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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology JOIN (Jurnal Online Informatika) Jurnal Informatika Jurnal Komtika (Komputasi dan Informatika) JUTIS : Jurnal Teknik Informatika Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Abdidas DEDIKASI PKM Jurnal Sisfotek Global Electron: Jurnal Ilmiah Teknik Elektro Pena Dimas Jurnal Pengabdian Masyarakat Nasional ORAHUA : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknologi Informatika Jurnal Komtika (Komputasi dan Informatika)
Juniawan, Fransiskus Panca
STMIK Atma Luhur
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Mathematics Public Health Social Sciences Other

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Augmented Reality as Learning Medium for Preservation of Traditional Musical Instruments in Bangka Juniawan, Fransiskus Panca; Sylfania, Dwi Yuny
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.416

Abstract

Nowadays the use of technology is something that can be found anywhere. This condition has an impact on the loss of awareness of the Indonesian cultural treasures value in the form of traditional tools. No exception to traditional musical instruments on Bangka Island which began to lose its popularity. At present, most teenagers on Bangka Island cannot play traditional musical instruments. Likewise with the children who do not know and not even know their own regional musical instruments. With the continuation of this condition, it is feared that the existence of traditional Bangka musical instruments will disappear, as well as human resources that can play it. Augmented Reality (AR) is a visual technology that can display objects in 3D. The advantage of this technology is being able to give a display of real-time musical instrument in the form of dynamic 3D visualization of objects and it is in accordance with the movements of the user's smartphone camera. AR has also been applied in various cases as a solution to problem-solving. Therefore, to overcome this problem, an application to preserve the traditional Bangka musical instruments using Augmented Reality (AR) is forming. The 3D objects of musical instruments are made using Maya. Unity is also used as an engine for the application of 3D modeling on the Android system, and Vuforia SDK as it’s augmented reality engine. The results of performance testing obtained 100% running well. From the results of testing the user experience with the HARUS method, it is proven that the system has comprehensibility aspects of 75.98% and manipulability aspects of 80.74% so that the total value HARUS be 78.36%.
Deteksi Backdoor Berdasarkan Static Code Analysis Dan Software Component Analysis Juniawan, Fransiskus Panca; Sylfania, Dwi Yuny; Hulvi, Alfajri; Juwariyah, Siti
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Di era transformasi digital yang pesat, perangkat lunak telah menjadi komponen penting di berbagai sektor. Namun, di lain sisi perangkat lunak juga semakin menjadi sasaran ancaman keamanan siber. Salah satu ancaman yang paling mengkhawatirkan adalah backdoor, yaitu kerentanan tersembunyi yang disisipkan secara tidak sah ke dalam perangkat lunak sehingga memungkinkan akses tanpa izin dengan menghindari mekanisme keamanan tradisional. Penelitian ini bertujuan mengembangkan metode deteksi backdoor yang lebih akurat dan komprehensif melalui integrasi Static Code Analysis dan Software Component Analysis. Representasi fitur dilakukan menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), kemudian digunakan sebagai masukan bagi beberapa algoritma Machine Learning dan Deep Learning, yaitu Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, Decision Tree, Gradient Boosting, XGBoost, LightGBM, AdaBoost, Multi-Layer Perceptron (MLP), dan Long Short-Term Memory (LSTM). Dataset penelitian terdiri atas 128 sampel source code, yang mencakup 60 sampel backdoor dan 68 sampel benign, dikumpulkan dari repositori publik serta dikurasi secara manual untuk menjamin kualitas dan relevansinya. Hasil eksperimen menunjukkan bahwa XGBoost dan KNN memperoleh akurasi tertinggi sebesar 96,15%, diikuti oleh MLP, LSTM, dan AdaBoost dengan akurasi 92,31%. Temuan ini menunjukkan bahwa integrasi analisis statis, inspeksi kerentanan berbasis komponen, dan teknik Machine Learning mampu meningkatkan efektivitas deteksi backdoor. Selain itu, pendekatan yang diusulkan berpotensi mendukung implementasi deteksi backdoor secara otomatis pada pipeline DevSecOps untuk meningkatkan keamanan perangkat lunak sebelum tahap deployment.   Abstract In the era of rapid digital transformation, software has become a critical component across various sectors. However, it has also become an increasingly attractive target for cybersecurity threats. One of the most concerning threats is the backdoor, a hidden vulnerability that is illicitly inserted into software, enabling unauthorized access while bypassing traditional security mechanisms. This study aims to develop a more accurate and comprehensive backdoor detection method by integrating Static Code Analysis and Software Component Analysis. Feature representation is performed using Term Frequency–Inverse Document Frequency (TF-IDF), and the resulting feature vectors are used as input for several Machine Learning and Deep Learning algorithms, including Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, Decision Tree, Gradient Boosting, XGBoost, LightGBM, AdaBoost, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). The dataset consists of 128 source code samples, comprising 60 backdoor and 68 benign samples, collected from public repositories and manually curated to ensure data quality and relevance. Experimental results demonstrate that XGBoost and KNN achieve the highest classification accuracy of 96.15%, followed by MLP, LSTM, and AdaBoost with an accuracy of 92.31%. These findings indicate that integrating static code analysis, component-based vulnerability inspection, and machine learning techniques can significantly improve the effectiveness of backdoor detection. Furthermore, the proposed approach has the potential to support automated backdoor detection within DevSecOps pipelines, thereby enhancing software security before deployment.
Co-Authors Abadi, Mu’taman Agrina, Dini Agusti, Leny Albuchori, Ikhwan Fitrah Ali, Firman Almira, Almira Anggraini, Catrine Widya Assobry, Muhammad Habib Cahyono, Tri Ari Carissa, Fihan Chrisna Putra, Rendy Rian Dwi Yuny Sylfania Endraswari, Putri Mentari Firmansyah, Dimas Hamidah . Hendrik, Maulina Hengki Hengki Hulvi, Alfajri Iski Zaliman Isnanto, Burham Juniawan, Tri Ari Cahyono Kodri, M. Adha Al Laurentinus, Laurentinus Linda Lestari Megawati - Nayla, Adinda Nur Annisa Nurhaeka Tou Okkita Rizan Perkasa, Eza Budi Pradana, Harrizki Arie Putri Mentari Endraswari Rahmat Sulaiman Saputra, Dhito Siti Juwariyah Syafiranur Iftizam Syahputra, Razen Sylfania, Dwi Yuni Tou, Nurhaeka Umar Faruq Vista, Umar Faruq Yuliana, Fanika Zaliman, Iski