al., 2018; Samudro et al., 2011; Waremra and Bahr, 2018). 3. RESULTS AND DISCUSSION Tests carred out on the system that has been bult to determne the performance of the system and conduct an evaluaton process of the test results and calculate accuracy Results The process of complng a case base The data entered n the case database s player data obtaned from the management of PSS Sleman to be used as the next data reference or later problem. The data entered s the data of the player's name and the condton of the player. In ths study 22 features of the player's condton were used, namely strength, speed, stamna, acceleraton, aerobc endurance, aerobcs endurance, aglty, reacton coordnaton, mult poston, usng both legs n kckng, ballless movements, accuracy, decson makng, drbble, take advantage of shootng opportuntes, the ablty to seze the ball, the ablty to control the opponent's movements, change from attack to defense, change from defense to attack and headng ablty. In addton to the data base case also has a soluton or decson to play poston from each data, namely the goalkeeper, defender, mdfelder and forward. The amount of data taken from the management of Sleman PSS s as many as 36 case data whch are then used as a bass for 24 cases and 12 cases as test data. The crtera weghtng preparaton process Each attrbute or crteron of players has dfferent weghts for each player poston whose value s determned by the coach. Case weghtng s carred out by assgnng weght values to the crtera of strength, speed, stamna, acceleraton, aerobc endurance, aerobc endurance, aglty, reacton coordnaton, mult poston, usng both legs n kckng, movement wthout balls, accuracy, decson makng, drbble, take advantage of shootng opportuntes, the ablty to seze the ball, the ablty to control the opponent's movements, change from attack to defense, change from defense to attack and headng ablty. Weghtng ths feature shows the edtor@aeme.com

6 Applcaton of Case Based Reasonng and Nearest Neghbor Algorthm for Postonng Football Players nfluence of the crtera for a poston n a football game. The greater the weght, the greater the level of mportance or nfluence of these features on a poston. Ranges used by traners to determne weght values for each crteron are between 1 and 10. The process of determnng poston by case based The process of determnng the poston of a player begns by enterng the condton of the player made by the management team to get a soluton, namely the poston of the player. The data entered s the player's dentty along wth the player's crtera. The crtera of the players used n ths study were strength, speed, stamna, acceleraton, aerobc endurance, aerobc endurance, aglty, reacton coordnaton, mult poston, usng both legs n kckng, ballless movement, accuracy, decson makng, drbble, take advantage of shootng opportuntes, the ablty to wn the ball, the ablty to control the opponent's moton, change from attack to defense, change from defense to attack and headng ablty. After the nput process for the condton of new players, the smlarty calculaton process s then carred out, the system wll calculate the level of smlarty between the condtons of new players and each case stored n the base of cases usng the formula of local smlarty and global smlarty. The results of the smlarty calculaton then carred out the threshold test process by the system to be taken nto consderaton for determnng the rght soluton for the new problem. If the hghest smlarty value s the same or above the threshold value, the canddate soluton s the soluton to the problem, namely the poston of the player. Reuse process Reuse s done by checkng the hghest smlarty value from the results of the retreval process. If the value s the value of the threshold, the soluton of the case wth the hghest smlarty s set as the soluton to the problem, namely the poston for the player The threshold lmt specfed n ths study s 0.8. These results are then stored n the case base as new knowledge. Revson process Revsons are part of the system adaptaton to cases that have not been found a soluton. Adaptaton s done by checkng the hghest smlarty value from the results of the retreval process. If the value s smaller than the threshold, whch s 0.8, the problem s saved to awat revsons by the traner Dscusson The process of testng and results The data used are data obtaned from the PSS Sleman management team, as many as 36 cases, 24 cases as a base case and 12 cases for testng data. Testng s done by several experments, namely testng by enterng data one by one and by usng k-fold cross valdaton. Scenaro 1 Testng s done by enterng the test data one by one where the test results are sad to be true f the test results are the same as the data obtaned from the PSS Sleman management team wth the hghest smlarty value exceedng the predetermned threshold. In ths study usng a threshold value of 80%. If the smlarty value s below the threshold value then t cannot be used to determne the player's poston but wll be saved and revsed by the coach. The recaptulaton of the test results s shown n Table edtor@aeme.com

