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This study examines the application of Financial Accounting Standards Statement 45 on Financial Reporting of Non-Profit Entities at the Istiqomah Foundation, which operates in the education sector. Non-profit organizations, especially educational foundations, require an accountable financial reporting system to maintain stakeholder trust. The purpose of this study is to analyze the conformity of the Istiqomah Foundation's accounting practices with PSAK 45 standards and to identify obstacles encountered in its implementation. The research method uses a descriptive qualitative approach with data collection techniques through interviews, observation, and documentation. The results show that the Istiqomah Foundation has prepared a statement of financial position and activity report, but there are still deficiencies in the presentation of the cash flow statement and notes to the financial statements. The classification of net assets is not fully in accordance with the provisions of PSAK 45, which distinguishes between permanently restricted, temporarily restricted, and unrestricted net assets. The main obstacles found include limited human resources who understand non-profit accounting and the absence of an adequate computerized accounting system. The study recommends the need for non-profit accounting training for foundation financial managers and the development of an accounting information system that is appropriate to the characteristics of non-profit educational entities.

Tomat merupakan komoditas hortikultura bernilai ekonomi tinggi dengan permintaan pasar yang luas, baik domestik maupun internasional. Salah satu tantangan utama dalam distribusinya adalah menjaga kualitas produk, khususnya tingkat kematangan buah. Penilaian kematangan yang akurat sangat penting karena berdampak pada masa simpan, cita rasa, dan kelayakan konsumsi. Namun, metode konvensional yang mengandalkan pengamatan visual manusia cenderung subjektif, memerlukan banyak tenaga kerja, dan kurang efisien dalam skala besar. Penelitian ini bertujuan mengembangkan sistem klasifikasi tingkat kematangan tomat menggunakan pendekatan transfer learning dengan arsitektur ResNet50. Dataset terdiri atas 2.400 citra yang terbagi ke dalam tiga kelas matang (ripe), belum matang (unripe), dan tidak layak konsumsi (reject). Model dilatih menggunakan teknik fine-tuning pada sepuluh lapisan terakhir dari ResNet50, dioptimalkan dengan algoritma Adam dan learning rate sebesar 0,00001. Hasil evaluasi menunjukkan akurasi validasi rata-rata sebesar 98,08% dengan nilai precision, recall, dan F1-score yang tinggi di semua kelas. Model yang telah dilatih kemudian diimplementasikan dalam aplikasi berbasis web menggunakan kerangka kerja streamlit, yang memungkinkan pengguna mengunggah citra tomat dan memperoleh hasil klasifikasi secara instan melalui antarmuka yang sederhana dan mudah digunakan. Dengan tingkat akurasi yang tinggi serta kemudahan akses, sistem ini berpotensi menjadi solusi praktis untuk mendukung digitalisasi proses penyortiran tomat serta mendorong pemanfaatan teknologi kecerdasan buatan di sektor pertanian.

Thesis by Gisela Nana, Nim 2386206182 entitled “ The influence of the media images of the students learning in subjects IPA about animal classification according to the type of food in class IV SDI Manumuti Malaka District”. This research of this study is to determine the students learning outcomes after using picture media in science subject on animal classfication based of food types. The method used the quantitative descriptive method. This study was conducted in SDI Manumuti, Umanen Lawalu Village, District Malaka Tengah, District of malaka. Data collection technique is a test and analysis technique used are simple linear regression statistical analysis. From the results of the study prove that as many as 18 people or 99% of students gaining higt grades in learning outcomes, whereas 1 or equal to 1% less value in learning outcomes. It is evident from the results of test calculations of  Fcount  = 5,70 which is significantly larger than the Ftable at the significant level of 5% N= 18 at 4,49 or equal to Fcount  ≥ Ftable  or 5,70 ≥ 4,49. Based on the above results the value of Fcount  is greater than Ftable namely Fcount = 5,70 and F table =4,49. Then Ho is rejected and Ha accepted. It can be concluded that there is the influence of the media images of the students learning in subjects IPA about animal classifacation according to the type of food in class IV SDI Manumuti Malaka District.  

