WEBSITE STRUCTURE IMPROVEMENT USING ANT COLONY TECHNIQUE

WEBSITE STRUCTURE IMPROVEMENT USING ANT COLONY TECHNIQUE Wiwik Aggraei 1, Agyl Ardi Rahmadi 1, Radityo Prasetyo Wibowo 1 1 Iformatio System Departmet, Faculty of Iformatio Techology, Istitut Tekologi Sepuluh Nopember Gedug Sistem Iformasi, Kampus ITS, Surabaya, Idoesia Email: wiwik@its-sby.edu ABSTRACT The developmet of the Iteret ad the icreasigly widespread use of it make the compay more ad more ito e-busiess activities. I the e- busiess idustry, a web site is the place where the compay services are placed. So that the websites holds importat role i the success of a e-busiess. Creatig web sites that effectively ad efficietly could icrease user satisfactio, because they ca fid the iformatio they wat quickly ad easily, or i other words to save time ad costs. Oe way to icrease the effectiveess of a web site is by improvig the structures of its liks. For site owers, improvig structure may mea costs ad time which is ot small. Therefore, a right ad efficiet method for restructurig a web site is eeded. Oe method that ca be used is by usig Quadratic Assigmet Problem (QAP) approach. QAP used the graph cocept to map the structure of web sites ad to defie the coectivity degree ad relatios betwee web pages. The the QAP is used to modelig the page positioig problem. To resolve the problem, at coloy techique is used. Usig the QAP method solved by at coloy techiques, ew structures ca be obtaied ad aalyzed. From aalysis we could kow whether the ew structure is better tha old website structure or ot from QAP objective fuctio, closeess aalysis, out degree aalysis, ad also best practices aalysis poit of views. Keywords: At Coloy Meta-heuristic, QAP, Web Sites Structure, Websites, e-busiess, Website structure improvemet 1. PREFACE Buildig a effective website as the ceter of iformatio is importat for a compay. Because it will idirectly improves customer ad website visitors satisfactio. I additio to e-busiess idustry, ature of a website as the ceter of iformatio also meas that a website should have good level of effectiveess ad usability, so that users or iformatio seekers ca easily ad quickly retrieve desired iformatio. There are may ways for icreasig iformatio retrieval efectivity i a website, amely the site map, search egie, ad itelliget avigatio aid tools [6]. As iformatio provider,.. that the ower provides a website with good level of usability [4]. Especially i e-busiess idustry, where compay use the website to get iformatio about customer, competitor, ad busiess parter ad also to provide iformatio about their compay. A web site structure improvemet method coducted i research [1] by usig Quadratic Assigmet Problem modelig. By usig QAP modelig, website structure problem could be aalogous to QAP problem. This aalogy also defies the basic assumptios required for the problem of site structure to be modeled i the QAP. After modeled ito QAP, the problem will be solved by usig At Coloy Optimizatio techique. By usig at coloy, it is hoped a better ew website structure ca be foud. Oce the ew structure foud, the assessmet of the ew structure is usig Key Performace Idicator (KPI) bechmarkig which is predefied. Based o KPIs, better or ot the ew structure is ca be defied. 2. BASIC THEORY 2.1 Graph Graph is a discrete structure that is used to represet discrete objects ad relatioships betwee those objects (Rosse, 2003). Visual represetatios of graphs cosistig of odes or vertices (V) i the form of dots to represet the object. To represet the relatioships betwee objects expressed by lies or the edge or edges (E) / arcs (A) which are coectig vertices. There are several differet types of graphs, which are distiguished by the ature of the iter-coect of the coected vertices. Graph is used to solve problems i may areas. As a example of graphs ca be used to study the structure of the Iteret or World Wide Web (WWW). Other examples of graphs ca be used i the problem of fidig the shortest path from oe place to aother i a city or a regio. VII-37

