Pub Date : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234214
Risanuri Hidayat, A. Winursito
Development of speech recognition systems continues to be carried out by many researchers. In many researches, system recognition accuracy is still as a main point which need to be improved. In addition to accuracy, systems algorithms computational time also becomes an important point that must be considered in developing a speech recognition system. This paper carries out a research on an analysis of initial processing stages in a speech recognition system. The initial processing stage of a speech recognition system is filtering which includes threshold analysis of filter and number of speech signal indicator data cuts. Research was carried out by testing range values of threshold and speech signal data cuts as well as observing effect of speech recognition systems accuracy. This research employed Mel Frequency Cepstral Coefficients (MFCC) as a feature extraction method, while the Euclidean distance method was used for classification. Results show that threshold values and number of speech signal data cuts affect speech recognition systems accuracy level. The highest speech recognition system accuracy is of 90% and is achieved at threshold value of 0.025, and of 3600 data cuts length. In addition, computational time of speech recognition system algorithm also influences speech signal data numbers used in computing process.
{"title":"Analysis of Amplitude Threshold on Speech Recognition System","authors":"Risanuri Hidayat, A. Winursito","doi":"10.1109/iSemantic50169.2020.9234214","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234214","url":null,"abstract":"Development of speech recognition systems continues to be carried out by many researchers. In many researches, system recognition accuracy is still as a main point which need to be improved. In addition to accuracy, systems algorithms computational time also becomes an important point that must be considered in developing a speech recognition system. This paper carries out a research on an analysis of initial processing stages in a speech recognition system. The initial processing stage of a speech recognition system is filtering which includes threshold analysis of filter and number of speech signal indicator data cuts. Research was carried out by testing range values of threshold and speech signal data cuts as well as observing effect of speech recognition systems accuracy. This research employed Mel Frequency Cepstral Coefficients (MFCC) as a feature extraction method, while the Euclidean distance method was used for classification. Results show that threshold values and number of speech signal data cuts affect speech recognition systems accuracy level. The highest speech recognition system accuracy is of 90% and is achieved at threshold value of 0.025, and of 3600 data cuts length. In addition, computational time of speech recognition system algorithm also influences speech signal data numbers used in computing process.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121409444","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234244
R. Yusianto, Marimin Marimin, Suprihatin, H. Hardjomidjojo
At present, post-harvest loss is an increasingly interesting issue. When farmers have used quality seeds, improved on-farm handling and advanced harvesting technology, the researchers began to focus on post-harvest problems. In Indonesia, the potatoes post-harvest loss is high at 32.8 kg/ton. Transportation and distribution problems have an effect of 20.43%. The contribution of this research is an Android-based advanced navigation system based on navigation radius for the route guidance system (RGS). We used the Dijkstra algorithm which we combined with latitude and longitude-based dynamic maps using Google APIs server for determining the shortest potatoes distribution route. To optimize the distance of the navigation radius, we used the radians approach on dynamic coordinates. The route that we calculated was all the nodes that were in the navigation radius. We can display distribution centers (DC) at a certain radius with the Google Maps fragment activity that embedded into the application and navigate to that place. So this method made it easy for decision makers to distribute their potatoes via the shortest route. The RGS using an android-based navigation that we proposed was implemented on a mobile application, and a comparison with the classical Dijkstra algorithm was performed. The results showed that this navigation system was better, more reliable with an accuracy rate of 99.83%. This proved that the android-based navigation system that we developed can be used. For future research, spatial analysis needs to be considered.
