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This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).

The rapid development of information and communication technology allows society to access various information needed in daily life. The Law of the Republic of Indonesia Number 23 of 2006 concerning population administration serves as an important element in population management. Population documents are issued by official institutions and have legal legitimacy as valid evidence. The method used in this research regarding public sentiment towards e-ID card services is the survey method, which aims to collect data from a large population using a smaller sample. The steps or processes in this research using the SVM method consist of case folding, cleaning, tokenizing, normalization, stopword removal, and stemming. Based on the classification of 150 test data using SVM, the number of positive sentiments recorded is 110 opinions, while negative sentiments recorded are 40 opinions.

Workload analysis provides an opportunity to smooth workload or reduce work activities that do not provide added value. Detailed evaluation and analysis of each work element of a position will display a more detailed workload map. PT. XYZ requires detailed workload mapping to be able to display the load profile for each position. The results of this load mapping can be used for workload normalization analysis. This research uses the Full Time Equivalent (FTE) method. The data used in this research are job descriptions, frequency and working hours. The research results show that the FTE-based workload value of the Vacum Geiss AG operator is FIT (value 1-1.28), namely on 03-10-2023 it is 1.030 and on 04-10-2023 it is 1.030 which is fit because the FTE value is between The value is 1-1.28 so that the operator's work load is appropriate on that day, then on 05-10-2023 on the Geiss AG vacuum machine it is 0.915, it is still underload because the FTE value is below the value of 0.99 so the operator on that day is underloaded. the work is not suitable and additional work is needed.

This study presents the implementation of Transfer learning using the ResNet-18 architecture for classifying 10 musical instrument categories based on visual representations of audio signals. The audio waveform is transformed into image-like inputs appropriate for CNN processing, accompanied by data augmentation and ImageNet-standard normalization. ResNet-18 is utilized due to its efficient feature extraction capability enabled by residual blocks, which help overcome vanishing gradient issues. The model was trained for 10 Epochs using the AdamW optimizer and Cross-Entropy Loss. Experimental results show that the model achieved a maximum validation accuracy of 77.35%, with a stable downward trend in training loss, indicating effective feature learning. However, several misclassification cases were observed, particularly among instruments with similar spectral characteristics, such as drum–violin and tabla–sitar. These findings demonstrate that while ResNet-18 performs reliably for musical instrument classification, further improvements remain possible through deeper architectures like ResNet-50, more comprehensive hyperparameter optimization, and the use of richer audio representations such as Mel-Spectrograms. This research provides an essential foundation for developing automated music analysis systems powered by Deep Learning.

Diabetes Mellitus (DM) is a metabolic disorder characterized by high blood sugar levels due to insulin deficiency. Factors causing Diabetes Mellitus (DM) are lifestyle which includes diet, lack of exercise, monitoring blood sugar, and medication. Most people do not realize that they have DM and only find out when they experience severe symptoms. To avoid this, the k-Nearest Neighbor (KNN) method can be used to predict the possibility of developing diabetes. The aim of this research is to classify diabetes mellitus using the K-Nearest Neighbor (KNN) method and make people more aware of the risk of disease through healthy lifestyle changes. Data received from the Dharma Husada Clinic is categorized based on researchers' needs, including age, BMI, insulin, skin thickness, glucose, diabetes, genetics, and insulin. This research was carried out in three main steps: dataset input, preprocessing, and evaluation. The first stage is data analysis which begins by entering a dataset to train and test the model, where each data element has certain characteristics (attributes) and classes. Preprocessing steps include training data generation and data cleaning, which includes sanitization, lowercase, normalization, stopwords, stemming, and tokenizing. The final step is evaluating. Evaluation includes building an evaluation model and measuring the level of accuracy, building a predictive model, and saving the model. This research shows that the K-Nearest Neighbor (KNN) method can be used to classify diabetes mellitus (DM), but especially in a small dataset consisting of 245 dates and 8 attributes it is not accurate for patients aged 30 years. . A k value that is too small can cause overfitting, and a k value that is too large can cause underfitting. However, if the amount of data is small, the choice of k can have a large impact.    

This qualitative study investigates the impact of streamers’ swearing on the morality of Vietnamese teenagers, a topic of increasing importance in the era of digital streaming and online entertainment. The research was conducted through semi-structured interviews with nine Vietnamese teenagers, providing in-depth insights into their perceptions and attitudes towards the language used by streamers and its influence on their behavior. The thematic analysis of the interview data revealed four key themes: normalization of swearing in digital spaces, delineation between online and offline behaviors, influence of streamers as role models, and critical reflection and selective adoption. The study found that while swearing by streamers was normalized in digital contexts, participants also demonstrated a clear distinction between acceptable behaviors in online and offline environments. Streamers were often viewed as influential role models, affecting the language and attitudes of the adolescents. However, participants also engaged in critical reflection, selectively adopting behaviors that aligned with their personal and cultural values. These findings contribute to the understanding of digital media’s impact on adolescent development, particularly in non-Western contexts. The study underscores the complex interplay between cultural norms, digital media consumption, and moral development, highlighting the need for comprehensive digital literacy and ethical content creation in the digital age.

