Jurnal SINTA 2
Hybrid K-means Dan Particle Swarm Optimization Untuk Clustering Nasabah Kredit
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 4 No. 2 (2017) · pp. 104–110
- ISSN
- 2528-6579, 2355-7699
- Published
- 2017-05-07
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- en
Abstract
AbstrakKredit merupakan suatu pendapatan terbesar bagi bank. Akan tetapi, bank harus selektif dalam menentukan nasabah yang dapat menerima kredit. Permasalahan ini menjadi semakin komplek karena ketika bank salah memberikan kredit kepada nasabah dapat merugikan, selain itu banyaknya parameter penentu dalam penentuan nasabah yang kredit. Clustering merupakan salah satu cara untuk dapat menyelesaikan permasalahan ini. K-means merupakan metode yang simpel dan popular dalam menyelesaikan permasalahan clustering. Akan tetapi, K-means murni tidak dapat memberikan solusi optimum sehingga perlu dilakukan improve untuk mendapatkan solusi optimum. Salah satu metode optimasi yang dapat menyelesaikan permasalahan optimasi dengan baik adalah particle swarm optimization (PSO). PSO sangat membantu dalam proses clustering dengan melakukan optimasi pada titik pusat tiap cluster. Untuk meningkatkan hasil yang lebih baik pada PSO ada beberapa improve yang dilakukan. Pertama penggunaan time-variant inertia untuk membuat nilai w atau inertia dinamis ditiap iterasi. Kedua melakukan kontrol kecepatan partikel atau velocity clamping untuk mendapatkan posisi terbaik. Selain itu untuk mengatasi konvergensi dini dilakukan hybrid PSO dengan random injection. Hasil pengujian menunjukan hybrid PSO K-means memberikan hasil terbesar dibandingkan K-means dan PSO K-means, dimana silhouette dari K-means, PSO K-means, dan hybrid PSO K-means masing-masing 0.57343, 0.792045, 1.Kata kunci: Kredit, Clustering, PSO, K-means, Random InjectionAbstractCredit is the biggest revenue for the bank. However, banks have to be selective in deciding which clients can receive the credit. This issue is becoming increasingly complex because when the bank was wrong to give credit to customers can do harm, apart of that a large number of deciding parameter in determining customer credit. Clustering is one way to be able to resolve this issue. K-means is a simple and popular method for solving clustering. However, K-means pure can’t provide optimum solutions so that needs to be done to get the optimum solution to improve. One method of optimization that can solve the problems of optimization with particle swarm optimization is good (PSO). PSO is very helpful in the process of clustering to perform optimization on the central point of each cluster. To improve better results on PSO there are some that do improve. The first use of time-variant inertia to make the dynamic value of inertial w each iteration. Both control the speed of the particle velocity or clamping to get the best position. Besides to overcome premature convergence do hybrid PSO with random injection. The results of this research provide the optimum results for solving clustering of customer credits. The test results showed the hybrid PSO K-means provide the greatest results than K-means and PSO K-means, where the silhouette of the K-means, PSO K-means, and hybrid PSO K-means respectively 0.57343, 0.792045, 1.Keywords: Credit, Clustering, PSO, K-means, Random Injection
Collaboration network of the authors
40 authors · 111 co-authorship links · 3 across different organisations · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Universitas Amikom Purwokerto
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Wayan Firdaus Mahmudy | University of Brawijaya | 4 |
| Arief Andy Soebroto | University of Brawijaya | 3 |
| Daneswara Jauhari | University of Brawijaya | 3 |
| Dhimas Anjar Prabowo | University of Brawijaya | 3 |
| Sutrisno . | Affiliation unknown | 2 |
| Nurul Hidayat | University of Brawijaya | 2 |
| Canny Amerilyse Caesar | University of Brawijaya | 2 |
| Latifah Hanum | University of Brawijaya | 2 |
| Muhammad Fhadli | University of Brawijaya | 2 |
| Anang Hanafi | University of Brawijaya | 2 |
| Listiya Surtiningsih | University of Brawijaya | 2 |
| Fitra Abdurrachman Bachtiar | University of Brawijaya | 2 |
| Ahmad Afif Supianto | University of Brawijaya | 1 |
| Randy Cahya Wihandika | University of Brawijaya | 1 |
| Maria Tenika Frestantiya | University of Brawijaya | 1 |
| Ziya El Arief | University of Brawijaya | 1 |
| Candra Dewi | University of Brawijaya | 1 |
| Rina Christanti | University of Brawijaya | 1 |
| Budi Darma Setiawan | University of Brawijaya | 1 |
| Rekyan Regasari Mardi Putri | University of Brawijaya | 1 |
| Lailil Muflikhah | University of Brawijaya | 1 |
| Muhammad Syafiq | University of Brawijaya | 1 |
| Achmad Jafar Al Kadafi | University of Brawijaya | 1 |
| Rizka Husnun Zakiyyah | University of Brawijaya | 1 |
| Wanda Athira Luqyana | University of Brawijaya | 1 |
| Uswatun Hasanah | Universitas Amikom Purwokerto | 1 |
| Lintang Resita Mayangsari | University of Brawijaya | 1 |
| Andhica Pratama | University of Brawijaya | 1 |
| Mochammad Ainun Najib | University of Brawijaya | 1 |
| Handika Agus Fauzi | University of Brawijaya | 1 |
| Sutrisno Sutrisno | University of Brawijaya | 1 |
| Aryeswara Sunaryo | University of Brawijaya | 1 |
| Maryamah . | University of Brawijaya | 1 |
| Moh. Fadel Asikin | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.