Pub Date : 2019-12-30DOI: 10.17933/jppi.2019.090206
Wisda Wisda, M. Mashud
In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelves based on the level of frequency of goods purchased together by customers. Therefore, this study suggests the creation of an application to analyze consumer spending patterns using the frequent pattern growth algorithm method to ensure appropriate placement of goods to increase sales at Giant Express Tamalanrea. The purpose of this study is to develop an application that can analyze consumer spending patterns to increase sales by positioning goods based on consumer shopping patterns, as well as implementing the Frequent Pattern Growth Algorithm method to determine customer spending patterns to increase sales. Stages of research methods conducted begin with data collection at the study site, system requirements analysis, system design with UML, and system testing with the Black Box method.
{"title":"Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm","authors":"Wisda Wisda, M. Mashud","doi":"10.17933/jppi.2019.090206","DOIUrl":"https://doi.org/10.17933/jppi.2019.090206","url":null,"abstract":"In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelves based on the level of frequency of goods purchased together by customers. Therefore, this study suggests the creation of an application to analyze consumer spending patterns using the frequent pattern growth algorithm method to ensure appropriate placement of goods to increase sales at Giant Express Tamalanrea. The purpose of this study is to develop an application that can analyze consumer spending patterns to increase sales by positioning goods based on consumer shopping patterns, as well as implementing the Frequent Pattern Growth Algorithm method to determine customer spending patterns to increase sales. Stages of research methods conducted begin with data collection at the study site, system requirements analysis, system design with UML, and system testing with the Black Box method.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48362545","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 : 2019-12-30DOI: 10.17933/jppi.2019.090203
Muhammad Insa Ansari
This study discusses electronic business licensing in Indonesia, by reviewing and analyzing the development of the regulations on electronic business licensing, electronically integrated business licensing reguations, and electronically integrated business licensing implementation. This research was conducted using normative legal research methods, with primary legal materials, secondary legal materials, and tertiary legal materials. The results of the study indicate that the development of regulations on business licensing is inseparable from the development of one-stop integrated licensing. However, the Online Single Submission system has not been implemented in all business licensing, leaving some with the use of offline arrangement. Proper implementation of electronic business licensing at the central government level, the provincial government level, to the regency level has not been achieved.
{"title":"Electronic Business Licensing in Indonesia","authors":"Muhammad Insa Ansari","doi":"10.17933/jppi.2019.090203","DOIUrl":"https://doi.org/10.17933/jppi.2019.090203","url":null,"abstract":"This study discusses electronic business licensing in Indonesia, by reviewing and analyzing the development of the regulations on electronic business licensing, electronically integrated business licensing reguations, and electronically integrated business licensing implementation. This research was conducted using normative legal research methods, with primary legal materials, secondary legal materials, and tertiary legal materials. The results of the study indicate that the development of regulations on business licensing is inseparable from the development of one-stop integrated licensing. However, the Online Single Submission system has not been implemented in all business licensing, leaving some with the use of offline arrangement. Proper implementation of electronic business licensing at the central government level, the provincial government level, to the regency level has not been achieved.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42502823","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 : 2019-12-30DOI: 10.17933/jppi.2019.090205
H. Ignatius, R. Chandra, N. Bohdan, A. Dharma
Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.
{"title":"Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy","authors":"H. Ignatius, R. Chandra, N. Bohdan, A. Dharma","doi":"10.17933/jppi.2019.090205","DOIUrl":"https://doi.org/10.17933/jppi.2019.090205","url":null,"abstract":"Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41798597","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 : 2019-12-30DOI: 10.17933/jppi.2019.090202
F. Tempola
This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.
{"title":"Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes","authors":"F. Tempola","doi":"10.17933/jppi.2019.090202","DOIUrl":"https://doi.org/10.17933/jppi.2019.090202","url":null,"abstract":"This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48774386","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 : 2019-10-01DOI: 10.17933/JPPI.2019.090103
Iswaya Maalik Syahrani
Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.
