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Nuansa Informatika
Published by Universitas Kuningan
ISSN : 18583911     EISSN : 26145405     DOI : https://doi.org/10.25134/nuansa
Core Subject : Science,
Computer Science & IT
NUANSA INFORMATIKA adalah jurnal peer-review tentang Informasi dan Teknologi yang mencakup semua cabang IT dan sub-disiplin termasuk Algoritma, desain sistem, jaringan, game, IoT, rekayasa Perangkat Lunak, aplikasi Seluler, dan lainnya
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 112 Documents
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Deep Learning Evaluation for Interactive Dashboard-Base Mail Classification: Evaluasi Pembelajaran Mendalam untuk Klasifikasi Email Berbasis Dasbor Interaktif Ruth Diana Purnamasari; Nisa Hanum
NUANSA INFORMATIKA Vol. 20 No. 1 (2026): Nuansa Informatika 20.1 Januari 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i1.546

Abstract

The management of incoming mail archives at a large national logistics company in Indonesia generates a large volume of unstructured textual data, making manual classification inefficient and error-prone. This study evaluates the performance of deep learning models for administrative mail archives classification using data collected between 2023 and 2025. Three models are examined, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN). Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. Experimental results indicate that CNN achieves the highest accuracy of 85.82%, outperforming LSTM and Bi-LSTM models. This superior performance is attributed to CNN’s ability to capture local textual patterns through convolution operations, which are well-suited to the structured and repetitive language characteristics of official correspondence. To support practical interpretation, an interactive dashboard is implemented as a visualization tool for model evaluation results, classification outcomes, and clustering analysis. These findings demonstrate that deep learning-based approaches integrated with visual analytics can significantly improve the efficiency and accuracy of unstructured mail archive management
Development of a Mobile Application for Booking Meeting Rooms at PLN UP2D West Java: Pengembangan Aplikasi Mobile untuk Pemesanan Ruang Rapat di PLN UP2D Jawa Barat Wafid Adzka Haifan Mukhayyar; Nisa Hanum
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.556

Abstract

At PLN UP2D West Java, the meeting room booking process still often faces several problems. Employees frequently experience schedule conflicts, difficulty finding clear information about room availability, and delays caused by manual confirmation. These issues can make employees confused and waste time, especially when meetings need to be arranged quickly. As a result, work activities do not always run smoothly and meeting schedules are sometimes disrupted. To solve these problems, a mobile meeting room booking application was developed. This application allows employees to check room availability in real time and make reservations easily without going through a long manual process. The system also helps prevent double bookings, so each meeting room can be used more effectively. The application is designed with a simple and user-friendly interface, making it easy for all employees to use anytime and anywhere. With this application, the meeting room booking process becomes more organized, efficient, and supports smoother daily work activities at PLN UP2D West Java
Hybrid PSO-Adaptive Boosting Regression for Employee Salary Prediction and Recommendation M. Amran Hakim Siregar; Bachtiar Ramadhan; Syafrial Fachri Pane
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.561

Abstract

Recommending appropriate employee salaries is important for supporting employee performance and data-driven managerial decisions. This study develops a hybrid machine learning model to recommend employee salaries and identify influential factors affecting monthly income. The dataset was obtained from Kaggle and consisted of 1,029 employee records with 34 variables covering company, personal, and demographic characteristics. Data preprocessing included categorical encoding, missing-value handling, duplicate checking, and outlier removal using the Interquartile Range method. The proposed approach combines Particle Swarm Optimization for variable optimization with an AdaBoost Regressor selected through TPOT Regression. Model performance was evaluated using R-Square and Mean Absolute Percentage Error. The PSO-AdaBoost Regressor achieved an R-Square value of 0.88 and a MAPE value of 0.22. Feature importance analysis identified Job Level as the most influential feature, with a score of 0.97156. The results were implemented in a Django-based web application
Development of a Web-Based Library Information System Using the Laravel Framework: Pengembangan Sistem Informasi Perpustakaan Berbasis Web Menggunakan Framework Laravel Adrian Rama Putra; Arif Bakti Nugraha
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.564

