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All Journal Jurnal Pekommas Journal of Sustainable Engineering: Proceedings Series Jurnal Ilmiah Realtech Journal of Electrical Engineering and Computer (JEECOM)
Steven Pandelaki
Program Studi Teknik Informatika, Universitas Katolik De La Salle Manado
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Journal : Journal of Electrical Engineering and Computer (JEECOM)

 1 
Sistem Pendeteksi Jatuh Berbasis Internet of Things Steven Pandelaki; Lanny Sitanayah; Micael Liem
Journal of Electrical Engineering and Computer (JEECOM) Vol 5, No 1 (2023)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v5i1.5802

Abstract

Jatuh merupakan kejadian yang cukup berbahaya karena dapat menimbulkan cedera pada tubuh. Risiko jatuh pada setiap orang berbeda-beda dan ditentukan oleh banyak faktor, mulai dari keadaan lingkungan, kondisi kebugaran tubuh, dan faktor usia. Jatuh tidak hanya menimbulkan bahaya pada orang lanjut usia tetapi juga pada orang di segala usia dan dapat menjadi pertanda dari kambuhnya penyakit-penyakit tertentu. Penyakit yang diderita dapat bermacam-macam, akan tetapi penyakit yang umumnya terjadi adalah penyakit jantung, penyakit ayan, dan tekanan darah tinggi. Untuk meminimalkan dampak dari kondisi jatuh maka akan dibuat suatu Sistem Pendeteksi Jatuh Berbasis Internet of Things. Sistem ini memanfaatkan nilai keluaran dari sensor accelerometer dan gyroscope MPU6050 dengan menggunakan mikrokontroler NodeMCU ESP8266 untuk mengirimkan data ke database. Sensor dan mikrokontroler ditempatkan pada posisi pinggang pengguna. Aplikasi kemudian mengambil data yang dikirimkan dan mengklasifikasikan data tersebut menggunakan algoritma C4.5. Jika hasil klasifikasi adalah jatuh maka aplikasi akan menampilkan peringatan serta bunyi notifikasi. Berdasarkan hasil pengujian yang telah dilakukan, diperoleh hasil bahwa sistem yang dibangun memiliki akurasi sebesar 95% dalam menampilkan peringatan kondisi jatuh.
Retinocare: A Web-Based Intelligent System for Early Detection of Diabetic Retinopathy Using CNN Adrian, Angelia Melani; Pandelaki, Steven; Ratuliu, Gladys; Kamagi, Jonathan
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.13568

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide and is becoming a significant public health concern in Indonesia due to the rising prevalence of diabetes. Early detection is critical, yet access to ophthalmologists and conventional fundus cameras remains limited in many primary healthcare facilities. To address these challenges, this study proposes a cost-effective, web-based intelligent system for early detection of DR using smartphone-based fundus adapters and deep learning.A hybrid dataset was employed, combining publicly available fundus image repositories with locally collected retinal images from Indonesian healthcare facilities, annotated by ophthalmologists. Images were preprocessed through normalization, cropping, artifact removal, and augmentation to address variability, particularly from smartphone acquisitions. A DenseNet-121 convolutional neural network was fine-tuned on this hybrid dataset to classify DR into five severity levels according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Model performance was evaluated using accuracy as the primary metric, with results compared against ophthalmologist annotations.The proposed system demonstrated promising performance in classifying DR severity levels, showing that combining public and local datasets improves contextual relevance and model robustness. Furthermore, integration into a web-based platform enables healthcare workers in primary care to upload fundus images, obtain real-time classification results, and facilitate referral decisions for severe cases.This study contributes to the development of an accessible and scalable screening tool for DR in Indonesia by integrating affordable imaging hardware, locally relevant datasets, and an AI-powered classification system. The approach has the potential to reduce reliance on expensive equipment and specialists, supporting national efforts to prevent diabetes-related blindness.
Co-Authors Angelia Melani Adrian Anthoni Pulakian Dame, Kristian Debby Paseru Hendri Hermawan Adinugraha Henrico Kurnia Kamagi, Jonathan Kondo, Kristian Y. Kristian Dame Kristian Dame Kristian Kondo Kurnia, Henrico Lahama, Artquito Beltsazar Lanny Sitanayah Micael Liem Michael George Sumampouw Polly, Victori Ratuliu, Gladys Sengkey, Blessynta Christesa Steven Koloay Sumajouw, Karen Verna Y.P. Bokau Victory Polly Victory Polly Wikarsa, Liza