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Wali Ja'far Shudiq
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COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi
Published by Universitas Nurul Jadid
ISSN : 27750124     EISSN : 27747875     DOI : https://doi.org/10.33650/coreai
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering Transportation
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan beberapa hasil dari penelitian dan pemikiran untuk pengabdian kepada masyarakat luas. Situs Jurnal COREAI ini menyediakan artikel-artikel jurnal untuk dibaca maupun diunduh secara gratis. Jurnal kami adalah jurnal ilmiah nasional yang merupakan sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal COREAI menerima artikel ilmiah dengan lingkup penelitian pada: Technology Management. Business Intelligence and Knowledge Management. Teknik Komputer Pengolahan Citra. Sistem Pendukung Keputusan. Data Mining. Robotik. Algoritma Genetika. Sistem Kecerdasan Buatan. Jaringan Komputer. Big Data. Enterprise Computing. Internet of Things. Sistem Database. Energy Management. Sistem Pakar. Sistem Penunjang Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 146 Documents
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Implementation of Business Intelligence for Performance Evaluation of a Professional Certification Institution Using Microsoft Power BI Irsyadulloh Ramadhan Bagus Nuryono; Eman Setiawan; Natalia Damastuti
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15179

Abstract

The Cipta Unggul Human Resource Management Professional Certification Body (LSP) faces challenges in its organizational performance evaluation process because operational certification data is still stored in various separate spreadsheet files that have not been optimally integrated. This situation results in manual report preparation, which is time-consuming, prone to data processing errors, and makes it difficult for management to monitor performance achievements quickly and accurately. This study aims to design a Business Intelligence system to evaluate the institution’s performance using Microsoft Power BI. The research method employed is Design Science Research with a qualitative descriptive approach. The research stages included identifying information needs, collecting data for the 2023–2024 operational period, designing a Data Warehouse using the Star Schema model, performing the Extract, Transform, Load (ETL) process, and developing a dashboard based on key performance indicators (KPIs) aligned with the Balanced Scorecard. The research results show that the designed system is capable of integrating previously fragmented data into a single, concise information source, accelerating the reporting process, improving information accuracy, and facilitating interactive monitoring of performance indicators.
Customer Relationship Management Strategy for Enhancing Customer Loyalty at MM Beauty Clinic Using the K-Means Algorithm Hazlita Hazlita; Hafizhah Mardivta; Aysyah Rengganis
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15780

Abstract

MM Beauty Clinic is one of the beauty service providers in North Sumatra Province that still carries out most of its service processes manually. This condition has the potential to reduce service quality and increase the risk of customers switching to other clinics. This study aims to design and implement a web-based Customer Relationship Management (CRM) system to strengthen customer loyalty. The system was developed using the PHP programming language and a MySQL database. The approach used is Recency, Frequency, and Monetary (RFM) analysis combined with the K-Means algorithm to segment customers based on their transaction patterns. The data used are patient transaction data for April 2026. The segmentation results are used as the basis for determining appropriate service strategies for each customer group. The implementation of this CRM system is expected to improve the effectiveness of customer relationship management, as well as facilitate transaction processing, consultation services, complaint submission, and access to service information more quickly, effectively, and efficiently.
Implementation of Artificial Neural Networks in an Android-Based Online Course Recommendation Application Muhamad Syaiful Anwar
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.13177

Abstract

The growing interest in online courses has caused information overload. Users now struggle to choose suitable programs. Machine Learning (ML) can address this problem through a Recommendation System Model based on an artificial neural network (ANN). This research aims to address this issue by designing an intelligent, ML-based recommendation system integrated within an Android app. The system uses a TensorFlow-built ML model to provide personalized course recommendations. The native Android application is developed with Kotlin and Jetpack Compose. Results show that the developed model provides recommendations that match user preferences and is successfully implemented in the online course recommendation application 'Rekomendasi Kursus Online.' Supporting features include Google account authentication, cloud storage, synchronization for favorite course lists, and dark mode. These features work as expected. User testing with the System Usability Scale (SUS) yielded a score of 83.17. This places the app in the Grade B category with a "Good" rating, showing the application is well-designed and suitable for use.
Integration of Artificial Intelligence Technology as an Innovation in Learning Resources in Islamic Education Annisa Maulida Setiawan
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15375

