Perbedaan tingkat akurasi metode k-means dan hierarchical clustering di bidang peramalan dan klasifikasi

Linita Sulistina, Syaharuddin Syaharuddin, Malik Ibrahim, Habib Ratu Perwira Negara

Abstract


Abstract: K-Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters/groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other groups. Hierarchical methods are clustering techniques to form a hierarchy or based on a certain level so that it resembles a tree structure. Thus, the grouping process is carried out in stages or stages. This research was conducted by reviewing research in national journals with topics that match the different levels of accuracy of the k-means and hierarchical clustering methods in the field of forecasting and classification. The purpose of this study was to determine the significant difference in the level of accuracy in forecasting and classification results between using the K-Means clustering method or using the Hierarchical clustering method. This research method uses a meta-analysis method by reviewing several articles from 2012-2022 related to differences in the level of accuracy of the k-means method and hierarchical clustering in the field of forecasting and classification. Data is collected from indexer databases such as Scopus, DOAJ, WorldCat, and Google Scholar. The data used is the result of research that contains the value of the correlation (r), and the number of data subjects (N). From the search results obtained publication data that meets as many as 60 publications. Based on the results of the analysis using JASP software, it was obtained that the k means method, the summary effect value of the forest plot was 0.67, in other words, the effect of the k means forecasting model on the accuracy rate was 67% with a moderate category, while in the hierarchical method the summary effect value of the forest plot was 0.61. in other words, the influence of the hierarchical method of forecasting models on the level of accuracy is 61% in the medium category.

Keywords


k-means, hierarchical, forecasting, meta-analysis, level of accuracy

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References


Agustina, S., Yhudo, D., Santoso, H., & ... (2012). Clustering Kualitas Beras Berdasarkan Ciri Fisik Menggunakan Metode K-Means. Universitas Brawijaya …. https://www.academia.edu/download/46692771/clustering-kualitas-beras-dengan-k-means.pdf

Aimmah, F. (2019). Implementasi Algoritma K-Means Clustering dan Double Exponential Smoothing Untuk Prediksi Penentuan Jumlah Produksi Jilbab. Theses, 91.

Atma Wijaya, K., & Swanjaya, D. (2021). Integrasi Metode Agglomerative Hierarchical Clustering dan Backpropagation Pada Model Peramalan Penjualan. 132–141. https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/1092/703

Auliasari, K., Kertaningtyas, M., Informatika, J., Auliasari, K., Kertaningtyas, M., & Industri, F. T. (2018). Studi Komparasi Klasifikasi Pola Tekstur Citra Digital Menggunakan Metode K-Means Dan Naïve Bayes. Jurnal Informatika, 18(2), 175–185.

B, A. T., & W, N. I. (2015). Web Content Mining Menggunakan Partitional Clustering K-Means Pada News Aggregator. 5(2), 42–46.

Dari, V., & Dan, K. (n.d.). Tugas akhir – ks141501.

Defiyanti, S. (2017). Integrasi Metode Clustering dan Klasifikasi untuk Data Numerik. Citee, July, 256–261.

Eka Ratnawati, D., . M., & Muflikhah, L. (2014). Pengembangan Metode Klasifikasi Berdasarkan K-Means Dan LVQ. Jurnal Teknologi Informasi Dan Ilmu Komputer, 1(1), 1. https://doi.org/10.25126/jtiik.20141197

Fierdaus, V. R., Bijaksana, M. A., & Astuti, W. (2020). Building Synonym Set for Indonesian WordNet using Commutative Method and Hierarchical Clustering. Jurnal Media Informatika Budidarma, 4(3), 778. https://doi.org/10.30865/mib.v4i3.2254

Fikri, R., Mushardiyanto, A., Naufal, M., Banin, L., Maureen, K., & Patria, H. (2021). Pengelompokan Kabupaten / Kota di Indonesia Berdasarkan Informasi Kemiskinan Tahun 2020 Menggunakan Metode K-Means Clustering Analysis. 1(November), 190–199.

Harnanda, P. R., Damastuti, N., & Fahrudin, T. M. (2021). GIS implementation and classterization of potential blood donors using the agglomerative hierarchical clustering method. IJEEIT International Journal of Electrical Engineering and Information Technology, 3(2), 44–54. https://doi.org/10.29138/ijeeit.v3i2.1305

Jannah, A. R., Arifianto, D., & Kom, M. (2015). Penerapan Metode Clustering dengan Algoritma K-Means untuk Prediksi Kelulusan Mahasiswa Jurusan Teknik Informatika di Universitas Muhammadiyah Jember. Jurnal Manajemen Sistem Informasi Dan Teknologi, 1(1210651237), 1–10.

Kartyasa Pribadi Putra, A., Purwanto, Y., & Novianty, A. (2015). Analisis Sistem Deteksi Anomali Trafik Menggunakan Algoritma CURE (Clustering Using Representatives) dengan Koefisien Silhouette dalam Validasi ClusteringNo Title. E-Proceeding of Engineering, 2(2355-9365), 3837.

Kusrini, S. E. D. A. (2017). Algoritma K-Means untuk Diskretisasi Numerik Kontinyu Pada Klasifikasi Intrusion Detection System Menggunakan Naive Bayes. Konferensi Nasional Sistem & Informatika, 61–66.

Kusuma, D. T., & Agani, N. (2015). Prototipe Komparasi Model Clustering Menggunakan Metode K-Means Dan FCM untuk Menentukan Strategi Promosi : Study Kasus Sekolah Tinggi Teknik-PLN Jakarta. TICOM (Technology of Information and Communication), 3(3), 1–10. https://doi.org/10.13140/RG.2.2.35612.08326

Kusumawardani, Y., Hamzah, A., & Informatika, T. (2018). Jurnal SCRIPT Vol . 5 No . 2 Juni 2018 ISSN : 2338-6304 Jurnal SCRIPT Vol . 5 No . 2 Juni 2018 ISSN : 2338-6304. 5(2).

