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All Journal International Journal of Electrical and Computer Engineering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri
Moh Edi Wibowo
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering 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.
Multi-Class Semantic Segmentation of Oil Palm Areas Using a VGG-19 U-Net Improvement Maura Widyaningsih; Tri Kuntoro Priyambodo; Moh Edi Wibowo; Muhammad Kamal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7062

Abstract

UAV imagery-based semantic segmentation is crucial for mapping tropical agricultural areas such as oil palm plantations. The main challenges are overlapping vegetation objects, unclear boundaries, and spectral similarities between classes, which reduce the accuracy of conventional models. This study proposes a modified U-Net architecture with a VGG-19 backbone, achieved through hyperparameter tuning (M7) and the integration of residual blocks (M8), to enhance multi-class segmentation performance. Experiments were conducted on aerial imagery with two resolutions (512×512 and 256×256) using four-class and three-class scenarios. The results show that M7 and M8 consistently outperform the baseline model (M2) in terms of accuracy, precision, recall, and average Intersection over Union (IoU). In the 512x512 four-class scenario, M8 achieved the highest accuracy (87.40%), precision (88.32%), recall (86.32%), and MIoU (0.132). M7 reached similar accuracy (>86%) but trained significantly faster than the baseline. In the 256x256 scenario, M8 maintained strong performance with 86.44% accuracy and 0.302 MIoU. For the three-class experiment, M8 reached a top MIoU of 0.178. Accuracy, precision, and recall were all above 87%, showing improved recognition of minority classes such as waterways. Confusion matrix analysis confirmed that M8 provided more balanced class predictions. It also reduced false negatives for oil palm vegetation. M7 showed slight fluctuations, suggesting possible overfitting. These findings support M8 as a robust solution for UAV-based oil palm mapping and large-scale monitoring.
Co-Authors Doni Setyawan E. Elsa Herdiana Murhandarwati Maura Widyaningsih Muhammad Kamal Retantyo Wardoyo Tri Kuntoro Priyambodo