Selection of Home Wifi Internet: Machine Learning Implementation With Decision Tree C4.5 Algorithm Method

Dewi Khairani, Muhammad Ammaridho Romdhan Siregar, Siti Ummi Masruroh, Miftakhul Nuuril Azizah


The multiple bandwidths that internet service providers offer make it difficult for people to choose, especially for regular people unfamiliar with the internet; therefore, most people choose because the price is reasonable. Numerous users also lament the difficulty and slow internet usage. The issue is then concentrated on internet service providers, who are thought to be poor at offering services. The quantity of bandwidth consumed, which does not correspond to the user’s needs, is one factor contributing to slow internet. As a result, the appropriate bandwidth must be chosen based on the requirements of each user. Compared to other algorithms, the C4.5 decision tree method can deliver the best and correct decision, according to the current literature. As a result, this project will develop a web application based on the C4.5 decision tree algorithm that can assist in determining bandwidth and internet following community needs. Using this C4.5 Decision Tree, decisions are based on patterns identified in previously collected data. Predictions about various forms of internet use in the neighborhood may subsequently be produced from these patterns. Based on the calculation, the accuracy obtained is 0.54, or a percentage of 54%. The black box testing indicated that the bandwidth determination application was functioning correctly


Machine Learning, Decision Tree C4.5, Home-Wifi Selection

Full Text:



“Asosiasi Penyelenggara Jasa Internet Indonesia.” (accessed Aug. 19, 2022).

S. R. Pokhrel and M. Mandjes, “Improving Multipath TCP Performance over WiFi and Cellular Networks: An Analytical Approach,” IEEE Trans. Mob. Comput., 2019, doi: 10.1109/TMC.2018.2876366.

A. J. A. M. van Deursen, “Digital inequality during a pandemic: Quantitative study of differences in COVID-19-related internet uses and outcomes among the general population,” J. Med. Internet Res., 2020, doi: 10.2196/20073.

S. H. Chan, Q. Song, S. Sarker, and R. D. Plumlee, “Decision support system (DSS) use and decision performance: DSS motivation and its antecedents,” Inf. Manag., 2017, doi: 10.1016/

J. P. Shim, M. Warkentin, J. F. Courtney, D. J. Power, R. Sharda, and C. Carlsson, “Past, present, and future of decision support technology,” Decis. Support Syst., 2002, doi: 10.1016/S0167-9236(01)00139-7.

M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., 2021, doi: 10.30812/matrik.v21i1.1092.

Y. Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Arch. Psychiatry, 2015, doi: 10.11919/j.issn.1002-0829.215044.

Z. F. Firmansyah and N. F. Puspitasari, “ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 BERDASARKAN OPINI PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES,” J. Tek. Inform., vol. 14, no. 2, pp. 171–178, Oct. 2021, doi: 10.15408/JTI.V14I2.24024.

L. Sun, S. Fu, and F. Wang, “Decision tree SVM model with Fisher feature selection for speech emotion recognition,” Eurasip J. Audio, Speech, Music Process., 2019, doi: 10.1186/s13636-018-0145-5.

P. J. Lisboa and A. F. G. Taktak, “The use of artificial neural networks in decision support in cancer: A systematic review,” Neural Networks, 2006, doi: 10.1016/j.neunet.2005.10.007.

F. Blanco-Mesa, J. M. Merigó, and A. M. Gil-Lafuente, “Fuzzy decision making: A bibliometric-based review,” Journal of Intelligent and Fuzzy Systems. 2017, doi: 10.3233/JIFS-161640.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, 2021, doi: 10.32493/informatika.v5i4.7622.

X. Wang, C. Zhou, and X. Xu, “Application of C4.5 decision tree for scholarship evaluations,” 2019, doi: 10.1016/j.procs.2019.04.027.

L. Vanfretti and V. S. N. Arava, “Decision tree-based classification of multiple operating conditions for power system voltage stability assessment,” Int. J. Electr. Power Energy Syst., 2020, doi: 10.1016/j.ijepes.2020.106251.

A. R. Panhalkar and D. D. Doye, “Optimization of decision trees using modified African buffalo algorithm,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2021, doi: 10.1016/j.jksuci.2021.01.011.

B. Joa, A. Paulus, R. Mikoleit, and G. Winkel, “Decision making in tree selection – contemplating conflicting goals via marteloscope exercises,” Rural Landscapes, 2020, doi: 10.16993/rl.60.

Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Inform., 2017, doi: 10.22202/ei.2016.v2i2.1465.

L. R. Haidar, E. Sediyono, and A. Iriani, “ANALISA PREDIKSI MAHASISWA DROP OUT MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA ID3 dan C4.5,” J. Transform., 2020, doi: 10.26623/transformatika.v17i2.1609.

Y. P. Tanjung, S. R. Sentinuwo, and A. Jacobus, “Penentuan Daya Listrik Rumah Tangga Menggunakan Metode Decision Tree,” J. Tek. Inform., 2016, doi: 10.35793/jti.9.1.2016.14141.

O. Mitchell, “Experimental Research Design,” in The Encyclopedia of Crime and Punishment, 2015.

A. Nugroho et al., “Implementasi metode decision tree c4.5 dalam pemberian subsidi listrik kepada masyarakat,” Proceeding SENDIU 2020, pp. 978–979, 2020.

A.-A. B. Bugaje, J. L. Cremer, M. Sun, and G. Strbac, “Selecting decision trees for power system security assessment,” 2021, doi: 10.1016/j.egyai.2021.100110.

P. W. Wardhani, “Hubungan antara nilai,” Hub. antara nilai, pp. 42–52, 2009.

M. Kumar, A. Professor, S. Kumar Singh, R. K. Dwivedi, and A. Professor, “A Comparative Study of Black Box Testing and White Box Testing Techniques,” Int. J. Adv. Res. Comput. Sci. Manag. Stud., 2015.

DOI: Abstract - 0 PDF - 0


  • There are currently no refbacks.

Copyright (c) 2022 Dewi Khairani, Muhammad Ammaridho Romdhan Siregar, Siti Ummi Masruroh

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

3rd Floor, Dept. of Informatics, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No.95, Cempaka Putih, Ciputat Timur. 
Kota Tangerang Selatan, Banten 15412
Tlp/Fax: +62 21 74019 25/ +62 749 3315
Handphone: +62 8128947537

Creative Commons Licence
Jurnal Teknik Informatika by Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at


JTI Visitor Counter: View JTI Stats

 Flag Counter