Deforestation in peatland areas such as Kalimantan, Indonesia has been going on for decades. The deforestation has indirectly increased peatlands to become degraded and flammable. The Synthetic Aperture Radar (SAR) interferometry approach for identification of degraded peatlands can be performed using ALOS-2 PALSAR-2 data by converting land deformation data generated from SAR interferometry analysis into water table (WT) depth data using Wosten models. Peatlands with WT depth conditions of more than 40 cm are classified as degraded peatlands which are flammable. By using fire data from previous studies, this research confirms that identification of degraded peatlands using SAR interferometry approach by ALOS-2 PALSAR-2 is more reliable with high precision related to forest fires, with a precision level of 88% compared to 5% precision level using the WT depth monitoring system that has been installed in Central Kalimantan. The highest wavelength of ALOS-2 PALSAR-2 (L-Band) data can resolve the limitation due to temporal and volumetric decorrelation, compared to C-Band and X-Band satellite data. The combination methods of SAR interferometry approach and the real-time WT depth monitoring system to identify degraded peatlands can be more efficient, faster, and accurate. The advantage of this research result shows that SAR interferometry analysis can reach blank spot areas that are not covered by the observation station of WT depth monitoring system. It also gives a benefit as a guide to select precise locations of observation stations related to degraded peatland and forest fire.

Electromagnetic wave backscattering by corner reflectors in an anechoic chamber is studied using our developed computational tool. The tool applies the Finite-Difference Time-Domain (FDTD) method to simulate the propagation of the wave’s electric and magnetic fields. Experimental measurement in an anechoic chamber is also carried out as a comparison. The two results show agreement, including the finding that the backscatter intensity variation amongst the four circularly polarized modes is significantly smaller than the variation amongst the four linearly polarization modes.

Tajwid learning faces challenges in visually recognizing recitation rules from Arabic script, thus requiring an interactive and accurate digital medium. This study aims to develop a web-based application to automatically detect seven core tajwid rules using YOLOv8. This research follows a Research and Development approach adopting the ADDIE model, which consists of five systematic stages: analysis, design, development, implementation, and evaluation. The YOLOv8 model was trained using 200 annotated images of Qur’anic verses, with a data split of 70% for training, 20% for validation, and 10% for testing. Data augmentation was applied through rotation, flipping, and brightness adjustment, with training facilitated using Roboflow. Our main finding is an interactive web application capable of automatically detecting seven tajwid rules from Qur’anic verse images. The application allows users to upload images, which are then analyzed and displayed with colored bounding boxes and interactive captions. Testing results showed accurate and responsive detection performance, achieving a mAP@50 of 89.88% with high accuracy across several tajwid classes. These findings highlight the potential of Artificial Intelligence (AI) to support more interactive, independent, and adaptive tajwid learning, while also promoting the digitization of Islamic manuscripts.

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.

Permasalahan non-interoperabilitas antar metaverse menjadi tantangan utama dalam pengembangan dunia virtual yang terintegrasi. Ketidaksesuaian standar pengembangan antar platform menghambat mobilitas dan pertukaran data pengguna, khususnya dalam representasi avatar. Penelitian ini bertujuan untuk memvisualisasikan permasalahan tersebut melalui pengembangan model avatar tiga dimensi (3D) berbentuk humanoid menggunakan pendekatan komputasi grafis. Metodologi yang digunakan melibatkan serangkaian tahapan teknis mulai dari pembuatan objek 3D (bola dan silinder) sebagai representasi bagian tubuh, implementasi fungsi translasi dan rotasi berdasarkan tiga urutan koordinat (XYZ, YXZ, ZXY), hingga simulasi rotasi searah dan berlawanan arah jarum jam menggunakan pustaka numpy dan matplotlib dalam lingkungan Python. Penelitian ini berhasil membuktikan bahwa perbedaan urutan sistem koordinat (XYZ, YXZ, ZXY) dan arah rotasi menghasilkan visual avatar yang tidak konsisten meskipun diuji menggunakan parameter yang sama.

Javanese script is an Indonesian cultural heritage rich in historical, aesthetic, and spiritual values, but it is now becoming marginalized. To reintroduce its use, this research develops a Javanese script recognition application based on an Artificial Neural Network (ANN). In this study, the Javanese script was divided into 120 classes (ha, hi, hu, he, hee, ho, up to nga, ngi, ngu, nge, ngee, ngo). Each class was represented by 40 sample images of the script handwritten by 40 different respondents, resulting in 4800 samples. The research began with preprocessing, which included adding padding to the top, bottom, left, and right sides of the script; downsizing the image to a 33x33 resolution by applying average pooling; image segmentation to separate the script characters from the background; converting the color image to grayscale; and converting the grayscale image to a binary image with the help of thresholding. A number of images that had undergone preprocessing were then structured into a ready-to-use dataset of 4800 samples. This dataset was then divided with an 80:20 ratio, where 80% of the data was used to train the model and 20% was used to validate the model. An evaluation was conducted to measure the model's accuracy. Subsequently, the application was developed using PySide6 as the desktop interface. After the application development, the researchers provided an additional 600 images, where each class was represented by 5 samples, for real-world application testing. The evaluation results showed that the model achieved a validation accuracy of 70.21%. Meanwhile, testing with the application using the additional test images showed an accuracy of 73.83%.