VII-38 The 6 th Iteratioal Coferece o Iformatio & Commuicatio Techology ad Systems Graph G ca be defied as a pair of sets (V,E), which : V = o-empty set of vertices = { v 1, v 2,, v } E = edge set coectig a pair of vertices = { e 1, e 2,, e } Or ca be writte i brief otatio G = ( V, E ) I the defiitio, V otherwise ot be empty, while E may be empty. Sice a graph may ot have eve a sigle side, but the vertex must exist, at least oe. Vertex i the graph ca be labeled with letters like a, b, c,, z, with the origial umbers 1,2,3, or ay combiatio thereof. While the edge which is coectig vertex v i with vertex v j expressed by pairs ( v i, v j ) or by usig symbol e 1, e 2,. Thus, if e is ad edge coectig v i ad v j, e ca be writte as e = ( v i, v j ) Graphs ca be grouped ito several categories. It depeds o the parameter used for groupig. Oe of groupig that ca be used, based o the directio ad weight. Uder the directio ad the weights, the graph ca be divided ito four, amely: Directed weighted graph Directed uweighted graph Udirected weighted graph Udirected uweighted graph Graph is used for mappig the structure of website which will be modeled ito QAP. 2.2 Quadratic Assigmet Problem Quadratic assigmet problem (QAP) is oe of the fudametal problems i combiatorial optimizatio problem (COP) which is a brach of operatios research, from the category of facility locatio problems. Whe first itroduced, QAP is a mathematical model for the locatio of idivisible ecoomical activities. The goal is to place facilities to locatios at a cost comparable to flow betwee the facilities, multiplied by the distace betwee locatios, plus the cost to place a facility at each locatio. These problems ca be modeled with three matrices : A B C = (a ik ), flow from facility i to k = (b jl ), distace form locatio j ke l = (c ij ), cost of placig facility i at locatio j So that the QAP i Koopmas-Beckma form ca be writte as follows, mi S (1) a b ik ( i) ( k ) i1 k 1 i1 c i ( i) S is the set of all permutatios of itegers { 1,2,, }. a ik b π(i)π(k) is the trasportatio costs due to placig the facility i to locatio π(i) ad facility k to locatio π(k). A example of the QAP with iput matrices A, B, ad C ca be deoted by QAP(A, B, C). I additio to the mathematical model (1), ca also be modeled without usig the matrix C atau c ij = 0, for all 1 i, j. So that there are oly two matrices i existig model, flow matrix A = (a ik ), ad distace matrix B = (b jl ). So the form of Koopmas-Beckma QAP ca be also writte as follows mi S i1 k1 a b ik ( i) ( k) (2) With the same explaatio with a mathematical model (1), S is the set of all permutatios of itegers { 1,2,, } ad a ik b π(i)π(k) is the trasportatio costs caused by placig facility i to locatio π(i) ad facility k to locatio π(k). QAP will be used to model the problems of the website structure improvemet. 2.3 At Coloy Optimizatio (ACO) At coloy Optimizatio (ACO) is a populatio based o meta-heuristic techique that ca be used to fid approximate solutios to difficult optimizatio problems. I ACO, a set of software agets called artificial ats fid the best solutio for the give optimizatio problem. To apply the ACO, the optimizatio problem coverted ito the problem of fidig the best path i a weighted graph. The artificial ats, gradually build a solutio by movig o the graph. The Process of solutio costructio is stochastically, o-determiistic which meas the state system is determied both by the movemet that has bee estimated from the process itself ad by some radom elemets. I additio to stochastic process, the solutio costructio is also iflueced by pheromoe model, which is a set of parameters associated with compoets of the graph (either vertices or edges) whose value chages durig the costructio process. ACO is basically a paradigm or framework for desigig algorithm meta-heuristic for combiatorial optimizatio problems. ACO also