{"title":"ANALISIS PEMBANDINGAN TEKNIK ENSEMBLE SECARA BOOSTING(XGBOOST) DAN BAGGING (RANDOMFOREST) PADA KLASIFIKASI KATEGORI SAMBATAN SEKUENS DNA","authors":"Iswaya Maalik Syahrani","doi":"10.17933/JPPI.2019.090103","DOIUrl":"https://doi.org/10.17933/JPPI.2019.090103","url":null,"abstract":"Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42810875","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}
Vocational college Bogor Agricultural University (IPB) have utilized information technology (IT) to support business process. The problem is that existing information technology has not been effective in supporting the main business process. Data processing and information systems become one of the things that need to be improved. To apply information technology to align with the needs of business processes required a plan to minimize the occurrence of failure in the implementation phase. As the guidance and direction of IT Operation in each organization, IT Strategic Plan plays the very important role in organization. Many IT projects fail since there was no adequate IT planning. The stages of IS strategy formulation are performed based on Ward & Peppard framework. IS and IT strategic plan formulated in this study consist of some components such: application portfolio, IT management and architecture recommendation in Vocational college Bogor Agricultural University (IPB). This study results of IT strategic plan formulation for Vocational College Bogor Agricultural University (IPB).
{"title":"Information Technology Strategic Plan Using Ward and Peppard Method (Case Study of the Diploma Program of IPB University)","authors":"Ringga Gilang Baskoro","doi":"10.17933/jppi.v9i1.177","DOIUrl":"https://doi.org/10.17933/jppi.v9i1.177","url":null,"abstract":"Vocational college Bogor Agricultural University (IPB) have utilized information technology (IT) to support business process. The problem is that existing information technology has not been effective in supporting the main business process. Data processing and information systems become one of the things that need to be improved. To apply information technology to align with the needs of business processes required a plan to minimize the occurrence of failure in the implementation phase. As the guidance and direction of IT Operation in each organization, IT Strategic Plan plays the very important role in organization. Many IT projects fail since there was no adequate IT planning. The stages of IS strategy formulation are performed based on Ward & Peppard framework. IS and IT strategic plan formulated in this study consist of some components such: application portfolio, IT management and architecture recommendation in Vocational college Bogor Agricultural University (IPB). This study results of IT strategic plan formulation for Vocational College Bogor Agricultural University (IPB).","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47520520","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}
Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.
{"title":"Comparation Analysis of Ensemble Technique With Boosting(Xgboost) and Bagging (Randomforest) For Classify Splice Junction DNA Sequence Category","authors":"Iswaya Maalik Syahrani","doi":"10.17933/jppi.v9i1.249","DOIUrl":"https://doi.org/10.17933/jppi.v9i1.249","url":null,"abstract":"Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49462451","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 : 2019-10-01DOI: 10.17933/jppi.2019.090102
Ringga Gilang Baskoro
Vocational college Bogor Agricultural University (IPB) have utilized information technology (IT) to support business process. The problem is that existing information technology has not been effective in supporting the main business process. Data processing and information systems become one of the things that need to be improved. To apply information technology to align with the needs of business processes required a plan to minimize the occurrence of failure in the implementation phase. As the guidance and direction of IT Operation in each organization, IT Strategic Plan plays the very important role in organization. Many IT projects fail since there was no adequate IT planning. The stages of IS strategy formulation are performed based on Ward & Peppard framework. IS and IT strategic plan formulated in this study consist of some components such: application portfolio, IT management and architecture recommendation in Vocational college Bogor Agricultural University (IPB). This study results of IT strategic plan formulation for Vocational College Bogor Agricultural University (IPB).