{"title":"The Route Guidance System using Android-Based Navigation to Determine the Shortest Potatoes Distribution Route","authors":"R. Yusianto, Marimin Marimin, Suprihatin, H. Hardjomidjojo","doi":"10.1109/iSemantic50169.2020.9234244","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234244","url":null,"abstract":"At present, post-harvest loss is an increasingly interesting issue. When farmers have used quality seeds, improved on-farm handling and advanced harvesting technology, the researchers began to focus on post-harvest problems. In Indonesia, the potatoes post-harvest loss is high at 32.8 kg/ton. Transportation and distribution problems have an effect of 20.43%. The contribution of this research is an Android-based advanced navigation system based on navigation radius for the route guidance system (RGS). We used the Dijkstra algorithm which we combined with latitude and longitude-based dynamic maps using Google APIs server for determining the shortest potatoes distribution route. To optimize the distance of the navigation radius, we used the radians approach on dynamic coordinates. The route that we calculated was all the nodes that were in the navigation radius. We can display distribution centers (DC) at a certain radius with the Google Maps fragment activity that embedded into the application and navigate to that place. So this method made it easy for decision makers to distribute their potatoes via the shortest route. The RGS using an android-based navigation that we proposed was implemented on a mobile application, and a comparison with the classical Dijkstra algorithm was performed. The results showed that this navigation system was better, more reliable with an accuracy rate of 99.83%. This proved that the android-based navigation system that we developed can be used. For future research, spatial analysis needs to be considered.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129293160","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234281
K. Hartomo, Dhimas Rizaldhi
Landslide is an activity from balance disruption which triggers the movement of a mass of soil and rock down a sloped section of land. Boyolali Regency is one of 35 regencies in Central Java Province which has high vulnerability to landslide. In order to reduce the number of casualties and property loss, this study aims to create a new model of landslide prone area map using the parameters which cause landslide, such as rainfall, soil types, drainage, slope, and land cover. The parameters are processed and analyzed using the combination of the scoring method and the polygon thiessen method. The scoring method is implemented to determine landslide prone areas, while the polygon thiessen method is applied to do the overlay and spatial mapping of landslide prone areas. The hypothesis proposed in this study is that the combination of scoring method and the polygon thiessen can map landslide prone areas in Boyolali Regency accurately. The result of the study shows that the model of the landslide prone area’s accuracy is 83.3%. The landslide prone area map shows the four sub districts in Boyolali Regency which meet the criteria of high landslide vulnerability level are Solo, Ampel, Musuk and Cepogo Sub Districts.
{"title":"A New Model of Landslide Prone Map Using a Combination of Scoring and Polygon Thiessen Methods","authors":"K. Hartomo, Dhimas Rizaldhi","doi":"10.1109/iSemantic50169.2020.9234281","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234281","url":null,"abstract":"Landslide is an activity from balance disruption which triggers the movement of a mass of soil and rock down a sloped section of land. Boyolali Regency is one of 35 regencies in Central Java Province which has high vulnerability to landslide. In order to reduce the number of casualties and property loss, this study aims to create a new model of landslide prone area map using the parameters which cause landslide, such as rainfall, soil types, drainage, slope, and land cover. The parameters are processed and analyzed using the combination of the scoring method and the polygon thiessen method. The scoring method is implemented to determine landslide prone areas, while the polygon thiessen method is applied to do the overlay and spatial mapping of landslide prone areas. The hypothesis proposed in this study is that the combination of scoring method and the polygon thiessen can map landslide prone areas in Boyolali Regency accurately. The result of the study shows that the model of the landslide prone area’s accuracy is 83.3%. The landslide prone area map shows the four sub districts in Boyolali Regency which meet the criteria of high landslide vulnerability level are Solo, Ampel, Musuk and Cepogo Sub Districts.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717452","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234242
Jimi Prasojo, R. Sarno
In this paper, a compact conformal antenna is proposed for vehicle to X (V2X) communication applications. The Hexagonal-shaped geometry is applied in the design to attain desired band in the vehicular communication spectrum. The proposed dimension antenna is 50mm x 50mm x 1.6 mm. By loading the hexagonal patch and annular slot with different sizes at each angle, it realizes to enhance bandwidth and increase the gain. This article explains how we found that tuning and overlapping of resonant frequency was mainly achieved by hexagonal parasitic element. The prototype antenna had been design using Ansys HFSS v.15. The simulation result shows that the antenna had resonant frequency at 5.9 GHz with return loss value of 32.95 dB. The antenna had VSWR value of 1.0189. This microstrip antenna had thickness of 1.6 mm, so it should be easy to fit up hidden in front of a vehicle for vehicular communication.
