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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.

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.

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 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.

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.

Customer churn is a strategic challenge for digital streaming platforms because it directly Impacts revenue and business sustainability. This study aims to analyze the factors influencing customer Churn and develop a churn prediction model using the Random Forest algorithm. The study uses a Quantitative approach with an explanatory design and utilizes secondary data from the Netflix Customer Churn and Engagement Dataset available on Kaggle. The dataset consists of 1,000 customer data with 16 Variables covering demographic characteristics, service usage behavior, financial condition, and customer Satisfaction level. The data was processed through preprocessing, one-hot encoding, and a 70:30 split Between training and test data. Model performance was evaluated using accuracy, precision, recall, F1 Score, and ROC-AUC metrics. The results show that the Random Forest model produces an accuracy of 53.7%, precision of 56.3%, recall of 63.6%, F1-score of 59.7%, and ROC-AUC of 0.534, indicating Moderate predictive ability and only slightly better than random classification. Feature importanceAn.evealed that user engagement levels, such as viewing duration and frequency of interactions, Were the most dominant factors influencing churn, followed by economic factors and customer satisfaction. The results of this study are expected to provide a basis for streaming platforms to design more effective Customer retention strategies.

State revenue holds a vital position in sustaining national development and the functioning of government, with taxation serving as the primary contributor to Indonesia’s State Budget (APBN). The substantial reliance on tax income obliges the government to manage the taxation system in an optimal, efficient, and sustainable manner. Nevertheless, the attainment of tax revenue targets in practice remains challenged by various issues, including structural, administrative, and strategic limitations. This study seeks to examine the role of strategic tax management in supporting the achievement of state revenue objectives. The research adopts a literature review approach by analyzing textbooks, national and international scholarly journals, official government publications, and relevant regulatory frameworks. The data are analyzed using a descriptive qualitative method through processes of classification, comparison, and synthesis of findings from previous studies. The findings reveal that strategic tax management has a crucial influence on enhancing state revenue performance through coherent policy formulation, flexible strategy execution, and ongoing performance assessment. The integration of information technology, the reinforcement of tax administration, and the improvement of taxpayer compliance emerge as key determinants in achieving revenue targets. Accordingly, strategic tax management constitutes a fundamental tool for ensuring fiscal resilience and promoting sustainable national development.

Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.

This study discusses reduplication as one of the important morphological processes in the Indonesian language. Reduplication plays a role in the formation of words and serves to express various grammatical meanings, such as plural, frequency, intensity, and continuity of an event. The purpose of this study is to describe the types of reduplication and the classes of words produced through this process. The research data is in the form of duplicated words, including materials, people, years, destruction, many times, and shouting. The method used is qualitative descriptive with analysis techniques in the form of classification based on the type of reduplication, namely intact reduplication, partial reduplication, reduplication with adjustment, and altered reduplication of sounds. The results of the study show that reduplication in Indonesian can form various classes of words, such as nouns, verbs, adjectives, and adverbs, with different grammatical functions according to the context of their use. These findings confirm that reduplication not only serves as a repetition of forms, but also has a significant semantic and syntactic role in the structure of language. This research is expected to make a theoretical contribution to the study of Indonesian morphology and become a reference for future linguistic research, especially in understanding the dynamics of word formation and meanings produced through the process of reduplication.

The phenomenon of the “Sandwich Generation” (SG) in Indonesia, where individuals of productive age (30-40 years old) bear a double financial burden—supporting the needs of their children and immediate family (furu') while also supporting their elderly parents (ushul)—has become a widespread social and financial challenge. This pressure, exacerbated by inadequate income and low financial literacy, forces 94% of SG respondents to set aside their personal interests. This dilemma calls for a clear Sharia analysis of the priority scale of financial support. This article aims to analyze the SG maintenance dilemma through the Qawa'id Fiqhiyyah (Fiqh Principles) framework to establish a hierarchy of financial obligations. The two main principles used are Al-Farḍu afḍalu mina an-Nafli (Absolute Obligation takes precedence over Sunnah) and Al-Wājib lā yutrak illā liwājibin (An Obligation cannot be abandoned except for another Obligation). Fiqh analysis shows that the resolution of priority conflicts is based on the classification of the legal status of the recipient of alms, distinguishing between absolute obligations (Adami rights, contractual) and conditional obligations (wajib zhanni, Allah's rights). Key findings establish Sharia priorities in conditions of limitation: Self, Wife and Children (Absolute Obligations), Parents (Conditional Obligations), Siblings/Relatives (Sunnah/Nafl). This priority is established to protect the nuclear family unit as the foundation of society, in line with Maqāṣid ash-Sharīʿah (Sharia Objectives).

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.

Correspondence management is very important in government administration because it helps all parts of the organization communicate more easily. The purpose of this study was to assess the manual mail management system at the Riau Province Human Resources Development Agency (BPSDM) and find barriers and conformity with applicable regulations, especially Riau Governor Regulation No. 45/2019. The research was conducted using a descriptive qualitative approach involving participatory observation, in-depth interviews, literature research, and visual documentation. The results showed that the manual system used causes recording errors, delays in letter distribution, and difficulties in archive retrieval. In addition, the current provisions have not fully digitized the official manuscript and archive classification. Limitations in budget, infrastructure and HR training lead to mismatches between practices and regulations. The results show that to increase bureaucratic efficiency and improve accountability of public information, we must shift to a digital-based mail management system. According to this study, digitization of archives through applications such as Srikandi should begin immediately. This is necessary so that BPSDM can meet the demands of fast and accurate information services in the era of information technology.