Published: 2024-12-01
Analysis of Scooter Spare Parts Sales at Harapan Indah Scooter Using the K-Means Algorithm
DOI: 10.35870/ijsecs.v4i3.3026
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Abstract
: K-means clustering algorithm has been used in this study to analyze the sales performance of scooter spare parts at Harapan Indah Scooter. By using the K-means method, researchers can classify products into 3 categories according to their sales volume. The purpose of this analysis is to identify patterns in sales data and compare the characteristics of each product group. Researchers can see the output from the previous step shows three clusters: Low, Medium, and High Sales. Associating products with these categories Empowers improved tracking of sales movements and fluctuation trends in product options. The findings of this study can be useful in the field of inventory management and to develop marketing strategies to increase product sales. Companies can find out which products fall into which categories and therefore can make better decisions on how to manage stock and promotional efforts. These findings are the first step to maintain and improve sales performance and optimize Harapan Indah Scooter business
Keywords
K-Means ; Analysis ; Spare Parts ; Scooter ; Sales ; Clustering ; Marketing Strategy
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 3 (2025)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i3.3026
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Frencis Matheos Sarimole
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Tracy Olivera Lingga
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
-
Davenport, T. H., Harris, J. G., Jones, G. L., Lemon, K. N., Norton, D., & McCallister, M. B. (2007). The dark side of customer analytics. Harvard Business Review, 85(5), 37.
-
Chopra, S., Meindl, P., & Kalra, D. V. (2007). Supply chain management by Pearson. Pearson Education India.
-
Bahtiar, A., & Ali, I. (2023). Transformasi strategi penjualan Batik Cirebon dengan pendekatan analisis pengelompokan K-Means. KOPERTIP: Scientific Journal of Informatics Management and Computer, 7(1), 1-7. https://doi.org/10.32485/kopertip.v7i1.309
-
Ariati, I., Norsa, R. N., Akhsan, L., & Heikal, J. (2023). Segmentasi pelanggan menggunakan K-Means clustering studi kasus pelanggan UHT Milk Greenfield. Cerdika: Jurnal Ilmiah Indonesia, 3(7), 729-743. https://doi.org/10.59141/cerdika.v3i7.639
-
Yulianti, Y., Utami, D. Y., Hikmah, N., & Hasan, F. N. (2019). Penerapan data mining menggunakan algoritma K-Means untuk mengetahui minat customer di toko hijab. Jurnal Pilar Nusa Mandiri, 15(2), 241-246. https://doi.org/10.33480/pilar.v15i2.650
-
Prasetya, A., Salkiawati, R., & Alexander, A. D. (2023). Analisis cluster K-Means dengan metode elbow untuk menentukan pola penjualan produk traffic room Summarecon Mal Bekasi. Journal of Students' Research in Computer Science, 4(1), 105-118. https://doi.org/10.31599/5xve0445
-
Anggraeni, Y., & Handayani, P. (2023). Penerapan metode K-Means untuk menentukan penjualan tiket renang pada Splash Swimming Pool & Gym. Jurnal Informatika dan Teknologi Informasi, 2(1), 173-181. https://doi.org/10.56854/jt.v2i1.167
-
Nurajizah, S., & Salbinda, A. (2021). Penerapan data mining metode K-Means clustering untuk analisa penjualan pada toko fashion hijab Banten. Jurnal Teknik Komputer AMIK BSI, 7(2). https://doi.org/10.31294/jtk.v4i2
-
Nahjan, M. R., Heryana, N., & Voutama, A. (2023). Implementasi RapidMiner dengan metode clustering K-Means untuk analisa penjualan pada toko Oj Cell. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 101-104. https://doi.org/10.36040/jati.v7i1.6094
-
Fahlevi, M. R., Putri, D. R. D., & Syahrin, E. (2023). Analisis pengelompokan data pelelangan barang dengan metode K-Means clustering. Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 8(1), 53-61. http://dx.doi.org/10.30645/jurasik.v8i1.541
-
Pambudi, W. T., & Witanti, A. (2021). Penerapan algoritma K-Means clustering untuk menganalisis penjualan pada toko Ayu Collection berbasis web. Jurnal Informatika Universitas Pamulang, 6(3), 645-650. https://dx.doi.org/10.32493/informatika.v6i3.12380
-
Sharyanto, S., & Lestari, D. (2022). Penerapan data mining untuk menentukan segmentasi pelanggan dengan menggunakan algoritma K-Means dan model RFM pada e-commerce. JURIKOM (Jurnal Riset Komputer), 9(4), 866-871. https://doi.org/10.30865/jurikom.v9i4.4525
-
Surapati, U., & Jannah, M. (2024). Penerapan data mining menggunakan metode K-Means untuk mengetahui minat customer dalam pembelian merchandise Kpop. Jurnal Sains dan Teknologi, 5(3), 875-884. https://doi.org/10.55338/saintek.v5i3.2739
-
Triyandana, G., Putri, L. A., & Umaidah, Y. (2022). Penerapan data mining pengelompokan menu makanan dan minuman berdasarkan tingkat penjualan menggunakan metode K-Means. Journal of Applied Informatics and Computing, 6(1), 40-46. https://doi.org/10.30871/jaic.v6i1.3824A
-
Sulistiyawati, A., & Supriyanto, E. (2021). Implementasi algoritma K-Means clustering dalam penetuan siswa kelas unggulan. Jurnal Tekno Kompak, 15(2), 25-36. https://doi.org/10.33365/jtk.v15i2.1162
-
Arvio, Y. (2023). Pemodelan segmentasi transaksi jual beli produk menggunakan pendekatan model K-Means dan subtractive clustering studi kasus survey pada beberapa cabang optik retail. PETIR, 16(1). https://doi.org/10.33322/petir.v16i1.1897
-
Handoko, S., Fauziah, F., & Handayani, E. T. E. (2020). Implementasi data mining untuk menentukan tingkat penjualan paket data Telkomsel menggunakan metode K-Means clustering. Jurnal Ilmiah Teknologi dan Rekayasa, 25(1), 76-88. http://dx.doi.org/10.35760/tr.2020.v25i1.2677
-
Hutabarat, S. M., & Sindar, A. (2019). Data mining penjualan suku cadang sepeda motor menggunakan algoritma K-Means. Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), 2(2), 126-132. https://doi.org/10.32672/jnkti.v2i2.1555
-
Darmi, Y., & Setiawan, A. (2017). Penerapan metode clustering K-Means dalam pengelompokan penjualan produk. Jurnal Media Infotama, 12(2). https://doi.org/10.37676/jmi.v12i2.418
-
Montero, Z. (2022). Customer grouping for customer relationship management optimization with the K-Means algorithm. Journal of Computer Science and Information Technology, 98-105. https://doi.org/10.35134/jcsitech.v8i4.46
-
Ridzki, M. (2023). K-Means algorithm method for clustering best-selling product data at XYZ grocery stores. International Journal of Social Service and Research, 3(12), 3354-3367. https://doi.org/10.46799/ijssr.v3i12.652
-
Wardhani, R. K. P., & Anjani, A. (2023). Sales Product Clustering Using RFM Calculation Model And K-Means Algorithm on Primskystore. Technium Romanian Journal of Applied Sciences and Technology, 16, 176-182. https://doi.org/10.47577/technium.v16i.9978.
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