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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Abdi Masyarakat Indonesia Jurnal Abdimas Maduma
Hadipurnawan Satria
Department Of Informatics, Faculty Of Computer Science, Universitas Sriwijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks Supardi, Julian; Samsuryadi, Samsuryadi; Satria, Hadipurnawan; Serrano, Philip Alger M.; Arnelawati, Arnelawati
Jurnal Elektronika dan Telekomunikasi Vol 24, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.653

Abstract

Remote sensing imagery is a very interesting topic for researchers, especially in the fields of image and pattern recognition. Remote sensing images differ from ordinary images taken with conventional cameras. Remote sensing images are captured from satellite photos taken far above the Earth's surface. As a result, objects in satellite images appear small and have low resolution when enlarged. This condition makes it difficult to detect and recognize objects in remote-sensing images. However, detecting and recognizing objects in these images is crucial for various aspects of human life. This paper aims to address the problem of remote sensing image quality. The method used is a convolutional neural network. The results show the proposed method can improve PSNR and SSIM compared to previous methods
Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM) Saputra, Heru; Stiawan, Deris; Satria, Hadipurnawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27559

Abstract

Portable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine learning approach. In this research, the Support Vector Machine (SVM) algorithm was used to detect PDF malware because of its ability to process non-linear data, and in some studies, SVM produces the best accuracy. In the process, the file was converted into byte format and then presented in Byte Frequency Distribution (BFD). To reduce the dimensions of the features, the Sequential Forward Selection (SFS) method was used. After the features are selected, the next stage is SVM to train the model. The performance obtained using the proposed method was quite good, as evidenced by the accuracy obtained in this study, which was 99.11% with an F1 score of 99.65%. The contributions of this research are new approaches to detect PDF malware which is using BFD and SVM algorithm, and using SFS to perform feature selection with the purpose of improving model performance. To this end, this proposed system can be an alternative to detect PDF malware.
Pemahaman Critical Thinking Dalam Menghadapi Olimpiade Sains Nasional (OSN) Untuk Guru SMA Al-Kautsar Bandar Lampung Rizki Kurniati; Osvari Arsalan; Anggina Primanita; Muhammad Fachrurrozi; Hadipurnawan Satria; Muhammad Qurhanul Rizqie; Ermatita
JURNAL ABDIMAS MADUMA Vol. 4 No. 2 (2025): Juli 2025
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v4i2.434

Abstract

Berpikir kritis adalah kemampuan yang dibutuhkan oleh pengajar, terutama yang akan menghadapi Olimipade Sains Nasional (OSN). Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah untuk meningkatkan kemampuan guru SMA dalam memahami permasalahan dengan berpikir kritis dalam menghadapi Olimpiade Sains Nasional (OSN). Melalui pelatihan dan pendampingan, para guru diberikan wawasan serta keterampilan praktis dalam mengintegrasikan metode berpikir kritis ke dalam pengajaran sehari-hari. Hasil kegiatan menunjukkan antusiasme tinggi dari peserta, yang terlihat dari interaksi aktif selama pelatihan. Kendala seperti keterbatasan infrastruktur jaringan diidentifikasi dan diusulkan solusi jangka panjangnya. Diharapkan dengan terselenggaranya kegiatan ini, kualitas pendidik dan pendidikan guru SMA mendapatkan dampak yang positif dan menjadi lebih baik. Hal ini dibuktikan dengan meningkatnya kemampuan guru SMA dalam memahmi soal berpikir kritis Kata Kunci : Berpikir Kritis; Gamifikasi; Olimpiade Sains Nasional; Pelatihan Guru; Pendidikan Digital
Multiclass Segmentation of Pulmonary Diseases using Convolutional Neural Network Arnaldo, Muhammad; Nurmaini, Siti; Satria, Hadipurnawan; Rachmatullah, Muhammad Naufal
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pulmonary disease has affected tens of millions of people in the world. This disease has also become the cause of death of millions of its sufferers every year. In addition, lung disease has also become the cause of other respiratory complications, which also causes the death of the sufferer. The diagnosis of pulmonary diseases through medical imaging is a significant challenge in computer vision and medical image processing. The difficulty is due to the wide variety in infected areas' shape, dimension, and location. Another challenge is to differentiate one lung disease from the other. Discriminating pulmonary diseases is a notable concern in the diagnosis of pulmonary disease. We have adopted the deep learning convolutional neural network in this study to address these challenges. Seven models were constructed using the Mask Region-based Convolutional Neural Network (Mask-RCNN) architecture to detect and segment infected areas within the lung region from CT scan imagery. The evaluation results show that the best model obtained scores of 91.98%, 85.25%, and 93.75% for DSC, MIoU, and mAP, respectively. The segmentation results are then visualized.
Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization Islami, Anggun; Nurmaini, Siti; Satria, Hadipurnawan
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization.
Comparative Analysis of Explainable AI Models for Pneumonia Detection in Chest X-rays Using Grad-CAM Richardo, M Denny; Ermatita, Ermatita; Satria, Hadipurnawan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2450

