PROCEEDINGS
The Jayakarta Bali Hotel, Legian-Bali, Indonesia
August 10-11, 2015
ISBN: 978-1-4673-7319-7
Contents
Oral
1. FPGAImplementationofCORDICAlgorithmsforSineandCosineGenerator.AntoniusP. Renardy,NurAhmadi,AshbirA.Fadila,NaufalShidqiandTrioAdionoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ 2. DesignandImplementationofDCBOTAinDeltaͲSigmaADCforCommunicationSystem.
VincentiusTimothy,AdityaCandra,KhafitMufadli,AchmadFuadMas'udandAmyHamidah SalmanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
3. DevelopmentofanFPGAͲBasedSubͲModuleasThreeͲPhaseSpindleMotorSpeedController forCNCPCBMillingMachine.FiqihTriFathulahRusfa,FarkhadIhsanHariadiandArif SasongkoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
4. MASH Delta Sigma ADC Layout for Dual Mode GSM & WLAN Application. Clement Christopher,AdityoPrabowo,MislyJuliani,AmyHamidahSalmanandAchmadFuadMas’udͲ
5. DesignandImplementationofVisibleLightCommunicationSystemUsingPulseWidth Modulation.AnggaPradana,NurAhmadiandTrioAdionoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
6. StructuralOfflineHandwritingCharacterRecognitionUsingLevenshteinDistance.Made EdwinWiraPutraandIpingSuprianaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
7. LenientNegotiationModelBasedonAltruisticUtilityanditsImplicationonAgentͲMediated Negotiation.MohammedAlͲAaidroos,NorleyzaJailaniandMuriatiMukhtarͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
8. HistogrambasedColorPatternIdentificationofMulticlassFruitusingFeatureSelection.Ema Rachmawati,MasayuLeyliaKhodraandIpingSuprianaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
9. DevelopmentofaPCͲBasedMarkerlessAugmentedReality.TanSiokYee,HaslinaArshadand AziziAbdullahͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
10. Development of IndonesianͲJapanese Statistical Machine Translation Using Lemma TranslationandAdditionalPostͲProcess.MohammadAnugrahSulaemanandAyuPurwarianti
11. ACombinationofStaticandStrokeGesturewithSpeechforMultimodalInteractionina VirtualEnvironment.LamMengChun,HaslinaArshad,ThammathipPiumsomboonandMark BillinghurstͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
12. MusicInformationRetrievalusingQueryͲbyͲHummingbasedontheDynamicTimeWarping.
RifkiAfinaPutriandDessiPujiLestariͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
13. LearningͲbasedAspectIdentificationinCustomerReviewProducts.WarihMaharani,DwiH. WidyantoroandMasayuLeyliaKhodraͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
14. BeatmapGeneratorforOsuGameUsingMachineLearningApproach.DestraBintangPerkasa andNurUlfaMaulideviͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
15. EmployingNaturalLanguageProcessingtoAnalyseGrammaticalErrorinaSimpleJapanese Sentence.AjiKasmajiandAyuPurwariantiͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
16. PreliminaryStudyDisruptioninSmallandMediumEnterpriseSupplyChain.NurAmlyaAbd Majid,NoraidahSahari,NurFazidahEliasandHazuraMohamedͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
17. The Capabilities of Offshore Information Technology Vendor. Yogi Wibisono, Rajesri Govindaraju,DradjadIriantoandImanSudirmanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
18. CriticalSuccessFactorsEnhancingEnterpriseResourcePlanningSystemsImplemintationin JordanianSMEs.MuhmmadI.NofalandZawiyahM.YusofͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
19. ValidationofanITValueModelforBranchlessBanking.SuhardiandRahardianDwiJuliartoͲͲͲ
20. TheEffectsofITInfrastructureTransformationonOrganizationalStructureandCapabilityin theCloudComputingEra:BeyondITProductivityParadox ͲACaseStudyinanIndonesian TelecommunicationCompany.RizalAkbar,RajesriGovindarajuandKadarsahSuryadiͲͲͲͲͲͲͲͲͲͲ
21. InventoryManagementOptimizationModelwithDatabaseSynchronizationthroughInternet Network(ASimulationStudy).YusufSutantoandRiyanartoSarnoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
