Application of Early Diagnosis of Diabetes Mellitus (DM) Equipped with Calorie Needs for DM Sufferers using the Fuzzy Mamdani Method
Corresponding Author(s) : Humaidillah Kurniadi Wardana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 5, No. 4, November 2020
Abstract
Diabetes Mellitus (DM) is one of the deadliest degenerative diseases in the world. The prevalence of DM in Indonesia from year to year shows a
significant increase. The high number of these causes the need for appropriate action and anticipation for health workers, DM families and DM people themselves. In this study, a system application model was created by using informatics techniques in health for early diagnosis of DM and what calorie needs needed for DM sufferers. This system was created using a GUI application and the Mamdani fuzzy method. The purpose of creating this system is to help in making an initial decision for DM diagnosis. The results obtained, first a DM diagnosis system with 6 input variables, 3 output variables, and 155 rules with MAPE achieved 29.48%. The second is the calorie requirements system with 2 input variables, 2 output variables namely BMI with MAPE 10.57% BMR with MAPE 9.7% and 9 rules with the results achieved by 99%.
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- S. A. Soelistijo et al., Konsensus Pengendalian dan Pencegahan Diabetes Melitus Tipe 2 di Indonesia 2015. 2015.
- R. Fajrunni’mah, D. Lestari, and A. Purwanti, “Faktor Pendukung dan Penghambat Penderita Diabetes Melitus dalam Melakukan Pemeriksaan Glukosa Darah,” Glob. Med. Heal. Commun., vol. 5, no. 3, pp. 174, 2017. https://doi.org/10.29313/gmhc.v5i3.2181
- I. Huang, “Universitas Pelita Harapan 48 Case Report Patofisiologi dan Diagnosis Penurunan Kesadaran pada Penderita Diabetes Mellitus,” Medicinus, vol. 5, no. 2, pp. 48–57, 2016. http://dx.doi.org/10.19166/med.v5i2.1169
- I. W. Himawan, A. B. Pulungan, B. Tridjaja, and J. R. L. Batubara, “Komplikasi Jangka Pendek dan Jangka Panjang Diabetes Mellitus Tipe 1,” Sari Pediatri, vol. 10, no. 6, pp. 367, 2016. https://dx.doi.org/10.14238/sp10.6.2009.367-72
- M. J. Gibney, S. A. Lanham-New, A. Cassidy, and H. H. Vorster, Human Nutrition, Second. 2009.
- Riskesdas, Hasil Riset Kesehatan Dasar. 2013.
- G. Willa, K. Nim, E. Fatma, and A. Nim, “Diabetes Melitus Menggunakan Metode Fuzzy Inference System ( FIS ) TSUKAMOTO Kelompok B Kelas F,” Skripsi, no. Nim 115090600111029, 2014.
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- A. Tjokroprawiro, “Garis Besar Pola Makan dan Pola Hidup sebagai Pendukun Terapi Diabetes Mellitus,” Plenary Leacture, pp. 11–13, 2012.
- P. Velvizhy, Pavithra, and A. Kannan, “Automatic food recognition system for diabetic patients,” in 6th International Conference on Advanced Computing, ICoAC 2014, 2015, pp. 329–334. https://doi.org/10.1109/ICoAC.2014.7229735
- C. Chin, R. Lin, and S. Liu, “Personal Healthy Diet and Calorie Monitoring System using Fuzzy Inference in Smart Phones,” pp. 49–60, 2013.
- W. R. S. Emmanuel and S. J. Minija, “Fuzzy clustering and Whale-based neural network to food recognition and calorie estimation for daily dietary assessment,” Sadhana - Acad. Proc. Eng. Sci., vol. 43, no. 5, 2018. https://doi.org/10.1007/s12046-018-0865-3
- B. Irawan#1 and S. Achmady#2, “Pola Diet pada Penderita Diabetes Melitus Menggunakan Metode Fuzzy Inferensi Sistem,” Semin. Nas. Literasi Sist. USU, no. 77, pp. 381–384, 2014.
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- S. T. Suyanto and M. Sc, “Artificial Intelligence: Searching, Reasoning, Planning, dan Learning,” Inform. Bandung, Indones., pp. 2011, 2007.
- S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy untuk Pendukung Keputusan, 2nd ed. Yogyakarta: Graha Ilmu, 2010.
- H. Huzaimah, S. Musdalifah, A. Hendra, and I. W. Sudarsana, “Penentuan Tingkat Resiko Penyakit Diabetes Mellitus Dengan Metode Sugeno di RSUD UNDATA Provinsi Sulawesi Tengah,” J. Ilm. Mat. DAN Terap., vol. 9, no. 1, 2016.
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- O. Geman, I. Chiuchisan, and R. Toderean, “Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction,” in 2017 E-Health and Bioengineering Conference, EHB 2017, 2017, pp. 639–642. https://doi.org/10.1109/EHB.2017.7995505
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- R. A. Priyono and K. Surendro, “Nutritional Needs Recommendation based on Fuzzy Logic,” Procedia Technol., vol. 11, pp. 1244–1251, 2013. https://doi.org/10.1016/j.protcy.2013.12.320
- M. Mamat, S. K. Deraman, N. M. M. Noor, and N. F. Zulkifli, “Relationship between body mass index and healthy food with a balanced diet,” Appl. Math. Sci., vol. 7, no. 1–4, pp. 153–159, 2013.
