Analisis Perbandingan Kinerja Clustering Data Mining Untuk Normalisasi Dataset
DOI:
https://doi.org/10.30865/json.v5i2.6919Keywords:
Performance Comparison, Clustering, Data Mining, Normalization, Z-ScoreAbstract
Nowadays, the development and influence of technology in human life is very important, where the role of technology greatly influences the activities carried out by humans. In a company organization, technology is not only used as a process to speed up the processes carried out. The use of such important technology also increases the size or volume of available data information. A dataset is a collection of data obtained in a data warehouse. Data mining is a technique that is part of Knowledge Discovery in Database (KDD). Clustering is a grouping process carried out in data mining. The first problem that is central to the research is that the values obtained from the clustering process are sometimes still not considered optimal. The performance results of the data mining clustering algorithm cannot yet be fully used as a basis for decision making. Comparisons made in clustering data mining are used to assist in the decision making process. In this research, the algorithms that will be used for comparison of performance are the K-Means and K-Medoids algorithms. Another problem that needs special attention is the problem of data quality. The results obtained from the data mining process can be seen from the quality of the data stored or used in the data processing process. Normalization is part of preprocessing data mining which aims to re-reason it based on a new scale. Z-Score is a normalization carried out on data based on statistical functions. The results obtained in the research The role of normalization in the research is very important, this is because using Z-Score normalization can improve the performance of the K-Means and K-Medoids algorithms, this can be seen from the DBI value obtained which is smaller when normalization is carried out compared to before it is carried out normalization, which indicates that performance is better after normalization. In the comparison of algorithms, the K-Medoids algorithm gets better performance, this can be seen from the DBI value obtained at 0.773 at K=9 after normalization. Meanwhile, the K-Means algorithm obtained a value of 0.783 at K=9 after normalization as wellReferences
[1] S. Restillah and A. I. Purnamasari, “PENGELOMPOKAN PENYEBARAN VIRUS COVID-19 MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING YANG BERADA DI WILAYAH JAWA BARAT,” vol. 7, no. 3, pp. 1953–1957, 2023.
[2] A. Adiyanto and Y. Arie Wijaya, “Penerapan Algoritma K-Means Pada Pengelompokan Data Set Bahan Pangan Indonesia Tahun 2022-2023,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1344–1350, 2023, doi: 10.36040/jati.v7i2.6849.
[3] A. Waruwu, M. Yetri, and F. Setiawan, “Implementasi Data Mining Dalam Mengelompokkan Data penduduk Kurang Mampu Menggunakan Metode K-Means Clustering,” J. Sist. Inf. TGD, vol. 2, no. 6, pp. 945–955, 2023.
[4] A. P. Adistya, N. Lutfiyani, P. Tara, Rifaldi, R. Adriyan, and P. Rosyani, “Klasterisasi Menggunakan Algoritma K-Means Clustering Untuk Memprediksi Kelulusan Mata Kuliah Mahasiswa,” OKTAL J. Ilmu Komput. dan Sci., vol. 2, no. 8, pp. 2301–2306, 2023.
[5] D. Gustiara, A. D. Mulyaningsih, R. Anadra, and E. Widodo, “Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Dampak Kerusakan Bencana Banjir Menggunakan K-Medoids,” Indones. J. Appl. Stat., vol. 5, no. 2, pp. 109–120, 2023.
[6] F. P. Hidayat, R. P. Putra, M. D. Alfitrah, and E. Widodo, “Implementasi Clustering K-Medoids dalam Pengelompokan Kabupaten di Provinsi Aceh Berdasarkan Faktor yang Mempengaruhi Kemiskinan,” Indones. J. Appl. Stat., vol. 5, no. 2, pp. 121–130, 2023.
[7] E. N. Fitriyani and A. Iswani Achmad, “Penerapan Analisis K-Medoids Cluster untuk Mengelompokkan Wilayah di Provinsi Jawa Barat Berdasarkan Fasilitas Kesehatan Tahun 2021,” Bandung Conf. Ser. Stat., vol. 3, no. 2, pp. 283–293, 2023, doi: 10.29313/bcss.v3i2.8080.