22. RealͲTimeFeedbackforJawiCharactersTracingActivity.NorizanMatDiah,NorAzanMatZin andHiroyukiIidaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
23. TheSpatialRelationFeaturesforDescribingObjectsRelationshipswithinImage.MohdNizam
Saad,ZurinaMuda,NoraidahSahariandHamzainiAbdulHamidͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
24. ANovel4ͲPointDiscreteFourierTransformsCircuitbasedonProductofRademacher Functions.ZulfikarandHubbulWalidainyͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
25. ImprovedDescriptorforDynamicLineMatchinginOmnidirectionalImages.SophiaJamila Zahra,RizaSulaimanandAntonSatriaPrabuwonoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
26. AnImpulseͲBasedFrameworkforSignalFunctionalRepresentations.A.Z.R.LangiͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
27. AdaptiveVideoWatermarkusingBlockQP.DongHwanShinandJongukChoiͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
28. AMulticarrierModulationAudioWatermarkingSystem.GelarBudiman.DongHwanShinand AndriyanSuksmonoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
29. EfficiencyMeasurementofVariousDenoiseTechniquesforMovingObjectDetectionUsing AerialImages.ZainalRasyidMahayuddin,A.F.M.SaifuddinSaifandAntonSatriaPrabuwonoͲ
30. InverseKinematicsandGesturePatternRecognitionusingHiddenMarkovModelonBeatMe! Project:TraditionalDanceDigitalization.ZahrotulAisyahUlfah,AciekIdaSuryandariandYoga PriyanaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
31. InvestigationofDCCoronaDischargePropertiesUsingUHFMethod.FebriArwanNugraha, NoureddineHarid,NajiAlSayari,BrahamBarkat,ShinyaOhtsuka,KatsuhikoHaradaand ToyoakiSonodaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
32. DGAandTensileStrengthTestonAcceleratedThermalAgingofEsterOilandKraftPaper.
TaufikWidyanugraha,RizalRachmad,WendhyandSuwarnoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
33. PartialDischargeCharacteristicsofVoidDefectinSF6underSteppedACVoltage.Rusli
37. AlternativeMethodforTestingOvercurentProtectionFunctionbyusingLeastSquare Method.J.S.PanggabeanandB.AnggoroͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
38. Three Dimensional Analysis of Rotating Electric Field in ThreeͲPhase Gas Insulated Switchgear.Md.TanvirUddinandUmarKhayamͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
39. ElectricalTreeinginPolyethyleneͲAluminaͲFilledNanocompositesforHVDCApplications.Deni Murdany,XiarongChen,DongmingLiu,UlfGedde,SuwarnoandStanislawGubanskiͲͲͲͲͲͲͲͲͲͲͲͲ
40. StudyonStaticElectrificationofPalmFattyAcidEster(PFAE)OilUsingMiniStaticTester.
HarryGumilang,MotooTsuchie,MasahiroKozako,MasayukiHikita,AbdulRajab,Takashi Suzuki,SatoshiHatada,AkinoriKanetani,H.Futakuchi,UmarKhayamandSuwarnoͲͲͲͲͲͲͲͲͲͲͲͲͲ
41. WirelessPowerChargingSystemforMobileDeviceBasedonMagneticResonanceCoupling.
AchmadMunirandBiruTuturRanumͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
42. ComparisionofEfficiencyMeasurementTecniquesforElectricVehicleTractionInverters.
FatihAcar,SadikOzdemir,HakanAkcaandUgurSavasSelamogullariͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
43. DevelopmentofHardwareͲinͲtheͲloopSimultionforRocketGuidanceSystem.YulyanWahyu HadiandBambangRiyantoTrilaksonoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
44. ImplementationofImageͲBasedAutopilotControllerusingCommandFilteredBackstepping forFixedWingUnmannedAerialVehicle.AdityaWildanFarras,BambangRiyantoTrilaksono andFadjarRahinoPutraͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
45. DesignandImplementationofGainͲSchedulingFlightControlSystemonPC/104Platform.
MadeWidhiSuryaAtmanandBambangRiyantoTrilaksonoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
46. HardwareInͲtheͲLoopSimulationforVisualServoingofFixedWingUAV.YaqubArisPrabowo, BambangRiyantoTrilaksonoandFadjarRahinoTriputraͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
48. FrameworkforSoftwareTamperingDetectioninEmbeddedSystems.AbdoAliAbdullahAlͲ WosabiandZarinaShukurͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
