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The optimal therapeutic impact of local vaginal drug delivery systems is strongly influenced by the physical characteristics balance of Solid Vaginal Suppositories. A comprehensive review regarding the comparison of mechanical profiles, specifically melting time and crushing strength parameters, from various base classifications constitutes the primary objective of this literature research. The implementation of a Literature Review study design was executed through the extraction of empirical data from twelve experimental journals published within the last ten years. Excessively rapid phase transformation characteristics at physiological basal temperatures and low compression resistance were consistently demonstrated by lipophilic bases such as Oleum Cacao. The risk of structural deformation during the distribution process is highly susceptible to unmodified lipid preparations. High surface elasticity accompanied by a delay in molecular hydration duration reaching 120 minutes was recorded in the utilization of Glycerinated Gelatin Base. Structural rigidity exceeding 4 kgF and disintegration time efficiency under 60 minutes were optimally demonstrated by Polyethylene Glycol (PEG) Base. An enhancement in mechanical resistance against external shocks during the storage period is offered by the thorough modification of the synthetic polymer ratios. Therefore, the determination of the PEG base as the most optimal material is recommended to maintain the quality stability of pharmaceutical products. Compendial regulation standards regarding the physical strength testing of pharmaceutical preparations must be obeyed by every institution to ensure long-term treatment effectiveness. Thus, the alignment between active substance release duration and physical preparation endurance can be realized for absolute patient comfort.

The Qur'an al-Karīm employs at least four principal terms in referring to human beings, namely al-basyar, al-insān, al-nās, and banī Ādam. These terms are not merely synonymous; rather, each represents distinct yet complementary dimensions of humanity in constructing a holistic concept of the human being. This study aims to: (1) analyze the semantic meanings of these four terms based on mufrodat studies, Makkiyah-Madaniyah classification, and asbābun nuzūl; (2) compare the interpretations of classical scholars - Al-Ṭabarī, Ibn Kathīr, Al-Qurṭubī, and Fakhr Al-Rāzī - with those of contemporary scholars - Sayyid Quṭb, Ibn ‘Āshūr, M. Quraish Shihab, and Buya Hamka; and (3) formulate their implications for Islamic education. This research employs a library research method using the tafsīr maudhū‘ī approach integrated with Izutsu’s semantic analysis model. The findings reveal that al-basyar represents the physical-biological dimension of human beings; al-insān represents the spiritual dimension in relation to ‘ubūdiyyah toward Allah; al-nās represents the social-collective dimension; and banī Ādam represents the intellectual-rational dimension inherited from Adam through the divine gift of teaching al-asmā’ (Qur'an 2:31). Collectively, these four dimensions provide fundamental implications for the development of objectives, curriculum, methodology, and evaluation within holistic and comprehensive Islamic education.

Group-based learning in the classroom plays a vital role in enhancing social interaction, individual responsibility, as well as students' critical thinking and collaborative skills. However, its implementation often faces challenges such as the dominance of certain members, social loafing, low participation, and interpersonal conflicts that hinder group effectiveness. This study aims to comprehensively examine the dynamics of learning groups by integrating four key aspects: the concept of group dynamics based on the Tuckman model, the characteristics of effective groups in cooperative learning, group formation techniques, and conflict management strategies. The research utilizes a qualitative approach with a literature study method, analyzing 25 sources including nationally accredited journals, academic books, and theses published between 2020 and 2024. Data analysis was conducted through reduction, thematic classification, content analysis, and conceptual synthesis. The results indicate that effective group dynamics can be achieved through the Tuckman stages, the application of the five elements of cooperative learning, the selection of appropriate group formation techniques with risk mitigation, and the implementation of the Thomas-Kilmann conflict management styles.The scientific contribution of this research is the development of an integrative model based on these four aspects, which serves as a conceptual framework to strengthen collaborative learning practices in the classroom. Practical implications include the formation of ideal groups consisting of 4–5 students, the establishment of initial group contracts, the use of dual assessment rubrics (individual and group), and peer evaluation mechanisms to enhance accountability and reflection.

Ibuprofen is an antipyretic and anti-inflammatory drug classified as Biopharmaceutics Classification System (BCS) class II, characterized by low water solubility and high permeability. Its limited solubility may reduce the dissolution rate and influence therapeutic effectiveness. This study aimed to formulate ibuprofen suppositories using cocoa butter (oleum cacao) as the suppository base through the melting method. Each suppository was prepared with a total weight of 2500 mg containing 125 mg ibuprofen, oleum cacao as the base, tween 80 as an emulsifier, and liquid paraffin as a mold lubricant. Before formulation, a displacement value test was performed to determine the exact amount of base required. The prepared suppositories were evaluated through organoleptic examination, weight uniformity, melting time, and dissolution testing. The evaluation results demonstrated that the suppositories possessed acceptable physical characteristics, uniform weight distribution, appropriate melting properties, and satisfactory dissolution behavior. Based on these findings, ibuprofen suppositories formulated with oleum cacao fulfilled pharmaceutical quality requirements in accordance with the Indonesian Pharmacopoeia standards.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

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.

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.

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.

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.

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.

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.

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.

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.

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

Many students still face difficulties in understanding statistics because inaccurate preconceptions often develop into misconceptions. This condition is important to study since misconceptions can hinder the mathematics learning process and reduce the quality of students’ conceptual understanding. This study aims to analyze in depth how preconceptions affect the emergence of misconceptions among senior high school students in learning statistics. The research employed a qualitative descriptive method with a case study approach, involving three tenth-grade students from State Senior High School 1 Purwosari who were selected through purposive sampling based on high, medium, and low achievement categories. Data were collected through diagnostic tests in the form of essay questions to reveal students’ preconceptions and in-depth interviews to explore their reasoning, then analyzed descriptively. The findings show that students with accurate preconceptions did not experience misconceptions, students with partially correct preconceptions developed classificational, theoretical, and correlational misconceptions, while students with incorrect preconceptions experienced more complex misconceptions, such as considering the median as the largest value and failing to relate changes in data to the properties of the mean, median, and mode. The study concludes that inaccurate preconceptions directly contribute to the emergence of various forms of misconceptions. The implication is that teachers need to detect, identify, and correct students’ preconceptions from the beginning of the learning process so that misconceptions can be minimized and students’ understanding of statistics can develop more comprehensively.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

Wood consumption in Indonesia, which is expected to reach more than 64.84 million m³ by 2025, is putting enormous pressure on forests, as evidenced by the reduction in forest area through the clearing of 96,230 hectares of forest in 2023. To reduce dependence on wood as a building material, alternative materials with comparable physical and mechanical properties are needed. Bamboo, especially laminated bamboo, was chosen because it has high tensile strength, a short harvest time, and abundant availability. This study examines the behavior of hollow section laminated bamboo beam-column connections with a glue-in-rod-bracket system to determine the ductility of the connection under unidirectional (static) loads. An experimental method was used with the independent variables of diameter and number of bolts, while the dependent variables included the moment-rotation of the connection, stiffness, strength, and ductility. The results show that the average ductility values for 4 and 6 thread rods D6, D8, and D10 mostly meet the SNI 1729 (≥1.25) and AISC 360 (rotation >0.03 rad) standards, with classifications ranging from "partially ductile" to "fully elastic." However, the 10-diameter 6-thread rod only achieved a ductility of 1.2, thus failing to meet the SNI standard. Based on the Handbook of Structural Steel Connection Design and Details, all connections fall into the non-seismic category because their ductility values are less than 3. These findings confirm the potential of laminated bamboo as an environmentally friendly construction material, while also providing technical guidelines for the design of non-seismic beam-column connections.

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.