IoT-Based Prediction of Ornamental Plant Water Needs Using Sugeno Fuzzy Algorithm

Urban plant care is increasingly important amid growing concerns about air pollution and limited time for manual maintenance. In Indonesia, air quality has deteriorated significantly, with PM2.5 pollution levels exceeding World Health Organization standards, particularly in major cities like Jakarta. Ornamental plants play a crucial role in improving air quality; however, urban residents often struggle to consistently water them. This study addresses that problem by developing an Internet of Things (IoT)-based smart irrigation system that utilizes the Sugeno fuzzy algorithm to predict the water needs of ornamental plants. The system combines a capacitive soil moisture sensor and a DHT11 temperature-humidity sensor with an ESP8266 microcontroller to monitor environmental conditions. Data is transmitted to Firebase and visualized in an Android application, which provides real-time monitoring and specific volume recommendations ranging from 10 ml to 240 ml, calibrated for medium-sized plant pots which is also based on 27 fuzzy rules derived from three input parameters: air temperature, humidity, and soil moisture. Real-world testing with the Aglaonema Snow White plant confirmed that the system functions reliably, helping users optimize water usage and support sustainable, data-driven plant care in urban environments. The system achieved an average prediction accuracy of 89.14% and a mean absolute error of 7.6% in guiding soil moisture toward a 70% target, confirming its practical effectiveness. While the system was tested on Aglaonema Snow White, the fuzzy rule base can be recalibrated for other ornamental plant species with different water needs

Indonesian Food Classification Using Deep Feature Extraction and Ensemble Learning for Dietary Assessment

Food is a cornerstone of culture, shaping traditions and reflecting regional identities. However, understanding the nutritional content of diverse cuisines can be challenging due to the vast array of ingredients and the similarities in appearance across different dishes. While food provides essential nutrients for the body, excessive and unbalanced consumption can harm health. Overeating, particularly high-calorie and fatty foods, can lead to an accumulation of excess calories and fat, increasing the risk of obesity and related health issues such as diabetes and heart disease. This paper introduces a novel ensemble learning approach with a dictionary that contains food nutrition content for addressing this challenge, specifically on Padang cuisine, a rich culinary tradition from West Sumatera, Indonesia. By leveraging a dataset of nine Padang dishes, the system employs image enhancement techniques and combines deep feature extraction and machine learning algorithms to classify food items accurately. Then, depending on the classification results, the system evaluates the nutritional content and creates a dietary evaluation report that includes the amount of protein, fat, calories, and carbs. The model is evaluated using different evaluation metrics and achieving a state-of-the-art accuracy of 85.56%, significantly outperforming standard baseline models. Based on the findings, the suggested approach can efficiently classify different Padang dishes and produce dietary assessments, enabling personalised nutritional recommendations to provide clear information on a balanced diet to enhance physical and overall wellness

Evaluation of User Satisfaction on the Indonesian National Police Recruitment Website Using the EUCS Method

The digitalization of public services encourages government institutions to provide efficient and responsive information systems, including in the recruitment process of the Indonesian National Police (Polri). The Polri recruitment website was developed as an online registration platform to improve transparency, accessibility, and service effectiveness. However, systematic evaluations of user satisfaction with this website are still limited. This study aims to measure user satisfaction using the End User Computing Satisfaction (EUCS) model. A quantitative approach was applied, with data collected through questionnaires from 144 prospective applicants in the West Papua Regional Police area. Data were analyzed using the Partial Least Squares - Structural Equation Modelling (PLS-SEM) method. The findings reveal that ease of use and timeliness significantly influence user satisfaction, while content, accuracy, and format do not. This indicates that usability and information timeliness play a more critical role. The study encourages system developers to focus on enhancing functional and responsive features to improve digital public services.The digitalization of public services encourages government institutions to provide efficient and responsive information systems, including in the recruitment process of the Indonesian National Police (Polri). The Polri recruitment website was developed as an online registration platform to improve transparency, accessibility, and service effectiveness. However, systematic evaluations of user satisfaction with this website are still limited. This study aims to measure user satisfaction using the End User Computing Satisfaction (EUCS) model. A quantitative approach was applied, with data collected through questionnaires from 144 prospective applicants in the West Papua Regional Police area. Data were analyzed using the Partial Least Squares - Structural Equation Modelling (PLS-SEM) method. The findings reveal that ease of use and timeliness significantly influence user satisfaction, while content, accuracy, and format do not. This indicates that usability and information timeliness play a more critical role. The study encourages system developers to focus on enhancing functional and responsive features to improve digital public services