This research aims to predict customer churn in a telecommunications company using Logistic Regression (LR) and Gradient Boosting Classifier (GBC) algorithms. Customer churn poses a significant challenge as acquiring new customers is costlier than retaining existing ones. The dataset from Kaggle comprises 7043 records and 21 attributes. The process includes data pre-processing, cleaning, transformation, and normalization using a Min-Max Scaler. The data is split into features (X) and target (y), then divided into training and testing sets with an 80:20 ratio. Both models were trained and evaluated using a confusion matrix. Results show that the GBC model outperforms the LR model, with an accuracy of 83% compared to LR's 81%. This study demonstrates the effectiveness of GBC in predicting customer churn.

This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.

A substation is a transmission installation that distributes power to a load in a certain area. The Sekayu 150 KV Substation is the center for controlling electrical power load requirements and functions as a center for securing electrical power system equipment and as a center for the normalization process for disturbances in the Sekayu transmission area. The electrical power distribution system does not rule out the possibility of disturbances, especially disturbances caused by nature. If there is an unpredictable disturbance, appropriate and reliable safety equipment (protection system) is needed to be able to increase the distribution of electrical power to the load (consumer).  

The topic of LGBT and homosexuality seems to be endlessly discussed. This is due to several scientific fields such as health, psychology, Islamic law, and other related fields that can significantly influence the decision-making process related to these issues. The problems include positioning LGBT or homosexual relationships as nature or nurture, innate or socially constructed, genetic or deviant, normal or abnormal, and so on. This abnormal behavior creates misunderstandings about sex in this group. This shows that people who maintain LGBT or homosexual relationships are unable to reconcile their desires and principles of life. Therefore, this article aims to identify the perceptions and efforts of Islamic religious education students towards the increasing normalization of LGBT in Indonesia. It also aims to educate the public about the understanding, meaning, and law of LGBT in Islam. The research method used is qualitative and is an approach or survey to examine and understand the main symptoms. To understand the main symptoms, researchers conducted interviews with respondents, asking several more general questions. After conducting interviews, the researchers initially obtained conflicting results. Students' perceptions of LGBT were contradictory. Second, PAI students do not agree that LGBT is normalized in Indonesia, either officially (legalized by the government) or unofficially (considered normal by society). The third effort of PAI students in minimizing LGBT in Indonesia is by providing education to the community directly or indirectly.    

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

Recognizing outstanding students is an essential strategy to encourage learners to improve their academic performance and overall learning quality. However, the process of selecting the best students at SMK Negeri 1 Raman Utara still encounters significant challenges due to the complexity of the evaluation criteria, which include academic achievement, attendance, participation in extracurricular activities, and student behavior. The current manual selection method is considered less effective because it is time-consuming and susceptible to subjectivity in decision-making. This study aims to address these issues by developing a technology-based Decision Support System utilizing the Simple Additive Weighting (SAW) method. SAW was chosen for its efficiency in solving multi-criteria problems through matrix normalization and preference weighting for each alternative. The development process includes requirement analysis, system design, and the implementation of an automated ranking mechanism for student candidates. Based on the testing results, the implemented system can process assessment data rapidly and produce accurate and objective rankings of high-achieving students. The system offers a practical solution for the school by enhancing transparency and validity in the selection process, minimizing human calculation errors, and supporting more equitable and data-driven decision-making.

This research aims to design a Decision Support System for Blood Donation Eligibility Using the SAW Method at UTD PMI Bengkulu City. Based on the research, UTD PMI Bengkulu City still uses manual calculations in its data processing and does not use a special application. The calculation system still uses Microsoft Word and Microsoft Excel applications which are still less effective and fast. In this research, the method used is SAW (Simple Additive Weighting). This method requires the decision maker to determine the weight of each attribute. The total score for creating an alternative is obtained by adding up all the results of the multiplication between ratings (which can be compared across attributes). The rating for each attribute must be dimension-free in the sense that it has gone through a previous normalization process. In this method, an analysis of the data requirements needed to build the system is carried out, a design plan for the system to be created, compiling and implementing the data that has been obtained into the system. In the new system designed on this occasion several designs were created consisting of designing the File Menu, Data Menu, Process Menu and Report Menu. The programming language used is PHP with a MySQL database.

As Time And Human Civilization Progress, The Construction Industry Is Also Developing Rapidly, Starting From Advances In Technology Used To Increasing Competitors, Both Foreign Competitors And Local Competitors. The Application Of Supply Chain In The Construction Industry Is Believed To Be One Of The Strategic Efforts To Increase The Competitiveness And Performance Of A Construction Company. In This Research, An Analysis Of The Application Of Supply Chain Management (SCM) Will Be Carried Out In The Wadung Asri Market Drainage Channel Normalization Project. This Research Aims To Find Out Whether Contractors Have Implemented The Supply Chain Management (SCM) Concept In Their Material Procurement Process. The Research Results Show That In The Project Material Procurement Process, The Contractor Has Not Implemented The Supply Chain Management (SCM) Concept Significantly. This Can Be Seen From Decisions In Selecting Suppliers Which Are Only Based On The History Of Cooperation Without Considering Other Factors. Apart From That, There Were Delays In The Arrival Of Materials Which Resulted In Project Work Being Delayed From The Initial Plan. Therefore, It Is Necessary To Improve The Goods Procurement System That Has Been Running So Far By Implementing The Supply Chain Management Concept.