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Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

This study examines the role of the creative economy in enhancing sustainable tourism potential in Pampang Cultural Village, Samarinda, Indonesia, and identifies strategic priorities for its development. Pampang Cultural Village is recognized for its rich cultural heritage preserved by the Dayak Kenyah community, which serves as a valuable tourism asset. Despite this potential, the contribution of creative economy activities to tourism development has not been fully optimized. Therefore, a comprehensive analysis is needed to formulate effective development strategies. This research employs a mixed qualitative–quantitative approach using the SWOT-AHP method. SWOT analysis is utilized to identify internal strengths and weaknesses as well as external opportunities and threats affecting creative economy development. Subsequently, the Analytic Hierarchy Process (AHP) is applied to determine the priority level of each strategic factor and alternative strategy. The findings indicate that the village possesses significant creative economy potential in cultural performances, handicrafts, culinary products, traditional fashion, and cultural souvenirs. Cultural authenticity emerges as the primary strength, while limited innovation capacity and digital marketing skills remain the major weaknesses. The results further reveal that the highest-priority strategy is the development of innovative creative tourism products rooted in Dayak Kenyah cultural heritage while preserving cultural authenticity. This strategy should be supported by digital promotion, human resource capacity building, stakeholder collaboration, and improvements in tourism infrastructure to achieve sustainable tourism development.

Phishing is one of the most common and dangerous cyber attacks as it can trick users into providing sensitive information such as usernames, passwords, and personal data. This study aims to simulate Phishing techniques using the Zphisher tool as an educational medium to improve cybersecurity awareness. The method used is a quasi-experimental approach involving 30 participants exposed to Phishing scenarios in a controlled environment using Kali Linux. The results show that 83% of participants clicked the Phishing link and 60% entered their credentials, while 40% demonstrated awareness by avoiding further interaction. These findings indicate that cybersecurity awareness is still relatively low, but Phishing simulation is effective as an experiential learning tool.

Digitalization in the fitness industry is increasingly essential to improve efficiency and service quality. However, many fitness centers still manage data manually, including KRESNA GYM, which faces challenges in member registration, workout scheduling, and facility information delivery. This study aims to design a web-based e-fitness application using the Laravel framework to support digital service management. The development follows the Waterfall model through stages of requirement analysis, design, implementation, testing, and evaluation. The application includes features such as a landing page, admin/member login and registration, management dashboards, workout schedules, equipment guides, and supplement listings. Black-box testing confirms that all features function as expected. This system simplifies administrative processes and enhances member experience, making it effective in supporting digital transformation at KRESNA GYM.

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

The thesis supervision process in higher education institutions is still frequently conducted manually, which may lead to inefficiencies in recording supervision data. This study focuses on the development and implementation of a web-based thesis supervision attendance application designed to facilitate attendance documentation, supervision session administration, and systematic monitoring of thesis supervision history. The methodology applied in this study is the Waterfall model, which includes the phases of requirements analysis, system design, implementation, and testing. The application was developed using the Laravel framework and a MySQL database. The system design was modeled using Unified Modeling Language (UML), while the validation process was conducted through Black Box Testing techniques. The research findings indicate that the developed application is capable of performing real-time supervision attendance recording, managing supervision information, and generating attendance reports effectively and efficiently. It can be concluded that the web-based thesis supervision attendance application improves the efficiency and accuracy of supervision record management and supports a more effective thesis supervision monitoring process..

Consumption of sweetened coffee with various mixtures has become an unhealthy habit for the younger generation. Kopi Babah Kacamata, a legendary tubruk coffee in Salatiga, has been affected by this change. Many people are still unaware of the brand and the consumption of black coffee is declining. Therefore, this research will discuss the positive impact of consuming tubruk coffee with the Kopi Babah Kacamata brand as a soft selling medium for young people. This research uses mixed-sequel exploratory method and linear strategy in its design. The results of this study can be concluded that promotional videos with a storytelling approach for Kopi Babah Kacamata effectively introduce to the target audience.

Bank stock price prediction is an important topic in the application of information technology because stock price movements are dynamic, sequential, and influenced by historical market patterns. This study aims to predict Indonesian banking stock prices using the Long Short-Term Memory method and evaluate the effect of Bayesian Optimization on model performance. The data used in this study consists of daily historical stock data of BBCA, BBNI, BBRI, BBTN, and BMRI from May 4, 2020, to May 4, 2026, obtained from Yahoo Finance. The input features include opening price, highest price, lowest price, closing price, and trading volume, while the prediction target is the stock closing price. The results show that the baseline model produced MAPE values ranging from 1.892% to 3.147%. The best baseline performance was obtained on BBCA with an R² value of 0.933, followed by BBTN with an R² value of 0.902. After optimization, performance improvement occurred on BBTN, with MAPE decreasing from 3.147% to 2.482% and R² increasing from 0.902 to 0.935. For BMRI, MAPE decreased from 2.385% to 2.206%, and R² increased from 0.687 to 0.743. This study concludes that Long Short-Term Memory can be used to predict Indonesian banking stock prices, while Bayesian Optimization can selectively improve model performance depending on the characteristics of each stock dataset.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