Animasi Aset Permainan “Safe Space” Sebagai Edukasi Pencegahan Kekerasan Terhadap Anak

Violence against children remains a serious issue that continues to occur in many environments. Unfortunately, there is still a lack of effective visual educational media that help children recognize signs of danger or unsafe situations. Children need learning tools that are engaging, easy to understand, and appropriate for their developmental stage. Therefore, this study focuses on the design and development of two-dimensional animation assets for an educational game titled “Safe Space” as a medium for educating children about violence prevention. This research employs a product creation method, emphasizing the visual design and 2D animation production processes that support the game\u27s content. The stages in this method include observation, concept formulation, character and environment design, and animation production. The process is divided into pre-production, production, and post-production phases, which involve client brainstorming in pre-production, creation of 2D vector assets and animation using the cut-out technique during production, and rendering in post-production for game implementation. The final result of this project includes animated movements such as idle, jump, run, walk, slide, and fall, rendered as image sequences to be used as assets in the “Safe Space” educational game. These animations were created using the cut-out technique with the aid of Adobe Illustrator and Adobe After Effects. It is expected that the resulting animation will serve as an interactive and informative educational tool, helping children better understand preventive actions against violence.Kekerasan terhadap anak merupakan isu serius yang masih sering terjadi di lingkungan sekitar. Sayangnya, media edukatif visual yang secara efektif mengajarkan anak untuk mengenali tanda-tanda bahaya atau situasi yang tidak aman masih terbatas. Anak-anak membutuhkan media pembelajaran yang menarik, mudah dipahami, dan sesuai dengan perkembangan usia mereka. Oleh karena itu, penelitian ini berfokus pada perancangan dan pembuatan aset animasi dua dimensi untuk permainan edukatif berjudul “Safe Space” sebagai media edukasi pencegahan kekerasan terhadap anak. Penelitian ini menggunakan metode penciptaan produk, dengan menitikberatkan pada proses perancangan visual dan produksi animasi 2D yang mendukung konten permainan. Tahapan dalam metode ini meliputi observasi, perumusan konsep, desain karakter dan lingkungan, hingga proses animasi. Metode ini terdiri dari tahap pra produksi, produksi, dan pasca produksi, dimana tahap pasca produksi melakukan brainstorming Bersama klien, produksi membuat aset 2D vektor dan penganimasian dengan teknik cutout animation, serta pasca produksi tahap rendering yang siap digunakan untuk permainan.  Hasil akhir dari penelitian ini berupa gerakan animasi idle, jump, run, walk, slide, dan fall yang dirender image sequence untuk digunakan sebagai aset permainan edukatif “Safe Space” yang dibuat menggunakan teknik cut-out dengan bantuan perangkat lunak Adobe Illustrator dan Adobe After Effects. Animasi ini diharapkan mampu menjadi media edukasi yang interaktif dan informatif bagi anak-anak dalam memahami tindakan preventif terhadap kekerasan

Heart Disease Classification Using Extreme Learning Machine (ELM) Method With Outlier Handling One-Class Support Vector Machine (OCSVM)

Heart disease remains the leading cause of death globally, accounting for approximately 32% of all deaths. Developing countries are particularly affected due to prevalent risk factors such as hypertension, diabetes, and poor lifestyle habits. Accurate and early diagnosis is essential for effective treatment and prevention. Technological advancements have enabled the precise analysis of complex clinical data. This study investigates the application of the Extreme Learning Machine (ELM) algorithm combined with outlier handling using One-Class Support Vector Machine (OCSVM) for heart disease classification. The dataset, obtained from the University of California, Irvine Machine Learning Repository, consists of 1190 clinical records with 12 numerical features. The ELM model was evaluated using the Tanh activation function and 10-fold cross-validation. Among the tested configurations, the best performance was achieved using 450 hidden neurons, yielding a sensitivity of 92,52% with a standard deviation of 4,00%. These results indicate that ELM, when paired with effective outlier handling and properly tuned parameters, can provide reliable and stable performance in heart disease classification

Comparative Analysis of the C5.0 Algorithm and Other Machine Learning Models for Early Detection of Multi-Class Heart Disease

