TŁUMACZENIE MASZYNOWE Z KLASYFIKACJĄ POZIOMÓW JĘZYKA JAWAJSKIEGO

A hybrid corpus-based machine processing has been developed to produce a proper Javanese speech level translation. The developed statistical memory-based machine translation shows significantly accurate results. Integration of an automatic text classifier and an expert system is proposed to help Javanese in classifying the speech levels used for a specific interlocutor. Javanese rule-based expert system is designed while naive Bayes classifier is selected after outperforming simple logic probability approach. As a result, the average of translation accuracy (72.3%) indicates that the integrated intelligent interfaces could effectively solve the Javanese language pragmatic translation problems.Hybrydowy korpus maszynowy dla celów translacji został opracowany w celu uzyskania właściwego tłumaczenia poziomu języka jawajskiego. Rozwinięte tłumaczenie na bazie statystycznej wykazuje wyjątkowo dokładne wyniki. Integracja automatycznego klasyfikatora tekstu i systemu eksperckiego jest propozycja aby pomóc użytkownikom języka jawajskiego w klasyfikacji poziomów mowy wykorzystywanych dla konkretnego rozmówcy. Zaprojektowany system ekspertowy w powiązaniu z klasyfikatorem naive Bayes wykazuje przewagę nad prostym podejściem logiki prawdopodobieństwa.W rezultacie średnia uzyskana dokładność tłumaczenia (72,3%) wskazuje, że zintegrowane inteligentne interfejsy mogą skutecznie rozwiązywać problemy pragmatycznego tłumaczenia języka jawajskiego

Virtual Laboratory for Line Follower Robot Competition

Laboratory serves as an important facility for experiment and research activity. The limitation of time, equipment, and capacity in the experiment and research undertaking impede both students and college students in undertaking research for competition preparation, particularly dealing with line follower robot competition which requires a wide space of the room with various track types. Unsettled competition track influences PID control setting of line follower robot. This study aims at developing Virtual Laboratory (V-Lab) for students or college students who are preparing for line follower robot competition with unsettled and changeable tracks. This study concluded that the trial data score reached 98.5%, the material expert score obtained 89.7%, learning model expert score obtained 97.9%, and the average score of small group learning model and field of 82.4%, which the average score of the entire aspects obtained 90.8%

Remote and Proximal Delivery of the Laboratory Component of Electrical and Energy Systems Course

: This paper describes the practical activities of Electrical Energy Systems (EES) course at University of South Australia (UniSA). The practical subject is Laboratory Component (LC). It is a compulsory subject for first-year students of bachelor of the engineering program. Students may do practically in conventional laboratories or by using Internet-based remote laboratory (RL). An experiment was conducted to explore the students' preference for practical approaches. Most students prefer the RL schedule flexibility while the others like to do real component wiring in an electrical laboratory. The combination between proximal and remote laboratories may become the best approach to delivering the LC practical

Effect of continuity of potential on accuracy in magnetic field analysis using nonconforming mesh

Methods of analyzing magnetic fields in rotating machines using nonconforming meshes, in which only an interpolation technique is applied, are investigated. The effect of the continuity of potentials at nonconforming surfaces on accuracy is examined, it is shown that although the flux distribution is not affected by the discontinuity of the potential, the force and torque are fairly affected by the discontinuity. Therefore, it is shown that a continuous method, or a discontinuous method using a fine mesh should be used especially in force and torque calculation. An analysis of an induction motor is also carried out using a nonconforming mesh</p

Collaborative learning based on a micro-webserver remote test controller

This paper presents a remote test workbench that was developed to support on-line assignments dealing with the IEEE 1149.1 standard test access port and boundary-scan architecture. The remote test controller is based on the DS80C400 networked microcontroller from Maxim-Dallas, which offers a very cost-effective solution to the development of micro-webservers enabling low complexity data acquisition and control tasks. All remote experiments are integrated into Moodle in exactly the same way as the remaining courseware that is made available to the students. The use of Moodle facilitates the implementation of collaborative learning activities based on the remote test workbench, and the development of the workbench itself is the subject of a collaborative learning project involving students from the universities of Porto in Portugal and South Australia at Adelaide

Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second

Detecting emotions using a combination of bidirectional encoder representations from transformers embedding and bidirectional long short-term memory

One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively, BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts

Generating Javanese Stopwords List using K-means Clustering Algorithm

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable