Pub Date : 2018-11-01DOI: 10.1109/eiconcit.2018.8878522
{"title":"EIConCIT 2018 Welcome Editorial Remarks","authors":"","doi":"10.1109/eiconcit.2018.8878522","DOIUrl":"https://doi.org/10.1109/eiconcit.2018.8878522","url":null,"abstract":"","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126922127","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}
Prior to traveling, one of the most important things to pay attention to is to determine the travel route, especially the shortest path to be taken. In this study, the method used to determine the shortest route is the conventional method and the Heuristic method. These two methods will be compared to find out methods which can provide the best result. For conventional methods, the authors use the Floyd- Warshall algorithm whereas for the Heuristic method, the greedy algorithm is employed. The Floyd-Warshall algorithm takes into account all possible routes so that there are some routes are displayed while the greedy algorithm checks every node that is passed to select the shortest route (Local Optimum) so that the time needed in searching is faster. Based on the conducted testing, the final result obtained is the Floyd- Warshall algorithm provides a better solution, namely the mileage was 22.7 km and the greedy algorithm covered a mileage of 24.8 km. This result indicated that a longer time is required because it takes the distance to all points into account.
{"title":"Comparison of Floyd-Warshall Algorithm and Greedy Algorithm in Determining the Shortest Route","authors":"Huzain Azis, Rizaldi dg. Mallongi, Dirgahayu Lantara, Yulita Salim","doi":"10.1109/EIConCIT.2018.8878582","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878582","url":null,"abstract":"Prior to traveling, one of the most important things to pay attention to is to determine the travel route, especially the shortest path to be taken. In this study, the method used to determine the shortest route is the conventional method and the Heuristic method. These two methods will be compared to find out methods which can provide the best result. For conventional methods, the authors use the Floyd- Warshall algorithm whereas for the Heuristic method, the greedy algorithm is employed. The Floyd-Warshall algorithm takes into account all possible routes so that there are some routes are displayed while the greedy algorithm checks every node that is passed to select the shortest route (Local Optimum) so that the time needed in searching is faster. Based on the conducted testing, the final result obtained is the Floyd- Warshall algorithm provides a better solution, namely the mileage was 22.7 km and the greedy algorithm covered a mileage of 24.8 km. This result indicated that a longer time is required because it takes the distance to all points into account.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128235119","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878513
Morteza Nazeri, A. Rezai, Huzain Azis
The Golay codes are widely used Error Correction Codes (ECCs) that are used to recognize and correct errors in digital systems. This paper proposes an efficient architecture for hardware implementation of Golay code encoder. The proposed architecture has three important units: 1) data unit, 2) control unit and 3) conversion unit. These units are carefully designed such that the developed architecture can work for a message with ‘0’ and ‘1’ Most Significant (MS) bits. The performance of the developed encoder architecture is verified using FPGA devices. The results demonstrate that the developed encoder architecture provides a promising advantage compared to other encoder architectures for Golay codes.
{"title":"An Efficient Architecture for Golay Code Encoder","authors":"Morteza Nazeri, A. Rezai, Huzain Azis","doi":"10.1109/EIConCIT.2018.8878513","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878513","url":null,"abstract":"The Golay codes are widely used Error Correction Codes (ECCs) that are used to recognize and correct errors in digital systems. This paper proposes an efficient architecture for hardware implementation of Golay code encoder. The proposed architecture has three important units: 1) data unit, 2) control unit and 3) conversion unit. These units are carefully designed such that the developed architecture can work for a message with ‘0’ and ‘1’ Most Significant (MS) bits. The performance of the developed encoder architecture is verified using FPGA devices. The results demonstrate that the developed encoder architecture provides a promising advantage compared to other encoder architectures for Golay codes.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130900992","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878646
M. Wati, BambangEkoHari Cahyono, M. Firdaus
The government efforts to reduce society poverty rate by carrying out various programs in order to fulfill the basic needs of citizens properly and improve the socio-economic welfare of the poor to reach a prosperous Indonesian society. One such program is a social assistance program for poverty society. To reduce the risk of distributing aid that is not on target, it becomes very important for decision makers to determine citizens who are the priority of beneficiaries according to the applicable criteria. There are various Multi-Criteria Decision-Making (MCDM) methods that can be used in decision-making problems that involve many criteria. This paper tries to apply the PROMETHEE method in evaluating the advisability of the citizens' condition to receive government assistance with the weighting criteria based on entropy weight. There are six criteria used, namely, age, sex, education level, main occupation, skills, and marital status. The result showed that the determination of the feasibility of citizens as recipients of government assistance using entropy weight in PROMETHEE method which its output is the priority ranking of the alternative has an accuracy rate is 80.39 percent. The result of this research can be used to help a decision-maker to decide who are eligible for receiving the assistance government.
