Evaluasi Kombinasi Hipernin dan Sinonim untuk Klasifikasi Kebutuhan Non-Functional Berbasis ISO/IEC 25010

Hakim, Lukman; Rochimah, Siti; Fatichah, Chastine

doi:10.25126/jtiik.2019651422

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Wordnet digunakan untuk melakukan pengembangan term dari data latih dengan dari relasi hipernim (Hakim, Rochimah & Fatichah, 2019a).…”

Section: Wordnetunclassified

Optimalisasi Proses Klasifikasi Dengan Menambahkan Semantik Pada Kebutuhan Non-Fungsional Berbasis ISO/IEC 25010

Hakim,

Huda

2023

JTIIK

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Kebutuhan non-fungsional (NFRs) memiliki peranan yang sangat penting untuk merancang sebuah sistem. Tetapi, mengidentifikasi NFRs masih menjadi tantangan nyata. NFRs dapat digunakan untuk memastikan apakah sistem sesuai dengan kebutuhan pengguna. Oleh karena itu, dibutuhkan suatu sistem untuk dapat mengidentifikasi NFRs. Penelitian sebelumnya menggunakan dataset dengan jumlah NFRs lebih banyak dibanding kebutuhan fungsional (FRs) memperoleh nilai tinggi. Akan tetapi, faktanya adalah NFRs lebih sedikit dari pada FRs. Penelitian selanjutnya menggunakan dataset dengan jumlah NFRs lebih sedikit dibandingkan (FRs) memperoleh nilai rendah terutama precision dan recall. Berdasarkan permasalahan pada penelitian sebelumnya, perlu dilakukan optimalisasi dengan cara menambahkan semantik pada metode FSKNN. Sistem yang digunakan untuk mengidentifikasi NFRs terdiri dari dua tahap. Pertama, sistem akan menghasilkan data latih secara otomatis tanpa pengukuran semantik. Setelah itu, data latih tersebut ditambahkan pengukuran semantik dan menghasilkan data latih baru. Kedua, proses klasifikasi menggunakan FSKNN. Pengujian dilakukan berdasarkan nilai ketetanggaan tertinggi. Hasil dari pengujian menggunakan dataset dengan penambahan pengukuran semantik memperoleh nilai accuracy, precision, dan recall lebih baik yaitu sebesar 98,04%, 25,05%, dan 21,35. Data latih baru dengan penambahan semantik memperoleh hasil lebih tinggi dari data latih tanpa penambahan semantik. Nilai precision rendah karena dataset yang digunakan tidak seimbang. Abstract Non-functional requirements (NFRs) are very important to design a system. However, identifying NFRs remains a real challenge. NFRs can be used to determine whether the system meets user requirements. Therefore, we need a system to be able to identify NFRs. Previous studies using a dataset with a higher number of NFRs than functional requirements (FRs) obtained high scores. However, the fact is that there are fewer NFRs than FRs. Subsequent studies using datasets with fewer NFRs than FRs obtain low values, especially precision, and recall. Based on the problems in previous studies, it is necessary to optimize by adding semantics to the FSKNN method. The system used to identify NFRs consists of two stages. First, the system will generate training data automatically without semantic measurements. After that, the training data is added with semantic measurements and generates new training data. Second, the classification process uses FSKNN. Testing is done based on the highest neighbor value. The results of testing using a dataset with the addition of semantic measurements obtained better accuracy, precision, and recall values of 98,04%, 25,05%, and 21,35. New training data with semantic additions obtains higher results than training data without semantic additions. The precision value is low because the dataset used is unbalanced.

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“…Wordnet digunakan untuk melakukan pengembangan term dari data latih dengan dari relasi hipernim (Hakim, Rochimah & Fatichah, 2019a).…”

Section: Wordnetunclassified

Optimalisasi Proses Klasifikasi Dengan Menambahkan Semantik Pada Kebutuhan Non-Fungsional Berbasis ISO/IEC 25010

Hakim,

Huda

2023

JTIIK

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“…In this research which combines several methods, SVM has the best and highest accuracy among other methods. Hakim et al [2] proposed an evaluated combination of hypernym and synonyms for NFR based on ISO 25010. Lukman used KNN and SVM.…”

Section: Previous Researchesmentioning

confidence: 99%

“…In contrast, non-functional requirements describe how the software provides the means to carry out functional requirements [1] . Non-functional requirements are often mixed with functional requirements [2] . Sometimes, there are software requirements specification which is incomplete [3] and ambiguous [4] at the time of writing them.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Comparison of Software Requirements Classification

Baskoro

Andrahsmara

Darnoto

et al. 2021

JTS

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Software requirements specification (SRS) is an essential part of software development. SRS has two features: functional requirements (FR) and non-functional requirements (NFR). Functional requirements define the needs that are directly in contact with stakeholders. Non-functional requirements describe how the software provides the means to carry out functional requirements. Non-functional requirements are often mixed with functional requirements. This study compares four primarily used machine learning methods for classifying functional and nonfunctional requirements. The contribution of our research is to use the PROMISE and SecReq (ePurse) dataset, then classify them by comparing the FastText+SVM, FastText+CNN, SVM, and CNN classification methods. CNN outperformed other methods on both datasets. The accuracy obtained by CNN on the PROMISE dataset is 99% and on the Seqreq dataset is 94%.

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Implementation of the Requirement Elicitation Comparison Process with Step Performed Based on Text Weighting Using TF-IDF at SRS Onlinelaundry

Majoli,

Priyadi,

Dian Martha

2023

2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)

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Evaluasi Kombinasi Hipernin dan Sinonim untuk Klasifikasi Kebutuhan Non-Functional Berbasis ISO/IEC 25010

Cited by 3 publications

References 13 publications

Optimalisasi Proses Klasifikasi Dengan Menambahkan Semantik Pada Kebutuhan Non-Fungsional Berbasis ISO/IEC 25010

Optimalisasi Proses Klasifikasi Dengan Menambahkan Semantik Pada Kebutuhan Non-Fungsional Berbasis ISO/IEC 25010

A Systematic Comparison of Software Requirements Classification

Implementation of the Requirement Elicitation Comparison Process with Step Performed Based on Text Weighting Using TF-IDF at SRS Onlinelaundry

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