Studi Perbandingan Pembelajaran Online dan Offline: Implementasi Decision Tree di STT Terpadu Nurul Fikri

Ira Kusuma Wardani; Reza Maulana; Bambang Harie Wiyono

doi:10.54914/dbesti.v3i1.2154

Penulis

Ira Kusuma Wardani Sekolah Tinggi Teknologi Terpadu Nurul Fikri
Reza Maulana Sekolah Tinggi Teknologi Terpadu Nurul Fikri
Bambang Harie Wiyono Sekolah Tinggi Teknologi Terpadu Nurul Fikri

DOI:

https://doi.org/10.54914/dbesti.v3i1.2154

Kata Kunci:

Decision Tree, KNIME, Pembelajaran Offline, Pembelajaran Online, Pendidikan

Abstrak

The COVID-19 pandemic, spanning four years (2020-2024), has significantly impacted various sectors in Indonesia, including education. According to the 2024 data from the Indonesian Bureau of Statistics (BPS), the majority of students utilize the internet for entertainment purposes (90.76%), whereas online learning accounts for only 27.53%. This disparity highlights the need to optimize internet usage for educational purposes. STT Terpadu Nurul Fikri, a higher education institution, implemented online learning during the pandemic. This study aims to determine the most effective learning approach, whether online or offline, to enhance student capabilities and prepare them for the workforce. The research employs a quantitative method using the Decision Tree algorithm with KNIME software, which comprises the necessary Nodes. The analysis reveals that 76.3% of students prefer offline learning, particularly for Web Programming and Computer Mathematics courses. The accuracy of the model for Web Programming is 90.16%, and for Computer Mathematics, it is 88.52%. These findings suggest that students who learn offline tend to be more comfortable and can grasp the material more deeply. The study's results can be used to evaluate and improve learning models in higher education.

Unduhan

Data unduhan belum tersedia.

Referensi

Z. I. Lubis, “Analisis Kualitatif Penggunaan Telemedicine sebagai Solusi Pelayanan Kesehatan di Indonesia pada Masa Pandemik COVID-19”, PhysioHS, vol. 2, no. 2, pp. 76–82, Jan. 2021.

M. A. Septiadi, N. H. Prawira, S. Aepudin, and V. A. Lestari, "Dampak Covid-19 Terhadap Sistem Pendidikan," Khazanah Pendidikan Islam, vol. 4, no. 2, pp. 51–61, Oct. 2022. [Online]. Available: https://doi.org/10.15575/kp.v4i2.19478

Y. Fitriani, “Analisa Pemanfaatan Learning Management System (LMS) Sebagai Media Pembelajaran Online Selama Pandemi Covid-19,” J. Inf. Syst. Informatics Comput., vol. 4, no. 2, p. 1, 2020, doi: 10.52362/jisicom.v4i2.312.

S. Pujilestari, S. Harini, and N. Munah, “Analisis Kepuasan Mahasiswa Terhadap Perkuliahan Online,” J. Ris. Mhs. Mat., vol. 1, no. 5, pp. 211–221, 2022, doi: 10.18860/jrmm.v1i5.14174.

P. S. V. Utama, “Maukuliah | Sekolah Tinggi Teknologi Terpadu Nurul Fikri.” Accessed: Feb. 27, 2025. [Online]. Available: https://maukuliah.id/universities/sekolah-tinggi-teknologi-terpadu-nurul-fikri

A. N. Ikhsan, A. N. Fadilah, and A. Dafa Iftinani, “Performance Comparison of Decision Tree J48, CART and Naïve Bayes Algorithms for Predicting Chronic Kidney Disease,” Indones. J. Artif. Intell. Data Min., vol. 7, no. 1, pp. 64–70, 2024.

A. Iswanda, “Penerapan Data mining Dalam Menentukan Pola Pembelian Obat Menggunakan Metode Frequent Pattern Growth ( Fp- Growth ) ( Studi Kasus : Apotek Kharisma ),” Skripsi, Universitas Medan Area, 2023.

S. Y. Prasetyo and G. Z. Nabiilah, “Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform,” J. Teknol. Terpadu, vol. 9, no. 1, pp. 29–34, 2023, doi: 10.54914/jtt.v9i1.605.

F. Ardiani, Rodhiyah Mardhiyyah, I. A. Syahalam, and Nasmah Nur Amiroh, “Forecasting: Analyze Online and Offline Learning Mode with Machine Learning Algorithms,” IJID (International J. Informatics Dev., vol. 11, no. 2, pp. 252–261, 2023, doi: 10.14421/ijid.2022.3733.

C. Dietz et al., “Integration of the ImageJ Ecosystem in KNIME Analytics Platform,” Front. Comput. Sci., vol. 2, no. March, pp. 1–17, 2020, doi: 10.3389/fcomp.2020.00008.