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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 46 Documents
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Implementing Synchronize Service From Open Journal System into Laravel Journal House Information System Huygenz Widodo, Raden Ibnu; Nengsih, Titin Agustin
CoreID Journal Vol. 3 No. 1 (2025): March 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i1.65

Abstract

The increasing complexity of managing academic journals has highlighted the need for efficient systems that ensure seamless data synchronization across platforms. Existing solutions often involve fragmented workflows, resulting in redundant manual tasks and data inconsistencies. This study proposes an integration of the Open Journal System (OJS) with a Laravel-based Journal House Information System through a custom API synchronization service. The method employs Express.js and MySQL to facilitate real-time, automated data transfer, streamlining journal management and improving accuracy. Developed using the Agile methodology, the system was rigorously tested for performance and functionality, demonstrating its ability to efficiently handle large datasets. The proposed solution reduces administrative workload, enhances data consistency, and provides a scalable model for academic institutions seeking to improve their journal management workflows
REST-API Implementation for Optimizing Features in the K-Mob Jabar Smart ASN Application Halizah, Nur; Azkiya Jamaludin, M. Rifqi
CoreID Journal Vol. 3 No. 1 (2025): March 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i1.77

Abstract

The Jabar Smart ASN (JSA) application is a staffing application used to access other West Java Province staffing applications, one of which is the K-Mob application. The JSA application cannot be fully used, because there are still some features in K-Mob that are not optimal in accordance with the expected functions. So that from these problems, the implementation of REST-API on the features of the K-Mob application in Jabar Smart ASN is carried out. This research successfully implemented REST-API on the Working Hours, Claims and Appeals, and Recapitulation features with a response code of 200. However, the Subordinate Presence feature gave a response code of 500, caused on the server side. Although there are problems in the REST-API implementation process, the problems have been identified and can be the basis for further improvement.
Analysis Of E-Commerce Product With Web Scraping Technique Maulidiyah, Siti jahro; Syahyadi, Asep Indra
CoreID Journal Vol. 3 No. 1 (2025): March 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i1.90

Abstract

This research aims to implement a web scraping system to automatically extract product data from the e-commerce platform Bukalapak, with the goal of supporting statistical analysis at the Central Bureau Statistics (BPS) of West Java Province. The system utilizes a combination of API access and automation tools such as python, executed in the Google Colab cloud environment. Through this method, 74,796 product records were successfully collected, encompassing information such as product names, prices, categories, customer reviews, stock levels, and seller locations. The data was then processed and visualized using bar charts and histograms to analyze market trends, price distribution, and consumer behavior across regions in West Java. The results show that most products fall within affordable ranges, with certain categories like electronics and personal care dominating in volume. The scraping approach proved to be an efficient and scalable solution for acquiring real-time market data, supporting BPS in evidence-based decision-making and policy formulation.
Dynamic Decision-Making Model Analysis for Pandemic Spread Control Using Causal Loop Diagram Bakti Nugraha, Arif
CoreID Journal Vol. 3 No. 1 (2025): March 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i1.91

Abstract

Corona Virus Disease 2019 or better known as Covid 19 spreads very quickly and has become a pandemic for the world today. In its development process, it is very dynamic and complex, consisting of many interrelated and different components, each with its own purpose. As for the dynamic and complex issues in the spread of the Corona virus, the ability is absolutely necessary to limit the spread of the virus. For this reason, it is necessary to know the factors that influence and inhibit the spread of the virus as well as what strategies are needed and analyze the dynamic causality of these factors by using an approach method that uses a Causal Loop Diagram (CLD) system. The purpose of this study is to obtain an appropriate decision solution to manage the spread of the corona virus by using a causal graph approximation system.
UI/UX Design Of Website-Based E-Commerce Application Using Lean User Experience Method Arujisaputra, Erwin Teguh; Heriansyah, Madi; Zamani, Fadli Emsa; Husain, Adam
CoreID Journal Vol. 3 No. 1 (2025): March 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i1.92

Abstract

Tomkids Children's Clothing Store Bandung is a store engaged in the sale of children's clothing in Cimenyan-Bandung Regency. So far, activities in the store can be said to be less than optimal, because the transaction process is still carried out conventionally or buyers come directly to the Tomkids Store. This makes it difficult for customers who live far from the Tomkids Store. Customers find it difficult to find out what information is still offered by the Tomkids Store, therefore a website design is needed that can help increase sales at the Tomkids Store. The method used in designing a website at Toko Tomkids is using the Lean UX model based on the website. From the analysis and design process, a UI/UX E-commerce application will be produced at Toko Tomkids which can help in the promotion and sales process, and make it easier for customers to make online children's clothing purchases. And the data analysis and processing techniques use a questionnaire by giving statements to respondents. To reach respondents, 40 people were selected, consisting of two groups, the first is 20 respondents who are lay with e-commerce and experienced in online shopping. Second, there are 20 respondents who have or have a trading business. The results of this study are in the form of a better and optimal User Interface and User Experience design for the e-commerce application at Toko Tomkids Baju Anak Bandung based on the website. The purpose of designing this application is to display the interface and experience pages so that it can provide information to customers, make orders and also be able to make purchases so that customers can make transactions online.
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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