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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. 2 (2025): July 2025" : 5 Documents clear
Design and Evaluation of a Temperature–Humidity Control System for Mushroom Cultivation Using a DHT11 Sensor Suryaman, Suryaman; Yuningsih, Siti Hadiaty; Setiawan, Aan Eko; Zakaria, Kiki
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.103

Abstract

Oyster mushroom (Pleurotus ostreatus) cultivation requires stable temperature and humidity conditions to support optimal mycelial development and fruiting body formation. This study aims to develop and evaluate a low-cost temperature–humidity monitoring and control system for an oyster mushroom cultivation room using a DHT11 sensor integrated with an Arduino-based controller. An experimental evaluation was conducted by comparing DHT11 temperature and humidity readings with a reference measuring instrument under cultivation-room conditions, while the control function was tested using threshold-based rules for activating environmental actuators (heater, fan, and humidifier). The results indicate that the DHT11 sensor produced measurements close to the reference instrument within the tested range, with temperature differences of 0.1–0.3°C and humidity differences of 0.2–0.4%RH across the observations. These findings suggest that the proposed system is feasible for basic environmental monitoring and supports automated threshold-based control for maintaining cultivation conditions near recommended ranges. Sensor performance and measurement stability are influenced by practical factors such as airflow, proximity to heat or moisture sources, and sensor placement; therefore, appropriate placement and shielding are important to minimize local bias. The originality of this work lies in providing an implementable prototype and an empirical sensor performance assessment in a mushroom cultivation environment, offering practical guidance for low-cost smart farming applications.
Academic Data Quality Measurement in SALAM Application Using Six Sigma Method Firdaus, Imam; Alam, Cecep Nurul; Gerhana, Yana Aditia; Irfan, Mohamad; Iskandar, Ibrahim
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.136

Abstract

Data quality plays a critical role in ensuring the reliability and usefulness of information for decision making in higher education institutions. However, academic data within the SALAM application at UIN Sunan Gunung Djati Bandung has not previously undergone a systematic quality assessment, leading to uncertainty in several managerial and academic decisions. This study aims to evaluate the quality of academic data in the SALAM application using the Six Sigma method with the DMAIC (Define–Measure–Analyze–Improve–Control) framework. Five data quality dimensions completeness, consistency, conformity, uniqueness, and timeliness are employed to measure and analyze data quality performance. The measurement process begins with data definition and extraction, followed by quantitative analysis using sigma metrics. The results indicate that the overall quality of academic data is at a moderate level, with an average sigma score of approximately 3, primarily influenced by incomplete and inconsistent data. In contrast, the timeliness dimension demonstrates excellent performance, achieving a sigma metric of 6 due to the long-term availability of data over more than ten years. This study contributes by providing an empirical, data-driven evaluation of academic data quality and offers practical insights for implementing continuous monitoring and improvement strategies to enhance data reliability and support more effective decision making in higher education institutions.
Precision Diagnosis of Skin Cancer Using Convolutional Neural Networks Rizqulloh, Moh Hasbi; Pasha, Muhammad Kemal; Rizq H, Muhammad Andhika; Widyowaty, Dwi Sari
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.137

Abstract

Skin cancer is a prevalent and potentially life-threatening condition that requires accurate and timely diagnosis. This study explores the application of Convolutional Neural Networks (CNNs) for the detection and classification of skin cancer types, including mole, dermatofibroma, melanoma, and nevus, based on visual characteristics extracted from digital images. The research focuses on preserving color information in original images during preprocessing to enhance the model's ability to differentiate between these conditions. A dataset comprising a variety of skin condition images was utilized to train and evaluate the CNN model, which was designed with convolutional and dense layers for effective feature extraction and classification. The model achieved a test accuracy of 63.83%, indicating its potential as a tool for supporting dermatological diagnosis. This work contributes to advancing machine learning applications in dermatology, aiming to improve diagnostic accuracy and patient care outcomes in the detection of skin cancer.
Implementation of Template Matching Algorithm in Detecting Student Identification Numbers to Improve Student Services Cahya, Nurul Dwi; Irfan, Mohamad; Amin, Mohammad Badrudin
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.141

Abstract

The rapid progression of technological advancements, particularly in the digitalization of image data, has significantly facilitated numerous sophisticated applications, including pattern recognition. A prominent example can be observed within the education system of UIN Sunan Gunung Djati Bandung, where the Student Identification Number (NIM) constitutes a pivotal component in a wide range of academic service operations. At present, processes such as the verification of scholarship documentation, updating of PD DIKTI data, and the borrowing of library materials are predominantly executed through manual means, frequently resulting in operational inefficiencies and the occurrence of human errors. To address these challenges, this study investigates the application of the template matching algorithm for recognizing the NIM on the Student Identity Card (KTM). This study is conducted to systematically evaluate the implementation of template matching for NIM recognition, assess the performance of the proposed method, and ascertain its impact on enhancing student services. The experimental findings reveal that the template matching algorithm demonstrates variable success rates across three trials (9/20, 8/20, and 8/20 instances correctly identified). The detection accuracy is determined to be influenced by factors including, but not limited to, template values, the presence of noise, variations in lighting conditions, and the parameter settings of the Canny edge detection process. The results substantiate the potential of the template matching algorithm to significantly improve the efficiency of student services by automating the NIM recognition process. Nonetheless, several technical limitations, particularly those impacting detection accuracy, necessitate further refinement to optimize its performance. This research highlights the critical importance of enhancing the algorithm to establish a robust and effective system for academic service delivery.
Convolutional Neural Network for Soil Surface Image Classification in Six Soil Categories Fauzi, Muhamad Iqbal; Darraini, Nasywah; Septiansah, Muhamad Randi; Nur Afifi, Erwinestri Hanidar
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.142

Abstract

Soil type classification is important for agriculture, geology, and civil engineering because soil characteristics influence land suitability, tillage strategy, irrigation, fertilization, and foundation stability. However, manual soil identification through field observation or laboratory analysis can be time-consuming and may introduce subjective errors. This study proposes an automated soil image classification approach using a Convolutional Neural Network (CNN). The dataset comprises six soil categories-black soil (tanah hitam), yellow soil (tanah kuning), peat soil (tanah gambut), cinder/volcanic soil (tanah vulkanik), laterite soil (tanah laterit), and cracked soil (tanah retak) -collected from a public Kaggle dataset and complemented with web-extracted cracked-soil images. Images are preprocessed through resizing, normalization, and training-time augmentation before being split into training, validation, and testing subsets. Experimental results show that the proposed CNN achieves 91.61% test accuracy and substantially improves performance compared to training without preprocessing. These findings indicate that CNN-based models, supported by appropriate preprocessing, can provide practical decision support for rapid soil type identification under diverse image conditions.

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