Analysis of Specific Water Consumption Based on Water Discharge Case Study of Batang Agam Hydroelectric Power Plant

Renewable energy has an important role today, one of which is hydroelectric power plants which use water as the main resource. So the amount of water is very important for producing every 1 kWh of electricity, this is called specific water consumption. Each hydropower plant has different SWC standards. The research was carried out to determine the SWC value and generator efficiency at the Batang Agam Hydroelectric Power Plant in the period April 2022 to April 2023. The research method was carried out by observing and collecting the required data such as inflow, outflow and daily electrical energy distribution data. Calculate water volume, hydraulic energy and specific water consumption. The research results show that the swc is in the range of 3 -4 m3/kWh, which means this value is below the standard swc value for the Batang Agam Hydroelectric Power Plant, namely 4,808 m3/kWh. This is caused by the unstable condition of the water flow flowing from the river to the Batang Agam Hydroelectric Power Plant which is influenced by rainfall. And based on the electrical energy generated with the distributed electrical energy, the efficiency of the Batang Agam Hydroelectric Power Plant for one year is 71.66%

Loneliness and psychological well-being among adolescents K-Pop fans

Psychological well-being is important for adolescent, especially on their mental health. This study aimed to examine the relationship between loneliness and psychological well-being in adolescent K-pop fans. The population in this study were adolescents aged 13-18 years who were K-pop fans, with a research sample obtained from quota sampling with a total of 100 people. This research applied the quantitative method, while the data collection tools included the psychological well-being and loneliness scales. The data are then analyzed using the product moment technique. The study results show a significant negative relationship between loneliness and psychological well-being among adolescent K-pop fans. The level of psychological well-being and loneliness adolescent K-pop fans are in the moderate category. Based on the study results, the lower the level of loneliness among adolescents, the higher the psychological well-being will be and vice versa. The higher the level of loneliness, the lower the psychological well-being

GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness

AI in Law: Urgency of the Implementation of Artificial Intelligence on Law Enforcement in Indonesia

Introduction to The Problem: The advancement of Artificial Intelligence (AI) has marked the beginning of an age in digital technology, social economics, human needs, and professional conduct. A previous study shows a significant difference in the level of accuracy between Artificial Intelligence (AI) machines and human advocates in which AI machines turned out to be more accurate than advocates. However, the challenges are related to the inadequacy of laws in responding to the development of AI. Furthermore, Indonesian law enforcement officers lack awareness of the advantages of using AI to support their profession.Purpose/Objective Study: Hence, this study aims to analyze the urgency of implementing AI for law enforcement in providing legal services and the law enforcement process.Design/Methodology/Approach: The method used in this research is normative, empirical research with Statute and Conceptual Approach. Furthermore, the data uses primary and secondary data sources. Primary data was obtained through interviews with law enforcement officials. Meanwhile, secondary data sources are primary and secondary legal materials. Furthermore, it will be analyzed qualitatively and presented descriptively.Findings: Artificial Intelligence (AI) is crucial in assisting in developing services and law enforcement, especially for Indonesian law enforcement, which still relies on manual or conventional means to carry out its duties. Artificial Intelligence (AI) can bring benefits in terms of time efficiency and accuracy in assessing cases urgently needed by law enforcement. In terms of law enforcement's perception of the use of AI, they are placed as assistants who cannot entirely replace the law enforcement profession since Artificial Intelligence (AI) lacks human traits that law enforcement officers must possess.Paper Type: Research Articl

Sentiment Analysis of Customers’ Review on Delivery Service Provider on Twitter Using Naive Bayes Classification

Customer evaluations on social media may help us remain competitive and comprehend our business's target market. By analysing consumer evaluations, a business owner can identify common themes, pain points, and desired features or enhancements.  By analysing customer feedback across multiple channels, such as social media, online reviews, and customer service interactions, businesses can rapidly identify any negative sentiment or potential brand damage. The contribution of our study is to evaluate the performance of the Naive Bayes method for classifying customer feedback on courier delivery services obtained via Twitter. The Naive Bayes algorithm is selected due to its simplicity, which facilitates efficient computation, suitability for large datasets, outstanding performance on text classification, and ability to manage high-dimensional data. In this investigation, the Naive Bayes classifier accuracy is 0.506, which is considered to be low.  According to our findings, the irrelevant feature classification resulting in an error throughout the categorization process. A large number of data appearance characteristics that do not correspond to the testing data category have been identified as a result of this occurrence

Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification

Kesepian dan motivasi akademik mahasiswa

Kebijakan physical distancing yang diterapkan pemerintah tempo waktu rupanya masih berdampak pada kondisi kesepian di masa kini. Perasaan terisolasi dan terasing pasca pandemi Covid-19 berisiko menyebabkan siswa mengalami kesepian. Banyak studi telah menunjukkan bahwa kesepian membahayakan kesehatan fisik maupun mental. Tujuan penelitian ini adalah untuk mengetahui hubungan kesepian dengan motivasi berakademik mahasiswa UNY. Pendekatan yang digunakan adalah kuantitatif dengan metode penelitian korelasional. Jumlah sampel yang didapat sebesar 132 mahasiswa, dengan 35 laki-laki dan 97 perempuan. Teknik analis data yang digunakan dalam penelitian ini adalah Pearson correlation. Hasil penelitian menunjukkan bahwa hipotesis penelitian ini dapat diterima. Kesepian memiliki hubungan sebesar -0,297 (p<0,01) dengan motivasi intrinsik, -0,315 (p<0,01) pada motivasi ekstrinsik, dan -0,377 (p<0,01) pada amotivasi akademik. Hasil penelitian ini menyimpulkan bahwa ditemukan hubungan yang signifikan antara kesepian mahasiswa dengan motivasi akademik, baik dalam dimensi intrinsik, ekstrinsik maupun amotivasi. Ditemukan pula bahwa kesepian mahasiswa terpusat pada kategori kesepian sedang, motivasi intrinsik dan ekstrinsik pada kategori sangat tinggi, serta amotivasi pada kategori sangat rendah

TEACHING COVID-19 CONCEPTS TO LEARNERS: FILIPINO EARLY CHILDHOOD EDUCATORS’ NARRATIVES

What are the challenges faced by early childhood educators in teaching COVID-19 concepts to Kindergarten learners? What strategies do these educators employ and how are these addressed? The present study is anchored on these initial inquiries seeking to gather the narratives of Filipino early childhood educators as they navigate the education landscape amid the health crisis. Phenomenological design is used in the study in exploring what Filipino ECEd teachers have experienced in teaching COVID-19 concepts to learners. Six Filipino ECEd teachers were the participants of the study, representing the three main islands of the Philippines — Luzon, Visayas, and Mindanao. They have been teaching for at least 3 years prior to the pandemic and are still currently in the profession where they have experienced teaching COVID-19 concepts to learners. The COVID-19 concept although may be challenging to introduce, still needs to be delivered to allow learners to gain an understanding of the pandemic situation. COVID-19 can be vague to some young learners, for that reason, it requires being familiar with new vocabulary and additional visual cues for better understanding. Also, the reality of deaths caused by the virus creates fear and worries not only for the learners but the teachers as well.  The many challenges brought by the COVID-19 compels ECEd teachers to modify and employ creative strategies to let their learners be equipped with skills to practice health and safety protocols

Does Family Functioning Matter More Than School Culture? Understanding Deeper Junior High School Student’s Character Strength with Spirituality as mediator

Previous research has shown that character strength significantly impacts students' future success. This study examines the functioning of family and school culture as antecedents of character strength through spirituality as mediators. The research sample was 1871 junior high school students in Surabaya City consisting of 1010 female students and 861 male students selected by proportional cluster random sampling from five areas of Surabaya City. The research instruments used were the character strength scale of human dimension of Values in Action Inventory for adolescents (VIA-Youth), spirituality scale of Aspects of Spirituality Questionnaire (ASP) Version 2.1., family functioning scale of Family Assessment Device (FAD), and school culture scale of School   Climate and School Identification Measure—Student (SCASIM-St). The study was conducted by testing research instruments using confirmatory factor analysis and model testing using Structural Equation Modeling (SEM) analysis. The results showed that the fit, family functioning had a more significant influence than school culture did in shaping student character strength in the human dimension and spirituality model proved to significantly mediate the functioning of family and school culture and the strength of student character on the human dimension. The implications of the study results are expected to accelerate the improvement of student character strength in the humanitarian dimension and become policy recommendations for human resource development that are ready to face various challenges.

Implementation of Personal Protective Equipment Detection Using Django and Yolo Web at Paiton Steam Power Plant (PLTU)

Work accidents can occur at any time and unexpectedly, so work safety is associated with health because the work safety system in Indonesia is related to the K3 (Occupational Safety and Health) program. To create a safe and healthy work environment, occupational safety and health management are implemented to avoid work accidents by requiring every worker to use Personal Protective Equipment (PPE). This research aims to develop an immediate detection system for violations of Personal Protective Equipment (PPE) in the workplace using the Yolov8 Method and the Django web-based user interface framework. Yolov8 is one of the latest deep-learning object identification models while Django is the most popular Python developer framework. The system is designed to improve workplace safety and prevent accidents by monitoring compliance with PPE requirements. The research methodology involves literature study, image data collection, preprocessing, model training, and system deployment using the Django framework. There are four classes of detection based on the bounding box according to the specified color, the use of helmets and safety vests based on the red bounding box for helmets and blue for vests while when helmets and safety vests are not being used, based on green and yellow bounding boxes. The system successfully detected four PPE classes with an average accuracy of 82.3% from 230 test data, a mAP50 value of 81.6%, a precision value of 90.3%, and a recall value of 75.1%. The findings from this study indicate that the developed system can effectively improve occupational safety and health management. However, there is a detection error factor caused by the lighting and specifications of the camera used. Future research can focus on integrating the system with other work safety systems to provide a comprehensive solution for accident prevention