Analysis of Airline Passenger Satisfaction Using the Rough Set Method
DOI:
https://doi.org/10.55123/jomlai.v4i3.5946Kata Kunci:
Customer Satisfaction , Airlines , Rough Set, Data Analysis, Service QualityAbstrak
This study analyzes airline passenger satisfaction using the Rough Set method, an effective approach in handling complex data without requiring additional information such as probability. The main factors influencing customer satisfaction are identified based on data collected through questionnaires and analyzed using the attribute reduction method. The results show that flight punctuality, cabin crew service quality, and flight class type have a significant influence on customer satisfaction. From the survey results, 72% of respondents stated that they were satisfied, 18% were quite satisfied, and 10% were dissatisfied, with dissatisfaction generally related to flight delays and lack of comfortable facilities. The application of the Rough Set method has been proven to be able to identify passenger satisfaction patterns more accurately, so that it can be used by airlines to improve their service strategies.
Referensi
A. S. Khadijah and A. F. Waluyo, “Implementasi Algoritma FP Growth Untuk Menganalisis Pola Pembelian Konsumen Balcos Compound,” pp. 2450–2463.
I. D. Ayu, I. Saraswati, I. M. Agus, O. Gunawan, I. M. Agus, and W. Putra, “Analisis Keranjang Belanja pada Data Ritel Non- Toko menggunakan Algoritma FP-Growth,” pp. 1692–1704, 2023.
M. Hafizh, T. Novita, D. Guswandi, H. Syahputra, and L. Mayola, “Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 242–249, 2023, doi: 10.47233/jteksis.v5i3.847.
M. Arif Saifudin, H. Endah Wahanani, and A. Junaidi, “Implementasi Algoritma Asosiasi Fp-Growth Dan Klasifikasi K-Means Terhadap Pola Pembelian Konsumen Di Marketplace Shopee,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 764–771, 2024, doi: 10.36040/jati.v8i1.8848.
A. Fitriyah, K. Kaslani, E. Tohidi, M. Mulyawan, and F. Fathurrohman, “Optimasi Pola Pembelian Toko Sembako Dengan Algoritma Fp-Growth,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 1129–1136, 2024, doi: 10.36040/jati.v8i1.8435.
J. Jafar and N. Rahaningsih, “Menentukan Pola Reservasi Hotel Dengan Algoritma Fp-Growth,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 540–546, 2023, doi: 10.36040/jati.v7i1.6402.
Rhayatun Aviqah, A. Muhammad, and E. P. W. Mandala, “Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 3, pp. 821–831, 2024, doi: 10.37859/coscitech.v4i3.5487.
P. Nanda, P. Dewi, D. Sujadi, and U. Triatma Mulya, “Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Dalam Menggunakan Maskapai Garuda Indonesia Di Denpasar,” J. Res. Manag. (JARMA, vol. 4, no. 1, pp. 37–49, 2022.
R. Destriyanah, K. Kaslani, E. Wahyudin, G. Dwilestari, and M. Mulyawan, “Penerapan Algoritma Fp-Growth Untuk Menentukan Pola Pembelian Makanan Di Warmindo,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 2, pp. 2159–2165, 2024, doi: 10.36040/jati.v8i2.8969.
I. Juwita and I. Ali, “Penerapan Pola Penjualan Dengan Menggunakan Metode Algoritma Asosiasi Fp-Growth Bertujuan Untuk Meningkatkan Penjualan Kopi Di Point Coffee,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 2, pp. 1600–1607, 2024, doi: 10.36040/jati.v8i2.9025.
N. Asih and M. Martanto, “Penerapan Data Mining Pada Transaksi Penjualan Untuk Menentukan Pola Pembelian Produk Menggunakan Algoritma Fp-Growth,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 2, pp. 1425–1431, 2024, doi: 10.36040/jati.v8i2.8961.
D. S. Nugroho, N. Islahudin, V. Normasari, and S. Z. Al Hakiim, “Penerapan Market Basket Analysis (Mba) Data Mining Menggunakan Metode Asosiasi Appriori Dan Fp-Growth Pada Wan Caffeine Addict Yogyakarta,” JISI J. Integr. Sist. Ind., vol. 11, no. 1, p. 121, 2024, doi: 10.24853/jisi.11.1.121-134.
A. V. Al-haq, A. Fidela, W. Audiana, and Z. U. Hani, “Jurnal JTIK ( Jurnal Teknologi Informasi dan Komunikasi ) Penerapan Algoritma FP-Growth untuk Strategi Penjualan Toko,” vol. 9, no. June, pp. 444–451, 2025.
K. Metode and A. Dan, “Komparasi metode apriori dan fp-growth untuk meningkatkan pola penjualan,” vol. 10, no. 1, pp. 1–9, 2025.
D. Pratama, K. Kaslani, and E. Tohidi, “Market Basket Analysis Pada Data Penjualan Umkm Menggunakan Algoritma Fp-Growth,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 4, pp. 8197–8206, 2024, doi: 10.36040/jati.v8i4.10939.
F. Z. Ghassani, Asep Jamaludin, and Agung Susilo Yuda Irawan, “Market Basket Analysis Using the Fp-Growth Algorithm To Determine Cross-Selling,” J. Inform. Polinema, vol. 7, no. 4, pp. 49–54, 2022, doi: 10.33795/jip.v7i4.508.
D. Cahyanti and I. Permana, “COMPARISON OF BOOK SHOPPING PATTERNS BEFORE AND DURING THE COVID-19 PANDEMIC USING THE FP-GROWTH ALGORITHM AT ZANAFA BOOKSTORES PERBANDINGAN POLA BELANJA BUKU SEBELUM DAN MASA PANDEMI COVID-19 MENGGUNAKAN ALGORITMA FP-GROWTH PADA TOKO BUKU ZANAFA Abstrak,” J. Tek. Inform., vol. 3, no. 2, pp. 381–386, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.2.211
P. T. Raka and A. J. I. Sentosa, “PENENTUAN POLA PEMBELIAN CELANA ANAK MENGGUNAKAN ALGORITMA FP-GROWTH UNTUK STRATEGI PENJUALAN PADA DETERMINATION OF PURCHASING PATTERNS FOR CHILDREN ’ S PANTS USING THE FP-GROWTH ALGORITHM FOR SALES STRATEGY AT PT . RAKA AJI SENTOSA,” vol. 3, no. June, pp. 1817–1825, 2024.
Yoga Religia and A. Amali, “Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 527–533, 2021, doi: 10.29207/resti.v5i3.3086.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2025 Alisa Putri Amanda Nasution, Auralia Izmi, Aprillya Zahra Iswandy Lubis, Haya Atiqah Tampubolon, Victor Asido Elyakim P

Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
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



















