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All Journal ELKHA : Jurnal Teknik Elektro Indonesian Journal on Software Engineering (IJSE) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Engagement: Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Paramita, Cinantya
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

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Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti; Ahmed, Foez
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5207

Abstract

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.
Development of E-Catalog Design as a Promotional Medium to Support Digital Transformation and Professional Certification for Batik Communities Amalia, Amalia; Paramita, Cinantya; Izzhati, Dwi Nurul; Tjahyono, Rudi; Syamwil, Rodia; Febrian, Nanda Dwi; Nugroho, Adi
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 2 (2025): November 2025
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v9i2.2116

Abstract

The Indonesian government is encouraging the digitalization of public procurement to strengthen the use of domestic products and services, including competency certification. The LSP Batik is a professional certification institution that assesses the competencies of batik artisans and communities. They face challenges due to limited digitalization and restricted access to certification information. This project aims to develop an e-catalog design for the certification schemes and to enhance the understanding of digital procurement. A Participatory Design approach was adopted, actively engaging both end-users and key stakeholders across all phases of the co-creation and evaluation process. The project culminated in the development of an e-catalog integrated with the government's e-procurement portal, designed to modernize the services of LSP Batik. The design evaluation yielded high suitability scores for illustration and contrast (100%), followed by the completeness of information (88%), and branding and clarity of information (75%). Furthermore, user assessment indicated a high degree of comprehension in product search functionality (100%), while understanding of product comparison (63%) and showcase development (50%) was moderate, suggesting areas for future improvement.
Disparitas Efektivitas CLAHE pada Berbagai Arsitektur Deep Learning untuk Klasifikasi Katarak Berbasis Citra Fundus Ramadhani, Frida; Paramita, Cinantya; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8725

Abstract

This study aims to highlight and compare the performance of three deep learning architectures, namely CNN, VGG16, and EfficientNet-B1, in classifying cataract conditions based on retinal fundus images. A total of 2600 fundus images of two classes (normal and cataract) were collected from open sources and processed in two versions: the original images and contrast-enhanced images using Contrast Limited Adaptive Histogram Equalization (CLAHE). Each model was tested using both versions of the dataset, with evaluation based on accuracy, precision, recall, and F1 score. The results of this experiment show that the application of CLAHE is proven to improve the accuracy of CNN from 0.89 (89%) to 0.97 (97%) and, importantly for clinical diagnosis, improve the recall for cataract class from 0.81 (81%) to 0.97 (97%) with precision 0.98 (98%), f1 score 0.97 (97%) and reduce the number of False Negatives (FN) from 9 to 6. Similarly, it improves the accuracy of VGG16 from 0.93 (93%) (with precision 0.91 (91%), recall 0.96 (96%), f1 score 0.94 (94%)) to 0.96 (96%) (precision 0.94 (94%), recall 0.98 (98%), f1 score 0.96 (96%), and also reduces the number of FN from 9 to 6, thereby improving clinical reliability. In contrast to the EfficientNet-B1 Model, CLAHE does not provide significant improvement. significant. significant, with an accuracy of 0.97 (97%), precision of 0.98 (98%), recall of 0.98 (98%), and f1 score of 0.97 (97%), the accuracy performance actually decreased to 0.96 (96%) and precision to 0.94 (94%). This shows that the effectiveness of preprocessing techniques is highly dependent on the model architecture used. CLAHE has been shown to be effective on conventional models such as CNN and VGG16, but is less optimal for complex pretrained models such as EfficientNet-B1. These findings contribute to the development of adaptive and efficient medical image classification systems, particularly in the context of automated cataract screening in primary healthcare.
Interpretable Hybrid YOLOv8s-GWO Framework for Bounding-Box Viral Pneumonia Detection on Kaggle Chest X-ray Images Jalaluddin Amron, Azmi; Paramita, Cinantya; Šolić, Petar; Supratiknyo, Supratiknyo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5419

