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A flexible, fast and efficient
publication service,for an open access recording
of your event
and a wide dissemination of
your proceedings
A flexible, fast and efficient
publication service,for an open access recording
of your event
and a wide dissemination of
your proceedings
www.matec-conferences.org
MATEC Web of ConferencesOpen access conference proceedings
in Materials science, Engineering and Chemistry
Open Access Publishingfrom EDP Sciences
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MATEC Web of ConferencesVol. 58 (2016)
The 3rd Bali International Seminar on Science & Technology (BISSTECH 2015)
Bali, Indonesia, October 1517, 2015J. Jamari, R. Handogo and E. Suryani (Eds.)
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Statement of Peer review Published online: 23 May 2016PDF (232 KB)
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Preface 00001Published online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165800001Abstract PDF (25.85 KB)
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Opening Address 00002Dr. Ir. Ni Ketut SariPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165800002Abstract PDF (386.2 KB)
Chemical Engineering & Food Technology
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Activation oF Trass Rock aS Bleaching Palm Oil (CPO) 01001Laurentius Urip Widodo, Sukirmiyadi, Siswanto and Ely KurniatiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801001Abstract PDF (82.89 KB) References
Chemical Engineering & Food Technology
Industrial Engineering
Information Technology and Information Systems
Others
5/24/2016 MATEC Web of Conferences
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Antioxidant Activity and Sensory Properties Carrot (Daucus carrota) Soyghurt 01002Enny Karti Basuki Susiloningsih, Ulya Sarofa and Fauziah Imroatus SholihahPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801002Abstract PDF (110.8 KB) References
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Bioethanol Production from Liquid Waste of Rice Flour with Batch Process 01003Sari Ni Ketut, S Sutiyono, E Luluk, Ernawati Dira, Wesen Putu and Tatik Sri HajatiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801003Abstract PDF (100.9 KB) References
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Bioethanol Quality Improvement of Coffee Fruit Leather 01004Luluk Edahwati, P. Dyah Suci, S. Nana Dyah, Tri Widjaya, Ali Altway, Domas Cahyo and Atika NandiniPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801004Abstract PDF (120.9 KB) References
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Biogas Production and Removal COD – BOD and TSS from Wastewater Industrial Alcohol (Vinasse) byModified UASB Bioreactor 01005Isni Utami, Sri Redjeki, Dwi Hery Astuti and SaniPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801005Abstract PDF (169.7 KB) References
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Synthesis and Characterisation of Struvite Family Crystals by An Aqueous Precipitation Method 01006S. Sutiyono, Luluk Edahwati, Dyah Suci Perwitasari, Stefanus Muryanto, J. Jamari and Anatasius P. BayusenoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801006Abstract PDF (163.1 KB) References
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Effect of Calcium Additive on the Crystallization of Struvite 01007Dyah SuciPerwitasari, Luluk Edahwati, S. Sutiyono, Novel Karaman, J. Jamari, Stefanus Muryanto and Athanasius.P.BayusenoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801007Abstract PDF (391.5 KB) References
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Effectiveness of Environmental Management Based on Trash in The City of Depok 01008Ismiyati, Irfan Purnawan and Muh. KadarismanPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801008
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Abstract PDF (109.9 KB) References
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Effects of Giving Pedada Fruit Flour (PFF) on Digesta Profile and SCFAs HypercholesterolemicRats 01009Jariyah, Simon Bambang Widjanarko, Yunianta and Teti EstiasihPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801009Abstract PDF (81.39 KB) References
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Emulsion Copolymerization Of StyreneCoMaleic Acid and Incorporation of CU2O Into Its
Matrix 01010Yenny Meliana, Sri Fahmiati and Agus HaryonoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801010Abstract PDF (123.9 KB) References
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Estimating Greenhouse Gas Emissions Level of A Natural Gas Pipeline – Case Study from A to B Pointin West JavaIndonesia 01011Cindy Dianita and Asep Handaya SaputraPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801011Abstract PDF (82.25 KB) References
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Functional Properties of Leucaena Leucocephala Protein Concentrates Resulted Separation ofUltrafiltration Membrane 01012Dedin Finatsiyatull Rosida, Nur Hapsari and Taufik HidayahPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801012Abstract PDF (87.88 KB) References
