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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Nasional Komputasi dan Teknologi Informasi Infotek : Jurnal Informatika dan Teknologi Jurnal Minfo Polgan (JMP)
Andi Purnomo
Universitas Ary Ginanjar
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Neuroscience Social Sciences Transportation

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Efisiensi dan Akurasi dalam Deteksi Ekspresi Wajah: Studi Kasus Tiga Generasi Yolo Aliyah Kurniasih; Cantika Previana; Risman Nugraha; Andi Purnomo
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9674

Abstract

Abstrak − Ekspresi wajah merupakan indicator penting dalam interaksi manusia yang dapat dianalisis secara otomatis menggunakan teknologi deteksi objek. Penelitian ini bertujuan membandingkan performa YOLOv5, YOLO11, dan YOLO12 dalam mendeteksi tujuh kelas ekspresi wajah pada 2.935 dataset public. Model dilatih dengan konfigurasi yang seragam, kemudian di evaluasi berdasarkan nilai mean Average Precision (mAP) dan latensi inferensi. YOLOv5 mencatat nilai mAP tertinggi pada saat proses training dan validation, sedangkan YOLO11 memiliki lantensi terendah. Pada evaluasi model, YOLO12 unggul dalam nilai mAP, dan YOLO11 tetap tercepat dalam latensi. Model di deployment dengan 6 data citra yang memiliki 7 kelas. Hasil menunjukkan bahwa meskipun model YOLO12 unggul dalam akurasi evaluasi model, YOLO11 lebih optimal dari segi kecepatan inferensi.Kata Kunci: face expressions; accuracy-latency; yolo12; Abstract − Facial expression is an important indicator of human interaction that can be analyzed automatically using object detection technology. This study aims to compare the performance of YOLOv5, YOLO11, and YOLO12 in detecting seven classes of facial expressions on 2,935 public datasets. The models were trained with a uniform configuration, and then evaluated based on the mean Average Precision (mAP) value and inference latency. YOLOv5 recorded the highest mAP value during training and validation, while YOLO11 had the lowest latency. On model evaluation, YOLO12 excelled in mAP value, and YOLO11 remained the fastest in latency. The model was deployed with 6 image data that had 7 classes. Results show that while the YOLO12 model excels in model evaluation accuracy, YOLO11 is more optimal in terms of inference speed.Keywords: face expressions; accuracy-latency; yolo12
Peran Artificial Intelligence dalam Deteksi Dini Ancaman Keamanan Jaringan Andi Purnomo; Aliyah Kurniasih; Ahlijati Nuarminah; Sri Hartati
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14356

Abstract

Keamanan jaringan komputer menghadapi tantangan yang semakin kompleks seiring dengan meningkatnya volume dan variasi ancaman, seperti serangan Distributed Denial of Service (DDoS), malware, dan eksploitasi kerentanan. Pendekatan tradisional dalam deteksi dan mitigasi ancaman sering kali tidak cukup responsif terhadap pola serangan yang dinamis dan canggih. Teknologi kecerdasan buatan (Artificial Intelligence/AI), khususnya Machine Learning (ML), menawarkan pendekatan baru yang lebih adaptif dan proaktif.Penelitian ini bertujuan untuk menganalisis peran AI dalam meningkatkan keamanan jaringan melalui penerapan berbagai algoritma ML, seperti Naïve Bayes Classifier, Support Vector Machine (SVM), Decision Tree, dan Random Forest. Pendekatan ini memungkinkan analisis data dalam jumlah besar secara real-time, identifikasi pola anomali, dan deteksi dini terhadap serangan yang belum teridentifikasi sebelumnya. Hasil tinjauan literatur menunjukkan bahwa algoritma Machne Learning mampu meningkatkan akurasi deteksi ancaman hingga 95% dalam berbagai studi kasus. Meskipun demikian, beberapa tantangan masih dihadapi, seperti tingkat false positives yang tinggi, keterbatasan data pelatihan, dan kebutuhan infrastruktur yang signifikan. Untuk mengatasi tantangan ini, diperlukan pengembangan algoritma yang lebih efisien serta integrasi AI dengan teknologi lain, seperti blockchain dan Software-Defined Networking (SDN).Penelitian ini menyimpulkan bahwa AI memiliki potensi besar untuk menjadi komponen kunci dalam strategi keamanan jaringan modern, dengan memberikan solusi yang lebih cepat, akurat, dan skalabel dalam mendeteksi dan merespons ancaman keamanan siber.
Prediksi Kualitas Udara Berbasis Citra Menggunakan Pre-Trained Inception V3 dalam Perspektif Keberlanjutan Aliyah Kurniasih; Cantika Nur Previana; Andi Purnomo
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34839

Abstract

Advances in artificial intelligence are driving the optimization of deep learning models for image analysis. Air pollution is a significant environmental problem that impacts human health and the balance of ecosystems. Increased emissions from industrial activities, transportation, and the burning of fossil fuels are major factors contributing to deteriorating air quality in various regions. This study aims to analyze the influence of data preprocessing and training strategies on model performance, including the removal of duplicate image data, data splitting, data augmentation, the use of class weights on training data, and learning rate tuning using the Inception V3 transfer learning model. The results show that the combination of these strategies achieved an accuracy of 89.28% with an error rate of only 0.338, demonstrating stability and good model generalization capabilities. The application of appropriate and effective preprocessing and training strategies enhances model performance. Consequently, a more accurate and reliable model can support data-driven decision-making more efficiently and contribute to various sectors such as health and the environment in supporting sustainable development.
Penerapan Algoritma SVM pada Software Define Network untuk Mendeteksi dan Mitigasi Serangan DDOS pada Server Jaringan andi purnomo; Avrijsto Amandri Achyar
FORMAT Vol 14 No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.001

Abstract

Software Defined Network (SDN) is a network architecture that is very useful in the future where SDN can be used to manage network traffic on server networks. SDN can be implemented using a variety of controllers. In the controller the developer can configure it with various algorithms or other functions. At present, cyber crimes are increasingly numerous and dangerous. One of the most dangerous cyber attacks that is mostly carried out by both novice and professional hackers is the DDoS attack. DDoS attacks are aimed at crippling servers with server administration with multiple streams and packets. SDN as an architect for managing networks can be used to detect and counteract DDoS attacks so that servers are protected from these attacks. In this study researchers used SDN configured using the SVM algorithm to detect and mitigate DDOS attacks. In this study, the researchers obtained results where SDN with the SVM algorithm configuration obtained an accuracy rate of 99.67%. The SDN speed configured with the SVM algorithm does not exceed 0.30ms. Wireshark statistics show that SDN with the SVM algorithm configuration can stabilize and mitigate packets detected as DDOS.
Co-Authors Ahlijati Nuarminah Aliyah Kurniasih Avrijsto Amandri Achyar Cantika Nur Previana Cantika Previana Risman Nugraha Sri Hartati