49. Following theWiͲFiBreadcrumbs:NetworkBasedMobileApplicationPrivacy Threats.
MichaelKennedyandRossilawaiSulaimanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
50. ImprovingSecurityforIPv6NeighborDiscovery.AmjedSidAhmed,HassanandNorEffendy OthmanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
51. ListSteganographyBasedonSyllablePatterns.DavidMartinandAriM.BarmawiͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
52. MemcacheDBPersistentImplementationusingLevelDB.GabrielleWicesawatiPoerwawinata andAchmadImamKistijantoroͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
53. AndroidAnomalyDetectionSystemUsingMachineLearningClassification.HarryKurniawan, YusepRosmansyahandBudimanDabarsyahͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
54. Malware Detection on Android Smartphones using API Class and Machine Learning.
Westyarian,YusepRosmansyahandBudimanDabarsyahͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
55. CapacitorͲbasedPhaseShifterfor8ElementsAntennaFeedingNetwork.ChairunnisaC,Diana DesiyantiandAchmadMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
56. ImprovementofRadarPerformanceUsingLFMPulseCompressionTechnique.Ilfriyantri Intyas,BarokatunHasanah,RahmawatiHasanah,M.RezaHidayat,AchmadMunirand AndriyanBayuSuksmonoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
57. PerformanceAnalysiswithLMMSEforMIMOLTEontheHighAltitudePlatformStation.Catur BudiWaluyoandIskandarͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
58. ResonantFrequencyComputationofDielectricMaterialLoadedCircularWaveguideUsing CylindricalCoordinateSystemͲbasedFDTDMethod.AntrishaDaneraiciSetiawan,Hardi NusantaraandAchmadMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
59. DevelopmentofSARTransmitterforNanosatelliteͲbasedRemoteSensingApplication.Edwar EandAchmadMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
60. ExtractionofAnisotropicThinSlabArtificialDielectricMaterialPropertyUsingRectangular Waveguide.ZakiAbdurrasyid,MuhammadRezaHidayatandAchmadMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ 61. DevelopmentofHighSensitivityAmplifierforVLFReceiverApplication.Kusnandar,Kusmadi,
AsepNajmurrokhman,SSunobroto,CChairunnisaandAchmadMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
62. SimulationDesignofCompactSteppedͲFrequencyContinuousͲWaveThroughͲWallRadar.
KusmadiKandAchmadMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
63. BroadbandPerformanceofmultiͲsectionsofstepͲlineimpedancematchingofLDͲMOSFETRF Amplifierat900MHzLTEBand.BasukiRahmatulAlamͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
64. DesignandImplementationofRLSAAntennaforMobileDBSApplicationinKuͲBandDownlink Direction.JokoSuryanaandDimasBudiartoKusumaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
65. MatlabandGNURadioͲBasedSFCWRadarforRangeDetection.AtikCharisma,Antrisha DaneraiciSetiawan,SoniAuliaRahayu,AchmadMunirandAndriyanBayuSuksmonoͲͲͲͲͲͲͲͲͲͲͲ
66. PalmVeinRecognitionBasedͲonMinutiaeFeatureandFeatureMatching.AgungBudi WirayudaTjokordaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
67. QRSDetectionusingDigitalDifferentiators.KrishnaBattulaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
68. 3DScannerforOrthodonticUsingTriangulationMethod. MuhammadOginHasanuddin, IbrahimAkbar,GilangEkaPermanaandAciekIdaWuryandariͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