[8] D. U. Iswavigra, L. E. Zen, Okfalisa, and H. Hanim, “Marketing Strategy UMKM Dengan CRISP-DM Clustering &Promotion Mix Menggunakan Metode K-Medoids,” J. Inf. dan Teknol., vol. 5, no. 1, pp. 45–54, 2023, doi: 10.37034/jidt.v5i1.260.
[9] M. L. Radhitya and G. I. Sudipa, “Pendekatan Z-Score Dan Fuzzy Dalam Pengujian Akurasi Peramalan Curah Hujan,” SINTECH (Science Inf. Technol. J., vol. 3, no. 2, pp. 149–156, 2020, doi: 10.31598/sintechjournal.v3i2.567.
[10] R. G. Whendasmoro and J. Joseph, “Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 4, p. 872, 2022, doi: 10.30865/jurikom.v9i4.4526.
[11] I. W. P. Pratama, “Standarisasi Z-Score sebagai Pendekatan Alternatif dalam Evaluasi Prestasi Akademik Mahasiswa?: Studi Kasus di Politeknik eLBajo Commodus,” JPTM J. Penelit. Terap. Mhs., vol. 1, no. 2, pp. 77–85, 2023.
[12] N. Safitri, D. Kusnandar, and S. Martha, “IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR DENGAN NORMALISASI Z-SCORE DALAM KLASIFIKASI PENERIMA BANTUAN SOSIAL DESA SERUNAI,” Bul. Ilm. Math. Stat. dan Ter., vol. 13, no. 1, pp. 99–106, 2023.
[13] M. R. Alhapizi, M. Nasir, and I. Effendy, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru Universitas Bina Darma Palembang,” J. Softw. Eng. Ampera, vol. 1, no. 1, pp. 1–14, 2020, doi: 10.51519/journalsea.v1i1.10.
[14] S. Dewi, “Komparasi Metode Algoritma Data Mining pada Prediksi Uji Kelayakan Credit Approval pada Calon Nasabah Kredit Perbankan,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 59–65, 2019, doi: 10.31294/jki.v7i1.5744.
[15] H. Gunawan and V. Purwayoga, “Data Mining Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Potensi Penyebaran Virus Corona Di Kota Cirebon,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 1, pp. 1–8, 2022, doi: 10.32736/sisfokom.v11i1.1316.
[16] Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI.
[17] T. Hartati, O. Nurdiawan, and E. Wiyandi, “Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari,” J. Sains Teknol. Transp. Marit., vol. 3, no. 1, pp. 1–7, 2021, doi: 10.51578/j.sitektransmar.v3i1.30.
[18] Sekar Setyaningtyas, B. Indarmawan Nugroho, and Z. Arif, “Tinjauan Pustaka Sistematis: Penerapan Data Mining Teknik Clustering Algoritma K-Means,” J. Teknoif Tek. Inform. Inst. Teknol. Padang, vol. 10, no. 2, pp. 52–61, 2022, doi: 10.21063/jtif.2022.v10.2.52-61.
[19] J. Faran and R. T. Aldisa, “Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 543–552, 2023, doi: 10.47065/bits.v5i2.4313.
[20] N. Widiawati, B. N. Sari, and T. N. Padilah, “Clustering Data Penduduk Miskin Dampak Covid-19 Menggunakan Algoritma K-Medoids,” J. Appl. Informatics Comput., vol. 6, no. 1, pp. 55–63, 2022, doi: 10.30871/jaic.v6i1.3266.
[21] Y. Diana and F. Hadi, “Analisa Penjualan Menggunakan Algoritma K-Medoids Untuk Mengoptimalkan Penjualan Barang,” J. Inf. Syst. Informatics Eng. Vol., vol. 7, no. 1, pp. 97–103, 2023.
