A content-based image retrieval system, as an Indonesian traditional woven fabric knowledge base, can be useful for artisans and trade promotions. However, creating an effective and efficient retrieval system is difficult due to the lack of an Indonesian traditional woven fabric dataset, and unique characteristics are not considered simultaneously. One type of traditional Indonesian fabric is ikat woven fabric. Thus, this study collected images of this traditional Indonesian woven fabric to create the TenunIkatNet dataset. The dataset consists of 120 classes and 4800 images. The images were captured perpendicularly, and the ikat woven fabrics were placed on different backgrounds, hung, and worn on the body, according to the utilization patterns. The feature extraction method using a modified convolutional neural network (MCNN) learns the unique features of Indonesian traditional woven fabrics. The experimental results show that the modified CNN model outperforms other pretrained CNN models (i.e., ResNet101, VGG16, DenseNet201, InceptionV3, MobileNetV2, Xception, and InceptionResNetV2) in top-5, top-10, top-20, and top-50 accuracies with scores of 99.96%, 99.88%, 99.50%, and 97.60%, respectively.

Fruits are parts of plants that originate from the plant's pistils and usually contain seeds. Meanwhile, vegetables are leaves, legumes, or seeds that can be cooked. Fruits and vegetables have many variations that can be distinguished based on color, shape, and texture. However, the development of Artificial Intelligence (AI) technology has become pervasive in everyday life, one aspect of which is demonstrated through deep learning, a method of AI learning. Therefore, developing deep learning for tasks such as automatically detecting surrounding objects is necessary. This study aims to classify types of fruits and vegetables by applying a Convolutional Neural Network (CNN) with the MobileNetV2 architecture. In this study, fruits and vegetables encompassing 36 categories, including significant types in daily life, were considered. The results show that the classification system achieved an excellent accuracy rate of 97.31%, demonstrating the effectiveness of using deep learning techniques for this application

The woven ikat fabric from Nusa Tenggara Timur is a local wisdom that must be preserved. Due to its vast array of motifs, users often encounter challenges in its recognition. For this study, the TenunIkatNet dataset was employed. One prominent recognition method involves classification based on the motif type and geographical origin. The efficacy of the classification is heavily contingent upon the method of extraction employed. The Convolutional Neural Network (CNN) method is used for feature extraction and classification processes. This research compares the classification performance of the VGG16 baseline model and the proposed model. The proposed model modifies the baseline at the fully connected layer and the training process from the first convolution layer. Incorporating elements such as Global Average Pooling (GAP), Batch Bormalization (BN), and Dropout has proven instrumental in mitigating overfitting. The transfer learning strategy is used for feature extraction and classification because the model has been intelligently trained on a large dataset. The research findings unequivocally indicate that the performance of the modified model supersedes that of the baseline model. Based on the evaluation metrics, the proposed model is superior to the baseline model with precision, recall, accuracy, and F1-score, respectively 98.73%, 98.54%, 98.54%, and 98.53%

Advances in information technology currently play an important role in the rapid exchange of information. Information in the form of images requires adequate storage media due to the large size of images, thus requiring an efficient method, namely image compression. This study aimed to evaluate the performance of the Shannon-Fano and Huffman Coding algorithms in terms of compression ratio, processing time, and resistance to Additive White Gaussian Noise (AWGN). The solution in this study was the application and comparison of two compression algorithms to determine the more efficient compression algorithm. The algorithms used were Shannon-Fano and Huffman Coding for the compression of 256×256 and 512×512 BMP images. After the reconstruction process, the images were tested using the AWGN noise effect with noise variance ranging from 0.01 to 0.5 and filtered using a Gaussian filter to assess the image’s resistance to noise. The results showed that the Huffman Coding algorithm produced higher compression ratios of 5.60% and 5.65% compared to Shannon-Fano’s 5.51% and 5.58% for images measuring 256×256 and 512×512, respectively. However, the Huffman Coding algorithm required longer processing time than Shannon-Fano. AWGN noise testing showed that an increase in noise variance reduced image quality, but the application of a Gaussian filter could improve image quality. This study showed that Huffman Coding is more efficient in compression algorithms, while Shannon-Fano is superior in processing time, and the reconstructed image was able to maintain image quality after being affected by noise. The results can be used as a reference in selecting an efficient image compression algorithm to improve transmission channel performance

Empowering Cooperatives and Micro, Small, and Medium Enterprises (MSMEs) is strategic to boost the national economy. East Nusa Tenggara (ENT), as one of the cooperative provinces, is experiencing rapid growth in both quantity and quality. As pillars of regional and national economics, Cooperatives need to be developed, especially for small communities, in their various productive businesses. The development of cooperatives in NTT Province is quite significant until 2023, namely 4,301. Three thousand eight hundred sixty-six cooperatives are legal entities and active, while 435 cooperatives are not yet legal entities. The Koperasi Simpan Pinjam Sahabat Literasi Flobamorata (KSP Batera) is an institution founded by members who have the same destiny and share the same responsibility in the economic field. Its development is quite advanced regarding assets and the number of members. The main problem faced by KSP Batera is that financial management is still manual, and an Annual Member Meeting (AMM) has yet to be held. This happens because the educational background is inappropriate, and there needs to be more knowledge about the Cooperative Financial Accounting System (CFAS). Training activities for the management team on a computer-based financial accounting bookkeeping system is the best solution for the future development of this cooperative. The final result of the training activity is that the management team can complete a computer-based bookkeeping system and financial reports and prepare AMM reports to be accounted for after the close of the financial year. Maximum knowledge transfer occurs because the learning model in the form of theory and practice is 30% and 70%, respectively. Apart from that, assistance is also provided for managers both offline and online