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This study aims to determine the production process and the level of consumer acceptance of Bandung nagasari cake utilizing stevia as a natural sweetener and butterfly pea flower extract (Clitoria ternatea) as a natural coloring agent. The research employed a quantitative approach with an experimental method through organoleptic testing involving 20 panelists. Data collection techniques consisted of literature review, questionnaires, and organoleptic evaluation covering taste, texture, aroma, and appearance. The obtained data were analyzed using descriptive quantitative analysis with percentage calculations. The findings indicated that the Bandung nagasari formulation containing stevia and butterfly pea flower extract was more preferred than the formulation using granulated sugar. Approximately 90% of panelists preferred the taste attribute due to its healthier perception, while 80% of panelists favored the texture, aroma, and appearance attributes because of the softer texture and the attractive natural coloration produced by the butterfly pea flower extract. Furthermore, the product demonstrated a shelf life of 12 hours at room temperature and up to 3 days under refrigerated storage conditions. The study concludes that the incorporation of stevia and butterfly pea flower extract may serve as an innovative development of traditional Bandung nagasari cake without eliminating its traditional characteristics and shows favorable consumer acceptance.

This research aims to analyze how gender and culture are represented in the Grade VIII Indonesian language textbook of the Merdeka Curriculum. The focus is on various aspects of gender representation, such as the depiction of characters, their roles, activities, attributes, social status, gender equality, and stereotypes related to gender. Additionally, the study explores cultural representation, which encompasses cultural forms, diversity, local traditions, context, ways of presentation, and the cultural values expressed in the textbook's texts and illustrations. The methodology employed is descriptive qualitative research with a content analysis framework. Data were gathered through documenting and note-taking methods on the content of the textbook, followed by an analysis process that includes identification, classification, interpretation, and drawing conclusions. Findings indicate that the gender representation in the textbook predominantly portrays men as leading figures in public roles, leadership, and decision-making, whereas women are mainly shown in nurturing, domestic, and supportive capacities. On the other hand, the cultural representation illustrates the variety of Indonesian culture by showcasing regional customs, languages, art forms, traditional cuisine, practices, and societal norms. The study concludes that although the textbook presents cultural diversity adequately, there is a need for improvement in gender representation balance to better reflect equality values in the educational experience.

This research is based on the importance of studying Malay-Jawi religious manuscripts as a source of transmission of Islamic teachings in the archipelago, particularly in the field of monotheism. The study aims to examine the textual content of Jawi manuscripts containing the treatise of monotheism, especially the concept of the sentence of monotheism and the attributes of twenty, and to explain their position in the intellectual tradition of Malay Islam. The research employs This research is based on the importance of studying Malay-Jawi religious manuscripts as a source of transmission of Islamic teachings in the archipelago, particularly in the field of monotheism. The study aims to examine the textual content of Jawi manuscripts containing the treatise of monotheism, especially the concept of the sentence of monotheism and the attributes of twenty, and to explain their position in the intellectual tradition of Malay Islam. The research employs a qualitative method with a philological approach and content analysis. Primary data consist of Jawi manuscripts, while secondary data are obtained through library research. Data were collected through documentation and literature review and analyzed descriptively. The findings reveal that the manuscripts contain systematically arranged monotheistic teachings, including the meaning of lā ilāha illa Allāh through the principles of negation and affirmation, as well as the concept of faith involving the heart, speech, and actions. The manuscripts also explain the twenty attributes within the classifications of nafsiyah, salbiyah, ma‘ani, and ma‘nawiyah, reflecting the theological framework of Ahlussunnah wal Jama‘ah. These manuscripts function as both religious texts and pedagogical media, highlighting the importance of preserving Nusantara Islamic manuscripts as part of the region’s intellectual heritage.