Cardiovascular diseases represent the leading cause of mortality worldwide, making accurate and early detection a critical factor for effective medical intervention and improved patient prognosis. While machine learning (ML) offers promising tools for predictive diagnostics, many existing studies rely on single-algorithm approaches or less-than-robust validation methods, thereby limiting the generalizability and real-world applicability of their findings.This study aims to conduct a rigorous, head-to-head comparative evaluation of multiple machine learning algorithms for the multi-class classification of heart disease, with the goal of identifying the most effective and reliable model for this complex clinical task.We utilized a private dataset comprising 300 patient medical records, each described by 11 clinically relevant features. To ensure a robust and unbiased evaluation, a stratified 5-fold cross-validation methodology was employed. Five widely-used classification algorithms were evaluated: Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), a C5.0-analog Decision Tree (DT), and Support Vector Machine (SVM). Model performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score.The comparative analysis revealed that the Naïve Bayes algorithm delivered superior performance, achieving the highest mean accuracy of 43.33% (±4.22%). It also led in other key metrics with a mean precision of 43.40%, recall of 43.64%, and an F1-score of 41.26%. Other algorithms, such as Logistic Regression (40.67% accuracy) and Random Forest (39.33% accuracy), demonstrated competitive performance but were ultimately surpassed by the Naïve Bayes model in this specific multi-class classification context.This research underscores the critical importance of employing robust validation techniques and comprehensive comparative analyses to identify optimal models for clinical applications. The Naïve Bayes algorithm emerges as a strong candidate for developing a reliable clinical decision support system for the early differentiation of various heart conditions, providing a foundation for future data-driven diagnostic tools

Classification of Nutritional Status Using the Fuzzy Mamdani Method : Case Study at Banjar City Hospital

The problem of nutritional status in adults requires accurate and adaptive classification methods. This study aims to develop a decision support system using the Fuzzy Mamdani method to classify nutritional status based on Body Mass Index (BMI). A dataset consisting of 237 anthropometric records from Banjar City Regional General Hospital was utilized. The system applies five fuzzy rules to map BMI values into nutritional categories: malnutrition, underweight, normal, overweight, and obesity. The classification process involves fuzzification, inference, and defuzzification using the centroid method. System performance evaluation shows an overall accuracy of 91.13%, with the highest classification precision achieved in the normal category (98.54%) and the lowest in the malnutrition category (30.77%). The results demonstrate that the Fuzzy Mamdani method is effective for nutritional classification, although refinement is needed for underrepresented categories. This system can serve as a useful tool for supporting clinical decision-making in public health services.The problem of nutritional status in adults requires accurate and adaptive classification methods. This study aims to develop a decision support system using the Fuzzy Mamdani method to classify nutritional status based on Body Mass Index (BMI). A dataset consisting of 237 anthropometric records from Banjar City Regional General Hospital was utilized. The system applies five fuzzy rules to map BMI values into nutritional categories: malnutrition, underweight, normal, overweight, and obesity. The classification process involves fuzzification, inference, and defuzzification using the centroid method. System performance evaluation shows an overall accuracy of 91.13%, with the highest classification precision achieved in the normal category (98.54%) and the lowest in the malnutrition category (30.77%). The results demonstrate that the Fuzzy Mamdani method is effective for nutritional classification, although refinement is needed for underrepresented categories. This system can serve as a useful tool for supporting clinical decision-making in public health services

Implementation of Support Vector Machine for Classifying User Reviews on the Sentuh Tanahku Application

User reviews play a crucial role in the development of digital public service applications, as they reflect user satisfaction and service quality. This study aims to classify user reviews of the Sentuh Tanahku application into two sentiment categories, namely positive and negative, by applying the Support Vector Machine (SVM) algorithm. A total of 13,231 reviews obtained from Kaggle were processed through text preprocessing stages including case folding, tokenizing, stopword removal, and stemming. The TF-IDF technique was employed to convert text data into numerical vectors, followed by classification using SVM with hyperparameter tuning via RandomizedSearchCV. The evaluation results showed that the SVM model achieved an accuracy of 91% on training data and 84% on testing data. To assess its performance, the study compared SVM with baseline algorithms, namely Naïve Bayes and Logistic Regression. The comparison revealed that Logistic Regression and Naïve Bayes outperformed SVM with accuracy scores of 88.84% and 88.68%, respectively. Despite this, SVM remained competitive in maintaining balanced metrics across both classes. These findings highlight that algorithm performance in sentiment classification is highly influenced by the nature of the dataset. This study is expected to contribute as a reference for improving user opinion analysis methods in Indonesian-language public service applications.User reviews play a crucial role in the development of digital public service applications, as they reflect user satisfaction and service quality. This study aims to classify user reviews of the Sentuh Tanahku application into two sentiment categories, namely positive and negative, by applying the Support Vector Machine (SVM) algorithm. A total of 13,231 reviews obtained from Kaggle were processed through text preprocessing stages including case folding, tokenizing, stopword removal, and stemming. The TF-IDF technique was employed to convert text data into numerical vectors, followed by classification using SVM with hyperparameter tuning via RandomizedSearchCV. The evaluation results showed that the SVM model achieved an accuracy of 91% on training data and 84% on testing data. To assess its performance, the study compared SVM with baseline algorithms, namely Naïve Bayes and Logistic Regression. The comparison revealed that Logistic Regression and Naïve Bayes outperformed SVM with accuracy scores of 88.84% and 88.68%, respectively. Despite this, SVM remained competitive in maintaining balanced metrics across both classes. These findings highlight that algorithm performance in sentiment classification is highly influenced by the nature of the dataset. This study is expected to contribute as a reference for improving user opinion analysis methods in Indonesian-language public service applications