[22] A. Masitha and M. K. Biddinika, “Preparing Dual Data Normalization for KNN Classfication in Prediction of Heart Failure,” vol. 4, no. 3, pp. 1227–1234, 2023, doi: 10.30865/klik.v4i3.1382.
[23] M. Qori’atunnadyah, “Pengelompokkan Wilayah Berdasarkan Rasio Guru-Murid Pada Jenjang Pendidikan Menggunakan Algoritma K-Means,” J. Informatics Dev., vol. 1, no. 2, pp. 33–38, 2022.
[24] A. sami Jaddoa, S. J. Saba, and E. A.Abd Al-Kareem, “Liver Disease Prediction Model Based on Oversampling Dataset with RFE Feature Selection using ANN and AdaBoost algorithms,” Buana Inf. Technol. Comput. Sci. (BIT CS), vol. 4, no. 2, pp. 85–93, 2023, doi: 10.36805/bit-cs.v4i2.5565.
[2] A. Adiyanto and Y. Arie Wijaya, “Penerapan Algoritma K-Means Pada Pengelompokan Data Set Bahan Pangan Indonesia Tahun 2022-2023,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1344–1350, 2023, doi: 10.36040/jati.v7i2.6849.
[3] A. Waruwu, M. Yetri, and F. Setiawan, “Implementasi Data Mining Dalam Mengelompokkan Data penduduk Kurang Mampu Menggunakan Metode K-Means Clustering,” J. Sist. Inf. TGD, vol. 2, no. 6, pp. 945–955, 2023.
[4] A. P. Adistya, N. Lutfiyani, P. Tara, Rifaldi, R. Adriyan, and P. Rosyani, “Klasterisasi Menggunakan Algoritma K-Means Clustering Untuk Memprediksi Kelulusan Mata Kuliah Mahasiswa,” OKTAL J. Ilmu Komput. dan Sci., vol. 2, no. 8, pp. 2301–2306, 2023.
[5] D. Gustiara, A. D. Mulyaningsih, R. Anadra, and E. Widodo, “Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Dampak Kerusakan Bencana Banjir Menggunakan K-Medoids,” Indones. J. Appl. Stat., vol. 5, no. 2, pp. 109–120, 2023.
[6] F. P. Hidayat, R. P. Putra, M. D. Alfitrah, and E. Widodo, “Implementasi Clustering K-Medoids dalam Pengelompokan Kabupaten di Provinsi Aceh Berdasarkan Faktor yang Mempengaruhi Kemiskinan,” Indones. J. Appl. Stat., vol. 5, no. 2, pp. 121–130, 2023.
[7] E. N. Fitriyani and A. Iswani Achmad, “Penerapan Analisis K-Medoids Cluster untuk Mengelompokkan Wilayah di Provinsi Jawa Barat Berdasarkan Fasilitas Kesehatan Tahun 2021,” Bandung Conf. Ser. Stat., vol. 3, no. 2, pp. 283–293, 2023, doi: 10.29313/bcss.v3i2.8080.
[8] D. U. Iswavigra, L. E. Zen, Okfalisa, and H. Hanim, “Marketing Strategy UMKM Dengan CRISP-DM Clustering &Promotion Mix Menggunakan Metode K-Medoids,” J. Inf. dan Teknol., vol. 5, no. 1, pp. 45–54, 2023, doi: 10.37034/jidt.v5i1.260.
[9] M. L. Radhitya and G. I. Sudipa, “Pendekatan Z-Score Dan Fuzzy Dalam Pengujian Akurasi Peramalan Curah Hujan,” SINTECH (Science Inf. Technol. J., vol. 3, no. 2, pp. 149–156, 2020, doi: 10.31598/sintechjournal.v3i2.567.
[10] R. G. Whendasmoro and J. Joseph, “Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 4, p. 872, 2022, doi: 10.30865/jurikom.v9i4.4526.
[11] I. W. P. Pratama, “Standarisasi Z-Score sebagai Pendekatan Alternatif dalam Evaluasi Prestasi Akademik Mahasiswa?: Studi Kasus di Politeknik eLBajo Commodus,” JPTM J. Penelit. Terap. Mhs., vol. 1, no. 2, pp. 77–85, 2023.
