ANALISIS KLASTER KINERJA LAPTOP BERDASARKAN SPESIFIKASI HARDWARE MENGGUNAKAN ALGORITMA K-MEANS
Abstract
Customers often find it difficult to choose a laptop that suits their needs due to rapid market growth and the wide variety of specifications and price ranges. Existing approaches to laptop segmentation are often subjective or only consider a limited number of factors, which are insufficient to objectively represent the variation in device performance. Therefore, a data-driven approach is needed to classify laptops based on comprehensive hardware characteristics. This study aims to classify laptops into several performance clusters using the K-Means algorithm based on a combination of hardware specifications and price. It is assumed that laptops can be objectively grouped into different performance categories when several technical attributes are analyzed simultaneously. The data set was obtained from the Kaggle platform and consisted of 3,976 laptop records with eight main attributes: price, RAM capacity, processor speed, processor brand, screen size, SSD capacity, HDD capacity, and battery life. The results show that three performance clusters—entry-level, mid-range, and high-performance laptops—were optimally formed, with RAM, processor speed, and storage capacity as the most influential factors.
References
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[2] E. Denny, J. R. T. No, K. Gedong, P. Rebo, and J. Timur, “Perancangan Sistem Penentuan Merk Laptop Terbaik untuk Mahasiswa Informatika dengan Metode SAW,” J. Ris. dan Apl. Mhs. Inform., vol. 5, no. 04, pp. 886–893, 2024, doi: 10.30998/JRAMI.V5I4.12930.
[3] I. T. Prayudha, “Penerapan Kombinasi Metode Analytical Hierarchy Process Dan Simple Additive Weighting Dalam Mendukung Pengambilan Keputusan Pemilihan Laptop,” 2025.
[4] T. C. Saputra, S. M. Fadhilah, S. U. Mangkuto, and J. Heikal, “Segmentation, targeting and positioning analysis using k-means clustering model: A case study of the laptop market in Indonesia,” ijafibs.pelnus.ac.idTC Saputra, SM Fadhilah, SU Mangkuto, J HeikalInternational J. Appl. Financ. Bus. Stud. 2024•ijafibs.pelnus.ac.id, vol. 12, no. 2, pp. 195–203, 2024, doi: 10.35335/IJAFIBS.V12I2.313.
[5] N. N. M. B. Butar, T. Al Mudzakir, H. Y. Novita, and T. Rohana, “KLASTERING SPESIFIKASI DAN HARGA SMARTPHONE MENGGUNAKAN METODE FUZZY C-MEANS DAN PCA,” jurnal.uts.ac.idNNMB Butar, T Al Mudzakir, HY Novita, T RohanaJurnal Inform. Teknol. dan Sains (Jinteks), 2025•jurnal.uts.ac.id, vol. 7, no. 2, pp. 785–792, 2025, doi: 10.51401/JINTEKS.V7I2.5748.
[6] A. Sudrajat, K. Yohana, N. W.-J. K. dan …, and undefined 2025, “K-MEANS CLUSTERING: CUSTOMER SEGMENTATION ANALYSIS AND DIGITAL MARKETING STRATEGY DEVELOPMENT WITH ONLINE VALUE PROPOSITION,” ejurnals.comA Sudrajat, K Yohana, N Wahyuningsih, J HeikalJurnal Keuang. dan Manaj. Ter. 2025•ejurnals.com, vol. 6, no. 3, 2025, [Online]. Available: https://ejurnals.com/ojs/index.php/jkmt/article/view/2953
[7] P. Jangir, “Laptop Dataset.” [Online]. Available: https://www.kaggle.com/datasets/pradeepjangirml007/laptop-data-set
[8] S. Philip, P. Vashisth, A. Chaturvedi, and N. Gupta, “Imputation of Missing Values using Improved K-Means Clustering Algorithm to Attain Data Quality,” ieeexplore.ieee.orgS Philip, P Vashisth, A Chaturvedi, N Gupta2021 Third Int. Conf. Inven. Res. in, 2021•ieeexplore.ieee.org, pp. 295–301, 2021, doi: 10.1109/ICIRCA51532.2021.9544855.
[9] T. Hardiani, “Analisis Clustering Kasus Covid 19 di Indonesia Menggunakan Algoritma K-Means,” J. Nas. Pendidik. Tek. Inform. JANAPATI, vol. 11, no. 2, pp. 156–165, 2022, doi: 10.23887/JANAPATI.V11I2.45376.
[10] A. Azis, P. S. S. Informasi, S. T. I. K. C. K. Informatika, K. J. Timur, and D. K. I. Jakarta, “Penerapan Data Mining untuk Menentukan Ketersediaan Stok Barang Berdasarkan Permintaan Konsumen di PT Indonesia Thai Summit Plastech Menggunakan K-Means Clustering,” J. Indones. Manaj. Inform. dan Komun., vol. 5, no. 3, pp. 3099–3106, 2024, doi: 10.35870/JIMIK.V5I3.982.
