Classification of LPG Gas Usage Satisfaction Level Using The Naïve Bayes Algorithm

Angelia Melani Adrian, Bella Alisia Patras, Junaidy B. Sanger
DOI: https://doi.org/10.33650/jeecom.v7i1.11064



Abstract

LPG gas is a very important energy source in everyday life for cooking activities. Although the importance of LPG gas in supporting everyday life has been widely recognized, satisfaction with the use of LPG gas is an issue that should not be ignored. Often products or services that do not meet customer expectations can cause dissatisfaction. This can be caused by low quality, prices that do not match the quality received, or not in accordance with user expectations.

This study aims to classify the level of satisfaction of LPG gas usage using the Naïve Bayes algorithm. The data obtained from the survey results are 250 data using 5 attributes, namely meeting needs, good quality, affordable prices, repurchasing, and recommending products. And using 2 classes, namely satisfied and dissatisfied.

The model achieved an accuracy of 89.3% with a 70:30 training-to-test data split, 91.2% with an 80:20 split, and 94.0% with a 60:40 split, indicating that performance varied based on the proportion of training and test data used.

Keywords

Classification; Data Mining; LPG Gas; Satisfaction Level; Naïve Bayes Algorithm

Full Text:

PDF

References

M. Adriansa, L. Yulianti, and L. Elfianty, “Analisis Kepuasan Pelanggan Menggunakan Algoritma C4.5,” J. Tek. Inform. UNIKA St. Thomas, vol. 07, no. 21, pp. 115–121, 2022.

M. R. H. M. Saleh and I. W. Sudiarsa, “Rancang Bangun Pendeteksi Monitoring Gas LPG Berbasis Mikrokontroller dan Notifikasi Telegram Messenger,” J. Krisnadana, vol. 3, no. 1, pp. 310–319, 2023.

M. Siddik, H. Hendri, R. N. Putri, Y. Desnelita, and G. Gustientiedina, “Klasifikasi Kepuasan Mahasiswa Terhadap Pelayanan Perguruan Tinggi Menggunakan Algoritma Naïve Bayes,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 3, no. 2, pp. 162–166, 2020.

R. Andriana and E. Prasetyaningrum, “Sistem Informasi Pendistribusian Gas Lpg Di Pt . Budi Bhakti Kalimantan Berbasis Web,” J. Penelit. Dosen Fikom, vol. 10, no. 1, pp. 1–11, 2019.

S. Laitera, W. A. Dewa, and S. Arifin, “Penerapan Sistem Alarm Berbasis Arduino Uno Untuk Mendeteksi Kebocoran Gas LPG,” J. Janitra Inform. dan Sist. Inf., vol. 2, no. 2, pp. 96–106, 2022.

R. Alfiani and Y. Umaidah, “Prediksi Kepuasan Customer Terhadap Performance Terapis Baby Massage Menggunakan Algoritma Naive Bayes,” Bina Insa. Ict J., vol. 9, no. 1, p. 83, 2022.

P. L. Lokapitasari Belluano, I. Indrawati, H. Harlinda, F. A. . Tuasamu, and D. Lantara, “Analisis Tingkat Kepuasan Pengguna Sistem Informasi Perpustakaan Menggunakan Pieces Framework,” Ilk. J. Ilm., vol. 11, no. 2, pp. 118–128, 2019.

E. T. Naldy and A. Andri, “Penerapan Data Mining Untuk Analisis Daftar Pembelian Konsumen Dengan Menggunakan Algoritma Apriori Pada Transaksi Penjualan Toko Bangunan MDN,” J. Nas. Ilmu Komput., vol. 2, no. 2, pp. 89–101, 2021.

Oon Wira Yuda, Darmawan Tuti, Lim Sheih Yee, and Susanti, “Penerapan Penerapan Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Random Forest,” SATIN - Sains dan Teknol. Inf., vol. 8, no. 2, pp. 122–131, 2022.

S. Lestari, A. Akmaludin, and M. Badrul, “Implementasi Klasifikasi Naive Bayes Untuk Prediksi Kelayakan Pemberian Pinjaman Pada Koperasi Anugerah Bintang Cemerlang,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 7, no. 1, pp. 8–16, 2020.

Sutisna and N. M. Yuniar, “Klasifikasi Kualitas Air Bersih Menggunakan Metode Naïve baiyes,” J. Sains dan Teknol., vol. 5, no. 1, pp. 243–246, 2023.

I. Amillina and A. Qoiriah, “Penerapan Algoritma Naïve Bayes Dalam Klasifikasi Tingkat Kepuasan Siswa Terhadap Pembelajaran Daring,” J. Ilm. Teknol. Inf. dan Robot., vol. 3, no. 2, pp. 16–23, 2021.

N. Asmiati and Fatmawati, “Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Pengaruh Negatif Game Online Bagi Remaja Milenial,” JTIM J. Teknol. Inf. dan Multimed., vol. 2, no. 3, pp. 141–149, 2020.

A. C. Khotimah and E. Utami, “Comparison Naive Bayes Classifier, K-Nearest Neighbor, and Support Vector Machine in the classification of individual on twitter account,” J. Tek. Inform., vol. 3, no. 3, pp. 673–680, 2022.

A. Nugroho and Y. Religia, “Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 504–510, 2021.

K. Kadarsih and S. Andrianto, “Membangun Website SMA PGRI Gunung Raya Ranau Menggunakan PHP dan MYSQL,” JTIM J. Tek. Inform. Mahakarya, vol. 03, no. 2, pp. 37–44, 2022.


Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v7i1.11064


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Angelia Melani Adrian, Bella Alisia Patras, Junaidy B. Sanger

 
This work is licensed under a Creative Commons Attribution License (CC BY-SA 4.0)

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.