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JOURNAL OFFICIAL Indonesian Applied Research Computing and Informatics Official Publisher by PT. TERAS DIGITAL NUSANTARA KOTA BIMA, INDONESIA
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Indonesian Applied Research Computing and Informatics
Published by PT. Teras Digital Nusantara
ISSN : -     EISSN : 31108806     DOI : https://doi.org/10.64479/iarci
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Focus and Scope Indonesian Applied Research Computing and Informatics Indonesian Applied Research Computing and Informatics is a scientific journal that publishes applied research in the fields of computing and informatics. The journal aims to serve as a platform for academics, researchers, and practitioners to disseminate innovative, practical, and impactful technology-based solutions, particularly in the context of advancing science and technology in Indonesia. Scope of Topics Artificial Intelligence and Machine Learning Information Systems and Databases Cloud and Distributed Computing Image and Signal Processing Web and Mobile Technologies Software Engineering Intelligent Systems and Expert Systems Internet of Things (IoT) Cybersecurity and Cryptography Big Data and Analytics
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1: July (2025)" : 5 Documents clear
Development of Personalized Recommendation System for Online Educational Content Based on Machine Learning Dwi Remawati; Khairunnisa; Afril efan Pajri; Kumaratih Sandradewi; Sri Hariyati Fitriasih
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

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Abstract

The rapid growth of online educational platforms has increased the demand for intelligent recommendation systems that can personalize learning content to match individual learner needs. However, traditional methods such as Content-Based Filtering (CBF) and Collaborative Filtering (CF) often struggle with issues like data sparsity, limited adaptability, and cold-start problems. This study aims to develop a personalized recommendation system for online educational content by integrating Singular Value Decomposition (SVD) with an adaptive feedback loop to improve recommendation relevance and learner engagement. The proposed machine learning-based method captures latent user-item interactions and dynamically updates recommendations based on real-time user feedback. Experimental evaluation using a dataset of simulated learner interactions demonstrates that the proposed model significantly outperforms baseline methods, achieving higher scores in Precision (0.57), Recall (0.53), F1-Score (0.55), Mean Reciprocal Rank (MRR: 0.52), and Engagement Rate (72.1%). These results suggest that combining matrix factorization with adaptive learning can substantially enhance the performance of educational recommender systems, leading to more accurate, timely, and engaging content delivery.
Development of a Web-Based Rolling and Attendance System for News Coverage Employees at the Communication and Information Office (Kominfo) of Bima City Nabila Melani Putri; Muhammad Amirul Mu'min; Fathir; Suci Faaza Naafia
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

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Abstract

The management of attendance and assignment of news coverage tasks at the Public Relations Division of Kominfo Bima City has previously been handled manually, resulting in inefficiency and disorganization. This study aims to develop a web-based system that facilitates digital employee attendance recording and automated rolling of news coverage assignments. The development method used is the Waterfall model, involving stages of requirement analysis, system design, implementation, and testing. The system is developed using PHP as the programming language and MySQL as the database, with a responsive user interface built using HTML and CSS. Implementation results show that the system can record real-time attendance, distribute assignments fairly, and ease the performance monitoring of employees. This system is expected to improve work efficiency and support digital transformation in government institutions, especially in public relations workforce management.
Development of a Dashboard-Based Information System to Improve Prospective Customer Engagement at PLN UP3 Bima Aldillah; Zumhur Alamin; Lailia Rahmawati; Sutriawan; Teguh Ansyor Lorosae; Fitriani Ramadhani
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

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Abstract

PT PLN (Persero) UP3 Bima faces challenges in effectively managing and analyzing prospective customer data, resulting in delays in decision-making and suboptimal utilization of potential connected power. This study aims to develop an interactive dashboard system using Looker Studio and Google Sheets to improve operational efficiency and support digital transformation within PLN. The methodology includes user needs analysis, real-time data integration from Google Sheets, and the design of data visualizations in Looker Studio based on key parameters such as customer growth trends, sector classification, and potential connected power. The implementation results show that the system effectively delivers accurate and timely information, assisting management in identifying opportunities to increase new customer connections. The impact of this system includes enhanced effectiveness in managing prospective customer data, faster decision-making processes, and stronger support for data-driven strategies to increase customer acquisition in a measurable way.
Optical Character Recognition (OCR) Of License Plates Using the KNN Method Abim Tisanarada; Yo Ceng Giap
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

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Abstract

This study aims to implement an IoT-based security system with character recognition (OCR). The OCR system utilizes a webcam and the KNN method to recognize vehicle license plate text in real-time. This prototype was tested using six samples of the latest Indonesian license plates. The character detection process involves steps such as capturing images from the webcam, preprocessing images to improve contrast and convert them to grayscale, and applying calibrated transformations. Image inversion and thresholding are performed to separate characters from the background. Character segmentation and filtering criteria are also performed to clean the character image from noise and remove inappropriate backgrounds. The detected characters are identified using Region of Interest (ROI) detection to ensure the validity of the characters. The validated contours are sorted from left to right to form the complete license plate number. Subsequently, KNN implementation is used to recognize the detected characters. Test results indicate that the KNN-based webcam license plate detection system, with K set to 1, performs well and achieves a sufficiently high level of performance. Testing at camera-to-license plate distances of 60 cm, 70 cm, and 80 cm shows an average accuracy rate of 100% within 5 seconds. This research contributes to the development of an efficient and accurate vehicle license plate recognition system for various applications, including parking systems and access control.
Improving Thesis Title Classification Accuracy Using Ensemble Classifier and Modified Chi-Square Feature Selection Method Ritzkal; Wahyu Tisno Atmojo; Panji Novantara; Sabir Rosidin; Ahmad Dedi Jubaedi; Enggar Novianto
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

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Abstract

Text classification of academic documents, particularly thesis titles, poses challenges due to high dimensionality, sparsity, and topic heterogeneity. Conventional feature selection techniques, such as the standard Chi-Square, often fall short in capturing discriminative features effectively. This research aims to enhance classification accuracy by proposing a Modified Chi-Square feature selection method that integrates term frequency and class distribution information. The selected features are then classified using ensemble decision tree algorithms, including Random Forest, Gradient Boosting, and XGBoost. Experiments were conducted on a labeled dataset of thesis titles using TF-IDF for vector representation. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC were used to assess model performance. The results showed that the combination of Modified Chi-Square and XGBoost outperformed other models, achieving the highest accuracy of 93.8% and an AUC of 0.94. These findings demonstrate that the integration of advanced feature selection and ensemble learning techniques can significantly improve academic text classification performance, providing valuable implications for the development of intelligent digital repositories and recommendation systems.

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