Abstract

School libraries play an important role in supporting learning activities, but many are still managed manually, causing difficulties in managing book data, searching collections, and recording borrowing and returning transactions. SMP PGRI 9 Bandung faces similar challenges due to the absence of an integrated library management system. Previous studies have developed web-based library systems, but limited attention has been given to the combined integration of public catalog access, librarian-only administration, borrowing and returning transactions, fine payment recording, report generation, and website content management in a Laravel MVC-based system tailored to junior high school library operations. This study aims to design and develop a web-based library information system using the Laravel framework and the Waterfall method. The proposed system integrates book, category, publisher, member, borrowing, returning, fine payment, reporting, and public catalog search features into a single platform. Testing was conducted using black-box testing and user acceptance testing (UAT). The black-box testing results showed that all 10 functional scenarios were successful, achieving a 100% functional success rate. Meanwhile, UAT involving five respondents produced an average score of 4.52 out of 5.00, or 90.4%, which falls into the very feasible category. The specific contribution of this study is the development of an integrated Laravel MVC-based library system that combines administrative efficiency, public service accessibility, fine and report management, and customizable website content according to the operational needs of SMP PGRI 9 Bandung. The results indicate that the system can support more organized data management, improve service accessibility, and facilitate library transaction processing
Web Based Task Management System Integrating the Pomodoro Technique with Productivity Analytics Syafrial Fachri Pane; Muhammad Baihaqi Siregar; Zidan Ardiansyah; Muhammad Amran Hakim Siregar
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.565

Abstract

Effective time management plays a crucial role in maintaining academic productivity among university students. However, increasing academic workloads and digital distractions often make it difficult for students to maintain consistent focus while completing their tasks. Although numerous task management and Pomodoro-based applications exist, most tools address either task organization or timed focus sessions in isolation, without integrating both functionalities into a unified productivity monitoring system. This research gap motivates the development of a system that combines structured time management with data-driven productivity analytics, which is not found in widely used tools such as Todoist or standard Pomodoro timers. This study proposes a web-based task management system that integrates the Pomodoro Technique with productivity analytics to support structured productivity monitoring. The proposed system was evaluated using activity data from recorded focus sessions comprising 120 Pomodoro sessions across a four-week observation period. The productivity aggregation model achieved a task completion rate of 78.3%, with an average daily focus duration of 142 minutes. The system successfully processed all recorded sessions without data discrepancies, and trend visualizations confirmed consistent productivity improvements over the observation period. The results show that the integrated Pomodoro–analytics approach effectively transforms recorded focus session data into measurable productivity indicators, achieving a 78.3% task completion rate and an average daily focus duration of 142 minutes across 120 sessions the integrated system into a scientifically measurable and structured productivity monitoring tool, distinguishing it from existing standalone task managers and Pomodoro applications through its unified analytics capability.
Data Integrity Validation For Used Cooking Oil Logistics Based On Haversine Algorithm And Image Metadata: Validasi Integritas Data untuk Logistik Minyak Goreng Bekas Berdasarkan Algoritma Haversine dan Metadata Gambar Thoriq Haqqi Adha; Widya Wulandari; Azzahra Safitri; Fahreza; Gunawan
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.585

Abstract

The implementation of Indonesia’s B50 biodiesel mandate faces weak data verification within the used cooking oil (UCO) reverse logistics chain managed by MSMEs. Conventional trackers cannot validate the authenticity of field coordinates and photographic evidence, creating data vulnerabilities. This study developed the Capture-Guard Framework, a low-cost solution combining the Haversine Algorithm for spatial validation and EXIF metadata parsing for temporal-device validation. An experimental simulation was executed using an expanded synthetic dataset of 200 comparative entries. By evaluating logs against a 10-meter spatial boundary and a 120-second temporal tolerance protocol, the framework successfully filtered all malicious logs, yielding an overall binary classification accuracy of 93.5%. The system achieved a perfect Precision rate of 100% due to zero False Positive occurrences, while environmental noise and sensor drifts were safely isolated under a tiered verification class, resulting in a Recall rate of 87.0% and a robust F1-Score of 0.93. These empirical findings demonstrate that securing data integrity at the first-mile validation point provides a clean audit trail vital for supporting raw material certainty within the B50 biodiesel ecosystem
Implementation Of Extreme Programming Method In Financial Reporting Application With Laravel Framework Akhmad Syukron; Andria Bas Nando; Sardiarinto; Eko Saputro
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.588

