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All Journal Jurnal Ilmu Komputer dan Informasi JIKO (Jurnal Informatika dan Komputer) SKANIKA: Sistem Komputer dan Teknik Informatika
Nugraha, Made Prastha
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Computer Science & IT Control & Systems Engineering Engineering Library & Information Science

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Pemanfaatan Random Forest untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa Studi Kasus: Institut Desain dan Bisnis Bali Puspa, Gede; Rachmadhan Amri, Muhammad Febrian; Nugraha, Made Prastha
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1886

Abstract

Institut Desain dan Bisnis Bali, setiap tahun menghasilkan lulusan mahasiswa sesuai bidang yang ditempuhnya, dalam kurun waktu penyelesaian studi tepat waktu yaitu 4 (empat) tahun. Namun pada kurun waktu 3 (tiga) tahun terdapat penurunan jumlah presentase mahasiswa yang lulus . Hal ini tentu merupakan permasalah serius yang perlu ditindak lanjuti karena dapat berdampak pada nilai akreditasi perguruan tinggi. Belum diketahui penyebab pasti keterlambatan studi mahasiswa yang tidak lulus tepat waktu. Perlu adanya penggalian data yang masih tersembunyi serta pengolahan data sehingga menjadi pengetahuan dan informasi baru yang dapat dimanfaatkan untuk menindak lanjuti mahasiswa yang bermasalah pada tahun akademik berjalan. Penelitian ini bertujuan untuk memprediksi tingkat kelulusan mahasiswa tepat waktu dengan metode random forest untuk mengetahui metode yang lebih unggul dalam kasus tersebut. Dari penelitian ini yaitu sistem dapat memprediksi kelulusan mahasiswa tepat waktu dengan algoritma terbaik.
Deep Image Deblurring for Non-Uniform Blur: a Comparative Study of Restormer and BANet Nugraha, Made Prastha; Rahadianti, Laksmita
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1274

Abstract

Image blur is one of the common degradations on an image. The blur that occurs on the captured images is sometimes non-uniform, with different levels of blur in different areas of the image. In recent years, most deblurring methods have been deep learning-based. These methods model deblurring as an imageto-image translation problem, treating images globally. This may result in poor performance when handling non-uniform blur in images. Therefore, in this paper, the author compared two state-of-the-art supervised deep learning methods for deblurring and restoration, e.g. BANet and Restormer, with a special focus on the non-uniform blur. The GOPRO training dataset, which is also used in various studies as a benchmark, was used to train the models. The trained models were then tested on the GOPRO testing test, the HIDE testing set for cross-dataset testing, and GOPRO-NU, which consists of specifically selected non-uniform blurred images from the GOPRO testing set, for the non-uniform deblur testing. On the GOPRO testing set, Restormer achieved an SSIM of 0.891 and PSNR of 27.66 while BANet obtained an SSIM of 0.926 and PSNR of 34.90. Meanwhile, for the HIDE dataset, Restormer achieved an SSIM of 0.907 and PSNR of 27.93 while BANet obtained an SSIM of 0.908 and PSNR of 34.52. Finally, on the non-uniform blur GOPRO dataset, Restormer achieved an SSIM of 0.911 and PSNR of 29.48 while BANet obtained an SSIM of 0.935 and PSNR of 35.47. Overall, BANet shows the best result in handling non-uniform blur with a significant improvement over Restormer.
SEGMENTASI CITRA ECHOCARDIOGRAPHY MENGGUNAKAN DENSE-AIDAN Nugraha, Made Prastha; Rachmadan Amri, Muhammad Febrian; Sunarmodo, Wismu
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3654

Abstract

Congenital heart disease is a structural abnormality of the heart present from birth, affecting about 1% of all newborns, which make early detection of abnormal heart conditions is essential. Detection can be performed by calculating the traced area of end-systole and end-diastole segmentation in cardiac echocardiography videos. This study aims to perform segmentation on echocardiography images using the Dense-AIDAN method. The research workflow conducted in this study includes data collection and preparation, model development, and evaluation. The dataset used in this study is the public EchoNet-Dynamic echocardiography video dataset showing the four-chamber view of the heart. The echocardiography videos from the dataset are first converted into image frames. The image frames are generated based on the two tracings mentioned above. These images are then divided into training, validation, and test sets. The training images are used as input to train the Dense-AIDAN model. The trained model is then used to segment the left ventricle of the heart from the input test images. The implementation of the Dense-AIDAN method yields a Dice Similarity Coefficient (DSC) of 0.81 and an Intersection over Union (IoU) of 0.68. The study concludes that using DenseNet201 provides better segmentation results compared to ResNet50 on medical images, especially echocardiography images.
Co-Authors Muhammad Febrian Rachmadhan Amri Puspa, Gede Rahadianti, Laksmita Wismu Sunarmodo, Wismu