Article Per Year (5 Year)
p-Index From 2021 - 2026
1.126
P-Index
This Author published in this journals
All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JSAI (Journal Scientific and Applied Informatics) Jurnal Abdi Insani Jurnal Teknik Informatika (JUTIF) Dharmakayana: Journal of scientists, engineers, educators and scientific activists related to society development
Kurnia Anggriani, Kurnia
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Enhancing Malware Detection in IoT Networks using Ensemble Learning on IoT-23 Dataset Anggriani, Kurnia; Az Zahra, Syakira; Susanto, Agus
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.2025.6.4.4782

Abstract

The Internet of Things (IoT) has become a technological innovation that brings many benefits in various sectors, but also presents challenges, especially in terms of cybersecurity. One of the main threats is malware, which can damage devices, steal data, and disrupt system performance. With the increasing use of IoT, malware attacks on IoT devices are a serious concern. Previous research shows that malware detection models in IoT devices still have shortcomings, especially in terms of accuracy. One of the algorithms used in malware detection, Naïve Bayes, has been shown to provide low accuracy results. This study aims to improve the accuracy of malware detection on IoT networks by applying Ensemble learning techniques using traffic data from the IoT-23 dataset. The methodology used refers to the CRISP-DM (Cross Industry Standard Process for Data Mining) framework, which includes the stages of domain understanding, data understanding, data preparation, modelling, evaluation, and deployment. The results show that Ensemble learning improved the performance of individual models. Naïve Bayes as a single model produces an accuracy of 0.24, increasing to 0.35 when combined with AdaBoost, and 0.99 when combined with XGBoost. The combination of the three models also produced an accuracy of 0.99. These results demonstrate the effectiveness of ensemble learning in improving malware detection accuracy in IoT environments.
Herbal Plant Classification Using EfficientNetV2B0 Model and CRISP-DM Approach Sonita, Anisya; Anggriani, Kurnia; Vatresia, Arie; Putri, Tiara Eka; Darnita , Yulia; Zahra, Syakira Az; Aprilia, Vilda; Aziz, Dzakwan Ammar
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.2025.6.4.5141

Abstract

Herbal remedies have long been utilized by Indonesian communities as part of traditional medicine. However, identification of these natural resources is often challenging due to the morphological similarities among various species, which demand expert knowledge to differentiate. This study aims to implement the EfficientNetV2B0 model architecture for classifying medicinal leaves through an Android-based application designed to support recognition tasks. The dataset was composed of augmented images of plant foliage. The model was trained using the TensorFlow framework and evaluated to measure classification performance. Results demonstrate that EfficientNetV2B0 achieves excellent accuracy, with validation scores exceeding 97%, outperforming several other deep learning models. The resulting application allows the general public to identify local medicinal species more easily. This study contributes to the field of computer vision by providing an accurate and efficient classification framework, particularly beneficial for health-related informatics in biodiversity-rich regions.
Co-Authors Agus Susanto Aprilia, Vilda Arie Vatresia Arie Vatresia Az Zahra, Syakira Aziz, Dzakwan Ammar Bismantolo, Putra Darnita , Yulia Enda Esyudha Pratama, Enda Esyudha Nata Kusuma, Teguh Nurul Renaningtias, Nurul Okta Piani, Sri Pratama, Yogi Putra Bismantolo Putri, Tiara Eka Sonita, Anisya Stern Imanuel Kristian Utama, Ferzha Putra Vinalti, Gushe Zahra, Syakira Az