Website Structure Improvemet Usig At Coloy Techique-Wiwik Aggraei VII-39 could be cosidered as a class of algorithms. I ACO, artificial ats build solutios to the COP by traversig the costructio graphs that are fully coected defied as follows. First, each decisio variable X i is called the solutio compoets ad deoted by c ij. The set of all possible solutios compoets deoted by C. Costructio graph G C (V, E) is defied by associatig the compoets of C with the set of vertices V or with the set of edges E. The value of the pheromoe trail τ ij associated with the compoet c ij. Pheromoe values are geerally followed the fuctio of the iteratio algorithm t : τ ij = τ ij (t). Pheromoe values allow modelig of probability distributio from differet compoets. Pheromoe value is used ad modified by the ACO i solutio costructio process. Procedure ACO_metaheuristic Set parameters, iitialize pheromoe trails SCHEDULE_ACTIVITIES CostructAtSolutios DaemoActios UpdatePheromoes END_SCHEDULE_ACTIVITIES END_Procedure {optioal} Figure 2. 1 ACO Algorithm Ats are traversig from vertex to vertex through the edges of the costructio graph, utilizig iformatio from the pheromoe values ad gradually buildig a solutio. I additio, the ats storig a umber of pheromoe compoets both o the vertex ad the edge which they traversed. Amout fromi Δτ of pheromoe stored might depeds o the quality of curret solutio. Thereafter the ats utilize pheromoe iformatio as a guide to build a better solutio. Geerally, the ACO meta-heuristic ca be described i pseudo code i figure 2.1. ACO meta-heuristic cosists of iitializatio steps ad three compoets of the algorithm which is activated i Schedule_Activities procedure. This procedure is repeated util completio criteria are met. As a example of the criteria is the maximum umber of iteratios. Schedule_Activities procedure does ot specifically defie how the three algorithms is scheduled ad sychroized. Eve the they should be executed i parallel ad idepedetly, or if such sychroizatio is required. I most cases whe applyig ACO to the problem NP-hard, three of these algorithms perform a iteratio cosistig of: (i) The costructio of the solutio by all ats (ii) Improve the quality of the solutio by performig local search. This step is optioal. (iii) Update the pheromoe value 3. WEBSITE STRUCTURE IMPROVEMENT METHOD Web site structure improvemet method outlied as see i Figure 3.1. There are three parts, amely iput, process ad output. 3.1 Iput 3.1.1 Website Structure Oe of the iput data is a curret website structure. The structure here is the curret lik structure of the website. To be oted, the website structure as a iput igores such thigs as follows : Iterface desig of the website back fuctio from web browser software, whose fuctio is to retur to previously explored web pages Liks which are cross-liks ad liks to web page previously explored. Regardig cross-liks will be explaied. Those three are igored to elimiate the assumptio that might be developed, such as whether the desig of structures affectig the structure. I additio to those three, there are other ecessary assumptios, amely website has oly oe root page / homepage or start page. So that the structure of the web site will be mapped will always starts from oe iitial page. To get the website structure, the extractio of these structures ca be doe i two ways, automatically or maually. The automatic method ca be doe usig web crawler software, it is a software that ca browse the website ad collect ay iformatio from the website. For the maual method, it is doe by opeig the web site, view, ad the mappig maually. I this research, the extractio of the web site structure ad mappig is doe maually. After extractio of the website's structure, it eeds to be simplified. Simplifyig the structure was carried out to meet the eeds of the three issues metioed previously. It also cosidered simplifyig the computatio of the optimizatio. With a simple graph, the calculatio ca also be simpler. It based o assumptio that improvemets i the level of simple structure will also icrease the value at more complex structure.

