This study aims to analyze the effect of target setting management on the academic development of teacher careers. This study uses a quantitative method with a survey approach. The sample technique is a systematic sampling based on the order of teacher data as many as 108 respondents. Data collection is done by distributing questionnaires through googleform. Data analysis was carried out with validity and reliability tests, linear regression analysis. The results of the study obtained the value of the correlation coefficient which was included in the sufficient category, and a positive value, and there was a significant influence between the target setting variables on the academic development of teacher careers. Determination of target settings in the form of increasing knowledge and skills refers to the SMART model that can be used by teachers to achieve success in career development.
The background of this research is the preparation for face-to-face learning, so we need facilities and infrastructure. where learning that started at school has become at home with the issuance of government regulation number 57 years. 2021 regarding the standard of facilities and infrastructure which is used as a reference in face-to-face learning. This study uses a qualitative evaluation research where the evaluation model used is the discrepancy model. The discrepancy model consists of four stages, namely design, installation, process and comparison. Then the results of the design used are government regulation number 57 of 2021 concerning standard facilities and infrastructure, the form of installation is a discussion with the foundation then socialized with school residents, the process is data collection, the making of proposals, spending and realization and comparison there are facilities that do not yet exist, learning process related to the quality of education are also still lacking because teachers have not mastered technology and lack of facilities and infrastructure such as laptops and webcams, it can be said that the evaluation results regarding standards facilities and infrastructure are still lacking to improve the quality of education. suggestions from researchers to hold training and re-collection of data to add facilities and infrastructure that are lacking.
Stress is a serious challenge for students that can negatively impact physical health, mental well-being, and academic performance. However, accurate and effective stress detection approaches to support early intervention are still limited. This study aims to evaluate machine learning models for detecting student stress levels with optimal accuracy to facilitate early intervention. The research employs a quantitative approach using a dataset containing 1,100 student samples from Nepal, encompassing 20 stress-related features from psychological, social, academic, environmental, and physiological aspects. Data were collected via a self-report questionnaire, processed with StandardScaler scaling, and analyzed using 10-fold cross-validation. The models tested include Perceptron, Gradient Boosting Trees Classifier (GBTC), Naive Bayes (NB), Logistic Regression (LR), and AdaBoost. The results show that Perceptron performed the best with an accuracy of 97.27%, followed by NB (95.45%), GBTC (94.54%), LR (94.54%), and AdaBoost (93.63%). Perceptron, with its advantage in linearity and evaluation through 10-fold cross-validation, shows great potential as an effective classification model for student stress detection, which can accelerate early intervention and enhance student well-being and learning environments.
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 article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.
This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests.Invasive diagnostic technologies and expensive expenses are additional impediments to early diagnosis.To detect anemia, an effective, accurate, and non-invasive method is required.In this study, the conjunctival image of the eye is analyzed as a non-invasive method of detecting anemia.Various model approaches were tested in an endeavor to categorize anemic and healthy patients as accurately as possible.The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the most effective plan.With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%.These findings show that the model can successfully identify healthy patients while accurately identifying anemic patients.This method offers a non-invasive means of detecting anemia early on, making it promising for use in clinical settings.The SVM+MobileNetV2 technique relies on images of the conjunctiva of the eye and has the potential to improve healthcare by identifying people who may have had anemia earlier.This technique stands out as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificity are balanced.
Technology-based record management, which is part of the development of information and communication technology (ICT) plays an important role for organizations in managing documents to be more effective and efficient. Thus, such training needs to be carried out, one of which is through community service activities. The purpose of this training was to educate participants to understand the importance of this training topic and be able to practice it directly as part of a technology literate society. This training was conducted by the community service team consisting of three lecturers and two students The training was carried out virtually through the Zoom platform for four hours, which the community service partner was PCM Bekasi Timur. The stages are divided into four parts, 1) the delivery of core material and best practices; 2) a question-and-answer session; 3) the conclusion and summary of the material; and 4) the impressions and messages conveyed by representatives of the participants. The evaluation results showed that 6 (54.5%) participants were satisfied and 4 (36.4%) participants were very satisfied, and 1 (9.1%) participant was not satisfied with the activities. In addition, they mostly showed their satisfaction and enthusiasm with the presented material and the delivery of spokespersons, which can also be seen from the positive impression of the participants. It is inferred that this training provides great benefits for participants and organizations to improve their archival documentation system.
Lips animation plays an important role in facial animation. A realistic lips animation requires synchronization of viseme (visual phoneme) with the spoken phonemes. This research aims towards building Indonesian viseme by configuring viseme classes based on the clustering process result of visual speech images data. The research used Subspace LDA, which is a combination of Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA), as the extraction feature method. The Subspace LDA method is expected to be able to produce an optimal dimension reduction. The clustering process utilized K-Means algorithms to split data into a number of clusters. The quality of clustering result is measured by using Sum of Squared Error (SSE) and a ratio of Between-Class Variation (BCV) and Within-Class Variation (WCV). From these measurements, we found that the best quality clustering occurs at k=9. The finding of this research is the Indonesian viseme consisting of 10 classes (9 classes of clustering result and one neutral class). For a future work, the result of this research can be used as a reference to the Indonesian viseme structure that is defined based on linguistic knowledge.
A method of index measurement, the so-called rn-line technique has been applied to measure the refractive index of planar waveguides fabricated by ion exchange method in BK7 substrate. By placing a prism coupler on the surface of the planar waveguide, the coupling angle of modes guided in the waveguide was measured. The prism has apex angle of 44.9° and is made of ZnSe with refractive index 2.59073 . The values of the coupling angles were then processed mathematically to obtain the effective indices of the guided modes. The number of modes guided in the waveguide depends on the duration of ion exchange process, and the effective refractive indices have been determined for the respective modes. The result for zero order mode, ranging from 1.5183 to 1.6887 for TM modes and from 1.5182 to 1.6891 for TE modes. On the other hand,for the duration of ion exchange process of 48 hours, five modes were guided in the waveguide and the effective refractive indices were 1.6887; 1.6167; 1.5818; 1.5649 and 1.5465 for zeroth, the first, the second, the third and the fourth modes, respectively. The use of the rn-line technique has been proved to be simple and effective with high accuracy in the characterization purpose of waveguides.
A characterization method of planar waveguides, namely m- line measurement has been utilized to reconstruct refractive index profile in planar waveguide. This method gives some values of incident angle that can be coupled in to waveguide, which after some mathematical calculations can provide its mode indices. To reconstruct the refractive index profile from mode indices as a function of normalized film thickness we use Inverse Wentzel-Kramers-Brillouin method. Furthermore, we select the value of n0 that give the smoothest refractive index profile by finding the minimum sum of the squares of second differences of the profile. For this purpose, we implement reiterative, trial and error method on some values above the measured fundamental effective index value as a guess of the surface index. The result has been smoothed using curve fitting algorithm to exponential and Gaussian profile. The result confirm that index profile of planar waveguide can be reconstructed mathematically and the profile can be obtained more accurate by the proposed curve fitting technique than the basic IWKB method.