[11] V. R. Maulani, M. A. B.-J. of Applied …, and undefined 2025, “A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique,” jurnal.polibatam.ac.idVR Maulani, MA Barata, PE YuwitaJournal Appl. Informatics Comput. 2025•jurnal.polibatam.ac.id, vol. 9, no. 3, pp. 741–748, 2025, doi: 10.30871/JAIC.V9I3.9481.
[12] A. M. Nur, M. Saiful, H. Bahtiar, and M. T. Hidayat, “Penerapan Algoritma K-Means Clustering Dalam Mengelompokkan Smartphone Yang Rekomendasi Berdasarkan Spesifikasi,” e-journal.hamzanwadi.ac.idM Saiful, H Bahtiar, MT HidayatInfotek J. Inform. dan Teknol. 2024•e-journal.hamzanwadi.ac.id, vol. 7, no. 2, pp. 478–488, 2024, doi: 10.29408/JIT.V7I2.26283.
[13] G. R. Wahyudi, R. Rahmaddeni, E. Dini, S. Adrianto, and R. Fadila, “Pengelompokan Kabupaten di Indonesia untuk Pemetaan Pendapatan Daerah Menggunakan Algoritma K-Means: Clustering of Regencies in Indonesia for Regional,” journal.irpi.or.idGR Wahyudi, R Rahmaddeni, E Dini, S Adrianto, R FadilaMALCOM Indones. J. Mach. Learn. Comput. Sci. 2025•journal.irpi.or.id, vol. 5, no. 3, pp. 1143–1151, 2025, doi: 10.57152/MALCOM.V5I3.2206.
[14] K. Deeparani, P. S.-M. T. Proceedings, and undefined 2021, “Efficient image segmentation and implementation of K-means clustering,” ElsevierK Deep. P SudhakarMaterials Today Proceedings, 2021•Elsevier, vol. 45, pp. 8076–8079, 2021, doi: 10.1016/J.MATPR.2021.01.154.
[15] M. Amelia, A. Faqih, A. R. R.-J. I. dan, and undefined 2025, “PENERAPAN METODE K-MEANS CLUSTERING DALAM PEMETAAN KEMISKINAN KABUPATEN/KOTA DI INDONESIA UNTUK PERENCANAAN KEBIJAKAN,” journal.eng.unila.ac.idM Amelia, A Faqih, AR RinaldiJurnal Inform. dan Tek. Elektro Ter. 2025•journal.eng.unila.ac.id, vol. 13, no. 2, pp. 2830–7062, doi: 10.23960/JITET.V13I2.623.
[16] M. D. Salman et al., “Perbandingan Kinerja Algoritma Clustering K-Means dan K-Medoids dalam Pengelompokan Sekolah di Provinsi Riau Berdasarkan Ketersediaan Sarana dan Prasarana,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 5, no. 3, pp. 797–806, 2025, doi: 10.57152/MALCOM.V5I3.1950.
[17] I. F.-D. S. Insights and undefined 2024, “Cluster Analysis on Laptop Sales Data and Specifications Using K-Means and K-Medoids Methods,” citedness.orgI FajarData Sci. Insights, 2024•citedness.org, vol. 2, no. 2, pp. 105–113, 2024, doi: 10.63017/JDSI.V2I2.38.
[18] I. Gede, S. D. Putra, N. Tri, and A. Putra, “IMPLEMENTASI METODE NAÏVE BAYES PADA ANALISIS SENTIMEN PENGGUNA APLIKASI MOBILE KITA BISA,” journal.eng.unila.ac.idIGSD Putra, INTA PutraJurnal Inform. dan Tek. Elektro Ter. 2025•journal.eng.unila.ac.id, vol. 13, no. 2, pp. 2830–7062, 2025, doi: 10.23960/JITET.V13I2.6423.
[19] A. C. Purba, T. H.-K. J. I. K. dan, and undefined 2024, “Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis,” ojs.unikom.ac.idAC Purba, T HandhayaniKomputa J. Ilm. Komput. dan Inform. 2024•ojs.unikom.ac.id, vol. 13, no. 1, pp. 54–63, 2024, doi: 10.34010/KOMPUTA.V13I1.11710.
[20] N. Nurfiah, B. Satria, M. A. H.-J. (Jurnal, and undefined 2025, “PELANGGAN LAYANAN AIR BERSIH DI KAWASAN PEDESAAN MENGGUNAKAN K-MEANS CLUSTERING BERDASARKAN METRIK DURASI, KONSUMSI RATA …,” jurnal.stkippgritulungagung.ac.id, vol. 12, no. 2, pp. 311–320, 2025, doi: 10.25126/JTIIK.2025129456.
Published
2025-12-31
How to Cite
HIDAYAT, Amin et al.
ANALISIS KLASTER KINERJA LAPTOP BERDASARKAN SPESIFIKASI HARDWARE MENGGUNAKAN ALGORITMA K-MEANS.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 8, n. 2, p. 676-687, dec. 2025.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php?journal=Tekinkom&page=article&op=view&path%5B%5D=2536>. Date accessed: 21 apr. 2026.
doi: https://doi.org/10.37600/tekinkom.v8i2.2536.
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