This study aims to describe the implementation of instilling Islamic Religious Education values in the aspects of the Qur’an and Aqidah at TPQ Fattuhul Qulub, Doyo Baru District. The background of this research is based on the importance of Islamic religious education in shaping children’s character and morals from an early age amid the decline in the morality of the younger generation. TPQ, as a non-formal educational institution, has an important role in instilling Islamic values through learning the Qur’an, aqidah, and morals. However, in its implementation, several obstacles are still found, such as the low understanding of students regarding religious values, varying abilities in reading the Qur’an, and the lack of support from family and community environments.This study employed a descriptive qualitative approach. Data were collected through observation, interviews, and documentation at TPQ Fattuhul Qulub, Doyo Baru District. The focus of the research includes the planning, implementation, and evaluation processes of instilling Islamic Religious Education values in the aspects of the Qur’an and Aqidah.The results of the study indicate that the instillation of Islamic Religious Education values has been carried out through structured stages of planning, implementation, and evaluation. The learning process was conducted using the methods of Iqra’, tahsin, tahfidz, talaqqi, lectures, role modeling, habituation, and exemplary stories. In the Qur’anic aspect, students were guided to read the Qur’an according to tajwid rules and memorize short surahs, while in the Aqidah aspect, students were taught the pillars of faith, the attributes of Allah and His Messenger, and the formation of Islamic morals. The evaluation results showed improvements in Qur’anic reading skills, memorization, understanding of aqidah, and changes in students’ religious behavior, such as becoming more disciplined, polite, and diligent in worship. This success was supported by the exemplary behavior of the teachers and the involvement of parents in the learning process.

Financial reporting integrity is heavily reliant on audit quality. This research explores the effect of auditor attributes, specifically integrity and professional background, on audit standards in Timor-Leste. Quantitative data was collected from a survey of 60 auditors and analyzed using descriptive statistics, correlation analysis, and multiple linear regression techniques. The findings suggest that auditor ethics has a substantial positive impact on audit quality, indicating that conformity to professional ethical guidelines is vital for enhancing audit results. In contrast, experience in auditing does not demonstrate a statistically significant impact on the quality of audits. Visualization through scatter plots further supports the notion that the relationship between ethics and audit quality is more robust than that of other auditor characteristics. Strengthening ethical standards in the auditing profession is crucial to enhancing audit quality, as these findings demonstrate. This study contributes to the body of research on auditing behavior by presenting empirical findings from a developing institutional setting. The findings also have practical implications for policymakers and auditing bodies in Timor-Leste.

Facies analysis and thin-section petrography are complementary and essential approaches for determining the lithological characteristics of deep-water deposits. Deep-water sediments are formed by diverse depositional processes such as turbidity currents, debris flows, slumps, and pelagic suspension settling, producing a wide range of lithofacies with distinctive textural, compositional, and sedimentary structural attributes. This literature review synthesizes studies that apply facies analysis and thin-section petrography to characterize deep-water deposits across various basins and geological ages worldwide. Field-based facies analysis emphasizes lithology, sedimentary structures, bed geometry, and vertical–lateral facies relationships to reconstruct depositional environments. In contrast, thin-section petrography provides microscopic insights into mineral composition, grain texture, cement types, porosity, and diagenetic features that are not observable in the field. The integration of these methods, often combined with advanced techniques such as XRD, SEM, and geochemical analyses, has proven effective in distinguishing lithofacies, understanding sedimentary processes, and evaluating reservoir quality. The review highlights that grain size, sorting, cement type, pore-throat size, and diagenetic processes are key controls on lithological characteristics and reservoir potential in deep-water systems. Therefore, the combined application of facies analysis and thin-section petrography provides a comprehensive framework for interpreting deep-water sedimentary systems.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.

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.

