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All Journal Dinamik Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnal Sistem dan Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) WARTA Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Emerging Science Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) ILKOM Jurnal Ilmiah WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Tekno Kompak Journal of Electronics, Electromedical Engineering, and Medical Informatics Respati Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Melek IT: Information Technology Journal Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Sevanam: Jurnal Pengabdian Masyarakat Equivalent: Jurnal Ilmiah Sosial Teknik
Roy Rudolf Huizen
Institut Teknologi Dan Bisnis Stikom Bali
Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : juita jurnal informatika

 1 
Modified of Single Deepest Vertical Detection (SDVD) Algorithm for Amniotic Fluid Volume Classification Putu Desiana Wulaning Ayu; Gede Angga Pradipta; Roy Rudolf Huizen; Kadek Eka Sapta W; I Gede Edy Artana
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.18435

Abstract

Amniotic fluid a crucial role in ensuring the well-being of the fetus during pregnancy and is contained within the amnion cavity, which is surrounded by a membrane. Several studies have shown that volume of amniotic fluid can vary throughout pregnancy and is closely linked to the health and safety of the fetus. This indicates that it is essential to perform accurate measurement and identification of its volume. Obstetric specialist often use a manual method to identify amniotic fluid by visually determining the longest straight vertical line between the upper and lower boundaries. Therefore, this study aims to develop detection model, known as modified Single Deepest Vertical Detection (SDVD) algorithm to automatically measure the longest vertical line by following medical rules and regulations. SDVD algorithm was designed to measure the depth of amniotic fluid vertically by searching the column of pixels that comprised the image sample, excluding any intersection with the fetal body. Performance testing was carried out using 130 images by comparing the manual measurement results obtained by obstetric specialists and the proposed model. Based on the experimental results using modified SDVD, the average accuracy, precision, and recall achieved for amniotic fluid classification were 92.63%, 85.23%, and 95.6%, respectively.
Combination of VGG19 (Encoder) and U-Net (Decoder) for Colorectal Polyp Segmentation Image Nuri Sutiyaningsih; Putu Desiana Wulaning Ayu; Roy Rudolf Huizen
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.25783

Abstract

Health involves the proper function of the body and organs, with colon polyps being a common issue. Doctors often face challenges in segmenting medical images, especially endoscopic images for polyp detection. The complexity and variation in the appearance of polyps make accurate identification challenging, and the subjective manual segmentation process can result in misdiagnosis or delayed treatment.  This study examines the effectiveness of the combination of U-Net decoder model architecture and VGG19 encoder in segmentation of colon polyp images.  This study uses a public dataset, namely Kvasir-Seg with a total of 1000 images of colon polyps.  An innovative approach using VGG19 as encoder and U-Net as decoder improves colorectal polyp segmentation, achieving high performance with a Loss of 0.05, Accuracy 0.95, Precision 0.96, Recall 0.92, IoU 0.89, and Dice 0.94. Using optimal parameters such as Nadam Optimizer, 5 Fold Cross Validation, Learning Rate 0.0001, and 25 Epochs significantly improved performance, increasing the Dice Coefficient to 0.92 and IoU to 0.86 compared to previous studies.   This study concludes that the proposed architecture is reliable for colon polyp segmentation. Future work should explore attention mechanisms or transformer-based models to enhance accuracy and efficiency.
Co-Authors Adi Setiawan Adriani, Ni Putu Erica Puspita Andriyan Dwi Putra Bambang Soedijono Bambang Soedijono W Dadang Hermawan Dadang Hermawan Dadang Hermawan Dandy Hostiadi Dandy Pramana Hostiadi Dandy Pramana Hostiadi Dandy Pramana Hostiadi Daniel HF Manongga Eko Sediyono Erma Sulistyo Rini Erma Sulistyo Rini, Erma Sulistyo Firgiawan Faira Florentina Tatrin Kurniati Gede Angga Pradipta Gede Angga Pradipta, Gede Angga I Gede Edy Artana I Made Darma Susila I Made Darma Susila, I Made Darma I Made Liandana I Wayan Karang Utama Ipango, Rachmad Arissaputra Irwan Sembiring Iwan Setyawan Kadek Eka Sapta W karyanto, Nonot wisnu Leny Margaretha Huizen Lilik Jatmiko Prasetyo Lilis Yuningsih Luh Ayu Martini Made Liandana, Made Mahajaya, Nyoman Sarasuartha Mahendra, Tubagus Manongga, Daniel HF Ni Ketut Dewi Ari Jayanti Nonot Wisnu Karyanto Nuri Sutiyaningsih Perimawati, Ni Nyoman Eny Purnama, I Gusti Agung Vony Putu Desiana Wulaning Ayu Putu Desiana Wulaning Ayu Putu Desiana Wulaning Ayu Rihamzah, Muhamad Rustamaji, Abdullah Saputra, Made Wisnu Adhi Seneng, I Kadek Soedijono W, Bambang Sri Yulianto Joko Prasetyo Sudawati, Luh Dwi Ari Sudiatmika, Anop Sutarto Wijono W Mega Adhi Agam Pradhana Wiguna, I Wayan Adi Artha Wing Wahyu Winamo Wulaning Ayu, Putu Desiana Yoga, I Gede Dian Permana Yohanes Priyo Atmojo Yohanes Priyo Atmojo Yohanes Priyo Atmojo, Yohanes Priyo