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Inhibition of Struvite Crystal Growth in the Presence of Herbal Extract OrthosiphonAristatus BL.MIQ 01013Stefanus Muryanto, Sri Sutanti and Mega KasmiyatunPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801013Abstract PDF (1.001 MB) References
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Numerical of Bioethanol Production from Liquid Waste of Rise Flour by Distillation Process 01014Sari Ni Ketut, I Nyoman Abdi, Wesen Putu and Retno DewatiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801014Abstract PDF (243.8 KB) References
5/24/2016 MATEC Web of Conferences
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Pyrolisator Coal to be Cokes (Coal Cokes) Casting Metal Industry Standard 01015Sukamto, Didi Samanhudi, Sunardi and Nana Dyah SiswatiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801015Abstract PDF (243.9 KB) References
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Natural Medium for Growing of Endophytic Bacteria from Solanaceae in MalangIndonesia 01016Arika Purnawati, Wiwik Sri Harijani and Wiwin WindriyantiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801016Abstract PDF (143.7 KB) References
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Optimization of the Single Staggered Wire and Tube Heat Exchanger 01017I Made Arsana, Susianto, Kusno Budhikarjono and Ali AltwayPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801017Abstract PDF (1.359 MB) References
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Effects of the Optimised pH and Molar Ratio on Struvite Precipitation in Aqueous System 01018Luluk Edahwati, S. Sutiyono, Dyah Suci Perwitasari, Stefanus Muryanto, J. Jamari and A.P. BayusenoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801018Abstract PDF (482.7 KB) References
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Performance of Inclined Baffle Column for Pectin Continuous Extraction Process from Cocoa Peel(Theobromacacao) 01019Soemargono, Sutiyono, Edi Purnomo Sasongko and Bambang PriyantoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801019Abstract PDF (192.3 KB) References
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Physicochemical and Organoleptic Properties of Dried Synbiotics Yoghurt from Lesser Yam Tubers(Dioscoreaesculenta L.) 01020Sri Winarti and Erwan Adi SaputroPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801020Abstract PDF (130.4 KB) References
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Potassium Silicate Foliar Fertilizer Grade from Geothermal Sludge and Pyrophyllite 01021Srie Muljani, Bambang Wahyudi, Ketut Sumada and SuprihatinPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801021Abstract PDF (343.4 KB) References
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Removal of Mg2+, K+, SO42 Ions from Seawater by Precipitation Method 01022
Caecilia Pujiastuti, Ketut Sumada, Yustina Ngatilah and Prasetyo HadiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801022Abstract PDF (121.1 KB) References
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Removal of Organic Load in Communal Wastewater by using the Six Stage Anaerobic Baffle Reactor(ABR) 01023Minarni Nur Trilita, Novirina Hendrasarie and Iwan WahjudijantoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801023Abstract PDF (195.4 KB) References
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The Benefit of Cacao Peel’s Lignin as an Adhesive using Multi Function Extractor 01024Mu’tasim Billah, Titi Susilowati, Susilowati and Diah Hari SuryaningrumPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801024Abstract PDF (103.6 KB) References
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The Effect of Fluorescent Pseudomonads Application on the Resistance of Chili Plants Against theAttack of Ralstonia Solanacearum and Fusarium Oxysporum in the Field 01025Yenny Wuryandari, Sri Wiyatiningsih and Agus SulistyonoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801025Abstract PDF (159.0 KB) References
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The Extraction Process of Trimethyl Xanthina in Vitro Culture of Callus Camellia Sinensis with ethylAcetate Solvent 01026Sutini, Susilowati, Djoko Agus Purwanto and M. Rasjad IndraPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801026Abstract PDF (1.187 MB) References
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The Influence of Vibration on CaCO3 Scale Formation in Piping System 01027
W. Mangestiyono, S. Muryanto, J. Jamari and A.P. BayusenoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801027Abstract PDF (555.7 KB) References
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Effect of Humic Acid on Soil Chemical and Physical Characteristics of Embankment 01028Munawar Ali and Wanti MindariPublished online: 23 May 2016
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DOI: http://dx.doi.org/10.1051/matecconf/20165801028Abstract PDF (378.3 KB) References
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Calcium Carbonate Scale Formation in Copper Pipes on Laminar Flow 01029S Raharjo, A Bayuseno, Jamari and S MuryantoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165801029Abstract PDF (671.7 KB) References
