Ngaliah, Nafingatun (2022) Ekstraksi User Story dari Berita Daring Menggunakan Metode Featured-Based dan Maximum Entropy untuk Elisitasi Kebutuhan Perangkat Lunak. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Elisitasi kebutuhan perangkat lunak merupakan tahapan pertama dalam rekayasa kebutuhan perangkat lunak. Elisitasi merupakan proses identifikasi kebutuhan perangkat lunak yang berasal dari berbagai sumber seperti wawancara dengan narasumber, kuesioner, analisis dokumen, dll. User story mudah untuk diadaptasi sesuai dengan perubahan kebutuhan system dan merupakan semi-structure language, karena penyusunan user story harus mengikuti sintaks sebagai standar penulisan fitur dalam pengembangan perangkat lunak metode agile. Dalam pembuatan user story terdapat 3 aspek yaitu aspect of who (pelaku), aspect of what (aktivitas), dan aspect of why (alasan).
Penelitian ini mengusulkan metode ekstraksi user story dari berita daring (online). Informasi aktual yang terkait lesson learn didalam sebuah berita daring dapat digunakan oleh sistem analis untuk mendapatkan kebutuhan perangkat lunak yang dibutuhkan. Penelitian ini mengusulkan ekstraksi user story yang terdiri dari aspect of who dan aspect of what dari situs berita daring dengan metode ekstraksi fitur dan maximum entropy sebagai metode klasifikasi. Hasil yang diharapkan dari metode ekstraksi pada penelitian ini adalah menghasilkan user story yang relevan sebagai kebutuhan perangkat lunak, sehingga dapat membantu sistem analis dalam proses elisitasi kebutuhan.
Hasil dari metode usulan ini menunjukkan nilai rata-rata presisi dan recall masing-masing sebesar 98,21% dan 95,16% untuk aspect of who; 87,14% dan 87,50% untuk aspect of what; 81,21% dan 78,60% untuk user story. Sehingga dapat diindikasikan bahwa metode usulan menghasilkan aspect of who, aspect of what, dan user story yang relevan dengan fungsional perangkat lunak. Selain itu, keandalan metode usulan ini diukur berdasarkan kesepakatan annotator dengan metode Kappa yang menghasilkan tingkat kesepakatan sebesar 0,62. Dengan begitu, dapat diindikasikan bahwa proporsi kesepakatan antara annotator dengan metode ini adalah substantial atau banyak.
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Software requirements elicitation is the first stage in software requirements engineering. Elicitation is the process of identifying software requirements that come from various sources such as interviews with resource persons, questionnaires, document analysis, etc. User stories are easy to adapt according to changing system requirements and are semi-structured languages, because the preparation of user stories must follow the syntax as a standard for writing features in agile software development methods. In making user stories there are 3 aspects, namely the aspect of who (the actor), the aspect of what (activity), and the aspect of why (reason).
This study proposes a method of extracting user stories from online news. Actual information related to lesson learned in an online news can be used by system analysts to obtain the required software requirements. This study proposes the extraction of user stories consisting of aspects of who and aspect of what from online news sites with feature extraction methods and maximum entropy as classification methods. The expected result of the extraction method in this study is to produce user stories that are relevant as software requirements, so that they can assist system analysts in the process of eliciting requirements.
The results of this proposed method show that the average precision and recall are 98.21% and 95.16% for the aspect of who, respectively; 87.14% and 87.50% for the aspect of what; 81.21% and 78.60% for user stories. So it can be indicated that the proposed method produces aspects of who, aspect of what, and user stories that are relevant to the software's functionality. In addition, the reliability of this proposed method is measured based on the agreement of the annotator with the Kappa method which produces an agreement level of 0.62. Thus, it can be indicated that the proportion of agreement between the annotators with this method is substantial.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | elisitasi kebutuhan, user story, berita daring, ekstraksi fitur, maximum entropy, Elicitation of software requirements, user story, online news, feature-based |
| Subjects: | Q Science > QA Mathematics > QA76.76.A65 Application software. Enterprise application integration (Computer systems) T Technology > T Technology (General) |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55101-(S2) Master Thesis |
| Depositing User: | Nafingatun Ngaliah |
| Date Deposited: | 03 Feb 2022 04:35 |
| Last Modified: | 01 Nov 2022 03:43 |
| URI: | http://repository.its.ac.id/id/eprint/92689 |
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