SUKMAWATI, SAUFIKA (2020) PERBANDINGAN KLASIFIKASI DATA PENEMPATAN PEKERJA MIGRAN INDONESIA PADA KANTOR BP3TKI SEMARANG ANTARA ALGORITMA C4.5 DAN ALGORITMA NAIVE BAYES. Undergraduate thesis, UNIVERSITAS STIKUBANK.
![[thumbnail of HLM JUDUL]](/style/images/fileicons/application_pdf.png) |
PDF (HLM JUDUL)
Download (1MB) |
|
PDF (ABSTRAK)
Download (27kB) |
|
PDF (BAB I)
Download (174kB) |
|
PDF (BAB II)
Restricted to Repository staff only Download (265kB) |
|
PDF (BAB III)
Restricted to Repository staff only Download (770kB) |
|
PDF (BAB IV)
Restricted to Repository staff only Download (592kB) |
|
PDF (BAB V)
Restricted to Repository staff only Download (153kB) |
|
PDF (DAFTAR PUSTAKA)
Download (88kB) |
|
PDF (LAMPIRAN)
Restricted to Repository staff only Download (2MB) |
Abstract
Penempatan tenaga kerja Indonesia ke luar negeri merupakan salah satu upaya pemerintah dalam mewujudkan hak masyarakat untuk mendapatkan kesempatan bekerja serta meningkatkan perekonomian negara. Sebagai salah satu upaya pelindungan pekerja migran Indonesia maka dikembangkan sebuah sistem komputerisasi tenaga kerja luar negeri (SISKOTKLN) oleh Badan Nasional Penempatan dan Perlindungan TKI (BNP2TKI). Permasalahan yang menjadi kendala adalah adanya pekerja migran Indonesia (PMI) yang dipulangkan atau mendapat permasalahan ketenagakerjaan selama diluar negeri. Sehingga dibutuhkan sebuah interpretasi pada pola data penempatan PMI yang dapat digunakan sebagai prediksi negara tujuan penempatan para calon PMI kedepannya. Data yang digunakan dalam penelitian adalah data penempatan PMI pada wilayah BP3TKI Semarang dengan skema penempaan antara agency Indonesia dan agensi di negara tujuan. Penulis membandingkan dua algoritma klasifikasi dalam data mining yaitu algoritma C 4.5 dan algoritma Naïve Bayes. Percobaan dengan data training sebanyak 1802 dan data testing sebanyak 772 menghasilkan nilai akurasi paling tinggi bagi kedua algoritma. Algoritma C 4.5 mampu memprediksi lebih baik dengan tingkat akurasi sebesar 84.84% sedangkan Algoritma Naive Bayes menghasilkan nilai akurasi sebesar 58.29%. Abstract The placement of Indonesian migrant workers abroad is one of the government's efforts to make people get more chance to get job opportunities as well as improving their livelihood. The National Board on The Placement and Protection of Indonesian Migrant Workers (BNP2TKI), as a governmentorganization has developed digital based system called SISKOTKLN as an aegis for Indonesian migrant workers. The problem becomes an obstacle several Indonesian migrant workers have labor problems and some of them have been repatriated from their placement country. An interpretation of the migrant worker placement data pattern is needed which can be used as a prediction of the destination countries for migrant worker candidates ahead. This research focused on Indonesian migrant worker placement data from the BP3TKI Semarang office with private to private placement schemes which are facilitated by Private companies in both Indonesia and destination countries. This research compares the C 4.5 algorithm and the Naïve Bayes classifier which are the two classification algorithms in data mining. The highest accuracy values for both algorithms produced by using 1802 training data and 772 testing data. The C 4.5 algorithm can predict better with an accuracy rate of 84.84% while the Naive Bayes algorithm produces an accuracy value of 58.29%. Keywords: C 4.5, Naïve Bayes, Indonesian migrant worker
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | NIM:18.01.55.5004 SKR.I.05.02.1909 |
| Uncontrolled Keywords: | 4.5, Naïve Bayes, pekerja migran Indonesia |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty / Institution: | Fakultas Teknologi Informasi > Program Studi Sistem Informasi |
| Depositing User: | Ani Mariawati |
| Date Deposited: | 01 Oct 2020 08:26 |
| Last Modified: | 01 Oct 2020 08:26 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/7187 |
Actions (login required)
 |
View Item |