Abstract

This article aims to analyze the opportunities and challenges of integrating artificial intelligence (AI) tools as learning resources in Islamic education. The rationale behind this research arises from the demands for learning innovation in the digital era, which requires the use of AI to improve educational efficiency and relevance. This analysis aims to identify AI's potential to assist with learning adjustments, improve digital literacy, and expand access to learning resources, while also considering existing constraints such as teacher preparedness and infrastructure limitations. The method used was literature review. Data collection was conducted through secondary data collection. Secondary data was obtained from various sources, including scientific articles, journals, books, and relevant research reports. The study's findings indicate that AI integration offers significant potential for enriching learning methods and supporting personalized learning, access to learning resources, and increasing the effectiveness of digital-based learning. Within Islamic education, AI can help teachers design more interactive materials and provide learning simulations. However, careful implementation strategies are required to avoid conflicting with Islamic educational principles.
Application of K-Means Clustering to Group Islamic Boarding School Students' Kitab Kuning Reading Proficiency Based on Nahwu and Sharraf Luluk Suhartini; M. Burhanis Sulthan
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15769

Abstract

The ability to read classical Islamic texts (kitab kuning) is an important indicator for assessing students' mastery of Islamic sciences in Salafiyah Islamic boarding schools. However, the assessment of kitab kuning reading proficiency is often still conducted conventionally and tends to be subjective; therefore, a data-driven approach is needed to support a more objective student-grouping process. This study aims to apply the K-Means Clustering algorithm to group students based on their Nahwu and Sharraf scores as indicators of kitab kuning reading proficiency. The research methods included observation, interviews, literature review, data transformation, normalization using Min-Max Normalization, and clustering using Microsoft Excel and RapidMiner. The dataset consisted of 376 student records with the attributes of age, education level, Nahwu score, and Sharraf score. Manual grouping using Microsoft Excel produced three clusters: C1 with 57 students, C2 with 244 students, and C3 with 75 students. Meanwhile, the RapidMiner results showed C1 with 56 students, C2 with 244 students, and C3 with 76 students. The results indicate that K-Means can be used as a basis for grouping students' abilities in a more structured manner.
Comparative Analysis of Decision Tree, Neural Network, K-NN, GBT, SVR and Random Forest Algorithms for House Price Prediction Sahal Abdillah; Mochamad Taufiqurrohman Abdul Aziz Zein; Edy Sulistiyanto
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.13234

Abstract

This study aims to analyze and compare the performance of several machine learning algorithms for predicting house prices. The dataset was obtained from Kaggle with variables of house price, building area, land area, number of bedrooms, number of bathrooms, and garage. The algorithms used include Decision Tree, Neural Network, K-Nearest Neighbor, Gradient Boosted Trees, Support Vector Regression, and Random Forest. The study was conducted using Altair Studio through pre-processing, training, testing, and evaluation stages based on performance, computation time, and model complexity. The results showed that K-NN obtained the best RMSE value of 0.608, followed by Neural Network 0.630, Decision Tree 0.649, Random Forest 0.656, SVR 0.662, and GBT 0.747. Based on MAE, Neural Network obtained the best value of 0.464, followed by Random Forest 0.474, Decision Tree 0.478, K-NN 0.485, SVR 0.501, and GBT 0.589. Overall, K-NN was the best model because it had the lowest RMSE, the fastest training time of 0.004 seconds, and a relatively simple model complexity.
Analysis of the Implementation of a Learning Management System in Supporting the Digital Transformation of Education Based on Information Technology dhiyaa riha aisya
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15470