Abstract

Viral pneumonia continues to impose a substantial global health burden, making rapid and reliable radiographic detection essential for early clinical management. This study proposes a hybrid framework integrating the YOLOv8s detection model with the Grey Wolf Optimizer (GWO) to enhance hyperparameter tuning for Viral Pneumonia identification in chest X-ray images. A curated set of Normal and Viral Pneumonia samples was manually annotated and preprocessed before training. The optimization process involved multi-stage refinement of learning rate, momentum, weight decay, and loss-gain parameters to improve convergence stability and detection accuracy. The optimized YOLOv8s + GWO model demonstrated notable performance gains, achieving 0.965 recall, 0.983 mAP@50, and 0.827 mAP@50–95 on internal evaluations. External testing further validated its robustness, delivering 98.80% accuracy, 99.48% specificity, and 97.46% sensitivity. These results highlight not only enhanced clinical diagnostic reliability but also contributions to Informatics and Computer Science, demonstrating the effectiveness of metaheuristic-guided optimization in improving deep-learning model performance, generalization, and computational efficiency for AI-driven image detection tasks.
A Hybrid Deep Learning Architecture for Cost-Effective, Real-Time IV Infusion Anomaly Detection using IoT Sensors Brian Nafis, Muhammad; Paramita, Cinantya; Wright , Sasha-Gay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5440

Abstract

Intravenous (IV) infusion therapy is a critical medical procedure, yet manual monitoring increases the risk of complications such as air embolism and irregular infusion flow, particularly in resource-constrained environments. Although several automated infusion monitoring systems have been proposed, their high implementation cost limits practical adoption. This research develops a low-cost IoT-based infusion monitoring system capable of real-time anomaly detection using a multi-architecture machine learning approach. The proposed prototype integrates an ESP32 microcontroller with load cell (HX711) and optical (LM393) sensors to acquire time-series infusion data. Ten models from classical machine learning, deep learning, hybrid, and ensemble categories were evaluated using a dataset of 10,420 records under a unified experimental setup. The results show that XGBoost had a perfect recall (1.0000) and a strong PRAUC, while the LSTM Autoencoder had the highest F1-Score (0.9343) and precision (0.8934). The best overall performance came from hybrid and ensemble methods, with CNN–LSTM having an F1-Score of 0.89, a recall of 0.99, and a precision of 0.80. This means they would be great for clinics where being sensitive is very important. The research shows that using a low-cost IoT infrastructure with carefully chosen deep learning or ensemble models can help find problems in real time. A web dashboard explains how the technology operates and its capabilities. This study examines a cost-effective and easily scalable method to enhance infusion safety in hospitals with limited financial resources.
Co-Authors Abu Salam Adhitya Nugraha Adi Nugroho Ahmed, Foez Amalia Amalia Andreas Wilson Setiawan Astuti, Yani Parti Brian Nafis, Muhammad Catur Supriyanto Dwi Eko Waluyo Dwi Nurul Izzhati Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Fauzi Adi Rafrastara Febrian, Nanda Dwi Finoe, Albireo Musyaffa Guruh Fajar Shidik Hayu Wikan Kinasih Hussein, Jasim Nadheer Jalaluddin Amron, Azmi Junta Zeniarja Kencana, Lisdi Inu Manaf, Humam Wajdi Muljono Muljono Mulyanto, Edy Nohan, Rajendra Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Pergiwati, Dewi Pradana, Naufal Arsyaputra Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Ramadhani, Frida Rodia Syamwil Saputra, Filmada Ocky Savicevic, Anamarija Jurcev Shier Nee Saw Simbolon, Calvin Samuel Sindhu Rakasiwi Šolić, Petar Sudibyo, Usman Supratiknyo, Supratiknyo Swanny Trikajanti Widyaatmadja Syamsiar, Syamsiar Tanjung, Mohamad Aprilian Tasleem, Faathir El Tjahyono, Rudi Triono, Justin Matthew Widi Utomo, Emanuel Pinesthi Wildanil Ghozi Wright , Sasha-Gay ‘Aini, Fariida Qurrota