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All Journal Bulletin of Electrical Engineering and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Ekobistek Jurnal Teknologi Journal of Computer Networks, Architecture and High Performance Computing Journal of Computer Scine and Information Technology Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Journal Medical Informatics Technology Persepsi: Jurnal Riset Mahasiswa Psikologi
Fajrul Islami
Universitas Putra Indonesia YPTK Padang
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Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images Islami, Fajrul; Sumijan; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 8 (2024): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i8.7614

Abstract

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.
Hubungan antara Motivasi Belajar dan Dukungan Sosial Teman Sebaya dengan Student Engagement pada Siswa Islami, Fajrul; Shofiah, Vivik
Persepsi: Jurnal Riset Mahasiswa Psikologi Vol. 1 No. 1 (2022): Persepsi: Jurnal Riset Mahasiswa Psikologi
Publisher : Fakultas Psikologi UIN Suska Riau

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Abstract

Student engagement merupakan salah satu faktor penting untuk meraih kesuksesan pada siswa.Ketika siswa memiliki student engagement siswa akan cenderung terlibat secara perilaku, emosi dankognisi didalam proses pembelajaran, sehingga ini akan berdampak terhadap prestasi siswa.Penelitian ini bertujuan untuk mengkaji secara ilmiah hubungan motivasi belajar dan dukungan sosialteman sebaya dengan student engagement pada siswa MA Darussakinah Batu Bersurat. Sampelpenelitian adalah siswa MA Darussakinah Batu Bersurat dengan jumlah sampel 124 Siswa. Datapenelitian ini diperoleh dengan menggunakan skala motivasi belajar, dukungan sosial teman sebayadan student engagement. Data dianalisis dengan menggunakan analisis regresi berganda. Hasilpenelitian menunjukkan bahwa terdapat hubungan yang signifikan antara motivasi belajar dandukungan sosial teman sebaya dengan student engagement dengan nilai R=0,693 dan nilai sig=0,000(0,000 < p 0.05). Hal ini berarti hipotesis yang diajukan oleh peneliti diterima yaitu terdapat hubunganmotivasi belajar dan dukungan sosial teman sebaya dengan student engagement pada siswa MADarussakinah Batu Bersurat. Selain itu, dengan melihat nilai Adjust R Square maka sumbangan efektifmotivasi belajar dan dukungan sosial teman sebaya dengan student engagement sebesar 68.8%.Penelitian ini mengungkapkan bahwa motivasi belajar dan dukungan sosial teman sebaya merupakansalah satu faktor yang mempengaruhi student engagement siswa. Artinya dengan meningkatkanmotivasi belajar dan dukungan sosial teman sebaya pada siswa maka hal ini dapat pula meningkatkanstudent engagement.
Implementasi Computer Vision Dalam Deteksi Dan Klasifikasi Sampah Otomatis Pada Sistem Pengolahan Limbah Perkotaan Lusman, Akbar; Devita, Retno; Putra, Ondra Eka; Rianti, Eva; Islami, Fajrul
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

Waste is a very serious environmental problem commonly faced by Indonesians. According to data from the National Waste Management Information System (SIPSN), Indonesia's waste volume reached 20.02 million tons in 2022. In Indonesia, the amount of waste generated reached 65 million tons per day in 2016 and increased to 66.5 million tons in 2018. The amount of waste in Indonesia continues to increase annually. In large cities, waste management is an increasingly pressing challenge, given the negative impacts caused by improper management, such as waste accumulation in landfills (TPA), water and air pollution, and public health issues. This study aims to design and implement an automatic waste classification system based on Computer Vision technologies as a solution for urban waste management. The system utilizes an Arduino Mega 2560, camera, ultrasonic sensor, servo motor, and conveyor to detect and classify five main types of waste: plastic, paper, glass, metal, and organic materials in real time. The camera captures images of waste, which are then analyzed using a Computer Vision model, while sensors and actuators control the flow and physical sorting process. This research seeks to improve waste processing efficiency by reducing human involvement in hazardous tasks and to promote the application of intelligent technologies in supporting sustainable recycling systems and reducing the burden on final disposal sites (landfills). The system created can detect and classify waste types well.
Co-Authors Adrul Hutama Agung Ramadhanu Andre Gunawan Andrian, Reski Arsyah, Ulya Ilhami Devita, Retno Dodi Guswandi emil nafan Gushelmi Hadi Syahputra Putra Irfan Rizki Nur Lusman, Akbar Pratiwi, Mutiana Putra, Ondra Eka Rianti, Eva Sarjon Defit Sumijan Sumijan Sumijan Syafri Arlis Tahmid, Riskian Vivik Shofiah Raudatussalamah