69. ImplementationofFuzzyInferenceSysteminChildrenSkinDiseaseDiagnosisApplication.
AdityaAgungPutraandRinaldiMunirͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
70. Design ofECG Homecare:12ͲLeadECG Acquisition usingSingle Channel ECG Device DevelopedonAD8232AnalogFrontEnd.MuhammadWildanGifari,HasballahZakariaand RichardMengkoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
71. AProposalofQualityModelforMobileGames.AnggyTrisnadoli,BayuHendradjayaand WikanDanarSunindyoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
72. SystemDevelopmentforResearchMapVisualisation.DwiWidyantoroandYuliantiOenangͲͲͲ
76. RequirementsRefinementsandAnalysiswithCaseͲBasedReasoningTechniquestoReuseThe Requirements.FransiskusAdikara,BayuHendradjayaandBenhardSitohangͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
77. UsabilityGuidelinesforDevelopingMobileApplicationintheConstructionIndustry.Norleyza Jailani,ZuraidahAbdullah,MariniAbuBakarandHarniRohaidaHaronͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
78. WhiteBoxTestingToolPrototypeDevelopment.ArlintaChristyBarus,DianIraPutriHutasoit, JoelHunterSiringoringoandYusfiApriyantiSiahaanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
79. ModularisationofStateͲdependentCrosscuttingConcernsusingTinySAOP.NoorazeanMohd AliandRosilahHassanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
80. Study of Algorithmic Method and Model for EffortEstimationin Big DataSoftware Development.CaseStudy:Geodatabase.EzraHizkiaNathanael,BayuHendradjayaand WikanDanarSunindyoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
81. SoftwareMetricsSelectionMethods:AReview.ZubaidahBukhari,JamaiahYahayaandAziz DeramanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
82. UsableBibliographicSoftware.NurulAtiqahbintiZulkharnainandNoraidahSahariͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
83. TowardsanAutomatedTestSequenceGenerationforMobileApplicationusingColoredPetri Net.BlasiusNeriPuspika,BayuHendradjayaandWikanDanarSunindyoͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
84. BirdMatingOptimizerforDiscreteBerthAllocationProblem.AnasArram,MasriAyob,Mohd ZakreeAhmadNazriandAhmadAbunadiͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
85. SoftwareAgeingMeasurementModel(SAMM)theConceptualFramework.ZaihaNadiah ZainalAbidin,JamaiahYahayaandAzizDeramanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
86. AstudyonimprovementofInternetTrafficMeasurementandAnalysisUsingHadoopSystem.
LenaT.Ibrahim,RosilahHassanandAsrulAsatͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
87. ImplementingModelReconciliationforMetadataInteroperability.AtikaYusufandHira LaksmiwatiZoroͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
88. BusinessIntelligenceModelforUnstructuredDataManagement.MohammadFikryAbdullah andKamsuriahAhmadͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
89. GeohashIndexBasedSpatialDataModelforCorporate.IpingSuprianaSuwardi,Dody Dharma,DickyPrimaSatya,DessiPujiLestariͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
90. IncorporatingBusinessIntelligenceandAnalyticsintoPerformanceManagementforthe PublicSector,IssuesandChallenges.NurHaniZulkifliAbai,JamaiahHjYahayaandAziz DeramanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
91. OpenDataStrategyforEnhancingtheProductivityandCompetitivenessofFisherySMEsin Indonesia.InneGartinaHusein,WikanDanarSunindyo,RizalBahawares,YuliusNainggolan andSaifulAkbarͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
92. BehavioroftheResourcesintheGrowthofSocialNetwork.MahyuddinK.M.Nasution, MarischaElveny,RahmadSyahandShahrulAzmanNoahͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
93. HandlingImbalancedDatasetinMultiͲlabelTextCategorizationusingBaggingandAdaptive Boosting.GentaIndraWinataandMasayuLeyliaKhodraͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
94. EfficacyofArabicNamedͲEntityRecognition.SuhadAlͲShoukryandNazliaOmarͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
95. MissingDataSolutionofElectricityConsumptionbasedonLagrangeInterpolationCaseStudy: IntelligEnSiadatamonitoring.PinrolinvicD.K.Manembu,AngreineKewoandBrammyWelang
96. PreliminaryDesignofSpatioͲTemporalDisasterDatabaseinIndonesiatoSupportEmergency Response.YaniWidyaniandHiraLaksmiwatiͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
