Determining Product Suitability using Rule-Based Model with C4.5 Algorithm

Penulis

  • Chintya Carolina Situmorang STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Dedy Hartama STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Irfan Sudahri Damanik STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Jaya Tata Hardinata Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v2i1.1923

Kata Kunci:

Data Mining, Decision Tree, Rules Model, C4.5, Goods Eligibility

Abstrak

A hotel warehouse must have orderly, good, safe, comfortable, and usable procurement of goods. The common issue that occurs in a warehouse is damaged and unusable goods. The fluctuating production demand for goods sometimes leads to neglecting the quality of the goods in the warehouse. To determine usable goods, appropriate recommendations are needed. The C4.5 algorithm with data mining techniques is an appropriate recommendation for analyzing a large amount of data for classification. The data used in this study is the inventory data of Hotel Sapadia Pematangsiantar's warehouse. Implementing the C4.5 algorithm that produces a Decision Tree can assist the warehouse in determining which goods are still usable for hotel activities. This study resulted in the best variable from the rule model used to determine the feasibility of goods being the physical condition of the goods. The accuracy of the rule model generated from the C4.5 Algorithm modeling is 99.02% against the feasibility of goods.

Referensi

A. Dedy dan Y. Alfandi, “Pengaruh kualitas pelayanan dan fasilitas hotel terhadap kepuasan pelanggan di sari ater hot springs resort ciater,” Jurnal Sains Manajemen, vol. 4, no. 1, hal. 18–25, 2022.

S. S. S. P. Sitorus dan A. Suseno, “Evaluasi Pengendalian Persediaan Barang Dagang,” Jurnal Serambi Engineering, vol. 8, no. 2, hal. 5517–5527, 2023.

S. H. Amira, “Implementation and evaluation between single-used expandable strategy and environmental impact on supply chain and pallets on pt. sumber rezeki palletindo,” COSTING: Journal of Economic, Business and Accounting, vol. 6, no. 2, hal. 1715–1732, 2023.

I. S. Purba dkk., “Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia,” Journal of Physics: Conference Series, vol. 1255, no. 1, hal. 1–6, 2019.

W. Saputra, J. T. Hardinata, dan A. Wanto, “Resilient method in determining the best architectural model for predicting open unemployment in Indonesia,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, hal. 1–7, 2020.

I. A. R. Simbolon, F. Yatussa’ada, dan A. Wanto, “Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia,” Jurnal Informatika Upgris, vol. 4, no. 2, hal. 163–169, 2018.

A. Wanto dan J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, hal. 1–8, 2020.

R. E. Pranata, S. P. Sinaga, dan A. Wanto, “Estimasi Wisatawan Mancanegara Yang Datang ke Sumatera Utara Menggunakan Jaringan Saraf,” Jurnal semanTIK, vol. 4, no. 1, hal. 97–102, 2018.

W. Saputra, J. T. Hardinata, dan A. Wanto, “Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed,” JITE (Journal of Informatics and Telecommunication Engineering), vol. 3, no. 1, hal. 163–174, Jul 2019.

A. Wanto dkk., “Levenberg-Marquardt Algorithm Combined with Bipolar Sigmoid Function to Measure Open Unemployment Rate in Indonesia,” in The 3rd International Conference ofComputer, Environment, Agriculture, Social Science, Health Science, Engineering andTechnology (ICEST), 2021, no. 1, hal. 22–28.

M. A. Hanafiah dan A. Wanto, “Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra,” International Journal of Information System & Technology, vol. 3, no. 2, hal. 315–322, 2020.

A. Pradipta, D. Hartama, A. Wanto, S. Saifullah, dan J. Jalaluddin, “The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm,” IJISTECH (International Journal of Information System & Technology), vol. 3, no. 1, hal. 31–36, 2019.

I. Parlina dkk., “Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category,” Journal of Physics: Conference Series, vol. 1255, no. 1, hal. 012031, 2019.

N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, dan A. Wanto, “Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms,” IOP Conference Series: Materials Science and Engineering, vol. 1071, no. 1, hal. 1–7, 2021.

T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, dan Z. M. Nasution, “Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, hal. 9–20, 2021.

T. Imandasari, E. Irawan, A. P. Windarto, dan A. Wanto, “Algoritma Naive Bayes Dalam Klasifikasi Lokasi Pembangunan Sumber Air,” in Prosiding Seminar Nasional Riset Information Science (SENARIS), 2019, vol. 1, hal. 750–761.

Unduhan

Diterbitkan

2023-03-31

Cara Mengutip

Situmorang, C. C., Hartama, D. ., Damanik, I. S., & Hardinata, J. T. (2023). Determining Product Suitability using Rule-Based Model with C4.5 Algorithm. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2(1), 19–32. https://doi.org/10.55123/jomlai.v2i1.1923

Terbitan

Vol 2 No 1 (2023): March

Bagian

Articles

Lisensi

Hak Cipta (c) 2023 Chintya Carolina Situmorang, Dedy Hartama, Irfan Sudahri Damanik, Jaya Tata Hardinata

Creative Commons License

Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:their article is original, written by the mentioned author(s),

  • has never been published before,
  • does not contain statements that violate the law, and
  • does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

  • Copyright and other proprietary rights related to the article, such as patents,
  • The right to use the substance of the article in its own future works, including lectures and books,
  • The right to reproduce the article for its own purposes,
  • The right to archive all versions of the article in any repository, and
  • The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published