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Menampilkan 1–6 dari 6 artikel
Peningkatan Kualitas Pembelajaran Pengolahan Citra Digital Pada Program Studi Teknik Informatika Menggunakan Model Problem Based Learning
Mila Nurjanah
; Yovi Litanianda
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Vol 2
, No 3
(2024)
This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning p...
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Peningkatan Kualitas Pembelajaran Pengolahan Citra Digital Pada Program Studi Teknik Informatika Menggunakan Model Problem Based Learning
Mila Nurjanah
; Yovi Litanianda
Modem : Jurnal Informatika dan Sains Teknologi
Vol 2
, No 3
(2024)
This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning p...
Sumber Asli
Google Scholar
DOI
Pengujian Usabilitas Pada Penggunaan Platform Scratch
Zakia Access Asmaul Khusna
; Yovi Litanianda
Repeater : Publikasi Teknik Informatika dan Jaringan
Vol 2
, No 3
(2024)
This research tests the usability of the platform on new students of Informatics Engineering. Usability of is measured through three main variables: effectiveness, efficiency, and user satisfaction. Data was collected through pre-test and post-test involving 20 respondents using questionnaires and practical tasks. 20 respondents using questionnaires and practical tasks. The results of the study showed that the effectiveness and efficiency of the Scratch platform increased significantly from the...
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Klasifikasi Jenis Jamur Edible Menggunakan Convolutional Neural Network: Studi Kasus pada Jamur Tiram, Enoki, dan Truffle
Devitha Ratu Alamsyach
; Yovi Litanianda
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Vol 2
, No 3
(2024)
This study uses a Convolutional Neural Network (CNN) to develop a mushroom type classification model that can differentiate between truffles, enoki and oyster mushrooms very accurately. The dataset consisting of mushroom images is collected from various sources and processed through data augmentation techniques such as rotation, zoom, flip, and shifting to improve model generalization. For final classification, the CNN model used consists of several convolution and pooling layers, followed by a...
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Implementasi Metode Convolutional Neural Network (CNN) untuk Klasifikasi Jenis Ras Kucing
Aliefah Syalma Ratsdea Muftti
; Yovi Litanianda
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Vol 2
, No 2
(2024)
This research implements the Convolutional Neural Network (CNN) method to classify the various types of cat breeds that are common in Indonesia. This research attempts to create an automatic system that can definitely and accurately classify and identify the types of cat breeds that exist in Indonesia using image processing techniques. The data used contains a total of 600 images with each folder containing 200 images. Using this CNN method produces a validation accuracy rate of 54% in the proce...
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Klasifikasi Jenis Buah Jeruk Menggunakan Metode Convolutional Neural Network: Deep Learning Studi
Yazid Fauzan Nur Ashfani
; Yovi Litanianda
; Rizqy Amalia Putri
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Vol 2
, No 2
(2024)
This study analyzes the use of deep learning, primarily Convolutional Neural Networks (CNN), to categorize various types of citrus fruits. The study attempts to create an automated system that can accurately categorize citrus fruit kinds using image processing techniques. The collection contains 40 photos of four different citrus fruit types: pomelo, mandarin orange, kaffir lime, and lime. The methodology entails gathering photos, preprocessing them to improve quality, and then training a CNN mo...
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