Abstract

The advancement of information technology compels companies to adopt more efficient, accurate, and integrated approaches to financial data management. CV Mawar Magenta, a service-oriented company, still relies on manual bookkeeping and separate Microsoft Excel files for financial reporting. This method leads to duplicate tasks, a high risk of data entry errors, and delays in generating financial reports. To address these issues, a web-based Financial Reporting Application was designed and developed using the Laravel Framework. The system development employed the Extreme Programming (XP) methodology and includes core features such as cash inflow and outflow records, general journal, ledger, profit and loss reports, and balance sheet. The system was tested using the black-box testing method to ensure all functionalities align with user requirements. The implementation results show that the application simplifies financial record-keeping by integrating previously separate processes into a single system. Additionally, it streamlines data retrieval and enables business owners to monitor financial conditions in real time, thereby supporting faster and more accurate decision-making.
Development of an Anti-Cheating Online Examination System at MTs Assulaimaniyyah: Pengembangan Sistem Ujian Daring Anti-Kecurangan di MTs Assulaimaniyyah Sri Widaningsih; Agus Suheri; Muhamad Juanda
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.592

Abstract

Digital transformation in the field of education encourages the implementation of online examination systems to improve the effectiveness and efficiency of the learning evaluation process. However, the implementation of online exams still has weaknesses, especially related to supervision and the potential for cheating by exam participants. This study aims to develop a web-based online examination system with anti-cheating features at MTs Assulaimaniyyah. The system development method used is the waterfall method, which consists of the stages of communication, planning, modeling, construction, and deployment. The system was built using the PHP programming language with the Laravel framework and a MySQL database. The anti-cheating features implemented include fullscreen mode, prevention of copy-paste actions, prohibition of using two screens, screenshot restrictions, question randomization, exam time limitations, and tab-switching detection that can cause the exam to stop automatically. The results of the study show that the system is capable of helping the exam implementation process become more effective, simplifying the management of questions and exam results, and improving supervision during the exam. With the anti-cheating features, the system can support the creation of a more honest, fair, and accurate evaluation process, thereby contributing to the improvement of educational quality
Website-Based Potato Leaf Disease Detection Using the EfficientNetV2L Deep Learning Architecture Umi Khultsum
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.593

Abstract

Potato plants are seasonal crops, where potatoes are classified as a nutrient-rich food ingredient. The leaves of potato plants can be attacked by diseases caused by bacteria and viruses. The modern agricultural era makes farmers face many challenges related to the recognition of potato leaf diseases. Potato leaf diseases can be recognized visually, but the recognition carried out by farmers has a drawback, namely the process of identifying the type of disease that takes a long time, this can disrupt productivity and sustainability of the harvest. The purpose of this study is to produce a model that is able to recognize the type of potato leaf disease using the Convolution Neural Network (CNN) method using the EfficientNetV2L architecture. The EfficientNetV2Larchitecture has the ability to perform good feature extraction and high computational efficiency compared to other CNN architectures. The dataset used in this study is potato leaf images taken from Kaggle, there are 2 disease classes and 1 healthy class with a total of 2,300 potato leaf images. The model proposed in this study is able to identify potato leaf disease images well and has a testing accuracy of 99.00%, training accuracy of 99.35%, validation accuracy of 98.60%, precision of 98.95%, recall of 99.02% and F1-Score of 99.98%. The model was developed into a website-based system that can quickly and accurately identify potato leaf diseases. Implementing this website system offers the advantages of easy access and speed in the potato leaf disease identification process.
Implementation of CI/CD Architecture Based on GitHub Actions and Docker on Low Resource Servers Agun Guntara; Wildan Fathir Qinthara; Yanyan Sofiyan
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.596

Abstract

Manual application integration and deployment often introduce severe operational bottlenecks, including high human error configuration risks and strict dependency on individual system administrators. This research aims to design and implement an automated Continuous Integration and Continuous Deployment (CI/CD) pipeline architecture utilizing GitHub Actions and Docker containerization for an attendance web application at Puskesmas Cimalaka. Employing a descriptive qualitative method with a case study approach, this study focuses on evaluating the workflow transformation and operational efficiency within a limited, low-resource server environment. The implementation results reveal that the proposed architecture successfully shifts the operational workload from human iterative routines to external cloud runner infrastructures, isolating the intensive environment compilation and monolithic dependency builds away from the target host. In conclusion, this structural deployment framework radically compresses active developer engagement to near zero, heavily mitigates operational risks under rigid hardware boundaries, and provides a highly stable, secure, and easily replicable DevOps deployment blueprint for medium-to-low scale public institutions

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