Implementasi Algoritma Decision Tree dan Ensemble Learning Berdasarkan Spesifikasiperangkat Keras Untuk Klasifikasi Harga Laptop
-
Lian Galang Prayoga
*
Sekolah Tinggi Manajemen Informatika dan Komputer El Rahma Yogyakarta, Yogyakarta,
Indonesia
- Rachmad Sanuri Sekolah Tinggi Manajemen Informatika dan Komputer El Rahma Yogyakarta, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
The complexity of hardware specifications in laptops often makes it difficult for consumers to estimate price suitability in the digital market. This study aims to implement a laptop price classification system by comparing the performance of the baseline Decision Tree algorithm and the Ensemble Learning method (Random Forest) utilizing the Knowledge Discovery in Databases (KDD) approach. Contrary to the initial hypothesis that the ensemble model would provide significant performance improvements, the evaluation results revealed a paradoxical finding. Both classification models produced identically exact performance with an accuracy rate of 73.39% and an F1-Score of 73.41%. Technical analysis indicates that this anomaly is caused by the narrow dimension of deterministic features in the dataset, where RAM capacity and CPU architecture attributes dictate the decision boundaries absolutely, rendering the addition of hundreds of decision trees in the Random Forest computationally redundant. The results of this study contribute theoretical insights regarding the efficiency limitations of ensemble algorithms on low-dimensional datasets, while proving that a single Decision Tree is optimal and computationally efficient enough to be implemented as an inference engine in laptop price recommendation systems.
Downloads
References
Yustinus Liguori, I Wayan Sudiarsa, I Made Jagat Dita, I Gusti Ngurah Galih Jimbar Baskara, and Pande Wisnu Wijaya Putra, “Implementasi Algoritma Random Forest untuk Klasifikasi Rentang Harga Ponsel Berdasarkan Spesifikasi Teknis,” Router J. Tek. Inform. dan Terap., vol. 3, no. 4, pp. 135–143, 2025, DOI: https://doi.org/10.62951/router.v3i4.796
M. Juna Edi and Aryanto, “Implementasi XGBoost untuk Rekomendasi Smartphone Menggunakan Content-Based Filtering,” STATMAT J. Stat. dan Mat., vol. 7, no. 2, pp. 274–278, 2025, DOI: 10.32493/sm.v7i2.49850
D. Hartama and N. Amalya, “Perbandingan Algoritma Decision Tree, ID3, dan Random Forest dalam Klasifikasi Faktor-Faktor yang Mempengaruhi Karier Mahasiswa Ilmu Komputer,” J. Indones. Manaj. Inform. dan Komun., vol. 6, no. 1, pp. 72–80, 2025, doi: 10.35870/jimik.v6i1.1113. DOI:10.35870/jimik.v6i1.1113
D. Lestari and S. Lestari, “Penerapan Data Mining Klasifikasi Tingkat Pemahaman Siswa Pada Kegiatan Belajar Mengajar dengan Metode Decision Tree (Studi Kasus SDN Malaka Jaya 11 Duren Sawit),” J. Indones. Manaj. Inform. dan Komun., vol. 5, no. 2, pp. 1260–1268, 2024, doi: 10.35870/jimik.v5i2.662. DOI: 10.35870/jimik.v5i2.662
C. F. Hadi, R. M. Yasi, and A. Prasetyo, “Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen,” J. Telecommun. Electron. Control Eng., vol. 6, no. 1, pp. 29–38, 2024,DOI https://doi.org/10.20895/jtece.v6i1.1218
S. Kasus, S. M. A. Ypkpp, Н. Suhеndі, and F. N. Rаmadanis, “Klasifikasi Kelayakan Gaji Guru Menggunakan Algoritma Decission Tree menggunakаn SPSS . vіsual dаn mudаh diрahami , sеmentarа SPSS dіgunakan untuk mengujі hubungan аntаr tertentu , sehingga sangat relevan apabila diaplikasikan untuk menentukan kelayakan h,” vol. 4, no. januari 2026. DOI: https://doi.org/10.61132/merkurius.v4i1.1415
N. I. Yaman, A. R. Juwita, S. A. P. Lestari, and S. Faisal, “Perbandingan Kinerja Algoritma Decision Tree dan Random Forest untuk Klasifikasi Nutrisi pada Makanan Cepat Saji,” J. Algoritm., vol. 21, no. 2, pp. 184–196, 2024, doi: 10.33364/algoritma/v.21-2.1649.
D. Hartama et al., “Perbandingan Algoritma Decision Tree, ID3, dan Random Forest dalam Klasifikasi Faktor-Faktor yang Mempengaruhi Karier Mahasiswa Ilmu Komputer,” 2025. [Online]. Available: https://journal.stmiki.ac.id, 10.35870/jimik.v6i1.1113
C. F. Hadi, R. M. Yasi, and A. Prasetyo, “Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen,” J. Telecommun. Electron. Control Eng., vol. 6, no. 1, pp. 29–38, Jan. 2024, doi: 10.20895/jtece.v6i1.1218.
M. Y. Iskandar and H. W. Nugroho, “Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy,” J. Tek. Inform., vol. 6, no. 5, pp. 3392–3404, 2025, DOI: https://doi.org/10.52436/1.jutif.2025.6.5.4877
M. S. Efendi, Sarwido, and A. K. Zyen, “Penerapan Algoritma Random Forest Untuk Prediksi Penjualan Dan Sistem Persediaan Produk,” RESOLUSI Rekayasa Tek. Inform. dan Inf., vol. 5, no. 1, p. 20, 2024, doi: 10.30865/resolusi.v5i1.2149.