The concept of hubuddunya (love of the world) within the Akidah Akhlak subject holds a strategic role in shaping the moral and spiritual character of students, particularly in the midst of globalization, materialism, and consumerist culture that increasingly influence young generations. This study seeks to critically analyze the teaching materials of Akidah Akhlak for Grade X at MA YASPI Cicantayan, focusing on the hubuddunya topic. The main objective is to evaluate the relevance, depth, and effectiveness of these materials in fostering students’ awareness of moral values, as well as their ability to apply Islamic principles in daily life. This research employs a qualitative approach with a descriptive method, collecting data through documentation of learning resources, observation of lesson plans (RPP), and in-depth interviews with teachers. The data analysis process was conducted through reduction, classification, and interpretation of findings to obtain a comprehensive picture of the existing instructional practices. The results reveal that the current teaching materials tend to remain highly theoretical and emphasize the transfer of knowledge rather than transformative learning. Consequently, they do not adequately address contextual issues faced by students in their real-life environment. The affective and psychomotor aspects of students’ moral development are not optimally stimulated, leaving a gap between conceptual understanding and practical application. Furthermore, essential Islamic values such as zuhud (detachment from excessive worldliness), qanaah (contentment), and tawakal (trust in God) have not been integrated effectively as counterbalances to materialistic tendencies. The implications of this study underline the urgency of developing Akidah Akhlak teaching materials that are more contextual, student-centered, and relevant to contemporary challenges. Such materials should not only transmit religious knowledge but also foster reflective thinking, emotional maturity, and behavioral consistency, thereby preparing students to face globalization with strong faith, resilience, and anti-materialistic attitudes.

This study evaluates the consistency of transmitter level performance through calibration data obtained from the Instrumentation Maintenance Unit of the Cilegon PLTGU. The purpose of this study is to determine the accuracy, linearity, and stability of transmitter output against standard reference values. Qualitative methods were used, including direct observation, interviews, and literature studies to support the analysis of calibration procedures. Calibration data from the Masoneilan 12420-51 and Yokogawa EJA210E transmitters were analyzed using a comparative approach between normal and abnormal calibration results. Classification was based on the difference between the actual output and the 4–20 mA standard signal, with a tolerance limit of ±0.25% in accordance with ISA and IEC calibration standards. The results of the study show that the Level-3 Deaerator Storage Tank transmitter operates within the normal range, with excellent linearity and accuracy, while the Flash ST Tank Level transmitter shows minor deviations outside the tolerance limit, categorized as abnormal. These deviations suggest the potential for drift or zero shift influenced by environmental factors and aging. This study concludes that periodic calibration is necessary to maintain tranmitter performance reliability and ensure accurate signal transmission in the automatic control system at the Cilegon PLTGU facility.

This study aims to explore the relevance of Pupuh Kasmaran Mocoan in Lontar Yusup Banyuwangi to the culture of the Banyuwangi community through a literary anthropology approach. Pupuh Kasmaran Mocoan is part of the literary heritage of East Java and is preserved in Lontar Yusup Banyuwangi, reflecting the cultural values, traditions, and worldview of the local society. The research employed a qualitative descriptive method using reading and data classification techniques based on cultural elements. Data were analyzed by identifying cultural aspects embedded in the text and examining their relevance to the social and cultural life of the Banyuwangi community. The findings reveal that Pupuh Kasmaran Mocoan is not merely a literary work but also a representation of the cultural complexity of Banyuwangi society. It contains various cultural values, including social norms, customs, beliefs, and moral teachings that continue to be relevant in contemporary community life. These findings demonstrate that traditional literary works can serve as important cultural documents for understanding local identity and preserving regional wisdom. The study contributes to a deeper understanding of the cultural elements reflected in Lontar Yusup Banyuwangi and emphasizes the significance of literary anthropology in interpreting traditional literature. Furthermore, the findings provide valuable insights for the preservation and development of regional literature while enriching the discourse on literary anthropology.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

Background: Accurate coding of external cause in injury diagnoses is crucial to ensure the validity of medical records, support health policy decisions, and maintain the quality of morbidity reporting.  Objective: To determine the factors that influence the inaccuracy of external cause codes in injury diagnosis at Thalia Irham General Hospital, Panciro, Gowa Regency. Method: This study employed a descriptive qualitative approach using observation and in-depth interviews with outpatient coders handling injury cases. Result: The study found that only 36% of the medical record documents were coded accurately, 26% were inaccurately coded, and 36% lacked any external cause code. The main causes of inaccuracy included incomplete anamnesis, limited time, absence of specific standard operating procedures (SOPs), and the belief that external cause codes do not impact BPJS claims. Conclusion: The low level accuracy of external cause coding is caused by the lack of understanding of officers regarding the ICD-10 Chapter XX classification, the absence of a specific SOP for coding injuries, and the perception that external cause codes do not affect the claims system.

