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Journal : building of informatics technology and science

Klasifikasi Tingkat Serangan pada Log Jaringan Siber dengan Komparasi Naive Bayes dan K-Nearest Neighbor Apriliani, Evinda; Winiarti, Sri; Riadi, Imam; Yuliansyah, Herman
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.v7i3.8765

Abstract

The increasing threat of cybersecurity poses a significant impact on both organizations and individuals, necessitating a system capable of accurately detecting and classifying attack levels to support prioritization of responses. This study aims to analyze and compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbor (KNN), in classifying cyberattack levels, and to evaluate the effect of hyperparameter tuning on improving model accuracy. The research methods included utilizing the cybersecurity_attacks dataset, data preprocessing, model training at three data split ratios (70:30, 80:20, and 90:10), and parameter optimization using Randomized Search and Grid Search. Performance evaluation was based on accuracy, precision, recall, and F1-score values. The results showed that KNN performed best, with a peak accuracy of 0.96 at the 80:20 ratio after tuning, increased from an accuracy of 0.947 before tuning, with precision, recall, and F1-score values ​​ranging from 0.95 to 0.96. Meanwhile, Naive Bayes only achieved a peak accuracy of 0.8485 at the same ratio. Although the improvement after hyperparameter tuning was not significant, this process still resulted in a more stable and consistent model. Future research is recommended to explore ensemble methods and test them on other datasets to produce more adaptive cyberattack classification models.
Analisis Spasio-Temporal Berbasis Data Video untuk Identifikasi Bangunan Melayu Menggunakan Metode Hybrid CNN-LSTM Ines Triseptiani; Sri Winiarti
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9917

Abstract

Malay buildings have distinctive architectural characteristics and require technology-based identification systems to support cultural documentation and preservation. This study aims to develop an identification system for Malay and non-Malay buildings using video data extracted into frame-by-frame images. The use of video data in this study is not intended to analyze the physical movement of buildings, but to utilize visual variations caused by changes in camera angle, recording distance, lighting, object composition, and visible building elements. The proposed method is CNN-LSTM, where CNN extracts visual features from each frame, while LSTM learns inter-frame feature relationships as a sequence of visual information. To reduce redundant information between adjacent frames and minimize the risk of excessive similarity between training and testing data, the number of frames was limited to a maximum of 15 frames per video folder, and data splitting was performed by considering video source groups. The dataset consists of Riau Malay buildings, Kalimantan Malay buildings, and non-Malay buildings. The research stages include frame extraction, image resizing to 224×224 pixels, normalization, data augmentation, class labeling, train-test splitting, modeling, evaluation, GroupKFold validation, and web-based system implementation. The best testing scenario was obtained using an 80:20 data split, 80 maximum epochs, and a batch size of 16, achieving an accuracy of 0.9916 and a test loss of 0.0852. GroupKFold validation produced an average accuracy of 99.1% with a standard deviation of 0.5%. These results indicate that the model can recognize architectural visual patterns, such as roofs, windows, doors, ornaments, and overall building appearance, while the performance should still be interpreted within the scope of the dataset and evaluation scenario used in this study.
Co-Authors Abdul Fadlil Adil Pratama Afiat Triyuniarta An Nur, Fitrinanda Ana Distia Diva Andina Megawati Akase Andri Pranolo Anis Nurwanto Apriliani, Evinda Ardi Pujiyanta Aris Rakhmadi Astika AyuningTyas, Astika Astreanto Habibullah Astri Yatnasari Atik Mar’atun Sholihah Augit Indatmowo Bagus Imam S.N. Bagus Priangga Bagus Priangga Bagus Priangga, Bagus Cendani Wukir Choirul Fajri Cindy Mayeza Putri Daffa Alif Murtaja Dedi Nugraha Dedi Nugraha Desy Widayanti Dewi Soyusiawaty Dewi, Kharisma Kusuma Dian Sulistyo Distia Diva, Ana Dwi Oktavia Andriyanti Endriyono Endriyono Enggar Novianto Enita Try Saadyah Fadlillah, Umi Faisal, Ilyas Faza Akmal Fikamelyalla, Naura Fitriana Susanti Fitrinanda An Nur Galih Oktorika Isnawan Heri Pramono Herman Yuliansyah Herman Yuliansyah Herman Yuliansyah Herman Yuliansyah, Herman Ida Widaningrum, Ida Imam Riadi Imam Riadi Ines Triseptiani Irawan, Riki Islamey, Reyhanssan Izzati Muhimmah Kharisma Kusuma Dewi Lathifah Lathifah Meilawati, Noni Melanita Indrianis Miftahurrahma Rosyda Miksa Mardhia, Murein Muhammad Al Mahdi Muhammad Arifin Setyawan Muhammad Salman Al Farisy Murein Miksa Mardhia Murinto Murinto Nailut Thoyibah Nila Susanti Noni Meilawati Norma Sari Norma Sari Nungky Anjaswari Nur Azizah Nur Kahfi Ibrahim Nur Rachmaliany Nur Rochmah Dyah Pujiastuti Nurul Azizah Az zakiyyah Pandu Herwijaya Priranda Widara Ananta Puguh Drajat Eka Putra R. Panji Daru Tutuko Rachmaliany, Nur Rahmawati Witriani Witriani, Rahmawati Witriani Reni Wijayanti Rifki Pambudi Riki Irawan Rizka Gustikasari Rochmadi, Tri Rusydi Umar Safiq Rosad sapanti, intan rawit Saputro, Mochammad Yulianto Andi Silmina, Esi Putri Sonny Zulhuda Sri Kusumadewi Sri Wahyuni Sri Wahyuni Sulistyo, Dian Sunardi Sunardi Sunardi, Sunardi Supriyanto Taufiq Ismail Taufiq Ismail Taufiq Ismail Taufiq Ismail Tole Sutikno Tri Afriliyanti Tsaqila, Siti Lathifah Ulaya Ahdiani Ulaya Ahdiani Ulaya Ahdiani Ulfah Yuraida Ulfah Yuraida Wiwik Handayani Yuliansyah, Herman Yunita Tri Hernawati Yuraida, Ulfah Yusuf Sulistyo Nugroho Zainal Ihsanul F