Abstract

Pneumonia is one of the main reasons why young children die around the world, so it's essential to detect it early and make sure the methods used are straightforward to understand for doctors. This study aims to analyze and compare pneumonia detection systems based on Explainable Artificial Intelligence (XAI) using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique across four Convolutional Neural Network (CNN) architectures: VGG16, DenseNet, MobileNet, and EfficientNet-B0. The dataset used consists of approximately 5,800 chest X-ray images from Kaggle, split into training, validation, and test sets. The dataset underwent preprocessing, augmentation, and filtering. Each model was trained and tested using the accuracy, precision, recall, and F1-score measures. Additionally, the models were analyzed for explainability using Grad-CAM heatmaps. The results showed that MobileNet achieved the highest classification performance, attaining 99.6% accuracy, precision, recall, and F1-score, while EfficientNet-B0 demonstrated the highest explainability in a visual evaluation by medical practitioners. Explainability was assessed through a survey distributed to four medical professionals—two radiologists, a general practitioner, and a radiology technologist—using a Likert scale (1–5) to rate aspects such as focus accuracy, heatmap clarity, consistency of the area, and interpretability. EfficientNet-B0 achieved the highest average explainability score of 41.50, followed by MobileNet at 40.50. Thus, MobileNet is recommended for accuracy, while EfficientNet-B0 is the best choice if visual interpretability is a priority. This research underscores the importance of integrating explainability into the development of AI-based disease detection systems to enhance trust and safety in clinical applications.
Analisa Perbandingan Algoritma A* dan Dynamic Pathfinding Algorithm dengan Dynamic Pathfinding Algorithm untuk NPC pada Car Racing Game Sazaki, Yoppy; Satria, Hadipurnawan; Primanita, Anggina; Syahroyni, Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 1: Februari 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.231 KB) | DOI: 10.25126/jtiik.201851544

Abstract

Permainan mobil balap adalah salah satu permainan simulasi yang membutuhkan Non-Playable Character (NPC) sebagai pilihan lawan bermain ketika pemain ingin bermain sendiri. Dalam permainan mobil balap, NPC membutuhkan pathfinding untuk bisa berjalan di lintasan dan menghindari hambatan untuk mencapai garis finish. Metode pathfinding yang digunakan oleh NPC dalam game ini adalah Dynamic Pathfinding Algorithm (DPA) untuk menghindari hambatan statis dan dinamis di lintasan dan Algoritma A* yang digunakan untuk mencari rute terpendek pada lintasan. Hasil percobaan menunjukkan bahwa NPC yang menggunakan gabungan DPA dan Algoritma A* mendapatkan hasil yang lebih baik dari NPC yang hanya menggunakan Algoritma DPA saja, sedangkan posisi rintangan dan bentuk lintasan memiliki pengaruh yang besar terhadap DPA.
Adaptive Hint Generation for Educational Games Using Fuzzy Logic Primanita, Anggina; Satria, Hadipurnawan; Rizqie, Muhammad Qurhanul; Iskandar, Ananda Haykel; Nugraha, Wibisena
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41893

Abstract

The increasing interest in programming education has led to a wide variety of learner abilities. However, existing learning media often remain fragmented, necessitating the development of adaptive tools to cater to learners of varying skill levels. This study employs fuzzy logic to generate dynamic hints for players struggling to solve programming challenges in an educational game. The effectiveness of the system was evaluated through both simulation and real-world experiments. Simulation results indicate that the fuzzy logic system successfully generates personalized hints, with the highest frequency of hints provided to beginner players. Real-world testing using the GUESS-18 framework demonstrated high playability and excellent usability scores for the game.
Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means Nuzulastri, Sari; Stiawan, Deris; Satria, Hadipurnawan; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p176-185

Abstract

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.
Co-Authors Abdurahman Ahmad Nizar Ahmad Zarkasi Anggun Islami Arnaldo, Muhammad Arnelawati, Arnelawati Deris Stiawan Dian Palupi Rini Ermatita - Hasby Rifky Heru Saputra Iskandar, Ananda Haykel Islami, Anggun Jambak, Muhammad Ihsan Julian Supardi M. Nazhif Rizani Mohammad Eko Fitrianto Muhammad Afif Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Nugraha, Wibisena Nuzulastri, Sari Osvari Arsalan Primanita, Anggina Rahmat Budiarto Richardo, M Denny Rifkie Primartha Rizki Kurniati Samsuryadi Sahmin Samsuryadi Samsuryadi Serrano, Philip Alger M. Siti Nurmaini Sutarno Sutarno Syahroyni, Muhammad Widya Aprilini Yos Karimudin