[12] N. Safitri, D. Kusnandar, and S. Martha, “IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR DENGAN NORMALISASI Z-SCORE DALAM KLASIFIKASI PENERIMA BANTUAN SOSIAL DESA SERUNAI,” Bul. Ilm. Math. Stat. dan Ter., vol. 13, no. 1, pp. 99–106, 2023.
[13] M. R. Alhapizi, M. Nasir, and I. Effendy, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru Universitas Bina Darma Palembang,” J. Softw. Eng. Ampera, vol. 1, no. 1, pp. 1–14, 2020, doi: 10.51519/journalsea.v1i1.10.
[14] S. Dewi, “Komparasi Metode Algoritma Data Mining pada Prediksi Uji Kelayakan Credit Approval pada Calon Nasabah Kredit Perbankan,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 59–65, 2019, doi: 10.31294/jki.v7i1.5744.
[15] H. Gunawan and V. Purwayoga, “Data Mining Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Potensi Penyebaran Virus Corona Di Kota Cirebon,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 1, pp. 1–8, 2022, doi: 10.32736/sisfokom.v11i1.1316.
[16] Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI.
[17] T. Hartati, O. Nurdiawan, and E. Wiyandi, “Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari,” J. Sains Teknol. Transp. Marit., vol. 3, no. 1, pp. 1–7, 2021, doi: 10.51578/j.sitektransmar.v3i1.30.
[18] Sekar Setyaningtyas, B. Indarmawan Nugroho, and Z. Arif, “Tinjauan Pustaka Sistematis: Penerapan Data Mining Teknik Clustering Algoritma K-Means,” J. Teknoif Tek. Inform. Inst. Teknol. Padang, vol. 10, no. 2, pp. 52–61, 2022, doi: 10.21063/jtif.2022.v10.2.52-61.
[19] J. Faran and R. T. Aldisa, “Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 543–552, 2023, doi: 10.47065/bits.v5i2.4313.
[20] N. Widiawati, B. N. Sari, and T. N. Padilah, “Clustering Data Penduduk Miskin Dampak Covid-19 Menggunakan Algoritma K-Medoids,” J. Appl. Informatics Comput., vol. 6, no. 1, pp. 55–63, 2022, doi: 10.30871/jaic.v6i1.3266.
[21] Y. Diana and F. Hadi, “Analisa Penjualan Menggunakan Algoritma K-Medoids Untuk Mengoptimalkan Penjualan Barang,” J. Inf. Syst. Informatics Eng. Vol., vol. 7, no. 1, pp. 97–103, 2023.
[22] A. Masitha and M. K. Biddinika, “Preparing Dual Data Normalization for KNN Classfication in Prediction of Heart Failure,” vol. 4, no. 3, pp. 1227–1234, 2023, doi: 10.30865/klik.v4i3.1382.
[23] M. Qori’atunnadyah, “Pengelompokkan Wilayah Berdasarkan Rasio Guru-Murid Pada Jenjang Pendidikan Menggunakan Algoritma K-Means,” J. Informatics Dev., vol. 1, no. 2, pp. 33–38, 2022.
[24] A. sami Jaddoa, S. J. Saba, and E. A.Abd Al-Kareem, “Liver Disease Prediction Model Based on Oversampling Dataset with RFE Feature Selection using ANN and AdaBoost algorithms,” Buana Inf. Technol. Comput. Sci. (BIT CS), vol. 4, no. 2, pp. 85–93, 2023, doi: 10.36805/bit-cs.v4i2.5565.
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Published
2023-12-26
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Saqila, S. E., Ferina, I. P., & Iskandar, A. (2023). Analisis Perbandingan Kinerja Clustering Data Mining Untuk Normalisasi Dataset. Jurnal Sistem Komputer Dan Informatika (JSON), 5(2), 356–365. https://doi.org/10.30865/json.v5i2.6919
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