Design and Implementation of an Android-Based Student Stress Level Prediction System Using Naïve Bayes

Nidia Rintriana; Uliyatunisa Uliyatunisa

doi:10.55537/cosie.v5i4.1931

Authors

Nidia Rintriana Universitas Pamulang
Uliyatunisa Uliyatunisa Universitas Pamulang

DOI:

https://doi.org/10.55537/cosie.v5i4.1931

Keywords:

Naive Bayes, Prediksi Tingkat Stres, Mahasiswa, Klasifikasi, Sistem Android

Abstract

Students frequently face academic pressure that impacts their learning performance and mental health. The process of determining student stress levels at Universitas Pamulang is still carried out conventionally and is not yet integrated, resulting in a time-consuming process and subjective conclusions. This research focuses on the design and implementation of an Android-based student stress level prediction system to solve these problems. The main method applied in the system development is the Naïve Bayes algorithm with the Laplace Smoothing technique to resolve zero probability values. The questionnaire consists of 15 questions and has been validated by a psychology expert. The functional reliability and internal structure of the software are validated using Black Box and White Box testing, whereas the predictive performance validity of the model is evaluated separately using a confusion matrix against 60 test samples (comprising 5 low, 32 moderate, and 23 high-risk samples). The evaluation results indicate that the system is able to classify stress levels with an overall accuracy of 85% (with class-wise F1-scores of 0.50 for low, 0.87 for moderate, and 0.88 for high categories), although the relatively small sample size limits the generalization of the findings to a broader population. Through this application, students can independently monitor their psychological condition as a preventive measure to manage stress and support their academic success in the university environment.

Downloads

Download data is not yet available.

References

[1] E. Safittri, K. J. Renada, and K. E. Mawarni, “Prosiding Seminar Nasional Manajemen Analisis Faktor Penyebab Stres Pada Mahasiswa Dan Dampaknya,” vol. 3, no. 2, pp. 1020–1026, 2024.

[2] N. G. Widyawati, M. Khasanah, and E. Rasywir, “Prediksi Tingkat Stress Pada Mahasiswa Universitas Dinamika Bangsa Jambi Dalam Melakukan Perkuliahan Metode Hybrid Menggunakan Algoritma Naive Bayes Jurnal Manajemen Teknologi dan Sistem Informasi ( JMS ),” vol. 1, no. April, pp. 99–110, 2022.

[3] D. F. Putri, H. R. Mulyadi, and S. A. Pramana, “Prosiding Seminar Nasional Manajemen Gambaran Stres Mahasiswa saat E-learning,” vol. 4, no. 1, pp. 1031–1038, 2025.

[4] E. J. Izati, R. Hairisya, and E. Nurita, “Prosiding Seminar Nasional Manajemen Tantangan dan Solusi dalam Penanganan Kesehatan Mental di Kalangan Mahasiswa,” vol. 4, no. 1, pp. 463–467, 2025.

[5] Y. D. Megantara, “Pemanfaatan Metode Naive Bayes Untuk Diagnosis tingkat Stres Pada Mahasiswa Tingkat Akhir Berbasis Domain Expert,” J. SPIRIT, vol. 15, no. 1, pp. 74–82, 2023.

[6] L. Susanti, P. Studi, T. Informatika, R. Miner, and N. Bayes, “Klasifikasi Tingkat Stress Pada Mahasiswa Perkuliahan Metode Hybrid Menggunakan,” vol. 8, no. 3, pp. 243–248, 2024.

[7] T. O. Wikana, T. Pasaribu, and H. Manurung, “Penerapan Metode Naïve Bayes untuk Memprediksi Tingkat Kesehatan Mental Siswa Menjelang Akhir Masa Sekolah,” no. 4, pp. 296–306, 2024.

[8] R. P. Lestari, “Klasifikasi Tingkat Stres Pada Mahasiswa Tingkat Akhir menggunakan Metode Naive Bayes di Universitas Pelita Bangsa,” vol. 10, no. 2, pp. 481–493, 2025.

[9] A. W. O. Gama, “Penerapan Algoritma Naïve Bayes untuk Prediksi Penyakit Depresi Pada Mahasiswa,” vol. 6, no. 2, pp. 199–216, 2025.

[10] N. G. Widyawati, “Prediksi Tingkat Stress Pada Mahasiswa Universitas Dinamika Bangsa Jambi Dalam Melakukan Perkuliahan Metode Hybrid Menggunakan Algoritma Naive Bayes Jurnal Manajemen Teknologi dan Sistem Informasi ( JMS ),” J. Manaj. Teknol. dan Sist. Inf., vol. 1, no. April, pp. 99–110, 2022.

[11] M. Afriansyah, J. Saputra, V. Yoga, P. Ardhana, and Y. Sa, “Algoritma Naive Bayes Yang Efisien Untuk Klasifikasi Buah,” vol. 1, no. 2, pp. 236–248, 2024.

[12] A. Wijoyo et al., “Pembelajaran Machine Learning,” vol. 3, no. 2, pp. 375–380, 2024.

[13] Sibuea, “Aplikasi Mobile Collection Berbasis Android Pada Pt. Suzuki Finance Indonesia,” J. Inform. Dan Tekonologi Komput., vol. 2, no. 1, pp. 31–42, 2022.

[14] Sugiyatno, “Pengiriman Informasi Real Time Menggunakan Teknologi Database Firebase pada Aplikasi Mobile Android,” vol. 21, no. 2, pp. 46–55, 2023.

[15] R. Aditya, V. H. Pranatawijaya, and P. B. A. A. Putra, “Rancang Bangun Aplikasi Monitoring Kegiatan Menggunakan Metode Prototype,” J. Inf. Technol. Comput. Sci., vol. 1, no. 1, pp. 47–57, 2021.

[16] M. T. Wahyu and M. Afrizal, “Pengujian Blackbox Metode Equivalent Partitions Pada Aplikasi,” vol. 1, no. 2, pp. 1–10, 2023.

[17] A. C. Praniffa, A. Syahri, F. Sandes, U. Fariha, and Q. A. Giansyah, “Jurnal Testing dan Implementasi Sistem Informasi Parkir Berbasis Web: Black Box and White Box Testing of Web-Based Parking,” vol. 1, no. 1, pp. 1–16, 2023.