Mu’afa, S. F., & Ulinnuha, N. (2020). Regency grouping in East Java based on Variable Type of Agriculture uses Hybrid Hierarchical Clustering Via Mutual Cluster Method. InPrime: Indonesian Journal of Pure and Applied Mathematics, 2(1), 51–58. https://doi.org/10.15408/inprime.v2i1.14167

Muhammad, M., & Akhsani, L. (2016). Mathematics Communication Skills with K-Means Clustering Method Through Problem Based Learning Model. Phytagoras, 5(2), 120–130.

Muliono, R., & Sembiring, Z. (2019). Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosen. CESS (Journal of Computer Engineering, System and Science), 4(2), 2502–2714.

Niagara, Y., Ernawati, & Purwandari, E. P. (2020). Pemanfaatan Citra Penginderaan Jauh Untuk Pemetaan Klasifikasi Tutupan Lahan Menggunakan Metode Unsupervised K-Means Berbasis Web Gis (Studi …. Rekursif: Jurnal Informatika, 8(1), 100–110. https://ejournal.unib.ac.id/index.php/rekursif/article/download/8478/5706

Nickel, M. (2017). Nickel 2017 NIPS (Poincaré Embeddings for Learning Hierarchical Representations).pdf. Nips.

nurul rohmawati, sofi defiyanti, mohamad jajuli. (2015). Implementasi Algoritma K-Means Dalam Pengklasteran Mahasiswa Pelamar Beasiswa. Jitter 2015, I(2), 62–68.

Prasatya, A., Siregar, R. R. A., & Arianto, R. (2020). Penerapan Metode K-Means Dan C4.5 Untuk Prediksi Penderita Diabetes. Petir, 13(1), 86–100. https://doi.org/10.33322/petir.v13i1.925

Prasetyo, G. A., Santosa, R. G., & Chrismanto, A. R. (2017). Memprediksi Kategori Indeks Prestasi Mahasiswa. 5. https://doi.org/10.21460/jutei.2019.32.185

Qi, C., Yi, L., Su, H., & Guibas, L. (2017). PointNet++: Deep Hierarchical Feature Learning on. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, Dec, 5105–5114.

Red, P. A., Aqm, D. A. N., Jaringan, P., & Covid-, K. P. V. (2021). P s i f t u j. 6(3).

Riyadhi, M. F. (2019). Aplikasi Text Mining Untuk Automasi Penentuan Tren Topik Skripsi Dengan Metode K-Means Clustering ( Studi Kasus : Prodi Sistem Komputer ). 8(2), 59–64.

Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014). Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90(0), 449–468. https://doi.org/10.1016/j.neuroimage.2013.11.046

Somantri, O., Wiyono, S., & Dairoh, D. (2016). Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (SVM). Scientific Journal of Informatics, 3(1), 34–45. https://doi.org/10.15294/sji.v3i1.5845

Studi, P., Informatika, T., Informatika, J. T., Komputer, F. I., & Brawijaya, U. (2018). memperoleh gelar Sarjana Komputer Disusun oleh :

Suprihatin, S., Utami, Y. R. W., & Nugroho, D. (2019). K-Means Clustering Untuk Pemetaan Daerah Rawan Demam Berdarah. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 7(1). https://doi.org/10.30646/tikomsin.v7i1.408

Syaliman, K. U., Nababan, A. A., & Nasution, N. Wi. (2017). Pembentukan Prototype Data Dengan Metode K-Means Untuk Klasifikasi dalam Metode K- Nearest Neighbor ( K-NN ). Seminar Nasional Teknologi Informatika (Semantika), 185–190.

Syamala, M. P. (2013). Analisis Prediksi Churn Dan Segmentasi Pelanggan Speedy Retail Daerah Operasional Bandung Menggunakan Algoritma Decision Tree Dan K-Means. 32–37.

Trisnawan, A., Hariyanto, W., & -, S. (2019). Klasifikasi Beras Menggunakan Metode K-Means Clustering Berbasis Pengolahan Citra Digital. : Jurnal Terapan Sains & Teknologi, 1(1), 16–24. https://doi.org/10.21067/jtst.v1i1.3013

Turnip, T. N., Manik, P. O., Tampubolon, J. H., & Siahaan, P. A. P. (2020). Klasifikasi Aplikasi Android menggunakan Algoritme K-Means dan Convolutional Neural Network berdasarkan Permission. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(2), 399. https://doi.org/10.25126/jtiik.2020702641

Wilson, M.S. , Metink-Kane, M. M. (2012). NIH Public Access. Bone, 23(1), 1–7. https://doi.org/10.1016/j.neuroimage.2014.06.077.Hierarchical

Wulandari, A., Cholissodin, I., Si, S., & Kom, M. (2015). Implementasi Metode Asymmetric Agglomerative Hierarchical Clustering Pada Portal Jurnal Internasional Original Article : Hierarchical Clustering Pada Portal. January.

Yim, O., & Ramdeen, K. T. (2015). Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data. The Quantitative Methods for Psychology, 11(1), 8–21. https://doi.org/10.20982/tqmp.11.1.p008

Yusup, A. H., Maharani, W., & Telkom, U. (2021). Pembangunan Model Prediksi Kepribadian Berdasarkan Tweet Dan Kategori Kepribadian Big Five Dengan Metode Agglomerative. 1(1), 44–50.

Zuhal, N. K. (2022). Study Comparison K-Means Clustering dengan Algoritma Hierarchical Clustering. 1, 200–205.


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