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All Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Teknik Informatika (JUTIF) IC Tech: Majalah Ilmiah IC Tech: Majalah Ilmiah
NAWANGNUGRAENI, DEVI ASTRI
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Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mechanical Engineering Public Health Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
A COMPARATIVE STUDY OF MULTI-MASTER REPLICATION OF NOSQL DATABASE SERVER WITH VARYING DATA FORMATS Wibowo, Dwi Kurnia; Darmawan, Agus; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4371

Abstract

NoSQL Databases are currently an effective solution for managing large data sets distributed across many Servers. NoSQL Database design is usually based on its usability. Specifically related to the system or application to be built. This research aims to measure the Transfer Rate, CPU usage, Memory usage, query execution time for Create, Insert, Delete and remote replication query bandwidth in the Multi-Master Server replication process using two document stored NoSQL Database applications namely CouchBase and CouchDB by entering three different data models namely JSON, XML and CSV. The experimental results show that the Transfer Rate with CSV data format on CouchBase has the lowest value with an average of 111.41 kbps. CPU usage with XML data format on CouchBase has the lowest value with an average of 13.89%. Memory usage with JSON data format on CouchBase has the lowest value with an average of 1.68%. Query Execution Time Create with XML data format on CouchBase has the lowest value with an average of 1.16 seconds. Query Execution Time Insert on CouchBase with CSV data format has the lowest value with an average of 33.28 seconds. Bandwidth Query Execution Time Insert with CSV data format on CouchBase has the lowest value with an average of 24.78 mb. Query Execution Time Delete with JSON, XML and CSV data formats on CouchDB has the lowest value with an average of 1.5 seconds. Further research recommendations are to test Multi-Master Server Replication using other data formats and parameters or test the performance of data migration to other Databases with different data formats.
Comparison of Accuracy and Computation Time for Predicting Earthquake Magnitude in Java Island Yuniarto, Abdul Hakim Prima; Hariguna, Taqwa; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5044

Abstract

Java Island has numerous active faults, making earthquake magnitude prediction a crucial component of disaster mitigation efforts. This study conducted a rigorous comparative analysis of four machine learning algorithms—Random Forest, Neural Network, Linear Regression, and Support Vector Machine—to determine their effectiveness in this specific task. The methodology employed involved systematic hyperparameter optimization for each model to ensure a fair and robust evaluation, with performance measured by Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and training time. The results showed that all three nonlinear models significantly outperformed Linear Regression. Random Forest achieved the highest accuracy (RMSE 0.5445), but Support Vector Machine and Neural Network demonstrated very competitive and nearly equal performance. The study concluded that while Random Forest has a slight advantage, several state-of-the-art models are highly capable of addressing this problem after appropriate optimization. This underscores the critical role of methodical tuning and implies that model selection in practical applications depends on a trade-off between modest improvements in accuracy and computational efficiency.
An Integrated Pipeline with Hierarchical Segmentation and CNN for Automated KTP-el Data Extraction on the e-Magang Platform Syafrie Rahardian, Nuansa; Maryanto, Eddy; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5279

Abstract

In alignment with Indonesia's digital transformation agenda, this research addresses the inefficiencies and error-prone nature of manual data entry on the Foreign Policy Strategy Agency's (BSKLN) e-magang platform. This study introduces a comprehensive, end-to-end Optical Character Recognition (OCR) pipeline, specifically designed for structured identity documents and real-world government platform integration. The proposed methodology features a robust workflow, including image preprocessing with histogram matching, hierarchical segmentation using vertical projection, and intelligent postprocessing to structure the output. To overcome the limitations of a small dataset, three specialized Convolutional Neural Network (CNN) models were rigorously trained and validated using a stratified 5-fold cross-validation technique. The final system was successfully integrated, connecting a Flask-based model engine with the existing Laravel and React platform. End-to-end testing demonstrated strong performance, achieving an average character-reading accuracy of 93.31% with a mean processing time of 14.48 seconds per image. The primary contribution of this research to the field of informatics is the development of a complete and deployable system architecture that ensures data interoperability and reliability, providing a practical blueprint for integrating intelligent automation into digital public services.
Design and Implementation of Kernel-Based Quantum Classification Algorithms for Data Analysis in Software Engineering using Quantum Support Vector Machine (QSVM) Abdillah, M. Zakki; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5030

Abstract

With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.
Co-Authors Abdul Hakim Prima Yuniarto Abdul Razak Naufal Agus Darmawan Al’Amin, M. Amrullah, Hadi Eddy Maryanto M. Zakki Abdillah Ramadhan, Dicky Satria Suseno, Akrim Teguh Syafrie Rahardian, Nuansa Taqwa Hariguna Wibowo, Dwi Kurnia