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This study aims to redesign the raw material yarn warehouse layout at PT. XYZ using the Class Based Storage method to improve storage and retrieval efficiency. The main problem identified in the warehouse is random item placement, resulting in relatively long retrieval times of approximately 10–15 minutes per pallet. This research applies a descriptive quantitative approach using a case study method. The data used consists of inbound, outbound, and inventory records of yarn raw materials from November 2024 to April 2025. The analysis was conducted using the FSN (Fast Moving, Slow Moving, and Non-Moving) method through the calculation of consumption rate and average stay, combined with ABC classification to determine storage priority. The results show that 9 types of yarn are classified as Class A, 11 types as Class B, and 11 types as Class C. Based on this classification, a new warehouse layout was designed by placing Class A items near the input-output area, Class B items in the middle area, and Class C items in the back area of the warehouse, thereby improving storage efficiency and reducing retrieval time.

This study aims to redesign the raw material yarn warehouse layout at PT. XYZ using the Class Based Storage method to improve storage and retrieval efficiency. The main problem identified in the warehouse is random item placement, resulting in relatively long retrieval times of approximately 10–15 minutes per pallet. This research applies a descriptive quantitative approach using a case study method. The data used consists of inbound, outbound, and inventory records of yarn raw materials from November 2024 to April 2025. The analysis was conducted using the FSN (Fast Moving, Slow Moving, and Non-Moving) method through the calculation of consumption rate and average stay, combined with ABC classification to determine storage priority. The results show that 9 types of yarn are classified as Class A, 11 types as Class B, and 11 types as Class C. Based on this classification, a new warehouse layout was designed by placing Class A items near the input-output area, Class B items in the middle area, and Class C items in the back area of the warehouse, thereby improving storage efficiency and reducing retrieval time.

The idea of functions in mathematics and how they are used to build different mathematical models are methodically examined in this publication. Functions are basic mathematical constructs that show relationships between two or more variables in explicit equations, tables, or graphs. The fundamental building blocks of mathematics are functions, which enable the representation of variable interdependencies in a variety of formats, including formal mathematical expressions, data tables, and graphs. The classification of function types, such as linear, quadratic, and exponential, and their corresponding uses in the domains of physics, economics, and epidemiology are the main topics of this study, which takes a descriptive and exploratory approach.This article illustrates how knowledge of functions greatly aids processes through a review of the literature and an examination of secondary sources from current textbooks and academic publications. of judgment, forecasting, and analysis. In both academic and professional contexts, mathematical modeling based on functions has demonstrated efficacy in accurately and efficiently representing real-world occurrences. Thus, the significance of incorporating functional thinking into STEM education and multidisciplinary practice is emphasized in this essay.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

This study is motivated by the importance of politeness strategies in film discourse as a reflection of social interaction and cultural values. It aims to identify the types of politeness strategies used in the film Dilan 1990 and to determine the most dominant strategy along with its contextual use in interpersonal interactions, based on Brown and Levinson’s framework. Employing a descriptive qualitative method, the data consist of 40 utterances containing potential face-threatening acts. The data were analyzed through identification, classification, and interpretation according to four politeness strategies: Bald on Record, Positive Politeness, Negative Politeness, and Off-Record. The findings reveal that all four strategies appear in the film dialogues, with Positive Politeness as the dominant strategy, indicating closeness, humor, and social solidarity, particularly between the main characters, Dilan and Milea. Bald on Record is mainly used in instructional and hierarchical relationships, while Negative Politeness and Off-Record occur in more sensitive situations or when indirect meanings are intended. These results imply that Dilan 1990 represents politeness values within Indonesia’s collectivistic culture, especially in youth communication that emphasizes harmony and social cohesion.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Responsibility accounting is a management accounting system that emphasizes the distribution of authority and responsibility at each responsibility center to support the control and evaluation of organizational performance. There are five requirements for implementing responsibility accounting: organizational structure, budgeting, separation of controllable and uncontrollable costs, account code classification, and responsibility accounting reports. This study aims to understand the implementation of responsibility accounting in performance assessment at KSP Kopdit Hiro Heling. The method used in this study is qualitative descriptive, with data collection techniques through observation, interviews, and documentation. The results show that the implementation of responsibility accounting at KSP Kopdit Hiro Heling is still not effective based on the five indicators of responsibility accounting. This condition impacts the suboptimal performance assessment of responsibility centers.