{"title":"Hexagonal Patch Microstrip Antenna with Parasitic Element for Vehicle Communication","authors":"Jimi Prasojo, R. Sarno","doi":"10.1109/iSemantic50169.2020.9234242","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234242","url":null,"abstract":"In this paper, a compact conformal antenna is proposed for vehicle to X (V2X) communication applications. The Hexagonal-shaped geometry is applied in the design to attain desired band in the vehicular communication spectrum. The proposed dimension antenna is 50mm x 50mm x 1.6 mm. By loading the hexagonal patch and annular slot with different sizes at each angle, it realizes to enhance bandwidth and increase the gain. This article explains how we found that tuning and overlapping of resonant frequency was mainly achieved by hexagonal parasitic element. The prototype antenna had been design using Ansys HFSS v.15. The simulation result shows that the antenna had resonant frequency at 5.9 GHz with return loss value of 32.95 dB. The antenna had VSWR value of 1.0189. This microstrip antenna had thickness of 1.6 mm, so it should be easy to fit up hidden in front of a vehicle for vehicular communication.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117131109","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234205
Drajad Bima Ajipangestu, R. Sarno
Twitter is a social media that is often used by many people in the world. The information is spread and obtained through social media. For example, there is a company that is organizing a new event that many people need to know. This allows the creation of a system that supports the presentation of user information by detecting certain events from Twitter's social media data. In this study, tweet data will be retrieved using Twitter API and stored in JSON format. Furthermore, there will be a pre-processing which includes the deletion of characters, number, URL, stemming, and lower case. Furthermore, feature extraction is performed using Global Vector for Word Representation. we will classify into four classes, which are Competitions, Seminars, Festivals, and Other events. The classification is using SVM to predict the type of event. There are three experimental methods used, there is SVM C, SVM linear, and SVM Nu. SVM Nu was conducted with changes in the SVC parameters in the form of kernel and Nu to produce the best accuracy. Based on the experiments we have done, the best results are obtained with an accuracy of 85.2% by classification using the NuSVC method with an RBF kernel and nu parameter of 0.2.
{"title":"Event Classification in Surabaya on Twitter with Support Vector Machine","authors":"Drajad Bima Ajipangestu, R. Sarno","doi":"10.1109/iSemantic50169.2020.9234205","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234205","url":null,"abstract":"Twitter is a social media that is often used by many people in the world. The information is spread and obtained through social media. For example, there is a company that is organizing a new event that many people need to know. This allows the creation of a system that supports the presentation of user information by detecting certain events from Twitter's social media data. In this study, tweet data will be retrieved using Twitter API and stored in JSON format. Furthermore, there will be a pre-processing which includes the deletion of characters, number, URL, stemming, and lower case. Furthermore, feature extraction is performed using Global Vector for Word Representation. we will classify into four classes, which are Competitions, Seminars, Festivals, and Other events. The classification is using SVM to predict the type of event. There are three experimental methods used, there is SVM C, SVM linear, and SVM Nu. SVM Nu was conducted with changes in the SVC parameters in the form of kernel and Nu to produce the best accuracy. Based on the experiments we have done, the best results are obtained with an accuracy of 85.2% by classification using the NuSVC method with an RBF kernel and nu parameter of 0.2.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249116","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234254
Hesti Putri Winasih, Eko Hari Rachmawanto, C. A. Sari, De Rosal Ignatius Moses Setiadi
In an institution, the issue of a certificate document cannot be separated. Now, the development of technology makes certificate documents not only issued in paper form but can also be published online. The document must have security to prove its authenticity. If the document of the paper certificate there is a serial number or unique code to prove its authenticity, online certificate documents must also have a unique code to prove their authenticity. In this study, the LSB and RSA methods are used to prove the authenticity of the certificate. The secret message on the certificate document will be encrypted using the RSA algorithm. Encrypted messages will be entered into digital images using the LSB method. The results are represented in measurements, MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), and BER (Bit Error Ratio). The combination of algorithms in this study produced very good values, the average PSNR value reached 73,4252 dB and an average value of BER equal to 1.4939.