Application of Multi-Layer Perceptron neural network to fault classification in high-voltage transmission lines is demonstrated in this paper. Different fault types on protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of Discrete Wavelet Transform energy features combined with zero sequence current magnitude as input features for neural network classifier. The proposed method uses eight extracted features to learn hidden relationship in fault signal patterns. Using proposed approach, fault detection and classification of all 11 fault types could be achieved with high accuracy. Improved performance is experienced once the neural network is trained sufficiently with 1188 fault samples, thus performing correctly when faced with different system conditions. Results of performance studies show that proposed neural network-based classifier achieves 96.18% average accuracy, which demonstrates that it can improve the performance of conventional fault classification algorithms, which in turn can provide more efficient solutions in the management and protection of high voltage electrical systems.

The rapid growth of social media as a primary channel for information dissemination has triggered a significant surge in the distribution of hoaxes, potentially damaging social order, instigating mass disinformation, and threatening national security. This research aims to design an intelligent algorithm for hoax detection by integrating a critical thinking approach into Natural Language Processing (NLP)-based text processing. The algorithmic model is built using a combination of linguistic features, argument logic, and cognitive indicators such as the detection of unsubstantiated claims, identification of source bias, and evidence testing. To ensure accountability and transparency of the system, an Explainable AI (XAI) approach is applied so that classification results can be understood by non-technical users. The research results show that integrating critical thinking significantly improves detection accuracy to 93.1%, with an increase in precision and recall for detecting hoaxes based on emotional narratives. Beyond technical aspects, this model aligns with the mandate of Law of the Republic of Indonesia Number 11 of 2008 concerning Information and Electronic Transactions (ITE Law), particularly Article 28 paragraph (1), which prohibits the dissemination of false and misleading news that harms the public. Therefore, this system is not only scientifically relevant but also supports law enforcement and strengthens digital literacy in the post-truth era. These findings are expected to be a strategic contribution to the development of an ethical, critical, and responsible digital ecosystem.

Anxiety disorders are common mental health problems in society, often unrecognized by the sufferer. Identifying the type of anxiety disorder and its influencing factors is crucial for proper treatment. This research aims to apply the K-Nearest Neighbor (K-NN) method in identifying types of anxiety disorders based on influencing factors, focusing on patient data from Sylvani Hospital, Binjai. The K-NN method was chosen because of its ability to classify based on data proximity. This study used medical record data of patients with anxiety disorders, which were processed using MATLAB and Microsoft Excel software. The results show that the K-NN method is effective in identifying types of anxiety disorders, with a high level of accuracy, especially in the identification of Panic Disorder (K05) and Social Anxiety Disorder (K03). The use of MATLAB simplified the identification process by automating results, while data processing in Excel improved classification accuracy. This study concludes that the K-NN method can be an effective alternative in identifying anxiety disorder types based on the factors that influence them. It is recommended for future research to involve more variables and mental health experts for a more comprehensive validation of the results.

Unmonitored traffic conditions often hinder decision-making processes in traffic management, particularly on secondary roads. Jalan Raden Inten II in East Jakarta is one of the connecting routes with heavy traffic activity at certain times, yet no integrated data-based monitoring system is currently available. This study proposes an Internet of Things (IoT)-based traffic condition classification system to identify Clear, Normal, or Congested states based on vehicle counts and speed categorization. The system is designed using an ESP32 microcontroller, an HB100 sensor to detect vehicle speed, and two AJ-SR04M ultrasonic sensors to detect vehicle presence. Data on vehicle counts and the percentage of slow-moving vehicles are periodically transmitted to the ThingSpeak platform and processed using the Threshold-Based Classification method. The classification results are visualized on a dashboard-based website equipped with charts, traffic condition status, and notifications when consecutive congestion is detected. Testing was conducted using simulation data over a specific period. Qualitative validation was carried out by comparing the classification results with traffic indicators from Google Maps. The results show that the system can classify traffic conditions with a good degree of agreement with external references, although discrepancies occurred at certain times due to the limitations of simulated data. This research demonstrates that a simple IoT approach can provide an affordable and effective solution for monitoring and classifying traffic conditions, with potential for real-world implementation in future studies.

Rapid developments in information technology have made laptops an essential device for students, especially those in their final year of study. Choosing the right laptop plays an important role in supporting academic productivity, such as writing theses, analyzing data, and developing software. This study aims to classify the preferences of mathematics students at Gorontalo State University in choosing laptops based on usage characteristics and factors that influence purchasing decisions. The method used is Kernel Discriminant Analysis (KDA) with a Gaussian kernel function and an optimal bandwidth of 0.8. The research data involved 268 respondents divided into training and testing data. The analysis results show that the KDA model has an accuracy rate of 60% on the training data and 52% on the testing data, which indicates the model's ability to recognize student preference patterns despite a decrease in accuracy on new data. Based on the kernel density estimation results, Acer is the most widely used laptop brand, while Zyrex and Apple are rarely chosen. The most influential factor in purchasing decisions is processor specifications, with a contribution of 35.739%, followed by brand, warranty, and price. These findings indicate that hardware characteristics are the main consideration in laptop selection, with most students choosing laptops with Intel Core i5 processors, a minimum of 8GB of RAM, and SSD storage. The results of this study can also be used by universities to provide recommendations for selecting laptops that suit students' academic needs.  

