Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo

Penulis

  • Ardiansyah Universitas Ahmad Dahlan, Yogyakarta
  • Sri Handayaningsih Universitas Ahmad Dahlan, Yogyakarta
  • Deva Fathurrizki Universitas Ahmad Dahlan, Yogyakarta

DOI:

https://doi.org/10.25126/jtiik.2025128901

Kata Kunci:

effort estimation, software project, grey wolf optimizer, metaheuristic optimization, COCOMO

Abstrak

COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GWO) merupakan salah satu algoritma optimasi yang bisa menghindari jebakan optimum lokal yang sering dialami oleh algoritma berbasis swarm intelligence. Namun, GWO kurang dalam hal diversity populasi yang membuat banyak kandidat solusi tidak mampu menjangkau ruang pencarian secara merata. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan GWO termodifikasi berupa chaotic uniform initialization agar bisa meningkatkan diversity populasi. Metode yang diusulkan ini membangkitkan dua populasi awal yang masing-masing menggunakan teknik chaos dan acak. Setiap kandidat solusi pada kedua populasi tersebut diseleksi berdasarkan nilai fitness tertentu yang pada akhirnya akan membentuk satu populasi awal yang memiliki diversity yang lebih baik. Eksperimen pada penelitian ini menggunakan tiga himpunan data dari NASA. Untuk mendapatkan teknik chaos terbaik, dilakukan komparasi terhadap tujuh teknik chaos. Metode yang diusulkan kemudian dikomparasikan dengan algoritma GWO standar dan satu varian GWO terkini. Hasil penelitian menunjukkan bahwa metode yang diusulkan beserta teknik chaos circle terbukti mampu memperbaiki diversity populasi sehingga meningkatkan performa akurasi estimasi COCOMO. Metode yang diusulkan ini dimungkinkan untuk diintegrasikan pada alat bantu estimasi effort perangkat lunak yang biasa digunakan oleh para manajer proyek.

 

Abstract

COCOMO is a parametric-based software effort estimation method that is widely used and flexible to implement in small to large scale organizations. However, the two COCOMO parameters, namely multiplicative and exponential, often provide results that lack precision and are not realistic to apply to the current software development environment. To overcome this problem, several studies have proposed search-based approach to obtain appropriate parameter values using metaheuristic optimization algorithms. Grey Wolf Optimizer (GWO) is an algorithm that can avoid the local minimum trap that is often experienced by other swarm intelligence algorithms. However, GWO lacks population diversity which makes candidate solutions unable to cover the search space evenly. This study proposes chaotic uniform initialization to increase population diversity. The proposed method generates two initial populations using chaotic and random techniques respectively. Each candidate solution in the two populations is selected based on a certain fitness value which will ultimately produce an initial population that has better diversity. The experiment in this study used three data sets from NASA. To get the best chaos technique, a comparison of seven chaos techniques was carried out. The proposed method is then compared with the standard GWO algorithm and a current GWO variant. The research results show that the proposed method and the chaos circle technique are proven to be able to improve population diversity thereby increasing the accuracy of COCOMO. The proposed method is possible to integrated into software effort estimation tools commonly used by project managers.

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Referensi

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Diterbitkan

30-06-2025

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Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo. (2025). Jurnal Teknologi Informasi Dan Ilmu Komputer, 12(3), 671-680. https://doi.org/10.25126/jtiik.2025128901