A Banana Disease Detection Using MobileNetV2 Model Based on Adam Optimizer

The main objective of this study is to develop a deep learning-based disease detection system for banana plants using the MobileNetV2 architecture through a comprehensive comparison with VGG16. This study utilizes a dataset of 3,653 images categorized into 12 classes, including Aphids, Bacterial Soft Rot, Bract Mosaic Virus, Cordana, Insect Pest, Moko, Panama, Fusarium Wilt, Black Sigatoka, Yellow Sigatoka, Pestalotiopsis, and healthy specimens. The methodological framework includes architecture comparison, data balancing, preprocessing techniques, and performance evaluation. The dataset was divided with a distribution ratio of 75% for training, 15% for validation, and 10% for testing. Comparative analysis shows excellent performance of MobileNetV2 with an accuracy of 96.21% compared to 90.15% for VGG16, while maintaining a significantly smaller model size of 10.0 MB compared to 57.8 MB for VGG16. Statistical validation through the McNemar test confirms significant superiority with a p-value of 0.008. The findings of this study contribute positively to the development of agricultural technology, particularly in the development of automated systems for disease detection in banana plants

Personal Protective Equipment Completeness Monitoring System Using YOLO-Based Computer Vision

Workplace safety in the construction sector remains a critical concern, primarily due to low compliance with Personal Protective Equipment (PPE) standards. To address this, this study develops and evaluates a real-time PPE monitoring system, conducting a comparative analysis of two state-of-the-art object detection models: YOLOv8s and YOLOv11s. The system is designed to detect three essential PPE items: helmets, masks, and vests, and both models were trained on a custom dataset of 9,202 augmented images over 200 epochs. The final evaluation on an unseen test set revealed highly competitive performance. While YOLOv8s achieved a marginally higher [email protected] (90.8%), YOLOv11s demonstrated superior precision (92.0%) and better performance on the stricter [email protected]:0.95 metric (54.4%). Based on this nuanced trade-off and its significantly higher computational efficiency (15% fewer parameters), YOLOv11s was selected as the optimal model. The chosen model achieved a real-time inference speed of approximately 112 FPS. A functional web-based prototype was developed using Flask to demonstrate the system\u27s practical application. These findings confirm that YOLOv11s offers a more balanced and efficient solution for automating PPE compliance monitoring and highlight that a holistic evaluation beyond a single metric is crucial for deploying robust computer vision systems in real-world safety applications.Workplace safety in the construction sector remains a critical concern, primarily due to low compliance with Personal Protective Equipment (PPE) standards. To address this, this study develops and evaluates a real-time PPE monitoring system, conducting a comparative analysis of two state-of-the-art object detection models: YOLOv8s and YOLOv11s. The system is designed to detect three essential PPE items: helmets, masks, and vests, and both models were trained on a custom dataset of 9,202 augmented images over 200 epochs. The final evaluation on an unseen test set revealed highly competitive performance. While YOLOv8s achieved a marginally higher [email protected] (90.8%), YOLOv11s demonstrated superior precision (92.0%) and better performance on the stricter [email protected]:0.95 metric (54.4%). Based on this nuanced trade-off and its significantly higher computational efficiency (15% fewer parameters), YOLOv11s was selected as the optimal model. The chosen model achieved a real-time inference speed of approximately 112 FPS. A functional web-based prototype was developed using Flask to demonstrate the system\u27s practical application. These findings confirm that YOLOv11s offers a more balanced and efficient solution for automating PPE compliance monitoring and highlight that a holistic evaluation beyond a single metric is crucial for deploying robust computer vision systems in real-world safety applications