Industrial Engineering
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A Study on Home Based Enterprises in Kampoeng Pandean as Supporting SustainableArchitecture 02001Muchlisiniyati Safeyah and Eva ElvianaPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802001Abstract PDF (1.344 MB) References
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Accessibility to Public Transport Services (Case Study of Tabanan Region, BaliIndonesia) 02002Putu Alit SuthanayaPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802002Abstract PDF (314.8 KB) References
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Analysis of Balance Scorecards Model Performance and Perspective Strategy Synergized by SEM 02003Minto Waluyo, Syamsul Huda, Noer Soetjipto, Sumiati and HandoyoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802003Abstract PDF (241.4 KB) References
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Collaboration and Sustainable AgriFood Suply Chain: A Literature Review 02004Wike Agustin Prima Dania, Ke Xing and Yousef AmerPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802004Abstract PDF (143.0 KB) References
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Distribution Reliability of the Energy Supply Chain 02005Rachmad Hidayat and Sabarudin AkhmadPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802005Abstract PDF (365.9 KB) References
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5/24/2016 MATEC Web of Conferences
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Policy Strategies of Cocoa for Lead up Agroindustrial Food and Drinks in Jember Regency,Indonesia 02006Eko Nurhadi, Syarif Imam Hidayat, Pawana Nur Indah and Sri WidayantiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802006Abstract PDF (116.9 KB) References
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Sustainable strategies selection in SMEs Using MCDM approach 02007Ari BasukiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802007Abstract PDF (138.9 KB) References
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The Implementation of Vendor Managed Inventory In the Supply Chain with Simple ProbabilisticInventory Model 02008Ika Deefi AnnaPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165802008Abstract PDF (124.8 KB) References
Information Technology and Information Systems
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Analysis of the Effect of Information System Quality to Intention to Reuse of Employee ManagementInformation System (Simpeg) Based on Information Systems Success Model 03001Tri Lathif Mardi Suryanto, Djoko Budiyanto Setyohadi and Asif FaroqiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803001Abstract PDF (283.1 KB) References
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Determination of Selection Method in Genetic Algorithm for Land Suitability 03002Asti Dwi Irfianti, Retantyo Wardoyo, Sri Hartati and Endang SulistyoningsihPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803002Abstract PDF (203.6 KB) References
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Fake Review Detection From a Product Review Using Modified Method of Iterative ComputationFramework 03003Eka Dyar Wahyuni and Arif DjunaidyPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803003Abstract PDF (201.8 KB) References
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Forecasting of Computer Network Performance with Quadratic Equation 03004Achmad Ubaidillah and S. Ida Kholida
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Published online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803004Abstract PDF (419.8 KB) References
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Forecasting the Number of Patients Diseases Using Backpropagation 03005Aeri Rachmad and Devie Rosa AnamisaPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803005Abstract PDF (192.5 KB) References
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Gamification Approach to Enhance Students Engagement in Studying Language course 03006Andharini Dwi CahyaniPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803006Abstract PDF (100.6 KB) References
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Information Technology Implementation on Socialization of Harmonious Culture in Bali 03007I Putu Astawa, Ni Putu Wiwiek Ary Susyarini and Ni Nyoman TriyuniPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803007Abstract PDF (135.8 KB) References
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Management of Virtual Machine as an Energy Conservation in Private Cloud Computing System 03008Akhmad Fauzi, Edy Mulyadi, Abdullah Fadil and Mohammad IdhomPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803008Abstract PDF (464.9 KB) References
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Maturity Level at University Academic Information System Linking it Goals and Business Goal Based onCobit 4.1 03009Siti Mukaromah and Agung Brastama PutraPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803009Abstract PDF (122.2 KB) References