{"title":"Evaluation of Poverty Society for Social Assistance Recipients using PROMETHEE Method Based on Entropy Weight","authors":"M. Wati, BambangEkoHari Cahyono, M. Firdaus","doi":"10.1109/EIConCIT.2018.8878646","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878646","url":null,"abstract":"The government efforts to reduce society poverty rate by carrying out various programs in order to fulfill the basic needs of citizens properly and improve the socio-economic welfare of the poor to reach a prosperous Indonesian society. One such program is a social assistance program for poverty society. To reduce the risk of distributing aid that is not on target, it becomes very important for decision makers to determine citizens who are the priority of beneficiaries according to the applicable criteria. There are various Multi-Criteria Decision-Making (MCDM) methods that can be used in decision-making problems that involve many criteria. This paper tries to apply the PROMETHEE method in evaluating the advisability of the citizens' condition to receive government assistance with the weighting criteria based on entropy weight. There are six criteria used, namely, age, sex, education level, main occupation, skills, and marital status. The result showed that the determination of the feasibility of citizens as recipients of government assistance using entropy weight in PROMETHEE method which its output is the priority ranking of the alternative has an accuracy rate is 80.39 percent. The result of this research can be used to help a decision-maker to decide who are eligible for receiving the assistance government.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134257956","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878665
Yulita Salim, M. Latief, N. Kandowangko, R. Yusuf
The objective of this study was to analyze the comparison between artificial neural network algorithm and k-means clustering to see the extent of the effectiveness of this algorithm on the identification of Gorontalo herbal plant image. This study uses a digital imaging processing method with segmentation and extraction techniques. Segmentation proses used thresholding method. The next process was extraction process of the characteristics of the image of the herbal plant using the shape and color characteristics to obtain the metric, eccentricity, hue, saturation, and value of the plant was carried out. These five parameters were used as parameters to identify the herbal plant image. This study used 91 images which consisted of 80 imagery training and 11 test images. The study revealed that k-means clustering accuracy was 27.27% whereas the artificial neural network algorithm accuracy was 54.54%. In this case artificial neural networks had better accuracy than K-means.
{"title":"Comparison Analysis of the Artificial Neural Network Algorithm and K-Means Clustering in Gorontalo Herbal Plant Image Identification System","authors":"Yulita Salim, M. Latief, N. Kandowangko, R. Yusuf","doi":"10.1109/EIConCIT.2018.8878665","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878665","url":null,"abstract":"The objective of this study was to analyze the comparison between artificial neural network algorithm and k-means clustering to see the extent of the effectiveness of this algorithm on the identification of Gorontalo herbal plant image. This study uses a digital imaging processing method with segmentation and extraction techniques. Segmentation proses used thresholding method. The next process was extraction process of the characteristics of the image of the herbal plant using the shape and color characteristics to obtain the metric, eccentricity, hue, saturation, and value of the plant was carried out. These five parameters were used as parameters to identify the herbal plant image. This study used 91 images which consisted of 80 imagery training and 11 test images. The study revealed that k-means clustering accuracy was 27.27% whereas the artificial neural network algorithm accuracy was 54.54%. In this case artificial neural networks had better accuracy than K-means.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133789013","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878524
D. Hartina, A. Lawi, Benny L. E. Panggabean
GraphQL is a new concept in building an API. GraphQL is a Query Language developed by Facebook and implemented on the server side. Although it is a query language, the GraphQL is not directly connected with the database. In other words, GraphQL is not limited to both SQL and NOSQL databases. GraphQL which uses single endpoints is more efficient than RESTful which uses many endpoints but GraphQL will also be a little slower in querying complex databases and have many relationships beside that REST is built on multiple endpoints for specifying the return data, oftentimes multiple endpoints be required to be called when it needed. It will increase the number of client-server calls for displaying the data to the user and this could possibly result in poorer performance of the service in a Web Application needs. This paper analyses the performance calculation of the GraphQL and RESTful technologies in the web information services system of the Institute for Research and Community Service (LP2M) of the Hasanuddin University. The performance parameters used are Response Time and Throughput. Our results showed that in terms of speed RESTful is still superior to the GraphQL since the speed of RESTful is consistently stable in terms of access time and data size. Whereas the GraphQL is dynamic since it can be change depend on demand fluctuation.
