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Coreid Journal
Published by Generasi Intelektual Digital
ISSN : -     EISSN : 29876990     DOI : https://doi.org/10.60005/coreid.v1i2.14
Core Subject : Science,
Computer Science & IT
CoreID is a scientific journal that contains scientific papers from Academics, Researchers, and Practitioners about research on informatics and Computer. CoreID is published 3 times a year in March, July, and November. The paper is an original script and has a research base on Informatics. The scope of the paper includes several studies but is not limited to the following study. 1. Computer Sciences 2. Software Engineering 3. Information Technology 4. Digital Innovation
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 3 (2025): November 2025" : 5 Documents clear
Design and Development of an IoT-Based Prototype System for Monitoring the Care of Dendrobium Orchids Khoerunnisa, Ahshonat; Setiawan, Aan Eko; Ridwan, Azwar Mudzakkir; Nurshiyami, Alma; Yuningsih, Siti Hardianti
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.118

Abstract

The Dendrobium orchid (Dendrobium spp.) is widely appreciated for its beauty and durability in the horticultural industry. To ensure optimal growth, continuous monitoring of environmental conditions is essential. This research presents an Internet of Things (IoT)-based monitoring system that observes real-time temperature, humidity, and soil moisture levels around the orchid. The system utilizes a DHT22 sensor for measuring air temperature and humidity and a soil moisture sensor for detecting the moisture content of the growing medium. An ESP32 microcontroller processes the sensor data and transmits it to the Ubidots cloud platform for real-time visualization. Testing showed that the sensor system achieved an accuracy rate of 4%. Data consistency between the serial monitors and the cloud was maintained, except when network disruptions occurred. This system allows users to remotely monitor critical parameters necessary for orchid health, facilitating better decision-making and timely intervention, ultimately improving the effectiveness of orchid care and maintenance.
GreenEye: Plant Classification Using MobileNet V2 Hamami, Muhammad Syamil; Firdaus, Muhammad Rihap; Pasha, Pancadrya Yashod; Firdaus, Muhammad Raihan; Sugiarto, Awang
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.138

Abstract

Biodiversity in Indonesia includes more than 30,000 species of plantsand mushrooms, but public knowledge about these plants is still limited. The research aims to develop a mobile application called GreenEye that uses machine learning to detect and classify plants based on images. The model used is based on the MobileNet V2 architecture, a type of Convolutional Neural Network (CNN) designed for high-efficiency image classification tasks. Research data collected from PlantNet and Google Images, consisting of 2800 images covering seven plant species: Ananas comosus, Artocarpus heterophyllus, Carica papaya, Cocos nucifera, Musa spp, Nephelium lappaceum, and Salacca zalacca. Each species is categorized into four plant parts: fruit, flower, leaf, and habit. (habitus). This data is then processed through various preprocessing stages such as data cleaning, format conversion, resizing, cropping, and image augmentation. The results showed that the MobileNet V2 model was able to classify parts of plants with high accuracy, especially on fruits and leaves with accurations above 90%. However, the accuration was slightly lower for flowers and habits, which is about 70%. Classification errors occurred mainly in species with high visual similarities. To improve the performance of the model, it is recommended that further research increase the quantity and diversity of datasets.
Performance Comparison of K-Nearest Neighbor, Decision Tree, and Support Vector Machine Algorithms for Diabetes Classification Aria, Aria Octavian Hamza; Mulyana, Devi; Rifa’i, Akhmad Ridlo; Ikhsan, Muhammad
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.139

Abstract

This paper investigates the performance of three supervised machine learning algorithms K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) for diabetes classification using the Pima Indians Diabetes Dataset. The study aims to provide a fair and consistent comparison by applying unified preprocessing procedures, including median imputation for clinically invalid values, feature standardization, and stratified 5-fold cross-validation. Model performance is evaluated using accuracy, precision, recall, and F1-score, with particular emphasis on recall for the diabetic class due to its clinical significance in reducing false negative diagnoses. Experimental results show that the Decision Tree model achieves the most balanced performance, with an average accuracy of 0.78 and an F1-score of 0.75, while maintaining higher recall for diabetic cases compared to KNN and SVM. Although SVM and KNN demonstrate acceptable overall accuracy, both models exhibit limitations in identifying minority-class instances. These findings highlight the importance of algorithm selection based not only on accuracy but also on clinical priorities such as interpretability and sensitivity to positive cases. The study contributes practical insights for the development of reliable machine learning–based decision support systems for early diabetes screening.
Natural Language Processing and Random Forest for Mental Health Symptom Identification Using Social Media Data Sugara, Sigit; Dauni, Popon; Putri, Novianti Indah; Saputra, Yogi; Suryana, Nana
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.145

Abstract

This study explores the implementation of machine learning models, specifically Natural Language Processing (NLP) and Random Forest, for detecting mental health symptoms based on text analysis of web-sourced data. The research addresses the challenges of analyzing highly subjective and dynamic text in social media content to identify patterns associated with anxiety, depression, and stress. The methodology involves several preprocessing steps including case folding, cleansing, language normalization, negation conversion, stopword removal, and tokenization, followed by TF-IDF weighting and Random Forest classification. The model evaluation revealed a high accuracy rate of approximately 80%, although achieving a confidence level of 75% proved challenging. This research demonstrates that despite the inherent difficulties in predicting subjectively variable text, the machine learning approaches employed show satisfactory performance in identifying mental health symptoms, offering potential for early detection and intervention systems.
Short Message Spam Classification using Decision Tree, Naive Bayes, and Logistic Regression Aulia, Citra; Dinah, Azalia Fathimah; Zahratunnisa, Dzilan Nazira; Efendi, Rofik
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.146

Abstract

The increasing use of Short Message Service (SMS) in digital communication has been accompanied by a rise in spam messages, which threaten user convenience and information security. This study presents a comparative analysis of three classical machine learning algorithms—Decision Tree, Naïve Bayes, and Logistic Regression—for SMS spam classification. The research follows the CRISP-DM methodology, including data collection, understanding, preparation, modeling, and evaluation. The dataset used is the SMS Spam Collection (A More Diverse Dataset) from Kaggle, comprising 5,574 SMS messages labeled as spam or ham. Text preprocessing is performed through cleaning operations and feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The models are evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. Experimental results indicate that Logistic Regression achieves the most balanced performance, with an accuracy of 97.13%, precision of 99.23%, recall of 80.75%, F1-score of 89.04%, and an AUC of 98.72%. Naïve Bayes demonstrates high efficiency and perfect precision but lower recall, while Decision Tree offers interpretability with comparatively lower classification performance. The results suggest that Logistic Regression is the most suitable model for lightweight and reliable SMS spam detection systems, balancing accuracy and misclassification risk. This study provides practical insights for implementing efficient spam filtering solutions and serves as a reference for future research in text classification and natural language processing, particularly for short-message communication.

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