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All Journal Jurnal Buana Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Ilmiah Ekonomi Islam JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Jurnal Sains dan Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Martabe : Jurnal Pengabdian Kepada Masyarakat RISTEKDIK : Jurnal Bimbingan dan Konseling KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Riset Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan INVENTORY : Industrial Vocational E-Journal On Agroindustry Jurnal Abdimas Komunikasi dan Bahasa BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Accounting Information System Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat Artikula
Ernawati, Siti
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Journal : Jurnal Riset Informatika

 1 
IMPROVING IMAGE CLASSIFICATION ACCURACY WITH OVERSAMPLING AND DATA AUGMENTATION USING DEEP LEARNING: A CASE STUDY ON THE SIMPSONS CHARACTERS DATASET Maulana, Ilham; Ernawati, Siti; Indra, Muhammad
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.348

Abstract

The issue of data imbalance in image classification often hinders deep learning models from making accurate predictions, especially for minority classes. This study introduces AugOS-CNN (Augmentation and Over Sampling with CNN), a novel approach that combines oversampling and data augmentation techniques to address data imbalance. The The Simpsons Characters dataset is used in this study, featuring five main character classes: Bart, Homer, Agnes, Carl, and Apu. The number of samples in each class is balanced to 2,067 using an augmentation method based on Augmentor. The proposed model integrates oversampling and augmentation steps with a Convolutional Neural Network (CNN) architecture to improve classification accuracy. Evaluation results show that the AugOS-CNN model achieves the highest accuracy of 96%, outperforming the baseline CNN approach without data balancing techniques, which only reaches 91%. These findings demonstrate that the AugOS-CNN model effectively enhances image classification performance on datasets with imbalanced class distributions, contributing to the development of more robust deep learning methods for addressing data imbalance issues.
Prediction Of Flight Delays Using Feature Engineering, Catboost, And Bayesian Optimization To Improve Model Performance Maulana, Ilham; Ernawati, Siti; Wati, Risa
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.346

Abstract

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.
Digitalization Of Survey And Mapping Service Processes Through The Development Of A Web-Based System Ernawati, Siti; Hermawan, Deni
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.352

Abstract

PT. Cakrawala Pilar Nusantara is a private company engaged in survey and mapping consultancy services. Several challenges have been identified in its business processes, one of which is that service delivery for collaboration is still conducted manually. This study adopts a Design Science Research (DSR) approach, focusing on the development of an artifact in the form of a web-based service system for PT. Cakrawala Pilar Nusantara, in accordance with the objectives of the research. The DSR methodology consists of the following stages: Problem Identification and Research Motivation, Definition of Solution Objectives, Design and Development of the Artifact, Demonstration, Evaluation, and Communication. Data collection was carried out through observation and interviews with relevant parties. System design visualization was conducted using UML, represented by use case diagrams and activity diagrams. The programming language used is PHP, implementing the CodeIgniter framework. System testing was performed using the black-box testing method.The result of this research is a web-based information system that facilitates data entry, quotation submissions, reporting, and improves service processes by transforming manual record-keeping into a computerized system. The presence of this information system provides greater convenience for the company in managing its operational activities.
Co-Authors Abadi, Muhammad Djaka Ahmad Sopian, Ahmad Al Hakim, Imam Taftazani Alfahran Islami, Hasby Asri Utami, Lilyani Asy’ari, Hasyim Deni Hermawan Eka Rini Yulia Fatoni, Raden Mirza Kurnia Frieyadie Hasyim Asyari Hasyim Asyari Heriyani, Novia Ilham Maulana Lestari Yusuf Lisda Widiastuti Mahmudah, Sintia Masturoh, Masturoh Miharja, Rinaldi Budi Muhamad Ryansyah MUHAMMAD INDRA Muhammad, Katon Mutakin, Fakhruddin Prabowo, Sigit Putra Pratama, Andika Agus Putut Handoko, Nurul Rahayu, Septi Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Satrio, Muhammad Setiawan, Ronny Setyawati, Febryana Nabilla Tuslaela Utami, Cahyaning Budi