{"title":"Implementation of LSB-RSA Algorithm for the Authenticity of the JPG File Certificate","authors":"Hesti Putri Winasih, Eko Hari Rachmawanto, C. A. Sari, De Rosal Ignatius Moses Setiadi","doi":"10.1109/iSemantic50169.2020.9234254","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234254","url":null,"abstract":"In an institution, the issue of a certificate document cannot be separated. Now, the development of technology makes certificate documents not only issued in paper form but can also be published online. The document must have security to prove its authenticity. If the document of the paper certificate there is a serial number or unique code to prove its authenticity, online certificate documents must also have a unique code to prove their authenticity. In this study, the LSB and RSA methods are used to prove the authenticity of the certificate. The secret message on the certificate document will be encrypted using the RSA algorithm. Encrypted messages will be entered into digital images using the LSB method. The results are represented in measurements, MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), and BER (Bit Error Ratio). The combination of algorithms in this study produced very good values, the average PSNR value reached 73,4252 dB and an average value of BER equal to 1.4939.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125556631","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234252
Kevin Djajadinata, Hussein Faisol, G. F. Shidik, Muljono, A. Z. Fanani
News is information about knowledge or event that occurs within a certain period. In the text news, there are several categories can be classified. This research proposes an evaluation of feature extraction to classify Indonesian language news. The dataset are from www.cnnindonesia.com (May 2018 - July 2018) with 4 categories and has a total of 3677 data and www.liputan6.com with 4 categories and has a total of 3415 data. All existing data will be processed to structured form and then the feature is extracted with 8 feature extraction method (TF, TF-IDF, TF-RF, TF-Prob, TF-CHI, TF-IDF-ISCDF, TF-IGM, and RTF-IGM) combined with 6 classification algorithms (Gaussian Naïve Bayes, k-NN, Decision Tree, Neural Network, Logistic Regression, and Support Vector Machine). From this research can be concluded that the Gaussian Naïve Bayes algorithm with TF-Prob was able to obtain the best accuracy with 99.701% (CNN Indonesia) and 99.824% (Liputan6) from 5 fold cross-validation.
{"title":"Evaluation of Feature Extraction for Indonesian News Classification","authors":"Kevin Djajadinata, Hussein Faisol, G. F. Shidik, Muljono, A. Z. Fanani","doi":"10.1109/iSemantic50169.2020.9234252","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234252","url":null,"abstract":"News is information about knowledge or event that occurs within a certain period. In the text news, there are several categories can be classified. This research proposes an evaluation of feature extraction to classify Indonesian language news. The dataset are from www.cnnindonesia.com (May 2018 - July 2018) with 4 categories and has a total of 3677 data and www.liputan6.com with 4 categories and has a total of 3415 data. All existing data will be processed to structured form and then the feature is extracted with 8 feature extraction method (TF, TF-IDF, TF-RF, TF-Prob, TF-CHI, TF-IDF-ISCDF, TF-IGM, and RTF-IGM) combined with 6 classification algorithms (Gaussian Naïve Bayes, k-NN, Decision Tree, Neural Network, Logistic Regression, and Support Vector Machine). From this research can be concluded that the Gaussian Naïve Bayes algorithm with TF-Prob was able to obtain the best accuracy with 99.701% (CNN Indonesia) and 99.824% (Liputan6) from 5 fold cross-validation.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126955538","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234238
Reza Hermansyah, R. Sarno
Online reviews are very important for any business that wants to control its online reputation. This allows businesses to have active and positive participation from consumers. As an information and communication company in Indonesia PT Telekomunikasi Indonesia Tbk commonly called Telkom require a customer’s perspective or review to maintain the relevance of their digital products on the market. One method often used to analyze online reviews is sentiment analysis. Sentiment Analysis is used to gain an understanding of the opinions, attitudes, and emotions expressed in the mention of online by determining the emotional tone behind a series of words.This research tries to compare classifications in sentiment analysis of Telkom’s product from consumer reviews written in the form of tweets on Twitter. Each tweet about Telkom digital products such as Indihome, UseeTV, and Wifi.id will be collected as data. The use of classification types will be compared to help with the accuracy of sentiment analysis based on three types of methods TextBlob, Naïve Bayes & K-NN (K-Nearest Neighbor).The best result of this research is the K-NN algorithm with an accuracy score of 75% followed by Naïve Bayes 69.44% and the last is TextBolb with 54.67%.