VII-40 The 6 th Iteratioal Coferece o Iformatio & Commuicatio Techology ad Systems 3.1.2 Website Server Log File The log file for a website could be take from web server directly. After that, the log file processed by usig log file aalysis software. Example of the software used is SmarterStats 5.1, which is a product of SmaterTools Ic. The log file processed to get report about popular paths i the website. After get the report, ext is do the path aalysis. Path aalysis basically is aalyzes the report geerated from log file aalysis software. Especially the report about most frequet path used i explores the website. Path aalysis could be doe by read the report geerated from software. From report, usually it could be see the paths which is frequetly used by the visitor ad how much that path is used. Ad also from the report, User Visitig Sessios (UVSs) ad User Visitig Patter (UVP) ca be defied. User visitig sessios or UVSs is a usage patter from the paths traversed by website visitors. UVS data obtaied from processed website s log file, which is the paths that frequetly traversed by website visitor while i a website. Those paths are listed ad sorted accordig to how ofte it is crossed. Previously, website pages have also bee labeled by usig a umber or letter, as a marker of each page of the website. From those, oe row matrix ca be formed which cotais the represetatio of whether a website page visited or ot i the path. The umber of colums i that matrix is the umber of pages o the website. User visitig patter or UVPs is a matrix V M N, where M is the umber of UVS from a website ad N is umber of pages o that website. Table 1.Example of UVP with 10 UVS from a website with 14 pages 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 1 1 0 0 0 0 0 0 0 0 0 3 1 0 1 0 0 1 1 1 0 0 0 0 0 0 4 1 0 0 0 1 0 0 0 1 0 0 0 0 0 5 1 0 0 1 0 0 0 0 1 1 0 0 0 0 3.2.1 QAP Modelig If the graph of the popular path structure from a website is G, the G = ( P, L, W ) Defied as: N = total umber of pages o website P = {P i i [1,N]} is the set of all vertices i G, which i this case is the website pages L = {L(i, j) i j, i, j [1,N]} is the set of all edges i G, which i this case is the lik betwee website pages W = {Wij i j, i, j [1,N]} is the set of all edges weight i G W ij is the probability of L(i,j) selected by website visitors who have accessed P i defied as follows OD( i) W ij Rij Ridk (3) k1 With R ij (R ij [0,1]) is associatio degree from P i to P j which defied as follows R ij P( i, j) P i (4) If P i 0 ad R ij = 0, i, j [1, N]. Associatio degree is the level of a website pages associated with the other pages i the paths which website visitors traversed, or it ca be cosidered as the probability of coditios uder which the users have visited P j via P i. Also D i defied as set of all destiatios from P i. Weight matrix for QAP is the Coectivity Degree. Accordig to [2], C ij is used as coectivity degree betwee website pages. The greater the C ij, hece more easily the visitors foud P j through P i, with C ij defied as follows C ij = w 1 + w 2 + + w m (5) 6 1 0 0 0 0 0 0 0 0 0 1 1 0 0 7 1 0 1 0 0 0 0 0 0 0 1 1 0 0 8 1 0 1 1 0 0 0 0 1 1 0 0 0 0 9 1 0 0 0 0 1 0 0 0 0 1 0 1 0 10 1 0 0 0 0 0 0 1 0 0 0 0 0 1 3.2 Process After gettig the website structure data ad UVP data from processed website server log file, the ext step is modelig QAP from existig website structure problem. With m is the umber of liks route from P i ke P j, or i other words is how may paths that ca be traversed i order to get to P i from P j. I additio there are three basic assumptios that must be made i modelig the website structure improvemet problem ito QAP. These three assumptios are as see o table 3.2 As the objective fuctio for the QAP is,

Website Structure Improvemet Usig At Coloy Techique-Wiwik Aggraei VII-41 Mi TC ( a) [ Cij d ( ai, a j )] (6) 1i j Havig the assumptios defied ad objective fuctio is kow, the ext is how to form the QAP model. Namely the distace matrix ad weight matrix i the website structure problem. Both will be used i the calculatio whe solvig QAP model with at coloy techique. I formig the required distace matrix for QAP model, it is ecessary to calculate by performs operatio o the graph of the website structure that has bee mapped. The graph used is the oe which is the mappig of whole website. Table 1 QAP s Parameter ad basic assumptio i website structure The assumptio i the Param. QAP Defiitio structure of the web site The distace betwee two web pages based o the d(a i,a j ) The distace umber of liks/steps to betwee two reach ay target page facilities iitiatig from a iitial web page C ij TC(a) The iteractio cost of two facilities per distace uit Total cost of curret facility lay out The amout of iteractio or coectivity betwee two web pages based o the C parameter Total cost of curret website