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Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio 03010Fetty Tri Anggraeny and Intan Yuniar PurbasariPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803010Abstract PDF (230.6 KB) References
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Performance Evaluation of Dsr with Mobility Models 03011Henni Endah Wahanani, I Made Suartana and Yudhi Nur Hasyim
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Teratozoospermia Classification Based on the Shape of Sperm Head Using OTSU Threshold andDecision Tree 03012I Gede Susrama Masdiyasa, I Ketut Edi Purnama and Mauridhy Heri PurnomoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803012Abstract PDF (1.059 MB) References
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The Application of EEMD and Neural Network Based on PolakRibiére Conjugate Gradient Algorithm forCrude Oil Prices Forecasting 03013M. Latif and Sri HerawatiPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803013Abstract PDF (247.1 KB) References
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The Implementation of Caching Database to Reduce Query’s Response Time 03014Arnaldo Marulitua Sinaga and Poppy SibaraniPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803014Abstract PDF (126.8 KB) References
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WEBBased Traffic Ticket Data Processing Information System 03015Faikul Umam and Rachmad HidayatPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803015Abstract PDF (1.407 MB) References
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The Design of Mobile Application for Teacher and Parents Communication in Indonesian School 03016Sholeh Hadi Setyawan, Dhiani Tresna Absari, Susana Limanto and AndrePublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165803016Abstract PDF (295.9 KB) References
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A Comparative Study of Finite Journal Bearing in Laminar and Turbulent Regimes Using CFD(Computational Fluid Dynamic) 04001Susilowati, Mohammad Tauviqirrahman, J. Jamari, Athanasius P. Bayuseno and MuchammadPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165804001Abstract PDF (928.9 KB) References
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Heat Removal Using the Hollow Roof in the Javanese House 04003Pranoto S MohammadPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165804003Abstract PDF (972.4 KB) References
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Implementing Cleaner Production as an Environmental Management Efforts in Small Industries ofCassava Chips 04004Erina Rahmadyanti and Dwijanto W. AndrePublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165804004Abstract PDF (107.6 KB) References
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Temperature Approach Optimization in the Double Pipe Heat Exchanger with Groove 04006Putu Wijaya Sunu, Daud Simon Anakottapary and Wayan G. SantikaPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165804006Abstract PDF (165.3 KB) References
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Analysis of the Contact Area of Smooth and Rough Surfaces in Contact with Sphere Indenter UsingFinite Element Method 04007Kartini, E. Saputra, R. Ismail, J. Jamari and A.P. BayusenoPublished online: 23 May 2016DOI: http://dx.doi.org/10.1051/matecconf/20165804007Abstract PDF (1.301 MB) References
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FORECASTING THE NUMBER OF PATIENTS DISEASES USING BACKPROPAGATION
Aeri Rachmad, Devie Rosa Anamisa
Faculty of Engineering, University of Trunojoyo Madura, Indonesia Email: [email protected]
ABSTRACT
Forecasting with various types of disease is important for health centers, because it can be used to help the health center management in conducting strategic planning and decision making. Health Care Center Torjun in Indonesia has made estimationabout the number of patients with various types of diseases, such as Acute Respiratory Infections(ISPA), RA (Rheumatoid Arthritis), diarrhea, HT (Hypertension), Skin Allergies, Conjunctivitis, Asthma, Febrile, TB(Tuberculosis). Lung, scabies, Gastritis, typus and scarlet fever with reports the number of patients with certain diseases in the coming period and prepare the necessary needs both medical services and as well as drugs for use later. In this study, Artificial Neural Network (ANN) is one model that is used to identify patterns of images of people with various kinds of diseases. Backpropagation is one of the popular models of Neural Networkwhich is used for forecasting, prediction, and decision makers based on the input of data entry that has been studied in advance. The resultsis HT (0.35 %)with parameters for forecasting system using Neural Network Backpropagation is the best of the trial results that shows disease HT which are obtained from the experiments. They predict the number of patients with a disease that needs to be watched for in the coming period and prepare all the needs of both medical and medication needed to handle the number of people with the disease. Keywords: Forecasting, Health Care Center, Diseases, Backpropagation
INTRODUCTION
