SOFTWARE EFFORT ESTIMATION MENGGUNAKAN LOGARITHMIC FUZZY PREFERENCE PROGRAMMING (LFPP) DAN LEAST SQUARES SUPPORT VECTOR MACHINES (LSSVM)

• ADNAN PURWANTO
• 14002474

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ABSTRAK

 

ABSTRAK Nama : Adnan Purwanto NIM : 14002474 Program Studi : Ilmu Komputer Fakultas : Teknologi Informasi Jenjang : Strata Dua (S2) Konsentrasi : Software Engineering Judul : Software Effort Estimation Menggunakan Logarithmic Fuzzy Preference Programming Dan Least Squares Support Vector Machines Machine
Effort Estimation adalah proses dimana seseorang dapat memprediksi waktu dan biaya pengembangan untuk mengembangkan proses perangkat lunak atau produk. Banyak pendekatan telah dicoba untuk memprediksi probabilistik ini proses secara akurat, tetapi tidak ada teknik tunggal yang berhasil secara konsisten. Metode pengambilan keputusan multi-kriteria (MCDM) telah muncul sebagai metode yang memenuhi syarat pendekatan untuk menangani pengambilan keputusan multifaktor. Sedangkan machine learning banyak digunakan untuk membangun sebuah model untuk memprediksi. Sudah banyak penelitian tentang software effort estimation menggunakan Fuzzy ataupun menggunakan Machine Learning. Untuk itu, penelitian ini bertujuan untuk menggabungkan Fuzzy dan Machine Learning. Berbagai macam metoda dan kombinasi telah dilakukan penelitian sebelumnya, penelitian ini mencoba kombinasi metoda Fuzzy dan Machine Learning yaitu
Logarithmic Fuzzy Preference Programming (LFPP) dan Least Squares Support
Vector Machines Machine (LSSVM). LFPP digunakan untuk menghitung ulang bobot cost driver dan menghasil Effort Adjustment Point (EAP). Nilai EAP dan
Lines of Code kemudian dimasukkan sebagai input untuk LSSVM. Hasil outputnya kemudian diukur performasinya menggunakan Mean Magnitude Of Relative Error (MMRE) dan Root-Mean-Square Error (RMSE). Pada penelitian kali ini, digunakan COCOMO dan NASA dataset. Hasil yang didapat adalah MMRE sebesar sebesar 0.015019 dan RMSE sebesar 1.703092 pada dataset COCOMO, sedangkan pada dataset NASA didapatkan hasil MMRE sebesar 0.007324 dan RMSE sebesar 6.037986. Kemudian 100% hasil prediksi memenuhi range 1% dari
actual effort pada dataset COCOMO sedangkan pada dataset NASA didapatkan hasil 89,475 memenuhi range 1% dari actual effort dan 100% memenuhi range 5% dari actual effort. Hasil dari penelitian ini juga menunjukkan tingkat akurasi yang lebih baik daripada menggunakan metode Intermediate COCOMO. Kata kunci : LFPP,LSSVM, Software Effort Estimation, COCOMO.
 

KATA KUNCI

LOGARITHMIC FUZZY PREFERENCE PROGRAMMING (LFPP)

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DAFTAR PUSTAKA

 

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