structure The calculatio distaces ca be resolved by performig simple operatios o graphs. To be oted, distace matrix i QAP model is a matrix x. As metioed before, the elemets of weight matrix is C ij. I other words, to obtai C ij, after the logfile ad the structure is processed ad ready for use, the ext is doig calculatios to fid R ij which will be used to search for all W ij. Those W ij will be used to fid the value of all C ij. Same as the distace matrix, this weight matrix is. 3.2.2 Solvig the QAP model usig At Coloy techique After the QAP model formed, ext is to solve the model by usig at coloy techiques. With the aim of miimizig the cost of the structure, the the solutio of the existig model is to fid a ew structure of web sites that meet the objective fuctio. To solve the existig QAP model, at coloy techiques used to build the solutios which i this case is creates a geerator for ew website structure. The geerator worked by usig cost matrix ad umber of pages from the QAP model as iput. The geerator is made usig the basic cocepts of ACO that has described. The ACO cocept used for the geerator is the state trasitio rule ad pheromoe update rule of the at system. State trasitio rule from [1]: With, (33) P ij (t) is the probability of selectig the page j from i at the ext step τ ij (t) is the amout of pheromoe o the edge i j α is the weight of pheromoe i computig probability The pheromoe update rule: With, (34) τ ij ( t ) is the amout of pheromoe o the edge i j τ ij ( t +1 ) is the secodary amout of pheromoe o the edge i j ρ is the evaporatio rate of pheromoe Q is the total amout of pheromoe o the At System The geerator eed matrix C for iput. After that the matrix C is used to get the structure which has a smaller cost value tha the previous structure. How the geerator work is explaied as follows. Each at always starts from the vertex 1, which i this case is the iitial page of the website. Each at traverses from the iitial vertex to the ext vertex. Selectio of the ext vertex based o the state trasitio rule. Oce selected, the ats moved ito the vertex. After that ats choose the ext vertex to be visited. How may times do ats move from vertex 1 to aother depeds o the defied hop parameters. After a ats move as may as hops, the at is stopped ad the ext at do the same process. It is processed util all the ats had traverse or all the vertices that are available have visited. If the first at has visited vertex 4, the o the secod hop it is ot allowed to visit the vertex 4, ad so are for the ext hops. Whe the ext ats start to do the hops, for the first hop the at is allowed to visit the vertex 4, but ot o the secod ad so o.

VII-42 The 6 th Iteratioal Coferece o Iformatio & Commuicatio Techology ad Systems State trasitio ruie 2 2... 2 1 3 4... 3 3 4......... 1 st at 2 d at at Figure 1 Simple illustratio how the geerator work based o ACO So a vertex ot allowed to be visited agai if i the previous hop that vertex has bee visited. Differet at ca visit it if has the same hop umber whe will visit that vertex. Suppose that the first at visitig the vertex 4 i the first hop, the the umber-two ats ca also visit the vertex 4 i the first hop, but ot i the secod hop ad so o. For each vertex is visited, their status marked as visited. The purpose is to stop the curret iteratio. The process is limited by the umber of iteratios. Each iteratio has a umber of ats. Each at has a umber of steps (jumps) itself. A iteratio is completed whe each at has completed the hop, or if all odes have bee visited. I each iteratio, the global pheromoe update to chage the probability of each poit visited. So each at ca be expected to form a graph with o isolated vertices. Figure 1 is the illustratio how the geerator work. 3.3 Output Aalysis After QAP modelig ad the use of ACO algorithm, which i this case a geerator for website structure created, implemeted ad used to solve the existig QAP model, the expected to get the output i the form of optimized ew website structure. A aalysis of the ew website structure is coducted after get the ew structure. The aalysis was doe with referece to predefied KPI. I this case, the goal of the ew structure is how visitors ca be more quickly ad easily to fid the 4...... iformatio their eed. So that the KPIs established for the purpose are : Amout of time whe visitig the website Number of pages viewed per visit I the aalysis assumed that the umber of pages viewed per visit is a maximum for each visit, which meas all the pages i the web site visited. So every visitor explored every page i the website. The website visitig time take from the website log file aalysis for the average time per page visit. The every average time of the web pages are summed. I regards to defied KPIs, if the time required by based o sceario to browse the web site is less tha whe the sceario is ru