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Muhlis Tahir
Universitas Trunojoyo Madura
Religion Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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Journal : EMITTER International Journal of Engineering Technology

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Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization Tahir, Muhlis; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 6, No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.13 KB) | DOI: 10.24003/emitter.v6i2.287

Abstract

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria.  The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.
Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization Muhlis Tahir; Tessy Badriyah; Iwan Syarif
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.13 KB) | DOI: 10.24003/emitter.v6i2.287

Abstract

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria.  The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.
Co-Authors Achmad Sukriyadi Adinda Dwi Putri Andreani Agustina, Ais Zulaikha Ahmad Batsul Mushofi Septian Wachid Ahmad Faizal Prianggara Alfian Firdausi Alfian Syah Putra Alfinatul Hasanah Ali Mubarok Arromadhoni Aliffia Nurrohmah Zulkarnain Alvi Sakia Mardiana Amir Sulton Andi Akram Nur Risal Andi Setiawan Anggi Indri Wijayaningrum Anggun Dwi Lestari Ani Sofiyah Aqiqul Putra Zaibintoro Auliya Turrofifah Ayu Agustyas Ningrum Basri, Hasan Brikitha Olivia Putri Irine Irawan Cindi Ajeng S. A Delsa Yurina Cholili Desi Fitriani Ningrum Desta Chilyani Diana Iis Maulidia Dimas Mayoni Aji Sasono Dina Mulaikah Dwi Fatkhul Mu’in Edi Purwanto Eliza Permatasari Eva Nabila Aprilia Faisal Erfani Farah Nisa’ Salsabila Fathricia Angel M. V. Fatimatus Sahroh Fatimatus zahroh Fauziah Nur Faqih Fhatiah Adiba Fifi Rinazah Rofiq Husnul Amalia iwan Syarif Jannatul Firdausiyah Khairunnisa Nur Susanti Khoirul Amin Abidin Kinanti, Setyaning Puji Luluk Fariska Utami Lumatus Sa'adah M. Wildan Alvian Prastya Mar’atul Azizah Maulid Hidayat Miftakhul Hidayati Moch Shobibur Rohman Moch. Nasihuddin Moh Khoiruddin Moh Yasin Moh. Shaleh Helmi Mukhlis Ainur Rahman Nanda Afdlolul Basyar Nimas Ayu Windrastuti Noferianto Sitompul Nofiyanti, Nofiyanti Nova Estu Harsiwi Nur Wachid Hidayatulloh Nurul Septiana Putra, Aditya Eka Putri Qomariyah Putri Septiyawati Rikanawati Risma Dian Safira Rizkiyatul Zakiyah Robby Irsyad Faa'izzani Sabella Kalimatus Sa'diah Salman Alfarisi Salsabila Azura Septyan Hendra Sugara Sofyan Abduh F Sri Yuliani Susanti Susi Muslimah Terisha Sheline Shazhaq Tessy Badriyah, Tessy Tsasya Salsabila Umami Wahyuningsih Ummi Mutmainnah Vivia Auria Wahyu Dwi Angelina Puspitasari Zarwanda Ashfarina