{"title":"Performance Analysis of GraphQL and RESTful in SIM LP2M of the Hasanuddin University","authors":"D. Hartina, A. Lawi, Benny L. E. Panggabean","doi":"10.1109/EIConCIT.2018.8878524","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878524","url":null,"abstract":"GraphQL is a new concept in building an API. GraphQL is a Query Language developed by Facebook and implemented on the server side. Although it is a query language, the GraphQL is not directly connected with the database. In other words, GraphQL is not limited to both SQL and NOSQL databases. GraphQL which uses single endpoints is more efficient than RESTful which uses many endpoints but GraphQL will also be a little slower in querying complex databases and have many relationships beside that REST is built on multiple endpoints for specifying the return data, oftentimes multiple endpoints be required to be called when it needed. It will increase the number of client-server calls for displaying the data to the user and this could possibly result in poorer performance of the service in a Web Application needs. This paper analyses the performance calculation of the GraphQL and RESTful technologies in the web information services system of the Institute for Research and Community Service (LP2M) of the Hasanuddin University. The performance parameters used are Response Time and Throughput. Our results showed that in terms of speed RESTful is still superior to the GraphQL since the speed of RESTful is consistently stable in terms of access time and data size. Whereas the GraphQL is dynamic since it can be change depend on demand fluctuation.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365493","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 : 2018-11-01DOI: 10.1109/eiconcit.2018.8878625
{"title":"EIConCIT 2018 Committees","authors":"","doi":"10.1109/eiconcit.2018.8878625","DOIUrl":"https://doi.org/10.1109/eiconcit.2018.8878625","url":null,"abstract":"","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328673","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878608
Afrianda Cahyapratama, R. Sarno
Business process is a collection of all activities and tasks in achieving the goals of a company or an organization. This study aims to find the gap value obtained from the business process model matching technique. Process model matching can be used to identify similar processes or activities in a business process model. There are three things in finding similarities between each process model, including the similarity of label, structural similarity, and behavioral similarity. Probabilistic Latent Semantic Analysis is used to get the probability of topics. The calculation result of the probability of the document in the topic with the similarity value obtained is 0.063. Dice Coefficient is used to calculate the similarity of the structure of the business process model compared to showing the similarity results with a precision value of 0.695. Jaccard Coefficient is used to calculate the similarity of behavior of a business process model compared to showing the results of similarities with a precision value of 0.342. By looking at the precision value of the three mechanisms for measuring similarities, it can be said that business processes that are running are actually not in accordance with the business processes in the SOP.
{"title":"Gap Analysis of Business Processes using Behavioral, Structural, and Semantic Similarity Calculations","authors":"Afrianda Cahyapratama, R. Sarno","doi":"10.1109/EIConCIT.2018.8878608","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878608","url":null,"abstract":"Business process is a collection of all activities and tasks in achieving the goals of a company or an organization. This study aims to find the gap value obtained from the business process model matching technique. Process model matching can be used to identify similar processes or activities in a business process model. There are three things in finding similarities between each process model, including the similarity of label, structural similarity, and behavioral similarity. Probabilistic Latent Semantic Analysis is used to get the probability of topics. The calculation result of the probability of the document in the topic with the similarity value obtained is 0.063. Dice Coefficient is used to calculate the similarity of the structure of the business process model compared to showing the similarity results with a precision value of 0.695. Jaccard Coefficient is used to calculate the similarity of behavior of a business process model compared to showing the results of similarities with a precision value of 0.342. By looking at the precision value of the three mechanisms for measuring similarities, it can be said that business processes that are running are actually not in accordance with the business processes in the SOP.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934798","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878528
S. Ramdhani, R. Andreswari, M. A. Hasibuan
In this digital era, social media is used as a means to perform social activity and advertisements by companies. All of companies from small to big online shop created an endorsement to promote their products, and then it can be recognized. As a fast food restaurant, KFC launched the latest product KFC Salted Egg, As known, KFC often release unique products such as ChoChick, chicken sprinkled with chocolate spice. KFC created an endorsement by selecting Raditya Dika as an endorser. By using endorsement, KFC will get a good or bad sentiment. Analysis is needed to gain the sentiment’s effect of the endorsement. In conducting sentiment analysis, data was collected from two social media comments, YouTube, and Twitter. According to research conducted by Statista in 2007, the most widely used social media in Indonesia was YouTube while twitter was seventh. Even so, the development of twitter users time by time was increasing. It indicated that twitter was widely used. Naive Bayes was chosen to perform sentiment analysis because this method has a high accuracy in various studies. The stages of this research are divided into two periods, before and after endorsement. Data has been collected through the process of prepossessing, and then classification is done by using confusion matrix. The result showed that Naive Bayes has an accuracy rate more than 84%. However, negative sentiment rose by 12.51%. Neutral sentiment in this study contains neighbors of social media users who want to try the product, but the result after neutral sentiment endorsement decreased. It can be concluded that 9.77% of the decline has tried the product.