Granola bar is an instant snack product in the form of a bar that has high nutritional value and generally commercial products use imported ingredients such as oats and almonds, but can be developed using local food ingredients such as Job's Tears seeds and walnuts that have high nutritional content such as carbohydrates, protein, fiber, fat, and antioxidants. This study aims to obtain the ratio of Job's Tears seeds and walnuts to produce granola bars with the best physicochemical and organoleptic characteristics. The method used is a Randomized Block Design (RBD) with one treatment factor in the form of the ratio of Job's Tears seeds and walnuts (100:0, 80:20, 70:30, 60:40, and 50:50) with five replications to obtain 25 experimental units. Data were analyzed using Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) test. The results showed that the ratio of Job's Tears seeds and walnuts significantly affected the levels of fat, protein, carbohydrate, total calories, as well as organoleptic attributes including taste, aroma, texture, and overall preference. This research contributes to efforts Granola bar is an instant snack product in the form of a bar that has high nutritional value and generally commercial products use imported ingredients such as oats and almonds, but can be developed using local food ingredients such as Job's Tears seeds and walnuts that have high nutritional content such as carbohydrates, protein, fiber, fat, and antioxidants. This study aims to obtain the ratio of Job's Tears seeds and walnuts to produce granola bars with the best physicochemical and organoleptic characteristics. The method used is a Randomized Block Design (RBD) with one treatment factor in the form of the ratio of Job's Tears seeds and walnuts (100:0, 80:20, 70:30, 60:40, and 50:50) with five replications to obtain 25 experimental units. Data were analyzed using Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) test. The results showed that the ratio of Job's Tears seeds and walnuts significantly affected the levels of fat, protein, carbohydrate, total calories, as well as organoleptic attributes including taste, aroma, texture, and overall preference. This research contributes to efforts to diversify local Indonesian food as raw materials for highly nutritious functional food products diversify local Indonesian food as raw materials for highly nutritious functional food products.

The issue of illegal parking in the Pasar Jambi area remains unresolved despite various enforcement efforts carried out by the Jambi City Transportation Agency. Non-compliant parking practices, such as the presence of unauthorized parking attendants, the imposition of fees beyond official rates, and the lack of use of official attributes, indicate a discrepancy between established standards and actual field conditions. This study aims to analyze the effectiveness of the Transportation Agency’s supervision in controlling illegal parking in the Pasar Jambi area by applying George R. Terry’s supervision theory indicators. The research employs a qualitative method with a case study approach, using data collection techniques including observation, interviews, and documentation. The findings reveal that supervision has been implemented through stages of setting standards, measuring performance, comparing implementation with standards, and taking corrective actions. However, the effectiveness of supervision is still not optimal. This is reflected in the persistent gap between standards and practices in the field, the limited number of supervisory personnel, and corrective actions that have not been consistently effective in creating a deterrent effect. Although enforcement has been carried out progressively through warnings, guidance, and direct actions, violations continue to occur repeatedly. The implications of this study highlight the need to strengthen consistency in supervision, enforce stricter sanctions, and enhance resource capacity so that supervision is not merely administrative but can create sustainable parking order.

This study aims to evaluate the quality of public services at the Department of Population and Civil Registration of West Sumba Regency following the implementation of digitalization through the use of electronic signatures. A quantitative approach was employed, with data collected through questionnaires distributed to 100 respondents. Data analysis was conducted using the Importance Performance Analysis (IPA) method to identify gaps between the level of importance and service performance. The results indicate that, overall, service quality can be categorized as fairly good, as reflected by performance levels that are relatively close to public expectations. However, several service attributes remain in the priority quadrant, particularly in terms of system usability, service stability, and accessibility. Meanwhile, attributes such as service speed, data security, and staff responsiveness demonstrate satisfactory performance and should be maintained. These findings suggest that the implementation of electronic signatures has improved service efficiency and effectiveness, although further improvements are still required, particularly in technical aspects and system usability. Therefore, continuous improvement efforts are necessary to enhance public service quality, making it more responsive and oriented toward user satisfaction.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

Oil palm plantations are often confronted with the problem of uncontrolled land expansion and suboptimal management, which potentially threaten the sustainability of the business from environmental, social, and economic perspectives for surrounding communities. This condition calls for a comprehensive evaluation of management practices implemented by independent smallholders. This study aims to determine the sustainability status of smallholder oil palm plantations in Parindu Subdistrict, Sanggau Regency, assessed from five main dimensions: ecological, economic, social, institutional, and technological. In addition, the study analyzes attributes that have sensitivity levels affecting the future sustainability of oil palm farming as a basis for formulating improvement strategies. A total of 36 respondents were selected using purposive sampling techniques and in-depth interviews. The research method employed was quantitative descriptive using the Rap-Palmoil approach and qualitative descriptive supported by kite diagrams to clarify the analysis results. The findings indicate that the management status of oil palm plantations falls into the moderately sustainable category, with MDS values of 53.61 for Embala Village and 55.75 for Maju Karya Village. The dimensions that require priority improvement in both villages are the ecological and technological dimensions.

