The importance of early fire detection can help in providing warnings and avoid disaster that led to the economic damage and loss of life. Fire detection techniques with conventional sensors have limitations, which require a long time to detect a fire, especially in a large room and cannot work in the open area. This study proposed a fire detection method, to detect the possibility of fire based on visual sensor using multi-color feature such as color, saturation, luminance, background subtraction and time frame selection for fire detection. The evaluation in this studies conducted by calculating the error rate of the fire detection.

This paper presents Indonesian text emotion detection and evaluates the performances of four different classification methods: Naive Bayes (NB), J48, K-Nearest Neighbor (KNN) and Support Vector Machine-Sequential Minimal Optimization (SVM-SMO). The experiment uses Indonesian text corpus, containing 1000 sentences which consists of six emotion classes: anger, disgust, fear, joy, sadness, and surprise. Preprocessing step which consists of tokenization, case normalization, stopword removal, stemming and TFIDF are used to extract the features of text emotion. We conduct 10-fold cross validation and split validation for the experiment. Based on the result, we conclude that SVM-SMO classifier gives the best performance. In the 10-fold cross validation, the result shows that the accuracy of NB, J48, KNN and SVM-SMO are 80.2%, 80.8%, 68.1%, and 85.5% respectively. The same conclusion is also demonstrated by the split validation, the highest accuracy of 86% is also achieved by SVM-SMO.

This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN) to differentiate skin cancer categories. The public HAM10000 dataset was used to test how well the proposed model worked. Various pre-trained convolutional neural network (CNN) models, including Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19, were employed. Our approach demonstrates an accuracy of 96.90%, precision of 97.07%, recall of 96.87%, and F1-score of 96.97%, surpassing the performance of other state-of-the-art methods. The paper also discusses the use of Shapley Additive Explanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

Coconut tree grows rapidly in tropical region such as Indonesia. Coconut wood is used as alternative or complementary raw material for housing or making furniture. Abundant coconut trees are planted, however the utilization of coconut wood as raw material for furniture is still very rare in Indonesia. This is caused by the low quality of coconut wood, since it has not found adequate technology for the processing of coconut wood. This paper presents our experimental work on coconut wood quality classification using self-tuning MLP classifier (AutoMLP) and Support Vector Machine (SVM). For SVM classifier we used the LibSVM library, available in RapidMiner. The Gray-Level Co-occurrence Matrix (GLCM) is used to extract the texture features of coconut wood images. Experiment result shows that AutoMLP gives the best accuracy rate at 78.82%, which is slightly better than 77.06% of SVM.

Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source for both traditional and modern diagnostic approaches. This study aims to provide an overview of the significant role of medical images in skin cancer detection and highlight developments in the use of deep learning for early diagnosis. The scope of this survey includes an in-depth exploration of state-of-the-art deep learning methods, an evaluation of public datasets commonly used for training and validation, and a bibliometric analysis of recent advancements in the field. This survey focuses on publications in the Scopus database from 2019 to 2024. The search string is used to find articles by their abstracts, titles, and keywords, and includes several public datasets, like HAM and ISIC, ensuring relevance to the topic. Filters are applied based on the year, document type, source type, and language. The analysis identified 1697 articles, predominantly comprising journal articles and conference proceedings. The analysis shows that the number of articles has increased over the past five years. This growth is driven not only by developed countries but also by developing countries. Dermatology departments in various hospitals play a significant role in advancing skin cancer detection methods. In addition to identifying publication trends, this study also reveals underexplored areas to encourage new explorations using the VOSviewer and Bibliometrix applications.

Word processing tool is a basic need in learning a language. One of the word processors needed by a language learner is part of speech (POS) tagging. While many POS Tagging tools for Indonesian language have been developed, no systems have been addressed specifically for language learners. This paper presents a study on an Indonesian part of speech (POS) tagging system developed as one of word processing tools for language learners. We use resources from previous Indonesian POS tagging research, such as MorphInd for the morphological analysis and IPOSTagger for part of speech tagging. Objective and subjective tests are employed to evaluate this system. In the objective test the part of speech tagging results use a system model developed from IPOSTagger in combination with MorphInd as the morphological analyzer, and compared with the results of part of speech tagging produced from the original IPOSTagger system model. The results show that the part of speech tagging accuracy using this system model is higher than other models. For its subjective evaluation, Mean Opinion Score (MOS) is used to the 24 participating respondents. The MOS results obtained reach 3,61 for test-1, 3,87 for test-2, and 3,72 for test-3. From the results, we expect that this POS tagging system could be used to help language learners in their Indonesian language self-learning process.