Kidney trauma is a relatively common urological injury, often resulting from high-speed motor vehicle accidents, falls, or violent incidents. The kidneys are vulnerable to both blunt and penetrating trauma, with blunt mechanisms accounting for approximately 90% of all renal injuries. This paper reviews the anatomy of the kidney, mechanisms of injury, classification according to the American Association for the Surgery of Trauma (AAST), diagnostic evaluation, imaging modalities, and current management strategies. Most renal traumas are managed conservatively based on hemodynamic stability, while surgical exploration is indicated in severe or vascular injuries. Understanding the mechanism of injury and applying appropriate diagnostic evaluations are essential to determine the correct management approach and improve patient outcomes. Moreover, advancements in imaging techniques, such as CT scans, have significantly improved the accuracy of diagnosing renal injuries. Early intervention and appropriate monitoring are crucial in preventing complications such as hemorrhage or renal failure. This review aims to provide a comprehensive overview of renal trauma management to aid clinicians in improving patient care and optimizing recovery.

This study examines the role of classification, definition, and analogy as logical pillars that constitute the fundamental framework of knowledge. These three instruments of logic are closely interconnected in supporting the process of scientific reasoning. Classification functions to organize objects or phenomena into systematic categories, thereby facilitating identification. Definition provides clear boundaries of meaning to prevent conceptual ambiguity and ensure consistency of interpretation among researchers and practitioners. Meanwhile, analogy serves as a reasoning tool that bridges existing knowledge with new insights through rational comparison. This article employs a descriptive qualitative approach with a literature review of both classical and modern logic theories. The findings indicate that the integration of classification, definition, and analogy not only strengthens the consistency of scientific reasoning but also contributes significantly to education, communication, and the development of interdisciplinary knowledge. Thus, these three pillars of logic hold a strategic position in building a foundation of rationality and promoting the advancement of knowledge that is both comprehensive and applicablee.

This community service program aimed to enhance students’ pharmaceutical literacy through the introduction of drug classification logos at SMP Muhammadiyah 04 Pangkatrejo. The background of this activity was the low level of drug-use literacy among adolescents, often leading to irrational self-medication. The activity employed a quantitative descriptive design with a one-group pretest–posttest approach. Educational interventions included interactive counseling, PowerPoint presentations, visual media using logo sticks, and pretest–posttest evaluation. Data were analyzed using the Wilcoxon Signed Rank Test. The results showed a significant improvement in students’ knowledge (p = 0.000), with mean scores increasing from 58% to 81% after the intervention. The highest gain was observed in the recognition of logo colors and drug classifications. Satisfaction analysis revealed that 96% of participants expressed satisfaction with the learning process, especially regarding material clarity and the presenters’ readiness to answer questions. The study concludes that participatory educational methods and visual media effectively improve rational drug-use awareness among junior high school students. Future programs are recommended to expand similar activities to other schools and integrate digital media for sustained impact on public health literacy.

Drug stock-outs are an indicator of pharmaceutical management failure that directly affects patient safety and the quality of hospital services. Gatoel Hospital Mojokerto experienced an increase in the percentage of drug debt from 3.14% in January to 6.20% in July 2025, with 1,607 patients affected. This study aims to identify the factors causing drug stock-outs and formulate preventive strategies through the optimization of the Minimum-Maximum Stock Level (MMSL) system based on the Hospital Information System. A mixed-method approach was used, combining secondary data analysis (January–July 2025) and in-depth interviews. Fishbone analysis was applied to identify root causes, USG analysis to determine priorities, and SWOT analysis to formulate intervention strategies. Priority drug classification was carried out using the ABC-VEN method. The intervention involved implementing an MMSL pilot project for 150 drug items under Pareto category A. The analysis identified six dimensions of stock-out causes: man, materials, methods, machines, measurement, and environment. The highest priority issue was drug demand forecasting based on historical data (USG score: 125). SWOT analysis placed the organization in quadrant II, recommending a Weakness-Opportunities (WO) strategy. MMSL implementation was initiated through the development of SOPs and the entry of 150 priority drug items into the system. Drug stock-outs are caused by multifactorial issues that require systemic intervention. MMSL optimization has the potential to serve as a long-term solution, provided there is expanded coverage, strengthened human resource capacity, and comprehensive system integration.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.