Pub Date : 2019-12-15DOI: 10.46300/91015.2020.14.15
Arica Dwi Susanto, I. Apriyanto
The development of companies in the digital era especially product business in Indonesia is now increasingly prominent in complexity, competition, change, and uncertainty so that the company's marketing and sales systems have not reached a maximal capacity due to the lack of superior and appropriate strategy. The researcher considered several alternatives using SWOT analysis and the Analytical Hierarchy Process (AHP) method to overcome these problems. The results showed that using the SWOT-AHP Analysis, it was found that the Strength parameter got the highest score by 53% and Opportunity parameter by 21%. Through the SWOT sub-criteria, it was found that the Strenghts priority were S2 (Registered patent) with a score of 0.53, S1 (New product) with a score of 0.29, S3 (Mechanical technology) with a score of 0.28, respectively. While weaknesses priority were W2 (inoptimal product promotion) with a score of 0.63, W1 (product not widely known) with a score of 0.37. In addition, the Opportunities Priority were the order of O2 (market share's openness) with a score of 0.52, O3 (More efficient products) with a score of 0.29, and O1 (Switching products from manual to automatic) with a score 0.19. And finally, the Threats priority were T1 (raw material) with a score of 0.53, T2 (price competition) with a score of 0.26 and T3 (product fraud) with a score of 0.21. The top priority of leading marketing strategy are by increasing product quality by 39.3%, while the second priority is marketing cooperation by 21.4%, the third is the pricing strategy by 20.5% and the last is promotion by 14.8%.
{"title":"SWOT and AHP Analysis in Determining the Strategy of Product Marketing Excellence in Companies","authors":"Arica Dwi Susanto, I. Apriyanto","doi":"10.46300/91015.2020.14.15","DOIUrl":"https://doi.org/10.46300/91015.2020.14.15","url":null,"abstract":"The development of companies in the digital era especially product business in Indonesia is now increasingly prominent in complexity, competition, change, and uncertainty so that the company's marketing and sales systems have not reached a maximal capacity due to the lack of superior and appropriate strategy. The researcher considered several alternatives using SWOT analysis and the Analytical Hierarchy Process (AHP) method to overcome these problems. The results showed that using the SWOT-AHP Analysis, it was found that the Strength parameter got the highest score by 53% and Opportunity parameter by 21%. Through the SWOT sub-criteria, it was found that the Strenghts priority were S2 (Registered patent) with a score of 0.53, S1 (New product) with a score of 0.29, S3 (Mechanical technology) with a score of 0.28, respectively. While weaknesses priority were W2 (inoptimal product promotion) with a score of 0.63, W1 (product not widely known) with a score of 0.37. In addition, the Opportunities Priority were the order of O2 (market share's openness) with a score of 0.52, O3 (More efficient products) with a score of 0.29, and O1 (Switching products from manual to automatic) with a score 0.19. And finally, the Threats priority were T1 (raw material) with a score of 0.53, T2 (price competition) with a score of 0.26 and T3 (product fraud) with a score of 0.21. The top priority of leading marketing strategy are by increasing product quality by 39.3%, while the second priority is marketing cooperation by 21.4%, the third is the pricing strategy by 20.5% and the last is promotion by 14.8%.","PeriodicalId":158702,"journal":{"name":"International Journal of Systems Applications, Engineering & Development","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.22266/ijies2019.1031.30
E. Elsayed, Mohammed Aly
One of the main risks to food security is plant diseases, but because of the absence of needed infrastructure and actual noise, scientists are faced with a difficult issue. Semantic segmentation of images divides images into non-overlapped regions, with specified semantic labels allocated. In this paper, The QPSO (quantum particle swarm optimization) algorithm has been used in segmentation of an original noisy image and Ontology has been used in classification the segmented image. Input noisy image segmentation is limited to a classification phase in which the object is transferred to Ontology. With 49,563 images from healthy and diseased plant leaves, 12 plant species were identified and 22 diseases, the proposed method is evaluated. The method proposed produces an accuracy of 86.22 percent for a stopped test set, showing that the strategy is appropriate. EPDO (Enhance Plant Disease Ontology) is built with the web ontology language (OWL). The segmented noisy image elements are paired with EPDO with derived features that come from QPSO. Our results show that a classification based on the suggested method is better than the state-of-the-art algorithms. The proposed method also saves time and effort for removing the noise at noise level from the input image σ=70
{"title":"Hybrid Between Ontology and Quantum Particle Swarm Optimization for Segmenting Noisy Plant Disease Image","authors":"E. Elsayed, Mohammed Aly","doi":"10.22266/ijies2019.1031.30","DOIUrl":"https://doi.org/10.22266/ijies2019.1031.30","url":null,"abstract":"One of the main risks to food security is plant diseases, but because of the absence of needed infrastructure and actual noise, scientists are faced with a difficult issue. Semantic segmentation of images divides images into non-overlapped regions, with specified semantic labels allocated. In this paper, The QPSO (quantum particle swarm optimization) algorithm has been used in segmentation of an original noisy image and Ontology has been used in classification the segmented image. Input noisy image segmentation is limited to a classification phase in which the object is transferred to Ontology. With 49,563 images from healthy and diseased plant leaves, 12 plant species were identified and 22 diseases, the proposed method is evaluated. The method proposed produces an accuracy of 86.22 percent for a stopped test set, showing that the strategy is appropriate. EPDO (Enhance Plant Disease Ontology) is built with the web ontology language (OWL). The segmented noisy image elements are paired with EPDO with derived features that come from QPSO. Our results show that a classification based on the suggested method is better than the state-of-the-art algorithms. The proposed method also saves time and effort for removing the noise at noise level from the input image σ=70","PeriodicalId":158702,"journal":{"name":"International Journal of Systems Applications, Engineering & Development","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130773201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A turbine spirometer with an IR rotary encoder is designed and fabricated for performing Respiratory Function Tests (RFT). The system includes a hardware for gathering breath inspiratory flow rate and a user software which represents analyzed data of patients' breath flow and volume parameters in real-time. A major challenge in design of flow-based spirometers is the accuracy of device in measuring volume parameters of RFTs which is due to large effects of sensing data error and noise. The purpose of the paper is evaluating the efficiency of three different types of digital noise reduction filters in term of improving the accuracy of system in calculation of air volume passing through the spirometer turbine by use of data obtained by the innovative flow sensor. Three distinct kinds of flow waves are experimented and the most sufficient filter for corresponding respiratory function tests are reported.
{"title":"Empirical Investigation of Noise Reduction Filter for a Flow-based Spirometer Accuracy Improvement","authors":"H. Bagheri","doi":"10.7176/ceis/10-5-01","DOIUrl":"https://doi.org/10.7176/ceis/10-5-01","url":null,"abstract":"A turbine spirometer with an IR rotary encoder is designed and fabricated for performing Respiratory Function Tests (RFT). The system includes a hardware for gathering breath inspiratory flow rate and a user software which represents analyzed data of patients' breath flow and volume parameters in real-time. A major challenge in design of flow-based spirometers is the accuracy of device in measuring volume parameters of RFTs which is due to large effects of sensing data error and noise. The purpose of the paper is evaluating the efficiency of three different types of digital noise reduction filters in term of improving the accuracy of system in calculation of air volume passing through the spirometer turbine by use of data obtained by the innovative flow sensor. Three distinct kinds of flow waves are experimented and the most sufficient filter for corresponding respiratory function tests are reported.","PeriodicalId":158702,"journal":{"name":"International Journal of Systems Applications, Engineering & Development","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132469511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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