Forecasting is a process to predict the needs of the future which includes the need to measure the quantity, quality, time and location needed in order to meet the demand for goods and services. Forecasting demand is the level of demand for the products that are expected to be realized for a certain period in the future. Basically forecasting approaches can be classified into two approaches: qualitative approach and quantitative approach (Memmedli, 2012)
There are some solutions that are needed such as a good quality of initial data and in sufficient quantity, because principally, the method of prediction is to identify the pattern of data which is based on the existing data, so mathematically predicted results will be relatively accurate (Chang, 2012). One technique that can be considered in pattern recognition neural network for prediction is a clone. Neural Network Backpropagation is the development of least mean square algorithm that can be used to train the network with multiple layers which is based on steepest descent algorithm approach and uses its performance index as the mean square error (Kara, 2011).
The outcome of a forecasting considers that forecastingmust contain errors, meaning that forecasters could only reduce the uncertainty that will happen, but it can not eliminate this uncertainty. Forecasting should provide information about how large the size of the erroris, meaning that forecasting certainly contains errors.It is important for forecasters to inform how large the errors that may occur, or even short-term forecasting is more accurate than long-term forecasting (Santoso,2009).
Along with the development of technology, there are more and more emerging methods that can be used for forecasting such as the classic method and fuzzy time series. Time series methods for modeling and forecasting
monthly number of patients at a health clinic which has produced levels of forecasting accuracy of the existing methods are not good enough. They can only predict seasonal problem, but they failed to foresee problems with linguistic values. Moreover the model also requires a number great historical data to generate accurate forecasting results (Setiyoutami, 2012).
In a previous study, several approaches were made to obtain the results of the best forecasting such as Artificial Neural Network, Ensemble Neural Network, Backpropagation Networks, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, clustering Fuzzy, etc. To determine the daily weather forecast, it can use back propagation technique with 28 input parameters to foresee some parameters such as temperature, rainfall, humidity, clouds, and weather days (Meera 2015). Instead of predicting the weather phenomenon through traditional ways such as the estimated amount of rainfall, barometric pressure, wind speed, temperature etc, we can use a neural network. Back Propagation neural network has high accuracy and efficiency to predict the weather (Pooja 2014; Meera 2015).
By considering the results of the above studies, this research uses back propagation neural network to predict the number of patients with various types of 13 diseases at the health center. The predictive model is used to estimate the number of patients with a disease that needs to be watched in the coming period. METHODOLOGY
Neural networks are information processing systems that have certain characteristics by adapting performance of biological neural network(Deepthi 2015;
, Web of Conferences DOI: 10.1051/
conf/2016MATEC 58 matec 503005 (2016) 803005BISSTECH 2015
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution
License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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Eka, 2014;Santhosh 2010). An information processing systems is interconnected in a network that work together to produce an output. The output of a neural network has the size on the relationship of each individual node in the network operates. Information processing in neural network is often implemented in parallel and not sequentially. Therefore, the members of neurons which have the same size work together to carry out its function.It is unique from a function of the overall neural network that can still be carried out even if the majority of the neurons are not functioning. That is because the artificial neural networks are very tolerant to faults or failures(Meera 2015). Backpropagation Neural Network may be the reason behind the popularity of artificial intelligence. Because this metode does not restrict the types of algorithms which are used in the learning process. This method spread enthusiasm by the developers of artificial intelligence though it is still much controversy whether the model can be applied as the learning steps of the brain or not. Partly because the mechanism for reversing the signal or error, it can not be accepted. Moreover, because there is no reasonable source for learning signal or target output.Backpropagation uses a multilayer architecture with supervised training methods training. Behind Propagation models, they have a couple of units that existsin one or more hidden layers. Behind Propagation model architecture with n inputs plus a bias, a hidden layer consisting of p units plus a bias, and m pieces of unit output, as shown in Figure 1.
