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All Journal Jurnal Ilmu Pertanian Indonesia Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputer Terapan Indonesian Journal of Artificial Intelligence and Data Mining Prosiding Working Papers Series In Management Journal of Computer Science and Informatics Engineering (J-Cosine) The Asia Pacific Journal Of Management Studies TECHNO-SOCIO EKONOMIKA BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Sang Buana MATHunesa: Jurnal Ilmiah Matematika Jurnal Kesehatan komunitas Indonesia eProceedings of Engineering
Siti Saadah
Telkom University
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Journal : Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika

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Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms Redy Indrawan; Siti Saadah; Prasti Eko Yunanto
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.14629

Abstract

Diabetes Mellitus is one of the preeminent causes of death to date. Effective procedures are necessary to prevent diabetes and avoid complications that may cause early death. A common approach is to control patient blood glucose, which necessitates a periodic measurement of blood glucose concentration. This study developed a blood glucose prediction system using a convolutional long short-term memory (Conv-LSTM) algorithm. Conv-LSTM is a variation of LSTM algorithms that are suitable for use in time series problems. Conv-LSTM overcomes the lack in the LSTM algorithm because the latter algorithm cannot access the content of previous memory cells when its output gate has closed. We tested the algorithm and varied the experiment to check the effect of the cross-validation ratio between 70:30 and 80:20. The study indicates that the cross-validation using a ratio of 70:30 data split is more stable compared to one with 80:20 data split. The best result shows a measure of 21.44 in RMSE and 8.73 in MAE. With the application of conv-LSTM using correct parameters and selected data split, our experiment attains accuracy comparable to the regular LSTM.
Co-Authors . andriyanto . Soeripto Abadi Abbsumarmanali Firyabi Sakhtiyani Ade Romadhony Ananda Affan Fattahila Andrianawati, Aida Anggit Nourislam Aniq Atiqi R Aniq Atiqi Rohmawati Annisa Aditsania Aulia Andi Mustika Dadah Jubaedah Dety Mulyanti Didin Saepudin Didit Adytia Djoko Murdowo E Handayani Ecky Prasetyo Nugroho Fadhlurrahman Akbar Nasution Fakhira Zahra Z Farah Afianti Fendi Irfan Amorokhman Gia Septiana Wulandari Haifa Salsabila Hasna Haifa Z Hendy Irawan I NYM Yoga Saputra Idhar Resmadi Jondri Jondri Kaenova Mahendra Auditama Larasati Larasati Maesaroh Mario Adventino Hamboerh Maulana, Didit Johar Muhammad Muttabi Hudaya Mukti, Muhi Neni Neni Prasti Eko Yunanto Purnomo, Muhammad Ridaffa Rahminiwati, Min Ratri Wulandari Redy Indrawan Rendy Pandita B Risyad Faisal Hadi Sri Maywati Tunnufus, Zakiyya Unang P. Vika Haristianti Vip Paramarta Wibisono Tegar GP