- M. A. Hussain et al., “Income Based Food List Recommendation for Rural People Using Fuzzy Logic,” in Proceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018, 2018, pp. 116–121. https://doi.org/10.1109/ICIS.2018.8466403
References
S. A. Soelistijo et al., Konsensus Pengendalian dan Pencegahan Diabetes Melitus Tipe 2 di Indonesia 2015. 2015.
R. Fajrunni’mah, D. Lestari, and A. Purwanti, “Faktor Pendukung dan Penghambat Penderita Diabetes Melitus dalam Melakukan Pemeriksaan Glukosa Darah,” Glob. Med. Heal. Commun., vol. 5, no. 3, pp. 174, 2017. https://doi.org/10.29313/gmhc.v5i3.2181
I. Huang, “Universitas Pelita Harapan 48 Case Report Patofisiologi dan Diagnosis Penurunan Kesadaran pada Penderita Diabetes Mellitus,” Medicinus, vol. 5, no. 2, pp. 48–57, 2016. http://dx.doi.org/10.19166/med.v5i2.1169
I. W. Himawan, A. B. Pulungan, B. Tridjaja, and J. R. L. Batubara, “Komplikasi Jangka Pendek dan Jangka Panjang Diabetes Mellitus Tipe 1,” Sari Pediatri, vol. 10, no. 6, pp. 367, 2016. https://dx.doi.org/10.14238/sp10.6.2009.367-72
M. J. Gibney, S. A. Lanham-New, A. Cassidy, and H. H. Vorster, Human Nutrition, Second. 2009.
Riskesdas, Hasil Riset Kesehatan Dasar. 2013.
G. Willa, K. Nim, E. Fatma, and A. Nim, “Diabetes Melitus Menggunakan Metode Fuzzy Inference System ( FIS ) TSUKAMOTO Kelompok B Kelas F,” Skripsi, no. Nim 115090600111029, 2014.
N. Douali, J. Dollon, and M. C. Jaulent, “Personalized prediction of gestational Diabetes using a clinical decision support system,” in IEEE International Conference on Fuzzy Systems, 2015, vol. 2015-Novem. https://doi.org/10.1109/FUZZ-IEEE.2015.7337813
A. Tjokroprawiro, “Garis Besar Pola Makan dan Pola Hidup sebagai Pendukun Terapi Diabetes Mellitus,” Plenary Leacture, pp. 11–13, 2012.
P. Velvizhy, Pavithra, and A. Kannan, “Automatic food recognition system for diabetic patients,” in 6th International Conference on Advanced Computing, ICoAC 2014, 2015, pp. 329–334. https://doi.org/10.1109/ICoAC.2014.7229735
C. Chin, R. Lin, and S. Liu, “Personal Healthy Diet and Calorie Monitoring System using Fuzzy Inference in Smart Phones,” pp. 49–60, 2013.
W. R. S. Emmanuel and S. J. Minija, “Fuzzy clustering and Whale-based neural network to food recognition and calorie estimation for daily dietary assessment,” Sadhana - Acad. Proc. Eng. Sci., vol. 43, no. 5, 2018. https://doi.org/10.1007/s12046-018-0865-3
B. Irawan#1 and S. Achmady#2, “Pola Diet pada Penderita Diabetes Melitus Menggunakan Metode Fuzzy Inferensi Sistem,” Semin. Nas. Literasi Sist. USU, no. 77, pp. 381–384, 2014.
P. Pouladzadeh, S. Shirmohammadi, and R. Al-Maghrabi, “Measuring calorie and nutrition from food image,” IEEE Trans. Instrum. Meas., vol. 63, no. 8, pp. 1947–1956, 2014. https://doi.org/10.1109/TIM.2014.2303533
S. T. Suyanto and M. Sc, “Artificial Intelligence: Searching, Reasoning, Planning, dan Learning,” Inform. Bandung, Indones., pp. 2011, 2007.
S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy untuk Pendukung Keputusan, 2nd ed. Yogyakarta: Graha Ilmu, 2010.
H. Huzaimah, S. Musdalifah, A. Hendra, and I. W. Sudarsana, “Penentuan Tingkat Resiko Penyakit Diabetes Mellitus Dengan Metode Sugeno di RSUD UNDATA Provinsi Sulawesi Tengah,” J. Ilm. Mat. DAN Terap., vol. 9, no. 1, 2016.
J. Singla, “Comparative study of Mamdani-type and Sugeno-type fuzzy inference systems for diagnosis of diabetes,” in Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015, 2015, pp. 517–522. https://doi.org/10.1109/ICACEA.2015.7164799
S. Riyadhi, “Uji Coba Metode Mamdani Untuk Deteksi Penyakit Diabetes Di Rsud Dr. H. Soemarno Sosroatmojo Kuala Kapuas,” Pascasarj. Tek. Inform. Univ. Dian Nuswantoro, vol. 10, pp. 228–239, 2014.
O. Geman, I. Chiuchisan, and R. Toderean, “Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction,” in 2017 E-Health and Bioengineering Conference, EHB 2017, 2017, pp. 639–642. https://doi.org/10.1109/EHB.2017.7995505
R. Adrial, “Fuzzy Logic Modeling Metode Sugeno Pada Penentuan Tipe Diabetes Melitus Menggunakan MATLAB,” J. Ilm. Inform., vol. 6, no. 01, pp. 62, 2018. https://doi.org/10.33884/jif.v6i01.423
R. A. Priyono and K. Surendro, “Nutritional Needs Recommendation based on Fuzzy Logic,” Procedia Technol., vol. 11, pp. 1244–1251, 2013. https://doi.org/10.1016/j.protcy.2013.12.320
M. Mamat, S. K. Deraman, N. M. M. Noor, and N. F. Zulkifli, “Relationship between body mass index and healthy food with a balanced diet,” Appl. Math. Sci., vol. 7, no. 1–4, pp. 153–159, 2013.
M. A. Hussain et al., “Income Based Food List Recommendation for Rural People Using Fuzzy Logic,” in Proceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018, 2018, pp. 116–121. https://doi.org/10.1109/ICIS.2018.8466403