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All Journal Journal of Information Technology and Computer Science CogITo Smart Journal SEEIJ (Social Economics and Ecology International Journal) Jurnal Peduli Masyarakat Journal of Electrical Engineering and Computer (JEECOM)
Angelia Melani Adrian
Fakultas Teknik Program Studi Teknik Informatika, Universitas Katolik De La Salle Manado
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Social Sciences Other

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Journal : Journal of Electrical Engineering and Computer (JEECOM)

 1 
Decision Support System for Electric Vehicles Selection Using Simple Additive Weighting Suwanto, Thomas Christian; Koloay, Steven; Adrian, Angelia Melani
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10909

Abstract

Electric vehicles (EVs) are vehicles entirely powered by electric motors using energy stored in batteries. In Indonesia, interest in electric vehicles is increasing, supported by government initiatives to reduce carbon emissions and improve infrastructure. The main issues faced are potential buyers' hesitation in choosing electric vehicles due to the limited variety of models, high prices, and insufficient information provided to buyers.This research aims to build a decision support system for selecting electric vehicles using the Simple Additive Weighting (SAW) method. The selection of electric vehicles using the SAW method requires criteria derived from sales brochures, official dealer websites, automotive exhibitions, and trusted news sources. The criteria used include price, range, battery capacity, passenger capacity, and vehicle speed. In the application development process, the waterfall method was used. The modeling tools used in this research are Flowcharts, Data Flow Diagrams, and Entity Relationship Diagrams, while the application development uses HTML and JavaScript.Based on the research conducted, all features function well, and out of the five alternatives used in this study, the results show that the Hyundai Ioniq 6 has a score of 0.9, while the Wuling Air EV Long Range has a score of 0.59.
Classification of LPG Gas Usage Satisfaction Level Using The Naïve Bayes Algorithm Adrian, Angelia Melani; Patras, Bella Alisia; Sanger, Junaidy B.
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.11064

Abstract

LPG gas is a very important energy source in everyday life for cooking activities. Although the importance of LPG gas in supporting everyday life has been widely recognized, satisfaction with the use of LPG gas is an issue that should not be ignored. Often products or services that do not meet customer expectations can cause dissatisfaction. This can be caused by low quality, prices that do not match the quality received, or not in accordance with user expectations.This study aims to classify the level of satisfaction of LPG gas usage using the Naïve Bayes algorithm. The data obtained from the survey results are 250 data using 5 attributes, namely meeting needs, good quality, affordable prices, repurchasing, and recommending products. And using 2 classes, namely satisfied and dissatisfied.The model achieved an accuracy of 89.3% with a 70:30 training-to-test data split, 91.2% with an 80:20 split, and 94.0% with a 60:40 split, indicating that performance varied based on the proportion of training and test data used.
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 Aminuyati Apriandy Angdresey Immanuela Puspasari Saputro Jeniver Petronela Matrutty Junaidy Budi Sanger Kamagi, Jonathan Koloay, Steven Lanny Sitanayah Louis D.S Joseph Michael George Sumampouw Patras, Bella Alisia Ratuliu, Gladys Saputro, Immanuela Puspasari Steven Pandelaki Suwanto, Thomas Christian