{"title":"Sentiment Analysis of Product Reviews using Naive Bayes Algorithm: A Case Study","authors":"S. Ramdhani, R. Andreswari, M. A. Hasibuan","doi":"10.1109/EIConCIT.2018.8878528","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878528","url":null,"abstract":"In this digital era, social media is used as a means to perform social activity and advertisements by companies. All of companies from small to big online shop created an endorsement to promote their products, and then it can be recognized. As a fast food restaurant, KFC launched the latest product KFC Salted Egg, As known, KFC often release unique products such as ChoChick, chicken sprinkled with chocolate spice. KFC created an endorsement by selecting Raditya Dika as an endorser. By using endorsement, KFC will get a good or bad sentiment. Analysis is needed to gain the sentiment’s effect of the endorsement. In conducting sentiment analysis, data was collected from two social media comments, YouTube, and Twitter. According to research conducted by Statista in 2007, the most widely used social media in Indonesia was YouTube while twitter was seventh. Even so, the development of twitter users time by time was increasing. It indicated that twitter was widely used. Naive Bayes was chosen to perform sentiment analysis because this method has a high accuracy in various studies. The stages of this research are divided into two periods, before and after endorsement. Data has been collected through the process of prepossessing, and then classification is done by using confusion matrix. The result showed that Naive Bayes has an accuracy rate more than 84%. However, negative sentiment rose by 12.51%. Neutral sentiment in this study contains neighbors of social media users who want to try the product, but the result after neutral sentiment endorsement decreased. It can be concluded that 9.77% of the decline has tried the product.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114068513","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 : 2018-11-01DOI: 10.1109/EIConCIT.2018.8878534
J. A. Widians, N. Puspitasari, Ulvie Ameilia
Black Orchid is a typical plant originating from Borneo Island. Black orchid is protected because its presence in nature that begins to extinct. Therefore, cultivation and multiplication are urgently needed in order to overcome the extinction of black orchid. Yet, the cultivation of black orchids is a difficult thing to do. This is caused by the lack of information or knowledge in cultivating black orchid. This study was conducted by employing Certainty Factor method in expert system design that allowed users to know the conditions that happened in accordance with the selected symptoms of the black orchid. Moreover, the results showed that the expert system used in Certainty Factor method contributed to providing the condition analysis experienced by black orchid in accordance with the symptoms that have been selected. Furthermore, this system was also able to provide solutions to the conditions that occurred. In addition to that, based on the previous results of validity tests, this expert system contributed to yield accuracy results of 100%. Thus, these results indicate the suitability of information generated by the system with information from experts as well as observations of conditions performed on black orchid plants.
{"title":"Expert System of Black Orchid Cultivation using Certainty Factor Method","authors":"J. A. Widians, N. Puspitasari, Ulvie Ameilia","doi":"10.1109/EIConCIT.2018.8878534","DOIUrl":"https://doi.org/10.1109/EIConCIT.2018.8878534","url":null,"abstract":"Black Orchid is a typical plant originating from Borneo Island. Black orchid is protected because its presence in nature that begins to extinct. Therefore, cultivation and multiplication are urgently needed in order to overcome the extinction of black orchid. Yet, the cultivation of black orchids is a difficult thing to do. This is caused by the lack of information or knowledge in cultivating black orchid. This study was conducted by employing Certainty Factor method in expert system design that allowed users to know the conditions that happened in accordance with the selected symptoms of the black orchid. Moreover, the results showed that the expert system used in Certainty Factor method contributed to providing the condition analysis experienced by black orchid in accordance with the symptoms that have been selected. Furthermore, this system was also able to provide solutions to the conditions that occurred. In addition to that, based on the previous results of validity tests, this expert system contributed to yield accuracy results of 100%. Thus, these results indicate the suitability of information generated by the system with information from experts as well as observations of conditions performed on black orchid plants.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132020158","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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