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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.
Automated Video Recognition of Traditional Indonesian Dance Using Hyperparameter-Tuned Convolutional Neural Network Purwaningrum, Santi; Susanto, Agus; Susanti, Hera; Alkhafaji, Mohammed Ayad
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.2025.6.5.5157

Abstract

Traditional Indonesian dances serve as a vital expression of cultural identity and regional heritage, yet their preservation through intelligent video recognition remains limited due to technical challenges in motion complexity, costume variation, and the lack of annotated datasets. Prior research commonly employed Convolutional Neural Networks (CNNs) with manually defined hyperparameters, which often resulted in overfitting and poor adaptability when applied to dynamic and real-world video inputs. To overcome these limitations, this study proposes a robust and adaptive classification framework utilizing a hyperparameter-tuned CNN model. The approach automatically optimizes key training parameters such as learning rate, batch size, optimizer type, and epoch count through iterative experimentation, thereby maximizing the model’s ability to generalize across both static and temporal data domains. The model was trained using image datasets representing three traditional dances (Gambyong, Remo, and Topeng), and subsequently tested on segmented frames extracted from YouTube videos. Results indicate strong model performance, achieving 99.67% accuracy on the training set and 100% accuracy, precision, recall, and F1-score across all testing videos. The proposed method successfully bridges the gap between still-image learning and real-world motion recognition, making it suitable for practical applications in digital archiving and cultural documentation. This study’s contribution lies not only in the model’s technical effectiveness but also in its support for preserving intangible cultural assets through intelligent and automated video-based recognition. Future work may incorporate temporal modelling or multi-camera perspectives to further enrich motion understanding and extend the system to broader performance domains.
Co-Authors Achmad Junaedi Sitika Afiansyah, Laksmana Ilyas Afifah Bambang Sutjiatmo, Afifah Bambang Agus Sutamaya, Agus Ahmad Aniq Barlian Ahmad Nasihin, Ahmad Ahmad Sunardi Ahmad Sunardi Ahmad Sunardi, Ahmad Ahmaluddin, Ahmaluddin Ajat Rukajat Akhmad Aniq Barlian Albert Albert Aldi Budi Riyanta Alfianto, Dwi Alkhafaji, Mohammed Ayad Anak Agung Oka Andila, Shifa Anggraeni, Nadia Isabela Anggy Rima Putri Anik Andriani Anto Budi Listyawan Arrosida, Hanum Astini , Rahmi Ayu Rahayu Ayuni, Vini Az Zahra, Syakira Aziz, Farhan Abdul Azri, Huriati Badriah, Dewi Laelatul Balafif, Felisha Febriane Bari, Ichsan Nurul Baskoro Wicaksono, Baskoro Busori, Akhmat Damaranti, Maria Ervina Damayanti, Darwati Rossa Dattadewi Purwantini Daud, Nur Anisa Deawati, Miwha Dede Hadidjah, Dede Desi Fitriani Devanda, Noval Dewi Puspita Candrasari Diaz Fahrezi, Fathan Dicky Bayu Setiyono Dina Retnoningsih Drajat Tri Kartono Dwiyanti, Eka Dyah Nindita Carolina Eko Mindarsih Endah Yulia Engkesa, Penina Apricia Erni Setiyorini, Erni Euis Reni Yuslianti, Euis Eva Novita Sari Fadilah, Muhammad Arief Fitri Widiantini Gery Fifalia Finalis Guswin, Instreet Helnindys Hadiprasetyo, Dhanar Septyawan Hanniza Dwi Cahyani Harahap, Nur’ain Hartono Hasaniyah, Sitti Heni Purwantiningrum Heni Purwantiningrum Heni Purwantiningrum Heni Purwantiningrum Hera Susanti, Hera Hersanti - Heru Nurcahyo Hevi Susanti Hidayah, Chomsiatun Nurul Hidayat, Salman Husni, Thariq Zahir Ibrahim Rahmat Imbang Haryoko Ina Hendiani, Ina Indra Mustika SP, Indra Ismiyati, Ismiyati Januar, Dedy Joko Santoso Kasianingrum, Afinda Keliat, Chrisnasari Yanti Ketaren, Rayhan Kosasih, Hengky Krissanti, Petra Sulistya Dian Kristy Natalia Suwito Kurnia Anggriani, Kurnia Kussetyaningrum, Ratih Oktania Latifa, Saldila Lintang Putri Licia, Rizka Lisda Amalia Luisa, Nina Maharani, Nur Endah Dewi Mamlukah, Mamlukah Mangkuanom, Arwin Saleh Marpaung, Ade Putra Masruroh Masruroh Maulana, Gilang Akbar Meisiana, Rista Meliyana Perwita Sari Meliyana Perwita Sari Meliyana Perwita Sari Meliyana Perwita Sari Muhamad Janadin Ass Saff, Muhamad Muhammad Arsyad Muladi Putra Mahardika Mulya, Dody Mulyanto, Dody Nikous Soter Sihombing Noor Istifadah Noor, Muhammad Faizal Noorsakti Wahyudi Nugroho, Feri Nur Septiani Nurjihan, Khansa Oktaviani, Novia Cindi Oviyanti Mulyani Pambudi Yuwono Pambudi, Rahma Ayu Pambudi, Rilo Pane, Rico Purnawandi Priadi, Ramadhan Purba, Joshua Purwaningrum, Santi Purwantingrum, Heni Purwantini, Datta Dewi Purwantiningrum, Heni Purwanto, Diyan Sakti Putri Gami, Emelia Rahmadany Putri, Nadia Rahma Qunik Wiqoyah Raden Giazh Zuniar Maretha Rahmadhani, Alvin Ramdani, Jufri Ratih Sakti Prastiwi Ridho, Fikra Arsyad Rija Sudirja Riko Herdiansah Rista, Paulina Eva Rosaria Ika Pratiwi Rozzak, Ilham Abdul Ruli Haris Sahat Simbolon Saputri, Yunitya Eka Sari, A Anditha Sari, Kartika Indah Sari, Meliyana Perwita Savitri, Ulva Sembiring, Rinawati Sesdia Angela Setiyanto, Riyan Setiyanto, Riyan Setiyanto, Ryan Setya Agus Santosa Shahab, Dahris Siregar, Hot Berlian SIska Rasiska, SIska Sri Aprianti Tarigan Sri Hartati Subaer - Subakti Putri, Syifa Nabilah Suhadi, Miki Sukmawati, Rani Surna Lastri Susiyarti Sutrisno, Agus Tarkus Suganda Tri Mayanti Ulfatul Latifah, Ulfatul Wahyu Daradjat Natawigena, Wahyu Daradjat WAHYU WIDOWATI Wawan Sutari Widodo, Hermawan Setyo Wilyam Dwi Apri Windia, Alna Amelia Nur Wulur, Kevin Hanyu Clinton Yani Maharani, Yani Yasri, Kevin Assidiqi ZA, Saida Zainurossalamia