Jurnal SINTA 2
Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 5 No. 5 (2018) · pp. 567–576
- ISSN
- 2528-6579, 2355-7699
- Published
- 2018-10-30
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Klasifikasi merupakan teknik dalam data mining untuk mengelompokkan data berdasarkan keterikatan data terhadap data sampel. Pada penelitian ini, kami melakukan perbandingan 9 teknik klasifikasi untuk mengklasifikasi respon pelanggan pada dataset Bank Direct Marketing. Perbandingan teknik klasifikasi ini dilakukan untuk mengetahui model dalam teknik klasfikasi yang paling efektif untuk mengklasifikasi target pada dataset Bank Direct Marketing. Teknik klasifikasi yang digunakan yaitu Support Vector Machine, AdaBoost, Naïve Bayes, Constant, KNN, Tree, Random Forest, Stochastic Gradient Descent, dan CN2 Rule. Proses klasifikasi diawali dengan preprocessing data untuk melakukan penghilangan missing value dan pemilihan fitur pada dataset. Pada tahap evaluasi digunakan teknik 10 fold cross validation. Setelah dilakukan pengujian, didapatkan bahwa hasil klasifikasi menunjukkan akurasi terbaik diperoleh oleh model Tree, Constant, Naive Bayes, dan Stochastic Gardient Descent. Kemudian diikuti oleh model Random Forest, K-Nearest Neighbor, CN-2 Rule, AdaBoost dan Support Vector Machine. Dari keempat model yang menunjukkan hasil akurasi terbaik, untuk kasus ini Stochastic Gradient Descent terpilih sebagai model yang memiliki akurasi terbaik dengan nilai akurasi sebesar 0,972 dan hasil visualisasi yang dihasilkan lebih jelas untuk mengklasifikasi target pada dataset Bank Direct Marketing.AbstractClassification is a technique in data mining to classify data based on the attachment of data to the sample data.. In this paper, we present the comparison of 9 classification techniques performed to classify customer response on the dataset of Bank Direct Marketing. The techniques performed to find out the effectiveness model in the classification technique used to classify targets on the dataset of Bank Direct Marketing. The techniques used are Support Vector Machine, AdaBoost, Naïve Bayes, Constant, KNN, Tree, Random Forest, Stochastic Gradient Descent, and CN2 Rule. The classification process begins with preprocessing data to perform missing value omissions and feature selection on the dataset. Cross validation technique, with k value is 10, used in the evaluation stage. After testing, it was found that the classification results showed the best accuracy obtained when using the Tree model, Constant, Naive Bayes and Stochastic Gradient Descent. Afterwards the Random Forest model, K-Nearest Neighbor, CN-2 Rule, AdaBoost, and Support Vector Machine are followed. Of the four models with the high accuracy results, in this case Stochastic Gradient Descent was selected as the best accuracy model with an accuracy value of 0.972 and resulting visualization more clearly to classify targets on the dataset of Bank Direct Marketing.
Keywords
Collaboration network of the authors
37 authors · 88 co-authorship links · 43 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- National Research and Innovation Agency
- Indonesian Institute of Sciences
- Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
- 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 |
|---|---|---|
| Nanang Yudi Setiawan | University of Brawijaya | 2 |
| Vicky Zilvan | Indonesian Institute of Sciences | 2 |
| Asri Rizki Yuliani | Indonesian Institute of Sciences | 2 |
| Ade Ramdan | Indonesian Institute of Sciences | 2 |
| Sutrisno . | Affiliation unknown | 2 |
| Tibyani Tibyani | University of Brawijaya | 2 |
| Hilman Ferdinandus Pardede | National Research and Innovation Agency | 2 |
| Wahyu Teja Kusuma | Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW | 2 |
| Herman Tolle | University of Brawijaya | 2 |
| Nur Laita Rizki Amalia | University of Brawijaya | 1 |
| Imam Cholissodin | University of Brawijaya | 1 |
| Budi Darma Setiawan | University of Brawijaya | 1 |
| Farhanna Mar’i | University of Brawijaya | 1 |
| Rizal Arif Zulfikar | University of Brawijaya | 1 |
| I Putu Pradnyana Iswara | University of Brawijaya | 1 |
| Aditya Hari Bawono | University of Brawijaya | 1 |
| Nabila Divanadia Luckyana | University of Brawijaya | 1 |
| Yusi Tyroni Mursityo | University of Brawijaya | 1 |
| Yustinus Radityo Pradana | University of Brawijaya | 1 |
| Komang Candra Brata | University of Brawijaya | 1 |
| Santi Yunika Sufiana | University of Brawijaya | 1 |
| Mochammad Izzuddin | University of Brawijaya | 1 |
| Syaiful Anam | University of Brawijaya | 1 |
| Onky Prasetyo | University of Brawijaya | 1 |
| R. Budiarianto Suryo Kusumo | National Research and Innovation Agency | 1 |
| Satrio Hadi Wijoyo | University of Brawijaya | 1 |
| Abdul Harris Wicaksono | University of Brawijaya | 1 |
| Didik Krisnandi | National Research and Innovation Agency | 1 |
| Ana Heryana | National Research and Innovation Agency | 1 |
| Fawwaz Roja Mahardika | University of Brawijaya | 1 |
| Raden Sandra Yuwana | National Research and Innovation Agency | 1 |
| Endang Suryawati | National Research and Innovation Agency | 1 |
| Muhammad Aminul Akbar | University of Brawijaya | 1 |
| Carlista Naba | University of Brawijaya | 1 |
| Mochammad Isa Anshori | Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW | 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.