{"title":"INFORMATION TECHNOLOGY STRATEGIC PLAN USING WARD AND PEPPARD METHOD (CASE STUDY DIPLOMA PROGRAM OF BOGOR AGRICULTURAL UNIVERSITY)","authors":"Ringga Gilang Baskoro","doi":"10.17933/jppi.2019.090102","DOIUrl":"https://doi.org/10.17933/jppi.2019.090102","url":null,"abstract":"Vocational college Bogor Agricultural University (IPB) have utilized information technology (IT) to support business process. The problem is that existing information technology has not been effective in supporting the main business process. Data processing and information systems become one of the things that need to be improved. To apply information technology to align with the needs of business processes required a plan to minimize the occurrence of failure in the implementation phase. As the guidance and direction of IT Operation in each organization, IT Strategic Plan plays the very important role in organization. Many IT projects fail since there was no adequate IT planning. The stages of IS strategy formulation are performed based on Ward & Peppard framework. IS and IT strategic plan formulated in this study consist of some components such: application portfolio, IT management and architecture recommendation in Vocational college Bogor Agricultural University (IPB). This study results of IT strategic plan formulation for Vocational College Bogor Agricultural University (IPB).","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42729859","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 : 2019-10-01DOI: 10.17933/jppi.2019.090106
Riva'atul Adaniah Wahab
Pengembangan internet broadband penting diimplementasikan karena perannya dalam mendukung kegiatan ekonomi. Peningkatan penetrasi broadband 10% memicu pertumbuhan ekonomi sebesar 1,38% di negara low-income dan middle-income, sementara di negara high-income hanya sebesar 1,12%. Pada tahun 2050, Cina diprediksi tetap memimpin ekonomi dunia, sedangkan Indonesia bergerak dari posisi 8 ke 4. Dengan menggunakan pendekatan kualitatif melalui tinjauan literatur, penelitian ini bertujuan untuk membandingkan pengembangan internet broadband untuk ekonomi digital di Cina dan Indonesia dalam rangka merealisasikan posisi ekonomi kedua negara tersebut di tahun 2050. Berdasarkan hasil yang diperoleh, dapat disimpulkan bahwa infrastruktur telekomunikasi untuk mendukung internet broadband dan regulasi ekonomi digital di Cina lebih matang daripada di Indonesia. Meskipun demikian, Indonesia sangat aktif dalam proses pengembangan e-commerce saat ini. Namun, Indonesia perlu melakukan ekspansi dalam kegiatan ekonomi digital lainnya seperti fintech serta menyediakan sumber daya manusia yang memiliki pengetahuan dan keterampilan di bidang ini sebagai bagian dari komponen penting ekonomi digital. Indonesia juga perlu belajar dari Cina mengenai peraturan e-commerce, seperti perpajakan dan standar produk. Upaya ini membutuhkan kolaborasi semua pihak, yang terdiri dari pemerintah, akademisi, pelaku industri untuk memperkuat peran internet broadband dalam ekonomi digital, di Cina dan di Indonesia.
{"title":"Comparative Analysis of Broadband Internet Development for Digital Economy in China and Indonesia","authors":"Riva'atul Adaniah Wahab","doi":"10.17933/jppi.2019.090106","DOIUrl":"https://doi.org/10.17933/jppi.2019.090106","url":null,"abstract":"Pengembangan internet broadband penting diimplementasikan karena perannya dalam mendukung kegiatan ekonomi. Peningkatan penetrasi broadband 10% memicu pertumbuhan ekonomi sebesar 1,38% di negara low-income dan middle-income, sementara di negara high-income hanya sebesar 1,12%. Pada tahun 2050, Cina diprediksi tetap memimpin ekonomi dunia, sedangkan Indonesia bergerak dari posisi 8 ke 4. Dengan menggunakan pendekatan kualitatif melalui tinjauan literatur, penelitian ini bertujuan untuk membandingkan pengembangan internet broadband untuk ekonomi digital di Cina dan Indonesia dalam rangka merealisasikan posisi ekonomi kedua negara tersebut di tahun 2050. Berdasarkan hasil yang diperoleh, dapat disimpulkan bahwa infrastruktur telekomunikasi untuk mendukung internet broadband dan regulasi ekonomi digital di Cina lebih matang daripada di Indonesia. Meskipun demikian, Indonesia sangat aktif dalam proses pengembangan e-commerce saat ini. Namun, Indonesia perlu melakukan ekspansi dalam kegiatan ekonomi digital lainnya seperti fintech serta menyediakan sumber daya manusia yang memiliki pengetahuan dan keterampilan di bidang ini sebagai bagian dari komponen penting ekonomi digital. Indonesia juga perlu belajar dari Cina mengenai peraturan e-commerce, seperti perpajakan dan standar produk. Upaya ini membutuhkan kolaborasi semua pihak, yang terdiri dari pemerintah, akademisi, pelaku industri untuk memperkuat peran internet broadband dalam ekonomi digital, di Cina dan di Indonesia.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44486561","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 : 2019-10-01DOI: 10.17933/jppi.2019.090105
Nfn Bahrawi
Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.
{"title":"Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter","authors":"Nfn Bahrawi","doi":"10.17933/jppi.2019.090105","DOIUrl":"https://doi.org/10.17933/jppi.2019.090105","url":null,"abstract":"Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.","PeriodicalId":31332,"journal":{"name":"Jurnal Penelitian Pos dan Informatika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41905219","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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