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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Sainteks Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Indonesian Journal of Business Intelligence (IJUBI) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Just TI (Jurnal Sains Terapan Teknologi Informasi) International Journal of Industrial Optimization (IJIO) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Swadaya: Indonesian Journal of Community Empowerment Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi JHCLS Prosiding Seminar Nasional Program Pengabdian Masyarakat TECHSI - Jurnal Teknik Informatika Journal of Artificial Intelligence in Architecture KOPEMAS
Sri Winiarti
Universitas Ahmad Dahlan
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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.
Optimalisasi Kinerja Convolutional Neural Networks VGG16 dalam Identifikasi Bangunan Adat Melayu Sri Winiarti; Sunardi, Sunardi; Fadlil, Abdul
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 8 No. 3 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v8i3.58210

Abstract

Penggunaan deep learning dalam mendeteksi berbagai objek sudah banyak diterapkan, namun untuk identifikasi kemiripan bangunan untuk gaya arsitektur masih terbatas. Analisis klasifikasi model desain arsitektur bangunan adat Melayu dapat dilakukan dengan menerapkan metode Convolusional Neural Network (CNN). Pendekatan yang digunakan untuk menganalisis klasifikasi dan kemiripan model bangunan adat melayu menggunakan model arsitektur VGG16. Ekstraksi fitur menggunakan model deep learning untuk mengidentifikasi jenis bangunan adat Melayu menggunakan parameter atap, jendela, dan ornamen bangunan. Dataset citra bangunan adat Melayu didapatkan dari pengambilan langsung ke lokasi bangunan adat melayu Riau di Kawasan Jalan Muhammad Arifin Pekanbaru Riau untuk training sebanyak 644 gambar dan testing model sebanyak 106 gambar. Model yang digunakan adalah VGG16. Parameter ukuran kinerja meliputi accuracy, precision, recall, dan F1-score. Akurasi yang didapatkan dalam penelitian ini adalah 98,77% dari total 106 data yang diuji, sedangkan precision 0,8678, recall 0,9633, dan F1-score 0,9877. Hasil yang didapatkan ini melalui setting parameter learning rate 0,0001, drop out 0,20, dan epoch sebesar 25. Secara keseluruhan model VGG16 yang digunakan dalam penelitian ini menghasilkan akurasi yang baik.
Analisis Efektivitas Pelatihan Kecerdasan Artifisial untuk Peningkatan Kompetensi Guru dalam Pengembangan Media Kreatif Sutikno, Tole; Ayuningtyas, Astika; Riadi, Imam; Winiarti, Sri; Rochmadi, Tri; Rosad, Safiq
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 4 (2025): Edisi Oktober - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i4.7546

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan menganalisis efektivitas pelatihan pemanfaatan teknologi kecerdasan buatan (Artificial Intelligence/AI) dalam meningkatkan kompetensi guru, khususnya dalam pengembangan media pembelajaran kreatif. Pelatihan dilaksanakan di SMA Negeri 7 Yogyakarta dengan melibatkan guru-guru dalaliterasiemaparan teori dan praktik langsung menggunakan berbagai platform AI, seperti ChatGPT, Canva AI, serta generator gambar dan video berbasis AI. Evaluasi dilakukan melalui pre-test dan post-test untuk mengukur perubahan kemampuan peserta sebelum dan sesudah pelatihan. Hasil analisis menunjukkan peningkatan rata-rata skor dari 5,95 menjadi 9,50, yang menandakan adanya peningkatan signifikan pada aspek pengetahuan, keterampilan, dan sikap guru dalam menerapkan AI. Guru mampu menghasilkan media pembelajaran digital yang lebih interaktif dan relevan dengan kebutuhan siswa. Temuan ini menunjukkan bahwa pelatihan AI berperan penting dalam meningkatkan literasi teknologi guru sekaligus mendukung transformasi digital di lingkungan pendidikan menengah.
Sistem Pendukung Keputusan untuk Menilai Kesejahteraan Wilayah DIY dengan Metode SMART Muhammad Salman Al Farisy; Sri Winiarti
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3753

Abstract

Welfare in the Special Region of Yogyakarta (DIY) is disrupted by urbanization and development inequality, which leads to poverty and other social problems. This research uses the Waterfall method which includes requirements analysis, design, implementation, and testing. Data were collected through interviews, and literature studies. The results of the study show that: first, the system developed is able to provide welfare ratings with high accuracy. Second, system validation through MAE calculation produces a value of 0.285, indicating a minimum error rate, so that the system can provide reliable predictions and support data-driven decision-making. Usability evaluation using the System Usability Scale (SUS) obtained a score of 73.9 which is in the good category and shows that the system is easy to use, and the functionality test with the Black Box method shows a 100% success rate. In conclusion, SPK based on the SMART method is effective in supporting social policies.
Analisis Komparatif Random Forest dan Support Vector Machine untuk Klasifikasi Tingkat Keparahan Serangan Siber Reyhanssan Islamey; Sri Winiarti; Imam Riadi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36558

Abstract

The escalating volume and sophistication of cyberattacks on network infrastructures processing massive daily traffic have overwhelmed security teams in prioritizing incident responses rapidly and accurately, a phenomenon known as alert fatigue. This study aims to analyze and compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for classifying cyberattack severity levels (Low, Medium, and High). The study uses the public Cyber Security Attacks dataset, consisting of 40,000 network traffic records reduced to 20,000 clean entries through preprocessing and feature engineering. The methodology includes data cleaning, selecting 10 significant features using SelectKBest, standardizing numerical features, and evaluating models across three data split scenarios (70:30, 80:20, and 90:10) using a stratified splitting approach. Experimental results show that SVM consistently outperforms RF across all scenarios, with the best performance in the 80:20 split, achieving 98.92% accuracy and a weighted average F1-Score of 0.99 using hyperparameter configurations of C = 100 and gamma = 0.01. The superiority of SVM lies in its ability to model non-linear relationships and complex feature interactions in data with overlapping class boundaries. In contrast, RF exhibits an over-prediction bias toward the minority class (’Low’) due to the class_weight=’balanced’ mechanism and limitations of axis-based separation. These findings confirm that SVM with a Radial Basis Function (RBF) kernel is more suitable for cyberattack severity classification, particularly in automated incident detection systems requiring balanced precision and recall as well as reliable decision-making.
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 Irawan, Riki 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 Pandu Herwijaya Priranda Widara Ananta Puguh Drajat Eka Putra R. Panji Daru Tutuko Rachmaliany, Nur Rahmawati Witriani Witriani, Rahmawati Witriani Reni Wijayanti Reyhanssan Islamey 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