97. IntelligEnSiabasedElectricityConsumptionPredictionAnalyticsusingRegressionMethod.
99. SupportVectorMachineͲbasedAutomaticMusicTranscriptionforTranscribingPolyphonic MusicintoMusicXML.KrisnaFathurahmanandDessiPujiLestariͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
100. OnMachineLearningTechniqueSelectionforClassification.RahmadKurniawan,Mohd ZakreeAhmadNazriandAnisAklimaKamarudinͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
101. SingleCurrentSensorPMBLDCMotorDrivewithPowerQualityControllerforVariableSpeed VariableTorqueApplications.KanwarPal,SaurabhShuklaandSanjeevSinghͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
102. Modelingof1.6kWpSingleͲPhaseGridͲConnectedPhotovoltaicSystem.AgusPurwadi, ArwindraRizqiawan,RezaFachrizalandNanaHeryanaͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
103. ComparingDifferentSwitchingTechniquesForSiliconCarbideMOSFETAssistedSiliconIGBT BasedHybridSwitch.SadikOzdemir,FatihAcarandUgurSavasSelamogullariͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
104. SimpleSupercapacitorChargingSchemeinElectricalCarSimulatorbyUsingDirectCurrent Machines.AdnanRafiAlTahtawiandAriefSyaichuRohmanͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
105. OptimalSwitchPlacementinRadialDistributionSystemBasedonReliabilityworthAnalysis.
FitriRahmawatiTusriyati,MitaniYasunori,NanangHariyanto,MuhammadNurdinand KhairudinͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
106. EconomicRedispatchConsideringTransmissionCongestionforOptimalEnergyPriceina DeregulatedPowerSystem.MuhammadBachtiarNappu,ArdiatyAriefͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
107. DGPlacementandSizewithContinuationPowerFlowMethod.ArdiatyAriefandMuhammad BachtiarNappuͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲͲ
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661 665 670 676 682 687 693 699 704 710 716 720 726
Network
Mahyuddin K. M. Nasution
Centre of Information System, Universitas Sumatera Utara
Padang Bulan 20155 USU Medan Indonesia Email: mahyuddin@usu.ac.id
Marischa Elveny and Rahmad Syah
Information Technology Department, Fasilkom-TI Universitas Sumatera Utara
Padang Bulan 20155 USU Medan Indonesia Email: marischa@usu.ac.id,
Shahrul Azman Noah
Knowledge Technology Research Group, Faculty of Information Science & Technology
Universiti Kebangsaan Malaysia Bangi 43600 UKM Selangor Malaysia
Email: samn@ftsm.ukm.my
Abstract—Social network can be extracted from different sources of information, but the resources was growing dynamically requires a flexible approach. Each social network has the resources, but the relationship between resources and information sources requires ex-planation. This paper is aimed to address the behavior of the resource in the growth of social networks by using the association rules and statistical calculations to explain the evolutionary mechanisms. There is a strong effect on the growth of the resources of social networks. Keywords– Superficial method; independence; multiple regression; association rule; timeline; total effect.
I. INTRODUCTION
Automatic extracting the social network is an relatively approach which is formed through modal relations [1], that depends heavily on dynamically the Web as information source [2]. In discrete mathematic literature, the social network extraction formally considered as a Cartesian product, it could be represented as an n×n matrix M of vertices vi ∈ V
as a set of actors, i = 1, . . . , n, and for generating their relations ej ∈ E as a set of edges, j = 1, . . . , m, whereby ej = mik ∈ M is 1 for ej ∈ E, if two actors vi and vk are adjacent, 0 otherwise [3]. While the Web contained
enormous amount of information of social actors and clues about relations among them: We can always find new actors and add vertices to the resultant network, but also occasionally we cannot find old actors whereby their representation on vertices we cannot eliminate it. In other word, we may create the new connection between actors or disconnect the original relation between them [4], [5].
We considered that a social network as resources, i.e. actor/vertex, relation/edge, web/document, or connection/path, but no information about the relations of them as resources for explaining the dynamism of social network [6], [7]. Therefore, this paper is aimed at addressing the dynamism of social network based on resources. In this case, the evolutionary mechanisms, a guide to express an approach.
II. REVIEW ANDAPPROACH
The use of web is steadily gaining ground in extracting of social networks [1], but dealing with everything that can be changed dynamically in Web it needs a flexible approach [8]. There is most flexible method for extracting social network automatically from web, that is the superficial method, but the methods is less trustworthy [9]. Therefore, we use the evolutionary mechanism [10] for extracting social network and based on it do the designing and choosing the rules [11].
In line with superficial methods, we have developed an approach using association rules to enhance the methods for extracting the social network of specialized web pages [12]. In this case, we select some social actors as the seeds in order to we can declare their names properly. It is used to reduce bias. For that purpose, we define the association rule as follows:
Definition 1. Let Z ={z1, z2, . . . , z|Z|} is a set of attributes
literal, andMi is a set of transaction are subsets of attributes,
or Mi be subset of Z. The implication X ⇒ Y with two
possible values T = TRUE or F =FALSE as an association rule ifX, Y ⊂Z andX∩Y =∅.
