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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Teknologi Informasi Komunikasi dan Industri
Moh Edi Wibowo
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics

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RANCANGAN PELATIHAN PARALEL JARINGAN SYARAF DEEP LEARNING BERBASIS MAP-REDUCE Moh Edi Wibowo
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2017: SNTIKI 9
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (279.318 KB)

Abstract

Jaringan saraf deep learning telah menjadi model pembelajar dengan unjuk kerja yang tinggi pada beragam persoalan pengenalan pola. Meskipun demikian, pelatihan model ini seringkali terkendala oleh keterbatasan memori serta oleh kecepatan pengolahan yang rendah ketika data pelatihan yang digunakan berukuran besar. Untuk menyelesaikan persoalan tersebut, penelitian ini mengusulkan suatu rancangan pelatihan paralel jaringan saraf deep learning berdasarkan kerangka kerja map-reduce pada klaster komputer. Map-reduce diadopsi sebagai kerangka kerja pelatihan paralel karena memiliki dukungan implementasi yang kuat dan beragam.
Classification of plasmodium falciparum based on textural and morphological features Doni Setyawan; Retantyo Wardoyo; Moh Edi Wibowo; E. Elsa Herdiana Murhandarwati
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5036-5048

Abstract

Malaria is a disease caused by plasmodium parasites transmitted through the bites of female anopheles-mosquito that infect the human red blood cell (RBC). The standard malaria diagnosis is based on manual examination of a thick and thin blood smear, which heavily depends on the microscopist experience. This study proposed a system that can identify the life stages of plasmodium falciparum in human RBC. The image preprocessing process was done by illumination correction using gray world assumption, contrast enhancement using shadow correction, extraction of saturation component, and noise filtering. The segmentation process was applied using Otsuthresholding and morphological operation. The test results showed that the use of artificial neural network (ANN) using a combination of texture and morphological features gives better results when compared to the use of only texture or morphology features. The results showed that the proposed feature achieved an accuracy of 82.67%, a sensitivity of 82.18%, and a specificity of 94.17%, thus improving decision-making for malaria diagnosis.
Co-Authors Doni Setyawan E. Elsa Herdiana Murhandarwati Retantyo Wardoyo