Analysis of Land Plot Sales Using the C4.5 Algorithm in Property and Housing Sector
DOI:
https://doi.org/10.58777/ise.v2i1.239Keywords:
Data Mining, Plot Land Sales, C4.5 Algorithm, Decision Tree, Consumer Interest PredictionAbstract
This research examines the application of the C4.5 algorithm in analyzing land plot sales data at PT Piliruma Rosa Land. Using data from two main projects, namely Harmoni Farm House and Nuansa Alam Agroewisata and Luxury, this study aims to build a decision tree model that is effective in predicting consumer interest. The analysis was conducted by evaluating the contribution of various sales attributes, specifically Sold Area and Number of Sold Lots, which proved to have a significant impact on the classification of buyer interest. The results of the model showed an accuracy rate of 91.18%, signifying the effectiveness of the C4.5 algorithm in assisting strategic decision-making in the property sector. The findings provide important insights for real estate developers in optimizing marketing and sales strategies, while offering a reliable analytical method for similar sectors.
Penelitian ini mengkaji penerapan algoritma C4.5 dalam menganalisis data penjualan tanah kavling pada PT Piliruma Rosa Land. Melalui data dari dua proyek utama, yaitu Harmoni Farm House dan Nuansa Alam Agroewisata dan Luxury, penelitian ini bertujuan untuk membangun model pohon keputusan yang efektif dalam memprediksi minat konsumen. Analisis dilakukan dengan mengevaluasi kontribusi dari berbagai atribut penjualan, khususnya luas terjual dan jumlah kavling terjual, yang terbukti memberikan dampak signifikan terhadap klasifikasi minat pembeli. Hasil dari model ini menunjukkan tingkat akurasi sebesar 91.18%, menandakan efektivitas algoritma C4.5 dalam membantu pengambilan keputusan strategis di sektor properti. Temuan ini memberikan wawasan penting bagi pengembang real estat dalam mengoptimalkan strategi pemasaran dan penjualan, sekaligus menawarkan metode analitis yang bisa diandalkan untuk sektor serupa.
References
Anggraini, S., Defit, S., & Nurcahyo, G. W. (2018). Analisis Data Mining Penjualan Ban Menggunakan Algoritma C4.5. Jurnal Ilmu Teknik Elektro Komputer Dan Informatika, 4(2), 136–143. https://core.ac.uk/download/pdf/295348196.pdf
Azwanti, N. (2018). Analisa Algoritma C4.5 Untuk Memprediksi Penjualan Motor Pada Pt. Capella Dinamik Nusantara Cabang Muka Kuning. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 13(1), 33. https://doi.org/10.30872/jim.v13i1.629
Cherfi, A., Nouira, K., & Ferchichi, A. (2020). MC4.5 decision tree algorithm: an improved use of continuous attributes. Int. J. Comput. Intell. Stud., 9, 4–17. https://doi.org/10.1504/ijcistudies.2020.10028137
Dewi, K. R., Mauladi, K. F., & Masruroh. (2020). Analisa Algoritma C4.5 untuk Prediksi Penjualan Obat Pertanian di Toko Dewi Sri. Seminar Nasional Inovasi Teknologi, 25, 109–114.
Fadhila, F., & Hasugian, P. S. (2022). Application of C4.5 Algorithm to Prediction Sales at PT. Sumber Sayur Segar. Journal of Intelligent Decision Support System (IDSS), 5(1), 10–19. https://doi.org/10.35335/idss.v5i1.45
Fitriani, E., Aryanti, R., Saepudin, A., & Ardiansyah, D. (2020). Penerapan Algoritma C4.5 Untuk Klasifikasi Penempatan Tenaga Marketing. Paradigma - Jurnal Komputer Dan Informatika, 22(1), 72–78. https://doi.org/10.31294/p.v22i1.6898
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Elsevier Science. https://books.google.co.id/books?id=pQws07tdpjoC
Jin, Z., Shang, J., Zhu, Q., Ling, C., Xie, W., & Qiang, B. (2020). RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12343 LNCS, 503–515. https://doi.org/10.1007/978-3-030-62008-0_35
Kirkpatrick, K. (2019). 1. WHAT IS DATA MINING? Data Mining for the Social Sciences. https://doi.org/10.4135/9781526493095
Liu, J., Ning, B., & Shi, D. (2019). Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness. Journal of Physics: Conference Series, 1237(2). https://doi.org/10.1088/1742-6596/1237/2/022116
Mijwil, M. M., & Abttan, R. A. (2021). Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm. Asian Journal of Applied Sciences. https://doi.org/10.24203/AJAS.V9I1.6503
Muttaqien, R., Pradana, M. G., & Pramuntadi, A. (2021). Implementation of Data Mining Using C4.5 Algorithm for Predicting Customer Loyalty of PT. Pegadaian (Persero) Pati Area Office. International Journal of Computer and Information System (IJCIS), 2(3), 64–68. https://doi.org/10.29040/ijcis.v2i3.36
Puspita, D., Aminah, S., & Arif, A. (2022). Prediction System for Credit Eligibility Using C4.5 Algorithm. Journal of Informatics and Telecommunication Engineering, 6(1), 148–156. https://doi.org/10.31289/jite.v6i1.7311
Quinlan, J. R. (2014). C4.5: Programs for Machine Learning. Elsevier Science. https://books.google.co.id/books?id=b3ujBQAAQBAJ
Rohman, A., & Rufiyanto, A. (2019). Implementasi Data Mining Dengan Algoritma Decision Tree C4 . 5 Untuk Prediksi Kelulusan Mahasiswa Di Universitas Pandaran. Proceeding SINTAK 2019, 134–139.
Shurrab, S., & Duwairi, R. (2021). Effect of Missing Data Treatment on the Predictive Accuracy of C4.5 Classifier. International Journal on Communications Antenna and Propagation (IRECAP). https://doi.org/10.15866/irecap.v11i3.19721
Susanti, M., Kom, M., & Kom, M. (2018). Prediksi Pengangkatan Karyawan Kontrak Menjadi Karyawan Tetap Menggunakan Decision Tree Pada PT . Baskara Cipta Pratama. 6(1), 1–7.
Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. SIGMOD Rec., 31(1), 76–77. https://doi.org/10.1145/507338.507355
Yuni, R., & Putri, R. A. (2023). Penerapan Algoritma C4 . 5 Untuk Prediksi Jumlah Produksi Kelapa Sawit. Jurnal Media Informatika Budidarma, 7(4), 1749–1757. https://doi.org/10.30865/mib.v7i4.6861
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