Figurer-1. Backpropagation architecture (MePooja, 2014;Arti 2012)
ANN consists of many elements called neurons, unit, cell or science node connected to one another via the synaptic link / communication links that have weight-related (Kadar 2011). Weights represent information to be used by the network to resolve the issue. They can be applied to various types of problems, for example for pattern classification, making the mapping of input patterns, grouping with many constraints. Each neuron has an internal state called activity level, where the function of the input has been received. Neurons transmit a signal at a given moment; can be seen in Figure 2.
In the training, ANN Backpropagation initializedthe weights to determine the numbers of learning (α), determine the value of the error tolerance or threshold values (when using a threshold value as a condition to stop) or set maximum iterations (when using many iterations as
the stop condition), then performed step next-step for the stop condition is worth FALSE.
Figure-2. Neuron structure
Every unit of output (yk, k = 1,2,3, ..., m) receivesthe target pattern associated with the input pattern learning arithmetic error information and then calculate the correction weights (which will be used to improve the value wjk), as in equation 2 and compute their bias correction (which will be used to improve the value w0k), as in equation 1 (Noviani,2010).
(1)
Backpropagation ensures the completion of the minimum mean square error during the learning rate which is not too big. The drawback is when the learning rate is small, then the achievement of convergentvalue is slow, while if the learning rate is large, the achievement of rapid convergence gives a good value, but there is a danger that it can lead to global oscillations when the minimum value is not reached. To overcome this, it used backpropagation variations, such as momentum, where itworks with the aim to smooth oscillations that may occur. Filter momentum will be added to the equation weight matrix and bias. Variable Learning Rateworks by trying to raise the learning rate when it encounters a flat surface and then lowering the learning rate in the event of an increase in slope.
RESULTS
The system of forecasting the number of people with the disease at the health center with Backpropagation ANN method has the design development as in Figure 3.Model neural network with back propagation algorithm has three layers with an input layer consists of several unit cells (x1-xn) which contains the number of patients disease defined earlier. It can determine the number of people with the disease further, one hidden layer with the number of unit cells is determined at random (y1-yn), and one layer of output is amounted to one unit cell (z) as the target of the number of patients with disease outcome prediction next period
, Web of Conferences DOI: 10.1051/
conf/2016MATEC 58 matec 503005 (2016) 803005BISSTECH 2015
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Figure-3. Propose Model for number of patients
The training process of data patterns is normalized first so that the system can determine the weights that can map between data input with the desired output target data. Normalization is done by dividing the value of data with the value of the data range (maximum data value-minimum data value). Input data normalization is aimed to adjust the value range of data with activation function in the system backpropagation. This means that the value of the square of the input should be in the range of 0 to 1. Thus, input range that qualifies is the input data values from 0 to 1 or from -1 to 1. Therefore, the resulting output will be in the range of 0 to 1, such as in equation 2 (Frank 1997).
(2)where:
Xi =Data to iXmin = Data with a minimum valueXmax = Data with a maximum value
In the testing process, the output is produced by the network ranges between 0 and 1 so we need denormalization to convert the output result of network back into the normal number of people with the disease, as in Equation 3 (Noviani,2010). After that, it will be a comparison between the actual data with the data of the prediction, so the error can be calculated.or percentage of error
(3)where:
Xi = number of patients with the diseaseY = the output networkXmin = Data with a minimum valueXmax = Data with a maximum value
The accuracy of forecasting results are used to calculate the value of the error of the calculation process by the system to the initial value in accordance with the original data using the mean square error (MSE) because MSE is more sensitive to the value of the error. MSE does not have the magnitude of the value. A system is said to have good performance when the MSE value is close to zero (MSE ≈ 0) or can be said to be an error in the system. MSE is calculated by summing the squares of all the forecasting error in each period and dividing by the number of forecast period (Upadhyay 2011).
