Anto, Fajar Muji (2024) Penerapan Algoritma Naïve Bayes Dengan Feature Selection Pada Data Penjualan Konstruksi (Studi Kasus Pt. Maju Jaya Makmur Sejahtera). S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (COVER)
41818210014 - Fajar Putra Muji A - 01 Cover - Farah Apriliyanti.pdf Download (210kB) |
|
Text (BAB I)
41818210014 - Fajar Putra Muji A - 02 BAB 1 - Farah Apriliyanti.pdf Restricted to Registered users only Download (88kB) |
|
Text (BAB II)
41818210014 - Fajar Putra Muji A - 03 BAB 2 - Farah Apriliyanti.pdf Restricted to Registered users only Download (160kB) |
|
Text (BAB III)
41818210014 - Fajar Putra Muji A - 04 BAB 3 - Farah Apriliyanti.pdf Restricted to Registered users only Download (174kB) |
|
Text (BAB IV)
41818210014 - Fajar Putra Muji A - 05 BAB 4 - Farah Apriliyanti.pdf Restricted to Registered users only Download (857kB) |
|
Text (BAB V)
41818210014 - Fajar Putra Muji A - 06 BAB 5 - Farah Apriliyanti.pdf Restricted to Registered users only Download (35kB) |
|
Text (DAFTAR PUSTAKA)
41818210014 - Fajar Putra Muji A - 07 Daftar Pustaka - Farah Apriliyanti.pdf Restricted to Registered users only Download (101kB) |
|
Text (LAMPIRAN)
41818210014 - Fajar Putra Muji A - 08 Lampiran - Farah Apriliyanti.pdf Restricted to Registered users only Download (372kB) |
Abstract
Dalam jasa konstruksi, penerapan machine learning kerap digunakan pada proses pengolahan data yang berjumlah besar, salah satu contoh dari penerapan machine learning adalah menggunakan algoritma machine learning untuk mengklasifikasi data - data penjualan dari sebuah perusahaan yang hasil akhirnya berupa informasi untuk digunakan sebagai landasan pengambilan keputusan. Dalam classification data penerapan metode algoritma naïve bayes banyak digunakan karena hanya membutuhkan jumlah data pelatihan yang sedikit untuk menentukan parameter dalam proses classification. PT. Maju Jaya Makmur Sejahtera adalah perusahaan yang bergerak di bidang digital transformation di sektor jasa konstruksi. Berdasarkan hasil wawancara, masalah yang terdapat pada PT. Maju Jaya Makmur Sejahtera adalah banyaknya data client untuk konsultasi yang sebanyak kurang lebih 700 baris, menyebabkan sulitnya untuk mendapatkan informasi yang relevan sehingga diperlukannya analisis data dalam menentukan keputusan di PT. Maju Jaya Makmur Sejahtera. Hasil dari processing dan klasifikasi Algoritma Recursive Feature Elimination menyeleksi 10 fitur dataset menjadi total 6 fitur dan secara keseluruhan, akurasi yang didapatkan dari model algoritma naïve bayes sebesar 88%, precision 87%, recall 85%, dan F1-score 86%. Hasil Klasifikasi dapat dikatakan cukup bagus, tapi memiliki kekurangan dari segi atribut dataset sehingga menghasil skor rata rata dibawa 90%. In construction services, machine learning is often used in processing large amounts of data, one example of the application of machine learning is using machine learning algorithms to classify sales data from a company with the final result being information to be used as a basis for decision making. In Classification data on the application of algorithm methods naïve bayes are widely used because it only requires a small amount of training data to determine the parameters in the process classification. PT. Maju Jaya Makmur Sejahtera is a company engaged in digital transformation in the construction services sector. Based on the results of the interview, the problems found at PT. Maju Jaya Makmur Sejahtera is a data client for consultations of approximately 700 lines, making it difficult to obtain relevant information so that data analysis is needed in determining decisions at PT. Maju Jaya Makmur Prosperous. Results of processing and classification algorithm Recursive Feature Elimination selected 10 dataset features for a total of 6 features and overall, the accuracy obtained from the naïve Bayes algorithm model was 88%, precision 87%, recall 85%, and F1-score 86%. The classification results can be said to be quite good, but they have shortcomings in terms of dataset attributes so that the average score is brought to 90%.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41818210014 |
| Uncontrolled Keywords: | Classification, Decision Support System, Feature Selection, Naïve Bayes. Classification, Decision Support System, Feature Selection, Naïve Bayes. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | OKTAFIYANI AZ ZAHRO |
| Date Deposited: | 12 Feb 2025 03:18 |
| Last Modified: | 12 Feb 2025 03:18 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/94128 |
Actions (login required)
 |
View Item |