For representing some of attributes in search space Ω, we denote a keyword as tx, an actor name as seed is ta,
other actor names are tb, but in a document there are also
attributes like tt is title, te as event, ty as publication year,
etc. Supposing that the implementation of co-occurrence in a query as q = ”ta AND tx”, Db is a collection of
documents containing actor names tb. Then the transaction
be Mi ={q, bj},i= 1, . . . , n,j = 1, . . . , m for nseeds, or Mi+1={{q, bj}, ty}, thus we have Q⇒Db as implication,
whereq ∈ Q⊂ Ω,bj ∈ Db ⊂ Ω,ty ∈Db ⊂Ω. This is a
Corollary 1. If there are an actor in the search space and the actor as a seed, then via association rule there is one or more other actor in the search space.
In real world, each people connected with other people. The Web is representation of the real world [13]. Therefore, in the Web page is possible two or more actors exist (co-occurrence). Is not every web page created by the author as an actor is to discuss about other actor. Specially, in Ω, one and more of web pages contain the tables as presentation of the online database. If a seed exits in one of web pages and the seed is a part of query, then the web page with a unique URL is possible has other actor names. For example, let ta
= ”Mahyuddin K. M. Nasution” and tx = ”DBLP”, we have
a query q = ”Mahyuddin K. M. Nasution” AND ”DBLP”, andqsubmitted to the search engine like Google, we obtain a web page has a table contains other names [12]. Generally, the search engine got one or more snippets about q and by rank, from the top to the bottom consist of snippets sequentially according to their compatibility to ta andtx. A keyword have
been used for retrieving information appropriately as expected, if the mentioned information exist in the search space [14]. Thus a keyword and a seed together to remove impact of ambiguity and bias that comes from search space. In this case, a seed is as bait for fishing all documents related the seed. While a keyword to rank all documents according to suitability to information needed. A lot of web pages have connecting between any seed and other actors, where co-occurrences exist in the page, because of an event, and a record in the document. Therefore, if a title represented a document, there are one or more names as authors of documents or Web pages, then presence of new actor names and their relationships can be sorted by ascending based on published year of the document. Each seed would be established the relationship for the first time with a other actor, and the next relationship with more other different actors. The growth of social actors (vertices) and the relationships (edges) between a seed and other actors caused by increasing the number of documents on Web, and indirectly it have described the dynamics of social networks.
In the social network, the degree of a vertex d(v) is the number of other vertices to which it is connected. Or
d(v) =k,vi∈V,i= 1, . . . , nwherek=n−1.
Definition 2. LetSN is a social network withnvertices.SN
is a star network if
d(v) =nn−1 for one vertex only in SN 1 otherwise
On the one hand, the timeline of network growth based on a seed (as an actor) show the history of his/her social activities such as the relationship between the authors or the research collaboration in the academic social network. In this case, an actor is a center in a star network and the network will
Fig. 1. Timeline of social network based on a seed
grow with the emergence of other actors, where other actors might become a vertex with degreed(vi)greater than one, see
Fig. 1. Therefore, extracting social networks from the web is not only involve a number of documents / web pages, but also involves a dependence between the actors, relationships, and documents, whereby dependence test performed using chi squareχ2
to the contingency table withkrows andlcolumns, and by both k and l the degree of freedom defined [15] as follows
df = (k−1)(l−1), (1) if the cells of table containsuij,i= 1, . . . , k, andj= 1, . . . , l,
then the expected value E for all uij are E(uij) =
On the other hand, a social network by relying to the seed and year variables will be accompanied with a timeline of growth. A timeline can be used to show a snapshot of evolutionary mechanism in order to predict the extent to which the growth of a social network, a list of events in chronological order, but this is to understand events and trends for a particular subject. We use the multiple regression as an evolutionary mechanism for predicting a growth of social network, i.e. y to be regressed against two or more factors, and the relationship in multiple regression [16] is
ˆ
have the multiple regression ofy againtsx1andx2:
X
By dividing this equation by P x2
1, we get an effect total tr
as follows
tr=β1+β1β2+β1β2β3 (6)
whereβ1 means the direct effect,β1β2 andβ1β2β3 mean the
indirect effect.