Abstract

This study aims to examine the use of Learning Management Systems (LMS) by highlighting their advantages, disadvantages, and implementation solutions in the context of online learning. LMS is a digital platform designed to facilitate distance learning processes, especially amidst the increasing demand for technology-based education. Literature analysis shows that LMS has several advantages, such as simplifying access to learning materials, providing flexibility in learning time, enhancing two-way communication, and providing systematic evaluation tools. However, its implementation still faces various shortcomings, including low interaction between teachers and students, minimal learning motivation, and suboptimal utilization of available features. To overcome these obstacles, solutions are needed in the form of ongoing training for educators, development of engaging content, and selection of LMS features that are appropriate to learning needs. The study's conclusion confirms that optimal use of LMS, by considering its advantages and addressing its disadvantages, can be an effective strategy for implementing quality online learning.
Laravel Filament-Based Driver Commission Management and Calculation System Automation Alfito Aditya Trishardi
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15886

Abstract

Mentari Restaurant relies on delivery services provided by drivers as part of its daily operations; however, the processes for recording deliveries and calculating driver commissions—ranging from data compilation to payment processing—are still handled manually. This approach leaves the system vulnerable to calculation errors, data opacity, and payment delays, while the lack of an integrated system hinders comprehensive monitoring of driver performance. This research developed a web-based Driver Commission System using the Laravel framework and the Laravel Filament administration panel. The system automates delivery logging, commission calculations, driver data management, and reporting within a single integrated platform, utilizing the Iterative Waterfall development method and data collection techniques such as observation, interviews, and document analysis. Black-box testing across sixteen scenarios demonstrated that all system functions (100%) operated as required; consequently, the system is expected to provide a concrete solution for improving commission calculation accuracy, accelerating payment processes, and strengthening trust between drivers and Mentari Restaurant management.
Forecasting the Population of Balikpapan City Using the Artificial Neural Network Method Vira Oktavia; Andri Azmul Fauzi; Yuki Novia Nasution
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.14751

Abstract

The continuous growth of the population requires prediction methods capable of generating accurate population estimates as a basis for development planning. This study aims to predict the population of Balikpapan City for the period 2025–2029 using an Artificial Neural Network (ANN) with the Backpropagation algorithm. The dataset consists of annual population data of Balikpapan City from 2010 to 2024. The data were processed using the sliding window technique, followed by normalization, model training, and testing. The proposed ANN model employed a 3–4–1 architecture with a learning rate of 0,5, a sigmoid activation function in the hidden layer, a linear activation function in the output layer, and 150 training epochs. The model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The experimental results show that the proposed model produces predictions that closely match the actual population data, achieving a MAPE value of 0,57%, which indicates a high level of prediction accuracy. The trained model was subsequently used to forecast the population of Balikpapan City for the period 2025–2029. The prediction results are expected to provide useful references for local governments in formulating policies and development planning that are aligned with future population growth.
Application of the K-Nearest Neighbor (KNN) Algorithm in Data Mining for Heart Disease Prediction Nur Aida
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.13481

Abstract

Cardiovascular (heart) disease is a leading cause of high global mortality rates. This is often exacerbated by low public awareness and limited access to cardiac screening facilities. To address these challenges, this study aims to apply the K-Nearest Neighbors (KNN) algorithm to classify heart disease and evaluate the resulting accuracy. The KNN algorithm was selected for its efficiency and ease of use in classifying large datasets; it works by measuring the distance between objects using the Euclidean distance formula. This classification model was built using RapidMiner 9.10 software. The data used was sourced from the Cleveland UCI Machine Learning Repository database available via Kaggle, consisting of a total of 303 patient records and characterized by 14 features used for prediction. The target variable (heart disease) was set as the classification label. Test results using cross-validation demonstrated that the KNN implementation is effective for predicting heart disease. This model achieved an accuracy of 64.03% with the parameter setting K=5. Further analysis of the confusion matrix identified 124 true-positive patients, with a maximum precision of 64.58%. Overall, these accuracy results confirm the potential and capability of the KNN method in classifying diagnostic data for heart disease patients.

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