Hypertension is a major modifiable risk factor contributing to the development of Acute Coronary Syndrome (ACS), which includes STEMI, NSTEMI, and unstable angina. The increasing prevalence of hypertension worldwide raises concern regarding its impact on cardiovascular outcomes. This study aimed to describe the profile of ACS patients with hypertension receiving angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) therapy in the Intensive Coronary Care Unit (ICCU) of RSUD Dr. Soetomo Surabaya. Using a descriptive cross-sectional method, data from 91 patients treated between July 2021 and October 2024 were analyzed. Variables included demographic characteristics, clinical classification of ACS, hypertension degree, comorbidities, types and doses of ACEI/ARB administered. The results showed that most patients were male (73%) and aged over 65 years (40%). Chi-square analysis revealed no significant relationship between hypertension degree, ACS classification, or most comorbidities with drug selection or dosage (p>0.05), except for a significant association between coronary heart disease comorbidity and ARB selection. These findings suggest that in hypertensive ACS patients, the choice between ACEI and ARB therapy is predominantly based on individual comorbidity profiles rather than blood pressure severity or ACS type. The study highlights the importance of personalized treatment approaches considering patient comorbidities to optimize cardiovascular outcomes.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.

This study examines the distortion between civil and criminal perspectives in the legal considerations (ratio decidendi) of Judex Juris in Supreme Court Decision Number 121K/Pid.Sus/2020. The decision lacks substantial criminal law considerations regarding the alleged corruption offense. Instead, the legal reasoning focuses on the fault or negligence of company directors, particularly the exception under Article 97 of Law Number 40 of 2007 concerning Limited Liability Companies, which embodies the Business Judgment Rule doctrine. Furthermore, these considerations are distorted by tort (onrechtmatige daad) as regulated in Article 1365 of the Civil Code juncto Article 138 paragraph (1) letter b of the Company Law. This research employs a legislative approach by analyzing various legal instruments, including the 1945 Constitution, the Criminal Code, the Criminal Procedure Code, the Limited Liability Company Law, State-Owned Enterprises Law, Judicial Power Law, Supreme Court Law, and the Corruption Eradication Laws. A conceptual approach is also utilized to examine theoretical concepts concerning corporate crime, directors' liabilities, state losses, tort, negligence from criminal and civil perspectives, business judgment rules, collective collegiality principles, and formal-material classification of legislation. The data comprises primary legal materials (legislation and court decisions) and secondary legal materials (legal literature and scientific journals). Analysis is conducted qualitatively by interpreting legal principles and their relevance to the court's considerations in the decision.

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

This study evaluates public sentiment toward Constitutional Court Decision No. 90/PUU-XXI/2023 regarding the age limit for presidential and vice-presidential candidates, a controversial issue closely related to Indonesia’s democratic dynamics. Understanding public opinion on Twitter, as a major platform for political expression, is essential for informing electoral policy formulation. Data were collected using Tweet Harvest through Google Colab and analyzed using the Naïve Bayes algorithm as the primary sentiment classification method, with RapidMiner employed to support and streamline the analytical process. The analysis process included data cleaning, text normalization, stopword removal, manual labeling of 80 tweets as training data, and automatic sentiment classification to identify positive and negative sentiments. From a total of 151 analyzed tweets, 84 (55.63%) were classified as negative and 67 (44.37%) as positive, with the model achieving an accuracy of 66.67%. These findings suggest a tendency toward public opposition to the decision, reflecting dissatisfaction among Twitter users. The study demonstrates that Naïve Bayes is reasonably effective for sentiment classification with limited datasets and provides insights for policymakers in understanding public responses to election-related regulations.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

Sentiment analysis on social media is widely used to represent public perceptions of sports performance, particularly in international competitions. This study aims to analyze the sentiment of YouTube user comments regarding the performance of the Indonesian National Football Team during the FIFA World Cup 2026 Asian Qualifiers. The data were collected from user comments on videos related to the matches and analyzed using a machine learning–based sentiment analysis approach. Sentiment classification was performed using the Naive Bayes algorithm. The results indicate that the proposed approach is able to effectively identify public sentiment toward the national team’s performance during the qualification matches. The findings of this study are expected to provide insights into public perceptions and contribute to sentiment analysis research in the field of sports.

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.