在线评论对于任何想要控制其在线声誉的企业来说都是非常重要的。这使得企业能够得到消费者的积极参与。作为印度尼西亚的一家信息和通信公司，PT Telekomunikasi Indonesia Tbk通常被称为Telkom，需要客户的观点或审查，以保持其数字产品在市场上的相关性。情感分析是一种常用的在线评论分析方法。情感分析是通过确定一系列词语背后的情感基调，来了解网络话题中所表达的观点、态度和情感。本研究试图从推特上以推文形式写的消费者评论中比较电信产品的情感分析分类。每条关于电信数字产品的推文，如Indihome、UseeTV和Wifi。Id将作为数据收集。将基于TextBlob、Naïve贝叶斯和K-NN (k -最近邻)三种方法比较分类类型的使用，以帮助提高情感分析的准确性。本研究结果最好的是K-NN算法，准确率为75%，其次是Naïve Bayes 69.44%，最后是TextBolb，准确率为54.67%。
{"title":"Sentiment Analysis about Product and Service Evaluation of PT Telekomunikasi Indonesia Tbk from Tweets Using TextBlob, Naive Bayes & K-NN Method","authors":"Reza Hermansyah, R. Sarno","doi":"10.1109/iSemantic50169.2020.9234238","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234238","url":null,"abstract":"Online reviews are very important for any business that wants to control its online reputation. This allows businesses to have active and positive participation from consumers. As an information and communication company in Indonesia PT Telekomunikasi Indonesia Tbk commonly called Telkom require a customer’s perspective or review to maintain the relevance of their digital products on the market. One method often used to analyze online reviews is sentiment analysis. Sentiment Analysis is used to gain an understanding of the opinions, attitudes, and emotions expressed in the mention of online by determining the emotional tone behind a series of words.This research tries to compare classifications in sentiment analysis of Telkom’s product from consumer reviews written in the form of tweets on Twitter. Each tweet about Telkom digital products such as Indihome, UseeTV, and Wifi.id will be collected as data. The use of classification types will be compared to help with the accuracy of sentiment analysis based on three types of methods TextBlob, Naïve Bayes & K-NN (K-Nearest Neighbor).The best result of this research is the K-NN algorithm with an accuracy score of 75% followed by Naïve Bayes 69.44% and the last is TextBolb with 54.67%.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121613153","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234283
Mutia Rahmi Dewi, Nafingatun Ngaliah, S. Rochimah
Academic Information System (AIS) has become a mandatory application for universities nowadays. AIS is an academic information system that was built to provide convenience to users in campus's academic administration activities by online. Therefore, AIS must be a system that has good service quality. The many software quality standards that exist today show the importance of achieving software quality. The purpose of this study is to evaluate FRS module's maintainability quality measurements results in myITS application which can be used as a reference in further development. Quality software information that can be measured such as the amount of functions, the amount of lines of code, complexity, the amount of errors, and trials used to support management planning, organizing, implementing, and controlling. The research methods used consist of reverse engineering, quality matrix analysis, system quality testing, and evaluation. This research focuses on maintainability quality standards based on ISO 25010. The results of this study stated that the FRS module in the myITS application has good maintainability quality, this is evidenced by the quality score of the myITS Lecturer at 2.670 and myITS Student at 2.083.
{"title":"Maintainability Measurement and Evaluation of myITS Mobile Application Using ISO 25010 Quality Standard","authors":"Mutia Rahmi Dewi, Nafingatun Ngaliah, S. Rochimah","doi":"10.1109/iSemantic50169.2020.9234283","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234283","url":null,"abstract":"Academic Information System (AIS) has become a mandatory application for universities nowadays. AIS is an academic information system that was built to provide convenience to users in campus's academic administration activities by online. Therefore, AIS must be a system that has good service quality. The many software quality standards that exist today show the importance of achieving software quality. The purpose of this study is to evaluate FRS module's maintainability quality measurements results in myITS application which can be used as a reference in further development. Quality software information that can be measured such as the amount of functions, the amount of lines of code, complexity, the amount of errors, and trials used to support management planning, organizing, implementing, and controlling. The research methods used consist of reverse engineering, quality matrix analysis, system quality testing, and evaluation. This research focuses on maintainability quality standards based on ISO 25010. The results of this study stated that the FRS module in the myITS application has good maintainability quality, this is evidenced by the quality score of the myITS Lecturer at 2.670 and myITS Student at 2.083.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167529","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234201
Triyanna Widiyaningtyas, Ilham Ari Elbaith Zaeni, Tyas Ismi Zahrani
Fluctuations in food commodities price in East Java Province cause various negative impacts when there are significant changes. To avoid this problem, it is necessary to predict food commodities prices to prevent high price increases. This study aims to apply the Extreme Learning Machine (ELM) method to predict the price of staple food commodities in East Java Province and measure the performance of the ELM in predicting staple food commodities price. The ELM is a method develop from feedforward Artificial Neural Networks (ANN) with one hidden layer or commonly called Single Hidden Layer Feedforward Neural Networks (SLFNs). The prediction process of staple food commodities is carried out using 3 data features, 7 neurons, and composition of training and testing data is 80%: 20%. The results showed that the average level of prediction accuracy for all staple food commodities was 98.79%. This shows that the prediction error is very low, ie the predicted results approach the actual value.
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