7 Lusa Lamalewa, Gerzon J. Maulany Table 1 Test results Results No. Poston Total test True False data 1 Goalkeeper Defender Half-back Front player Total The accuracy of system testng s calculated by dvdng the amount of data correctly by the number of test data. Accuracy = x 100% = 100% The above calculaton results show that the system can correctly recognze the player's poston at 97.22%. Scenaro 2 The test uses the k-fold cross valdaton wth 36 cases of data, the data s dvded nto two parts, namely the base case and test data. The experment was carred out 3 tmes to measure the accuracy of the data, the frst experment used the 1st data up to the 12th data as the test data and the rest as the bass of the case, the second experment used the 13th data to the 24th data as test and expermental data the thrd uses data 25th to 36th data as test data. Measurement of system performance accuracy by calculatng the number of test data that s predcted correctly dvded by the number of test data. The test results n table 2 show that the hghest accuracy of classfcaton s 100% n the frst and thrd experments, the lowest accuracy n the second experment wth an accuracy rate of 91.66%. Table 2 3-fold cross valdaton test results Test result Correct data Incorrect data Accuracy (%) Fold 1 (1-12) Fold 2 (13-24) ,66 Fold 3 (25-36) Average Accuracy 97.22% The average result of accuracy for 3-fold cross valdaton s calculated by summng the classfcaton accuracy for each experment dvded by the number of experments performed. The average accuracy of the test results wth 3-fold cross valdaton s 97.22%. 4. CONCLUSIONS Based on the research and the results of the tests performed, several conclusons were obtaned, namely: Ths study produces a case based reasonng system for determnng the poston of soccer players based on potental players usng the nearest neghbor smlarty method. Testng by enterng the test data one by one usng 12 cases from a total of 36 cases wth 24 case data as the bass of the case resultng n an accuracy of 97% and testng usng the k-fold cross valdaton wth 3 fold of the total data as many as 36 cases yeldng even average accuracy of 97.22% edtor@aeme.com

8 Applcaton of Case Based Reasonng and Nearest Neghbor Algorthm for Postonng Football Players For the development of further research, t s necessary to ncrease the amount of data and crtera n determnng the poston of players to get more accurate results. And t needs to be tred wth other retreval methods that can mprove system accuracy. REFERENCES [1] Aamodt, A., and Plaza, E. (1994). Case-Based Reasonng: Foundaton Issues Methodologcal Varatons, and System Approaches, AI Communcaton IOS Press, vol 7, pp [2] Adawyah R., Hartat S. dan Musdholfah A. (2016). Case Based Reasonng Untuk Dagnoss Penyakt Akbat Vrus Dengue, Tess, S2 Ilmu Komputer UGM, Yokyakarta. [3] Kusrn, Emha T. L. (2009). "Algortma Data Mnng," And Offset.,Yogyakarta. [4] Lamalewa L. and Wardoyo R. (2017). Penempatan poss peman sepakbola menggunakan case based reasonng, Tess, S2 Ilmu Komputer UGM, Yogyakarta. [5] Latuheru, RD and Sahupala, P. (2018). Desgn of Methane Gas Capture Installaton from Garbage Waste, Internatonal Journal of Mechancal Engneerng and Technology, 9(10), pp [6] Mancasar, U.A. (2012). Sstem Pakar Menggunakan Penalaran Berbass Kasus untuk Mendagnosa Penyakt Syaraf pada Anak, Skrps, S1 Ilmu Komputer UGM, Yogyakarta. [7] Maulany, GJ; Manggau, FX; Jayad, Waremra, RS and Fenanlampr, CA. (2018). Radaton Detecton of Alfa, Beta, and Gamma Rays wth Geger Muller Detector, Internatonal Journal of Mechancal Engneerng and Technology, 9(11), 2018, pp [8] Mkkey Anggara Suganda, (2017). Pengaruh Lathan Lngkaran Pnball Tehadap Ketepatan Passng Datar Dalam Permanan Sepakbola Pada Sswa Ekstrakurkuler D SMK YPS Prabumulh, Jurnal Ilmu Keolahragaan, Vol. 16 No. 1. [9] Montan, S. and Jan, L. C. (2010). Successful Case-Based Reasonng Applcatons 1. Sprnger. Berln. [10] Pal, S.K., and Shu, S.C.K. (2004). Fondaton of Soft Case-Based Reasonng, John Wlley and Sons, Inc., New Jersey. [11] S. Kuper, (2009). Soccernomcs, Erlangga, Surabaya. [12] Salm, (2008). Buku Pntar Sepakbola, Penerbt Nuansa, Bandung. [13] Samudro, H., M. Faqh, E. Sudarma. (2011). Green archtecture crtera for hgh-rse buldng that serves as a rental offce n the cty of Surabaya. Journal of Appled Scences Research 7(5): [14] Tempola F., Abdullah D. S. (2018). Komparas Rule Based Reasonng (RBR) dan Case Based Reasonng (CBR) untuk Penentuan Kelayakan Mahasswa Penerma Beasswa, Jurnal PROtek, Vol. 05 No. 2. [15] Tomar, S. P. P., Sngh, R., Saxena, K. P., dan Sharma, J. (2016). "Case Based Medcal Dagnoss of Occupatonal Chronc Lung Dseases from Ther Symptoms and Sgns." Internatonal Journal of Bometrcs and Bonformatcs (IJBB), Vol.5, [16] Waremra, RS and Bahr, S. (2018). Identfcaton of Lght Spectrum, Bas Index and Wavelength n Hydrogen Lghts and Helum Lghts Usng a Spectrometer, Internatonal Journal of Mechancal Engneerng and Technology 9(10), pp [17] Watson, (1997). Applyng Case-Based Reasonng: Technques for Enterprse System, Morgan Kaufmann Publsher Inc., San Franssco, Calforna edtor@aeme.com