M. Ammar, F. Baihaqi, and K. D. Irianto, “Pengembangan Aplikasi Web Smart Waste Management Berbasis IoT dan Dashboard Spasial Untuk Optimasi Rute Pengangkutan Sampah,” vol. 7, no. 4, pp. 944–952, 2026, doi: 10.47065/josh.v7i4.10425.
S. Septiyanah and G. Athalina, “Implementasi Algoritma Random Forest Regression Dalam Prediksi Harga Laptop,” J. Mnemon., vol. 8, no. 1, pp. 70–73, 2025, doi: 10.36040/mnemonic.v8i1.12475.
P. A. Sholeha and R. T. Aldisa, “Penerapan Sistem Pendukung Keputusan Metode ROC dan ARAS dalam Seleksi Tim Kreatif Industri,” J. Inf. Syst. Res., vol. 5, no. 2, pp. 480–487, 2024, doi: 10.47065/josh.v5i2.4752.
H. Parsaulian, D. Maulana, and A. Maulana, “Design and Development of a Web-Based Student Discipline Evaluation Information System Using a Research and Development Approach,” vol. 7, no. 4, pp. 996–1004, 2026, doi: 10.47065/josh.v7i4.10469.
F. Nawandi, A. Muzhaffar, M. R. P. Tamtomo, and R. Setyadi, “Sistem Pendukung Keputusan Pemilihan Smartphone Bekas Menerapkan Metode SAW,” J. Inf. Syst. Res., vol. 4, no. 2, pp. 626–631, 2023, doi: 10.47065/josh.v4i2.2741.
C. M. Lauwl, H. Husain, B. N. Nuzululnisa, and H. Wijaya, “Komparasi Metode Random Forest Dan Support Vector Machine (SVM) Untuk Pemodelan Klasifikasi Serangan DDos,” J. Inf. Syst. Res., vol. 6, no. 2, pp. 0–7, 2025, doi: 10.47065/josh.v6i2.6684.
Z. Arifin, A. Al, and Z. Fatah, “Jurnal Ilmiah Multidisiplin Nusantara Analisis Faktor Penentu Harga Laptop Menggunakan Algoritma Decision Tree Jurnal Ilmiah Multidisiplin Nusantara,” vol. 2, no. November, pp. 155–160, 2024.
S. Muchammad Rosyid Aridho, A. Hasna Khaira Aswha, T. Dwi Wahyuni, and A. Arum Sari, “Analisis Faktor yang Mempengaruhi Harga Rumah Menggunakan Decision Tree,” Pros. Semin. Nas. Teknol. Inf. dan Bisnis, pp. 386–392, 2025, doi: 10.47701/f1bwa603.
N. Azwanti and N. E. Putria, “Analisis Kepuasan Customer pada Sdtechnology Computer dengan Algoritma Decision Tree,” J. Desain Dan Anal. Teknol., vol. 3, no. 2, pp. 137–148, 2024, doi: 10.58520/jddat.v3i2.62.
F. A. Artanto, I. Rosyadi, S. E. Rahmawati, and H. T. B. J. Pangestu, “Decision Tree Dalam Analisis Keputusan Pembelian Program Pada Perkumpulan Penggiat Programmer Indonesia,” J. Fasilkom, vol. 12, no. 3, pp. 141–144, 2022, doi: 10.37859/jf.v12i3.3948.
U. Firdaus, A. Alfiah, and L. Mohdo, “Analisis kinerja decision tree dan random forest menggunakan dataset breast cancer,” J. Pendidik. Sains dan Komput., vol. 6, no. 01, pp. 37–42, 2026, [Online]. Available: https://jurnal.itscience.org/index.php/jpsk/article/view/7892
R. A. Saputra and A. Pratama, “Implementasi Decision Tree Untuk Prediksi Harga Rumah Di Daerah Tebet,” J. Inf. Syst. Manag., vol. 6, no. 2, pp. 164–170, 2025, doi: 10.24076/joism.2025v6i2.1928.
N. Rizkyawan Maulana and M. Buana Yogyakarta, “Sistem Pendukung Keputusan Pemilihan Smartphone Android Bekas Menggunakan Metode ELECTRE Berbasis Web,” J. Komputer, Inf. dan Teknol., vol. 5, no. 1, pp. 1–12, 2025, [Online]. Available: https://penerbitadm.pubmedia.id/index.php/KOMITEK
M. Wahidin et al., “Projection of diabetes morbidity and mortality till 2045 in Indonesia based on risk factors and NCD prevention and control programs,” Sci. Rep., vol. 14, no. 1, pp. 1–17, 2024, doi: 10.1038/s41598-024-54563-2.
A. B. Colin and T. Ardiansah, “Sistem Pendukung Keputusan Penentuan Wajib Pajak Berprestasi Menggunakan Metode Multi-Attribute Utility Theory dan Rank Order Centroid,” J. Inf. Syst. Res., vol. 5, no. 2, pp. 488–497, 2024, doi: 10.47065/josh.v5i2.4686.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Algoritma Decision Tree dan Ensemble Learning Berdasarkan Spesifikasiperangkat Keras Untuk Klasifikasi Harga Laptop
Pages: 1314-1324
Copyright (c) 2026 Lian Galang Prayoga, Rachmad Sanuri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
