FADHILAH, MUHAMAD IRFAN (2023) CLUSTERISASI KEPADATAN PENDUDUK DI DKI JAKARTA DENGAN ALGORITMA K-MEANS DAN AGGLOMERATIVE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Indonesia is one of the countries with the largest population in the world. one of the largest cities in Indonesia is DKI Jakarta which is the capital city of Indonesia, this is evidenced by the increasing population in DKI Jakarta which has resulted in an increase in the number of residents in DKI Jakarta from year to year. So that the population in DKI Jakarta is getting denser and residential areas are decreasing. To avoid population accumulation in one sub-district, population density clustering is needed in each sub-district in DKI Jakarta. Therefore, clustering is carried out using 2 K-Means and Agglomeratie algorithms. This research phase was carried out using data from 2017 – 2020 in DKI Jakarta and then a search for the best K of the two algorithms was carried out and the results obtained K = 3. The next step was clustering using the K-Means and agglomerative algorithms which produced RMSE and MAE values, for the algorithm K-Means produces an RMSE value of 0.996 and an MAE value of 0.735 while the Agglomerative algorithm produces a RMSE value of 1,734 and an MAE value of 1.536, so it can be concluded that the K-Means algorithm is better than the Agglomerative algorithm with a lower RMSE number than the Agglomerative algorithm. Keyword : Clustering, Data Mining, K-Means, Agglomerative, Population Density indonesia menjadi salah satu negara dengan jumlah penduduk tersbesar di dunia. salah satu kota terbesar di indonesia adalah DKI Jakarta yang merupakan ibu kota Indonesia ,hal itu dibuktikan dengan meningkatnya jumlah penduduk di DKI Jakarta yang mengakibatkan peningkatan jumlah penduduk di DKI Jakarta dari tahun ke tahun. Sehingga populasi di DKI Jakarta semakin padat dan lahan-lahan pemukiman semakin berkurang. Untuk menghindari penumpukan penduduk di satu kelurahan maka diperlukan clustering kepadatan penduduk di setiap kelurahan di DKI Jakarta. oleh sebab itu dilakukan clustering dengan menggunakan 2 algoritma K-Means dan Agglomeratie. Tahap penelitian ini dilakukan dengan menggunakan data dari tahun 2017 – 2020 di DKI Jakarta kemudian dilakukan pencarian K terbaik dari kedua algoritma dan didapatkan hasil K = 3. Langkah selanjutnya dilakukan clustering menggunakan algoritma KMeans dan agglomerative yang menghasilkan nilai RMSE dan MAE, untuk algoritma K-Means menghasilkan nilai RMSE 0,996 dan nilai MAE 0,735 sedangkan algortima Agglomerative menghasilkan nilai RMSE 1.734 dan nilai MAE 1,536, jadi dapat disimpulkan algortima K-Means lebih baik dari algortima Agglomerative dengan jumlah RMSE yang lebih rendah dibanding algoritma Agglomerative. Kata Kunci : Clustering, Data Mining, K-Means, Agglomerative, Kepadatan Penduduk
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 086 |
| NIM/NIDN Creators: | 41518010067 |
| Uncontrolled Keywords: | Clustering, Data Mining, K-Means, Agglomerative, Kepadatan Penduduk |
| 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 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 > 003.5 Computer Modeling and Simulation/Model dan Simulasi Komputer |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | CALVIN PRASETYO |
| Date Deposited: | 22 Sep 2023 01:39 |
| Last Modified: | 22 Sep 2023 01:39 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81340 |
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