21 research outputs found
Perangkat Lunak Penganalisis Kemiripan Webpage Berdasarkan Konten Presentasional
No full textSebuah webpage selain berisi sekumpulan informasi utama (konten) juga mengandung konten presentasional yang digunakan untuk menampilkan isi informasi utama. Pada sebuah website, konten presentasional sebuah webpage cenderung mirip dengan konten presentasional dalam webpage lainnya di website tersebut. Meskipun mirip ataupun identik, setiap kali sebuah webpage dimuat dalam browser konten presentasional ini tetap mengalami proses pemuatan ulang. Jika kemiripan konten presentasional cukup besar, maka akan terjadi banyak pemborosan konten yang dimuat dari server. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat menganalisis kemiripan sekelompok webpage dalam sebuah website. Data yang digunakan adalah kumpulan webpage dari sebuah website yang diunduh menggunakan web crawler. Berdasarkan hasil analisis pada website www.pusbangdik.unsri.ac.id , didapatkan bahwa konten presesentasional dari masing-masing webpage cukup mirip, dengan rata-rata kemiripan 67% untuk semua webpage dan 58% untuk webpage yang terhubung saja
Automatic Data Extraction Utilizing Structural Similarity From A Set of Portable Document Format (PDF) Files
No full textInstead of storing data in databases, common computer-aided office workers often choose to keep data related to their work in the form of document or report files that they can conveniently and comfortably access with popular off-the-shelf softwares, such as in Portable Document Format (PDF) format files. Their workplaces may actually use databases but they usually do not possess the privilege nor the proficiency to fully utilize them. Said workplaces likely have front-end systems such as Management Information System (MIS) from where workers get their data containing reports or documents.These documents are meant for immediate or presentational uses but workers often keep these files for the data inside which may come to be useful later on. This way, they can manipulate and combine data from one or more report files to suit their work needs, on the occasions that their MIS were not able to fulfill such needs. To do this, workers need to extract data from the report files. However, the files also contain formatting and other contents such as organization banners, signature placeholders, and so on. Extracting data from these files is not easy and workers are often forced to use repeated copy and paste actions to get the data they want. This is not only tedious but also time-consuming and prone to errors. Automatic data extraction is not new, many existing solutions are available but they typically require human guidance to help the data extraction before it can become truly automatic. They may also require certain expertise which can make workers hesitant to use them in the first place. A particular function of an MIS can produce many report files, each containing distinct data, but still structurally similar. If we target all PDF files that come from such same source, in this paper we demonstrated that by exploiting the similarity it is possible to create a fully automatic data extraction system that requires no human guidance. First, a model is generated by analyzing a small sample of PDFs and then the model is used to extract data from all PDF files in the set. Our experiments show that the system can quickly achieve 100% accuracy rate with very few sample files. Though there are occasions where data inside all the PDFs are not sufficiently distinct from each other resulting in lower than 100% accuracy, this can be easily detected and fixed with slight human intervention. In these cases, total no human intervention may not be possible but the amount needed can be significantly reduced.
Predictive Modeling of Air Quality Index Using Ensemble Learning and Multivariate Analysis
No full textBreathing polluted air can result in multiple health problems. Thus, it is important to understand and predict the air quality in the environment. Air Quality Index (AQI) is a unit used to measure the air pollutants. In Indonesia, this value is measured and published by the Meteorological, Climatological, and Geophysical Agency regularly. In this research, four commonly used regression algorithms were used to analyzed AQI data, namely, Random Forest, Decision Tree, K-Neural Network, and Ada Boost. All the algorithms model were developed to analyzed 1096 AQI data. The Mean Squared Error value of each model was computed as a measure of comparison. It is found that the Random Forest is the best performing algorithm. It can generalize well without overfitting to the data set
Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization
No full textMulti-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
Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM)
No full textPortable 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
Analisa Perbandingan Algoritma A* dan Dynamic Pathfinding Algorithm dengan Dynamic Pathfinding Algorithm untuk NPC pada Car Racing Game
No full textPermainan 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
Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export
No full textNatural rubber is one of the pillars of Indonesia's export commodities. However, over the last few years, the export value of natural rubber has decreased due to an oversupply of this commodity in the global market. To overcome this problem, it is possible to predict the volume of Indonesia natural rubber exports. Predicted values can also help the government to compile market intelligence for natural rubber commodities periodically. In this study, the prediction of the export volume of natural rubber was carried out using the Automatic Clustering as an interval maker in the Fuzzy Time Series or usually called Automatic Clustering and Fuzzy Logical Relationship (ACFLR). The data used is 51 data per year from 1970 to 2020. The purpose of this study is to predict the volume of Indonesia natural rubber exports and compare the prediction results between the Automatic Clustering and Fuzzy Logical Relationship (ACFLR) and Chen's Fuzzy Time Series. The results showed that there was a significant difference between the two methods, ACFLR got 0.5316% MAPE with and Chen's Fuzzy Time Series model got 8.009%. Show that the ACFLR method performs better than the pure Fuzzy Time Series in predicting volume of Indonesia natural rubber exports
Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM)
No full textPortable 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
Comparative Analysis of Explainable AI Models for Pneumonia Detection in Chest X-rays Using Grad-CAM
No full textPneumonia 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
Komite Program
No full textiiiKOMITE PROGRAMProf. Ir. Zainal A. Hasibuan, MLS., Ph.D (Universitas Indonesia)Prof. Dr. Ir. Suhono Harso Supangkat, M.Eng (Institut Teknologi Bandung)Ir. Paulus Insap Santosa, M.Sc., Ph.D. (Universitas Gajah Mada)RetantyoWardoyo, M.Sc, Ph.D (Universitas Gajah Mada)Prof. Sri Hartati, M.Sc, Ph.D (Universitas Gajah Mada)Dr. Suryono, M.Si (Universitas Diponegoro)Ir. Kridanto Surendro, M.Sc., Ph.D (Institut Teknologi Bandung)Prof. Dr. Ir. Richardius Eko Indrajit, M.Sc (Perbanas)Dr. Djuniadi, M.T (UniversitasNegeri Semarang)Prof. Dr. Achmad Benny Mutiara Q.N. (Universitas Gunadarma)Tony Dwi Susanto, M.T., Ph.D. (Institut Teknologi Sepuluh November)Dr. Darmawijoyo (Universitas Sriwijaya)Prof. Dr. Siti Nurmaini, M.T (Universitas Sriwijaya)Dr. Ermatita,M.Kom (Universitas Sriwijaya)Dr. Saparudin, M.T (Universitas Sriwijaya)Syamsuryadi, M.T., Ph.D. (Universitas Sriwijaya)Deris Setiawan, M.T., Ph.D. (Universitas Sriwijaya)Reza Firsandaya Malik, M.T., Ph.D. (Universitas Sriwijaya)Hadipurnawan Satria, M.Kom, M.Sc., Ph.D. (Universitas Sriwijaya)Jaidan Jauhari, M.T (Universitas Sriwijaya