o old structures, ew structures will be cosidered better tha the old structure. I additio, aalysis i terms of graph aalysis is coducted from the statistical values geerated from the graph aalysis. Ad also i terms of practical aalysis, that is if the structure is used i the real world, how the structure affects the factors that are igored i the calculatio such as website iterface desig ad placemet of liks i a web site. 4. CASE STUDY 4.1 Iput, Process, Output I the case study, the website take as a case is the Eglish versio website of Istitut Tekologi Sepuluh Nopember (ITS), which has iitial page address i http://www.its.ac.id/e. Log files from the web site cases take directly from the web server where the website are hosted. The website is hosted i a Apache web server. The log files is located at the website directory / logs from the mai directory of website with the type of log file is the access log which records website access. Log files take are have recordig period from 22 December 2009 to 20 Jauary 2010. After get the website log files from website ad get it prepared, the ext is the selectio of software to process the log files. I this case study SmarterStats 5.1 software which is a product of SmarterTools Ic. is used. Software used to process the log files is used to geerate statistical reports from the website. Reports which is required, as described, is a report o the popular paths frequetly traversed by visitors of website. I the report, there are paths used by visitors while browsig the website. But due to the Idoesia ad Eglish versio from the ITS website proved to have joied access record, so that

Website Structure Improvemet Usig At Coloy Techique-Wiwik Aggraei VII-43 from the reports there are also paths used by visitors for Bahasa Idoesia versio of ITS website. for further processig. Furthermore UVP data obtaied from aalysis of the popular paths. Next is usig the UVP data ad structure of the web site obtaied as iput for the software made ad coded by usig Java laguage. The software ru with the parameter Q = 100, alpha = 0.1, rho = 0.9, ad the maximum is oe hudred iteratio. The the fial result has bee obtaied. For this case study, we got a good structure as a ew structure of the website case with less total cost value. With the old structure, the case website has a total cost of 3.902, after the software used, obtaied the total cost of 3.4509. Figure 5 is a ew structure geerated. Figure 2 Screeshot from the website used for case study Because of it, it is ecessary to segregate popular path report data that ca be doe maually or by usig the log files processig software used. I this case study, it is maually segregated. So we get the popular paths from the case website. Figure 4 Screeshot after the software s process fiished 4.2 Output Aalysis From the output obtaied from the software, we the aalyzed these results to be assessed whether these outcomes ca be judged better tha the old structure or ot. Figure 3 Results of mappig the structure of web sites used I this case study, because the small value of the use of some popular paths, the the popular paths have igored the miimum limits of the umber it is used. Of the two hudred data obtaied from the previous segregatio, the data suitable for use are selected agai because the data are still mixed with the Idoesia versio of ITS website data. From the selectio, it is foud approximately fifty data used

VII-44 The 6 th Iteratioal Coferece o Iformatio & Commuicatio Techology ad Systems From the average value of closeess, it ca be see that the old structure has a higher value tha the ew structure. From the decrease i the average value of closeess from the old structures, it ca be cocluded that the ew structure will be made visitors experiece more difficulties whe browse the website. 4.2.2 Out Degree Aalysis Out Degree aalysis is a aalysis to show the level of edges out from each vertex i a graph. The higher the more out degree value for each vertex i a graph. Table 3 shows the compariso value for the out degree aalysis of ew ad old structures for the case website. Table 3 Out degree compariso of old ad ew structures Notes Old Structure New Structure Miimum 0.023809524 0.45238096 Figure 5 The ew website structure obtaied from the software From the output results, the total costs were aalyzed. From the ew structure, the total cost value is 3.451; while the old structures, the total cost value is 3.902. It cocluded that the ew structure better tha the old structure i terms of total cost because the value has met the objective fuctio. For KPIs aalysis, from the reports geerated by the web log aalysis software, it caot be foud the average visit time for each website pages. So the KPI s aalysis caot be doe. Because the KPI aalysis caot be doe, aother aalysis eeds to be doe to evaluate the ew structure of the case website. Aalysis udertake is graph aalysis for ew structure, ad also practical aalysis. Those aalyze are Closeess aalysis, Out Degree aalysis, ad aalysis of practicability. 