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Journal : J-Intech (Journal of Information and Technology)

Pemanfaatan Deep Convolutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital Kurniawan S, Putu Widiarsa; Kristian, Yosi; Santoso, Joan
J-INTECH (Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.845

Abstract

Adversarial attacks on digital images pose a serious threat to the utilization of machine learning technology in various real-life applications. The Fast Gradient Sign Method (FGSM) technique has proven to be effective in conducting attacks on machine learning models, including digital images found in the ImageNet dataset. This research aims to address this issue by utilizing the Deep Convolutional Auto-encoder (AE) technique as a method for mitigating adversarial attacks on digital images.The results of the study demonstrate that FGSM attacks can be performed on the majority of digital images, although there are certain images that are more resilient to such attacks. Furthermore, the AE mitigation technique proves to be effective in reducing the impact of adversarial attacks on most digital images. The accuracy of the attack and mitigation models is measured at 14.58% and 91.67%, respectively.
Prediksi Student Performance Pada Hasil Penilaian Proses Pembelajaran Online Mata Pelajaran Informatika Di SMA Dipa, Sasra; Santoso, Joan; Chandra, Francisca H.
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1259

Abstract

In the Corona Endemic, we are not just returning to offline education patterns but are already moving towards education 5.0. Online, normal, blended learning patterns have become commonplace. Online learning assessment requires fast and precise predictions of student performance (high accuracy). The reason is first, due to limited direct interaction. Second, normal learning usually involves an assessment of the learning process and character assessment to be able to provide an accurate final assessment, which is difficult to implement in online learning accurately. Third, there is a lot of data to be processed quickly and precisely so that it can be reported to educational institutions and to students' families. Fourth, Informatics is a lesson that is 80% practical and 20% theory so that the assessment instruments used are 80% performance instruments (Bloom's taxonomy: C2, C3, C4, C5) and 20% multiple choice instruments (C1). Informatics correction and assessment requires more time because 80% cannot be assessed automatically. This research aims to predict student performance (Pass (1) or Intervention (0)) on the results of the online learning process assessment for informatics subjects in high school. If the student performance prediction results in an intervention, it will be immediately followed up by providing an intervention strategy to increase student performance. The target of the research results is to achieve > 70% accuracy on the processed dataset. This research uses the ensemble learning method random Forest Classification and XG Boosting classification. The research results of Student Performance Prediction using XG Boost Classification produce higher accuracy than RF Classification which has an average accuracy value = 93% while RF Classification has an average accuracy result = 92%. The research objectives have been achieved because the results of the 2 methods used have met the desired targets.
Co-Authors Aditya Dwi Aryanto Adriel Ferdianto Agung Dewa Bagus Soetiono Ahmad Syaifuddin Ali Djamhuri Ananta Tio Putra Andik Jatmiko Anita Guterres Bayu Anggara Putra Budi Irawan Chandra, Francisca H. Christian Nathaniel Purwanto Devi Dwi Purwanto Dewi, Nindian Puspa Dipa, Sasra Edwin Pramana Eka Rahayu Setyaningsih Eko Mulyanto Yuniarno Elizabeth Shirley, Stephanie Endang Setyati Ernest Lim Esther Irawati S. Esther Irawati Setiawan Esther Irawati Setiawan Eunike Kardinata F.X. Ferdinandus Fachrul Kurniawan Febriantoro, Erfan Francisca Chandra Fujisawa, Kimiya Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Hans Juwiantho Hans Keven Budi Prakoso Hartarto Junaedi Hendrawan Armanto Heppi Siswanto Herman Budianto Imron, Syaiful Indra Maryati Jatmiko, Andik Kristian Indradiarta Gunawan Kristina, Natalia Kurniawan S, Putu Widiarsa Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Luhfita Tirta Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Miftah Farid Mochamad Hariadi Muhammad Amfahtori Wijarnoko Mustaqin, Farhan Faisal Zainul Nagari, Widean Nikko Riestian Putra Wardoyo Nindian Puspa Dewi Ong, Hansel Santoso Patrick Sutanto Reddy Alexandro Harianto Ricky Sutanto Rossy P. C. Rully Widiastutik Samuel Budi Wardhana Kusuma Saputra, Daniel Gamaliel Setya Ardhi Soetiono, Agung Dewa Bagus Stefanie Hilda Kusumahadi Stella Vania Surya Sumpeno Syabith Umar Ahdan Syaiful Huda Syaiful Imron Tjendika, Patrick Tjwanda Putera Gunawan Tri Septianto Tuesday saka gustaf Ubaidi Ubaidi Ubaidi, Ubaidi Yosi Kristian