The trial conducted Backpropagation ANN forecasting model to determine the accuracy of the system in the process of disease predicted number. There are 13types of disease testing process based on the parameters that are used with real data scenarios comparison with predicted results. Test results of each disease prediction produce the best parameters of different configurations, such as in Table 1.
Each disease produces MSE and MAPE with the best parameter configuration. Configuration parameters generate value system accuracy,which is small.It shows that the difference between the data which predicts results with real data is not large, ascan be seen in Table 2. While the accuracy of the predictions for each disease can be seen in Table 3.
Table-1. The best parameter configuration
, Web of Conferences DOI: 10.1051/
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DISCUSSION
To evaluate the results of forecasting in this research, it used MAPE. MAPE is the percentage difference between the values of the error of the original data with the results of forecasting. MAPE measures the absolute error as a percentage instead of each period rather than the average absolute error in the number of periods of actual data. It can avoid the problems in the interpretation of measurement accuracy relative to the magnitude of the actual value and the predicted value. From 50 to 1000 iterations MSE and MAPE proportional same for any changes momentum, where the smaller iterations or momentum, the value of MSE and MAPE are getting smaller and vice versa, it is shown in Figure 4. When the comparison of accuracy and the learning rate changes, the smaller the value of learning the higher the rate the accuracy or otherwise. And the value of learning rate 0.008 is resulted in the MSE and MAPE smallest,as shown in Figure 5).
Figure-5. Forecasting evaluation results with learning rateshown in Figure 5).
Artificial neural network architecture consists of one input layer, one hidden layer, and one output layer. The number of input and output node in accordance with the number of lags is obtained from the autocorrelation,while the number of hidden layer is obtained from twice
Tabel-3.Prediction accuracy each disease
Table-2. Accuracy with real data and prediction
Figure-4. Forecasting evaluation results with iteration and momentum
, Web of Conferences DOI: 10.1051/
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the number of input layer nodes. The condition of the system stops if the system generated error value is less than or equal to the value specified error tolerances or the number of iterations performed has reached the maximum number of iterations specified. From some of the experiments that have been done, it can be concluded that the predicted number of patients using the model ANN Backpropagation disease depends on several parameters. In addition to the number of hidden layer, there are other parameters that need to be regulated, namely learning rate and momentum. Learning rate affects the speed and the accuracy of ANN in the learning process. The greater the learning rate, the faster ANN learns but the results are less accurate. The smaller the learning rate, the slower ANN studies but the results are more accurate (Tanu 2015).Momentum effects on the changes in the chart error. The larger the value of momentum, the shape of the curve is getting steeper. The smaller the value of momentum, then the error is getting sloping curve shape. The parameters are chosen to be the best parameter because it produces the number of iterations that has a value reasonably good accuracy error during the system test that is equal to 0.35%.
CONCLUSION
Configuration parameters for forecasting system using Neural Network Backpropagation is the best of the trial results that shows disease HT which are obtained from the experiments yield learning rate configuration at 0.008, 0.02 momentum, 0.000001 error tolerance, a maximum of 50 iterations and the number of hidden layer 2 as well as a percentage value error 0.35%. At scabies disease, the best parameters were 0.008 learning rate,momentum 0.02, 0.00001 of error tolerance, 1000maximum iteration, 10 hidden layers, and 0.001 MSE. This shows that the neural network Backpropagation method is suitable to be used for prediction of the number of people with the disease. The smaller the value of learning rate, the better the accuracy of the system. Momentum affects the changes in the chart error. The larger the value of the momentum, the steeper the shape of the error curve. On the opposite, the smaller the value of the momentum, then the flatter the shape of the error curve.
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