Finally, we proposed an approach to extract social network as base for revealing the dynamism of social networks. The approach is
1) Make a query for representing co-occurrence of an actor name and a keyword
2) Submit a query to any search engine
3) Make sure the actor name and the keyword contained are exactly same such as in the query.
4) Access the URL listed for extracting and then getting information
Furthermore, we will examine the dependence between all resources of social network and determine the behavior of dynamism based on predictive models.
III. EXPERIMENTS
By using the above approach and by involving 69 seeds grouped into 8 samples, we extract the social network, ob-tained information about
1) Papers with the title, one or more authors, publication year and their affiliation.
2) Vertices: the seeds and the new actor. 3) Edges: The relationship between actors.
We declare attributes of resources as follows: NoS as Number of Seeds, NoP as Number of Papers, NoNV as Number of New Vertices, and NoE as Number of Edges, see Table I. Based on publication year of documents we obtain an increase in the number of each factor in resource, see Table II.
If the sample contained in the Table I were calculated by using equation (2) such that the total expected through the equation (3) is greater than or same 0, then obtained χ2
= 468.5268. While based on equation (1) for k = 8, l = 4 so that df = 21 we have χ2
0.05 = 11.6. If the proposed null
hypothesis H0 and alternative hypothesisH1 as follows:
H0: The resources of social network are not independent.
H1: The resources of social network are independent.
Then H0 rejected because χ2 > χ20.05. In other words, the
emergence of a new vertex is associated with old vertex (a seed), the number of papers to be also dependent on the activities of the actor as a seed, and the number of edges are associated with the papers and the new vertices in social networks. Thus, there is a form of dependency between resources: actor/vertex, relation/edge, and web/document.
In context of the evolutionary mechanisms, the prediction models with resources take a position as a conduit of in-formation about dynamism of social network. The Multiple regressions are one of the methods to determine causal re-lationships between factors as resources of social network. Based on data in Table II we obtain an increase in the number of each factor in resource and then we conduct calculation.
Group NoS NoP NoNV NoE
I 8 115 112 422
VIII 11 120 133 413 Total 69 470 511 1935
TABLE II
TIMELINE OF SOCIAL NETWOR K GROWTH
Year NoS NoP NoNV NoE
1980 1 1 2 1
2005 23 109 121 220 2006 28 121 138 243 2007 32 139 159 298 2008 41 168 188 362 2009 45 243 284 676 2010 50 299 332 811 2011 64 451 496 1297
First model, therefore, involves papers as dependent variable
y and seeds as independent variablex1 based on (4) is
y=−3.7639 + 5.7799x1. (7)
Second model involves the relationship between authors, papers and seeds. Vertex is a dependent variable which is determined by two other factors as independent variables: seed and papers,
y=−3.1476 + 0.6580x1+ 1.0177x2 (8)
Meanwhile, with the involvement of all the resources we acquired third model,
y=−60.8712−0.01411x1+ 12.0181x2−0.81516x3 (9)
Briefly, three equations (7), (8) and (9) are summarized in Table III.
Fig. 2. An effect network for 4 resources of social network
TABLE III βOF MULTIPLE REGR ESSIONS
Model x1= seed x2 = papers x3= nodes y
I 5.7799 = papers
II 0.6580 1.0177 = nodes
III -0.0141 12.0181 - 0.8152 = edges
papers, while the papers have strongest effect for growing edge and vertices. The last, vertices have strong effect for existing edge. It is detailed as follows.
1) Direct effect of the seeds (x1) towardy=−0.0411.
2) Indirect effect via the papers (x2)= (5.7799) (12.0181) = 69.4634.
3) Indirect effect via the vertices (x3) = (0.6580) (−8.1516) = −5.3638.
4) Indirect effect via x2 and x3 = (5.7799) (1.0177) (−8.1516) = −47.9494.
And the effect total is tr = 16.1092. So even though the resources have different effects on the growth of social net-works, but overall all the resources affect the dynamics of social networks.
IV. CONCLUSION
In this study we have presented an analysis for finding behavior of resources of social network. The resources of social networks - actor/vertex, relation/edge, web/document, or connection/path - have different behavior toward growth of social networks. In total, the effects of resources positively affect the growth of vertices and edges in a social network based on predictions models. The future work will involve the extraction of a social network to describe the research collaboration.
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