4.2.1 Closeess Aalysis Closeess aalysis is a aalysis to determie the level of closeess of each vertex i a graph. The smaller the steps required, the higher the value of closeess. For the case of website structure, closeess shows the level of coveiece to roam from oe page to other pages. I this aalysis, closeess aalysis to the old ad ew structures of case website is show i table 2. Maximum 0.4047619 0.97619045 Mea 0.023255814 0.64451826 From the table we ca see that for the miimum ad maximum value ad the average of the out degree has icreased for the ew structure, which meas that i every page of a ew structure there are may ew liks to other pages. Thus the aalysis shows that the ew structure requires a cosiderable chage i the problem of placemet of ew liks o each page. 4.2.3 Practicability aalysis Practicability aalysis coducted here to see how the ew structure ca be used i terms of iterface desig ad liks layout of the website. From the graph i Figure 4.4 ca be see that from each vertex there are may edges to mutually coect with each other vertex. Thus o every page there is a ew lik that did ot exist. From the ew structure it ca be see that there are pages which is ot mutually coected. There are pages that are liked to all other pages, for example, are page 1. But there are also pages that are ot coected to all the pages. A example is page 2. If ew structures are used, from terms of the web site's iterface it ca use the rollover meu type to place liks. The example of rollover meu is show i Figure 4.5. Table 2 Closeess compariso of old ad ew structures Notes Old Structure New Structure Miimum 0.01369863 0.016393442 Maximum 1.0 0.024390243 Mea 0.032140605 0.018765671

Website Structure Improvemet Usig At Coloy Techique-Wiwik Aggraei VII-45 Figure 6 Rollover meu example at www.its.ac.id But from ew structure is obtaied, each page does ot always liked to every page. I terms of practicality, it would be very difficult for web desigers because it meas every page has a umber of differet liks. Yet at every website it is commo that each page has a form of the same view (template) with the same umber of liks. So that the ew structure from terms of practicality caot be implemeted. 5. CONCLUSION AND FUTURE DISCUSSION After doig the case study ad aalyzig results from the website structure improvemet method with at coloy techique, we ca coclude the followig thigs for the website structure problem issues: Website structure improvemet by usig at coloy techiques doe by modelig the website structure problem ito QAP where the solutio foud usig the website structure geerator that works with the cocept of at coloy. Implemetatio of improved methods of website structures improvemet usig at coloy has bee able to do with makig software for modelig QAP ad web log aalysis software for iitial aalysis of log files from a website. It has bee foud a web site proposal to improve the structure from Eglish versio of ITS website. The suggestios for future discussio related to the research that has bee doe ca be give as follows: 1. Whe mappig the structure of the website, it is doe maually. For a web site with a vast umber of pages ca be quite difficult. For the ext for mappig the structure of web sites ca be made a web crawler that ca map ad costruct graph of website structure automatically. 2. 2. The website structure geerator that is used oly uses the basic cocepts of ACO. The results obtaied from structure improvemet ca be cosidered bad i terms of closeess ad practical aalysis. If possible, the algorithm for website structure geerator is specially made so the result of improvemet will be better. 6. REFERENCES [1] Saremi, H. Qahri, Abedi, B., Karmei, A. Meima (2008) Website structure improvemet: Quadratic assigmet problem approach ad at coloy meta-heuristic techique, Applied Mathematics ad Computatio 195, 285 298. [2] Dorigo, M., Di Caro, G. (1999) At Coloy Optimizatio: A New Meta-Heuristic, IEEE Proceedigs of the 1999 Cogress o Evolutioary Computatio. Washigto, DC, 6 9 July. [3] Gambardella, L.M., Taillard, E.D., Dorigo, M. (1999) At Coloies for the Quadratic Assigmet Problem, The Joural of the Operatioal Research Society 50(2): 167-176. [4] Fag, X. ad Holsapple, C W (2006) A empirical study of web site avigatio structures impact o web site usability, Decisio Support System 43 (2007): 476 491. [5] Ye, Bejami (2007) The desig ad evaluatio of accessibility o web avigatio, Decisio Support System 42 (2007): 2219 2235. [6] Wag, Y., Dai, W., Yua, Y. (2007) Website borwsig aid: A avigatio graph-based recommedatio system, Decisio Support System 45 (2008): 387 400.

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