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.

A suitable and targeted marketing plan is required because of the intense competition in the retail drinking water sector. Customer segmentation using RFM (Recency, Frequency, and Monetary) analysis is one of the techniques employed. Additionally, K-Means clustering, a clustering technique based on machine learning, is employed. This study's goal is to present the findings in the form of graphs that can be used to examine consumer trends according to their attributes. With a value of 10286, the Calinski Harabaz index is a suitable metric to move on to the segmentation step in this study, which also tests three metrics using the clustering method. An ideal cluster is created for every cluster evaluation by dividing the Calinski Harabaz index into three more manageable clusters. This contrasts with other evaluation metrics that only yield two clusters. For instance, when XYZ drinking water sales transaction data was distributed, it was discovered that, out of the total drinking water sales, woodsale had 422 customers, diamond had 1061 customers, and star diamond had 2005 customers. The management of the XYZ drinking water company and other marketing fields are expected to encounter more intense competition as a result of the study's findings.

This study aims to analyze the performance of Android-based attendance applications used at PT PLN ULP West Sumba by integrating two evaluation methods, namely WebQual 4.0 and Importance Performance Analysis (IPA). This attendance application functions to monitor employee attendance digitally so that it is expected to be able to improve the efficiency and accuracy of data recording. Evaluations are conducted to assess the extent to which the application meets the needs of users as well as the expected performance. WebQual 4.0 is used to measure the quality of user experience in terms of ease of access, interactivity, trust, and satisfaction, while IPA is used to compare the level of user interest with application performance based on four main attributes: system quality, information quality, service quality, and usage quality. The research data was collected through a survey with questionnaires compiled according to WebQual 4.0 and IPA indicators, involving application users at PT PLN ULP West Sumba. The results show that the majority of users are satisfied with the ease of use and performance of the application, but there are aspects that need to be improved, especially the speed of the system and a more user-friendly interface design. The science analysis emphasizes that the quality of systems and information is a crucial factor that must be a priority for development. This research provides strategic recommendations for PT PLN ULP West Sumba to improve the performance of the attendance application and support the company's operational needs in a sustainable manner.

Traditional fermented foods made from glutinous rice play a significant role in Indonesia’s local food system, with fermented glutinous rice known as tape ketan being one of the most popular products. Tape ketan is produced through a fermentation process using traditional starter cultures that involve diverse microorganisms, resulting in distinctive sensory characteristics. This review article aims to examine and synthesize previous studies related to the fermentation process of tape ketan and the characterization of metabolites formed during fermentation. The study employed a qualitative approach with descriptive analysis based on a literature review of relevant national and international scientific journals. The findings indicate that tape ketan fermentation involves starch hydrolysis, sugar fermentation, and the formation of primary and secondary metabolites influenced by fermentation time and microbial composition. Primary metabolites such as simple sugars, organic acids, and alcohol contribute to the basic sensory attributes of the product, while secondary metabolites including phenolic compounds and bioactive components enhance its functional potential. The characterization of these metabolites demonstrates that tape ketan is not only a traditional fermented food but also has promising potential for development as a value-added functional food based on local wisdom.

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.

This study aims to elaborate on the mechanism of product design, word of mouth (WOM), price perception, and product quality on purchase decisions, with brand image serving as a mediating variable. The research focuses on consumers of Ogel-Ogel, a traditional snack product from Pemalang. Utilizing a quantitative descriptive method, data were gathered from 100 respondents through purposive and accidental sampling techniques. Data analysis employed path analysis and the Sobel test to measure the strength of the mediation effect. The results reveal that, partially, all independent variables and brand image exert a positive and significant influence on purchase decisions. The most crucial finding indicates that brand image acts as an effective mediating bridge, reinforcing the transformation of physical product attributes and social recommendations into actual purchasing actions. This underscores that for MSMEs in the culinary sector, visual uniqueness and quality are insufficient without consistent brand reputation management. This research provides strategic contributions for local product managers in integrating traditional marketing elements with brand equity strengthening to navigate an increasingly competitive market.