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All Journal JIEET (Journal of Information Engineering and Educational Technology) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Khairunisa, Mutiara
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Computer Science & IT Engineering

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Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction Khairunisa, Mutiara; Putri, Desak Made Sidantya Amanda; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9076

Abstract

This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. The dataset consists of 1,665 samples with 80 attributes encompassing information related to menstrual health. These methods were evaluated using accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. The results show that LSTM achieved the highest accuracy (91.3%), followed by CNN (88.9%) and Decision Tree (85.2%). LSTM excelled in capturing complex temporal patterns in menstrual cycle data, while CNN effectively identified key patterns, and Decision Tree offered interpretability despite lower performance. This study concludes that LSTM is the most effective model for menstrual cycle prediction. The findings highlight the potential for improved accuracy in reproductive health tracking, with future research opportunities to incorporate additional variables such as hormonal history and lifestyle factors, as well as a focus on data privacy.
Perbandingan Metode Machine Learning untuk Analisis dan Prediksi Siklus Menstruasi Putri, Desak; Khairunisa, Mutiara; Wijayakusuma, I Gusti Ngurah Lanang
JIEET (Journal of Information Engineering and Educational Technology) Vol. 8 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v8n2.p111-115

Abstract

Penelitian ini membandingkan metode machine learning—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), dan Decision Tree—untuk analisis dan prediksi siklus menstruasi. Menggunakan data sekunder, model-model ini dievaluasi berdasarkan akurasi, Mean Absolute Percentage Error (MAPE), dan Root Mean Square Error (RMSE). Hasil menunjukkan bahwa LSTM memiliki akurasi tertinggi (91,3%), efektif menangkap pola temporal kompleks pada data menstruasi, sedangkan CNN dan Decision Tree kurang konsisten. Hasil ini mendukung LSTM sebagai model yang disarankan untuk pelacakan siklus menstruasi, yang bermanfaat bagi pemantauan kesehatan reproduksi. Penelitian selanjutnya disarankan menambah variabel lain, seperti riwayat kesehatan hormonal dan gaya hidup, untuk meningkatkan akurasi prediksi serta memperhatikan privasi data pada aplikasi pelacakan menstruasi.
Co-Authors Putri, Desak Putri, Desak Made Sidantya Amanda Wijayakusuma, I Gusti Ngurah Lanang