The rapid growth of e-commerce among university students has provided greater convenience in conducting digital transactions. However, it has also created various challenges, including online fraud, discrepancies between advertised and delivered products, delayed shipments, and limited understanding of consumer rights and dispute resolution mechanisms. This community service program aimed to enhance legal literacy and increase students’ awareness of consumer protection in electronic transactions, particularly within the Faculty of Teacher Training and Education (FKIP) of Yapis University Papua. The program employed an educational-participatory approach through counseling sessions, interactive discussions, case simulations, and participant comprehension evaluations. The materials covered consumer rights and obligations, common violations in electronic transactions, complaint procedures on e-commerce platforms, and non-litigation dispute resolution mechanisms. The results demonstrated a significant improvement in students’ understanding of consumer protection laws, their ability to identify violations of consumer rights, and their knowledge of appropriate actions when disputes arise in online shopping transactions. Furthermore, participants showed strong engagement throughout the activities and were able to apply their knowledge effectively during case simulations. The program contributed to the development of more critical, informed, responsible, and legally aware digital consumers who are better prepared to engage safely and confidently in e-commerce activities.

This study was conducted to analyze the relationship between students’ critical thinking skills and mathematical problem-solving abilities through a literature review approach. The study is based on the importance of mastering higher-order thinking skills in the mathematics learning process, particularly when students are faced with complex problems related to real-life situations. The method used in this research was a literature review by examining various relevant scientific journals and academic books published between 2021 and 2026. The data analysis process was carried out through stages of identification, classification, evaluation, and synthesis of the collected sources. The findings revealed a significant and positive relationship between critical thinking skills and students’ mathematical problem-solving abilities. Critical thinking skills play an important role in helping students understand problems, process and analyze information, select appropriate solution strategies, and systematically review the results obtained. Students with strong critical thinking skills generally demonstrate more optimal mathematical problem-solving abilities. In addition, the implementation of learning models such as Problem-Based Learning and contextual approaches has been considered effective in improving both abilities. Therefore, critical thinking skills are regarded as an essential aspect that needs to be developed in mathematics learning in order to enhance students’ mathematical problem-solving abilities.

Chronic rhinosinusitis (CRS) is a long-term inflammatory condition affecting the nasal mucosa and paranasal sinuses, which significantly impacts patients’ quality of life due to persistent and recurrent symptoms. Limited epidemiological data regarding CRS are available in Central Kalimantan, particularly at RSUD dr. Doris Sylvanus in Palangka Raya, prompting this study to determine its prevalence and patient distribution characteristics. This research employed a quantitative descriptive design with a cross-sectional approach. Data were collected from medical records of patients diagnosed with CRS in 2023 and 2024. Univariate analysis was conducted, and results were presented in tables and diagrams illustrating frequency distributions by year, gender, age, and main complaints. The findings showed a total of 118 CRS patients, with 53 cases in 2023 and 65 cases in 2024. The prevalence of CRS increased from 4.91% in 2023 to 10.83% in 2024. Based on gender, males accounted for 50.8% (60 patients), while females represented 49.2% (58 patients). The age group most affected was 46–55 years (31%), indicating a predominance among individuals of productive age. Nasal congestion was the most frequent primary complaint, reported by 35% of patients. The study concludes that the prevalence of CRS at RSUD dr. Doris Sylvanus increased notably from 2023 to 2024, with a slightly higher incidence among males in the productive age group. These findings highlight the need for enhanced preventive measures and early diagnosis to manage CRS more effectively in the local population.

This study examines the transformation of financial management through the implementation of digital accounting in PPOB (Payment Point Online Bank) businesses, which still face manual recording problems such as input errors, delayed reconciliations, and data discrepancies. The research used a descriptive qualitative method with a case study approach involving PPOB agents in Sidoarjo. Data were collected through observation, interviews, and documentation. The findings show that digital accounting significantly improves operational efficiency through automated transaction recording, real-time data integration, and faster as well as more accurate reconciliation processes. In addition, features such as automatic validation, API integration, and audit trails help minimize recording errors and maintain data consistency. The implementation of digital accounting also supports transparency and improves the quality of financial information used in decision-making. Although challenges remain regarding agents’ technological understanding and infrastructure readiness, overall implementation has provided positive impacts on financial management effectiveness and business operations in PPOB services, making processes more efficient, accurate, and reliable.

Transportation plays an important role in supporting community mobility and enhancing socio-economic activities in urban areas. The increasing population growth and community activities in Sorong City have led to a higher demand for transportation modes. This study aims to identify the characteristics of transportation users in Kota Sorong and to analyze the probability of selecting different transportation modes using the Multinomial Logit (MNL) method. A quantitative approach was employed in this research. Questionnaires were distributed to 225 transportation users in Sorong City. The variables analyzed included gender, age, occupation, income, trip purpose, travel frequency, and reasons for choosing a transportation mode. The data were analyzed using multinomial logistic regression with SPSS software. The results indicate that transportation mode choice is influenced not only by socio-economic factors, but also by showed that income, trip purpose, travel frequency, and reasons for choosing the mode significantly influenced transportation mode choice, while gender, age, and occupation did not have significant effects. The probability analysis indicated that private vehicles had the highest probability of selection at 89.63%, followed by online transportation at 9.96%, and conventional motorcycle taxis at 0.41%. These findings imply that socio-economic and travel behavior factors strongly influence transportation mode preferences in Sorong City and may serve as a reference for future urban transportation planning and policy development.

Communication is an important aspect in the development of children aged 5–6 years, especially in supporting their language and social-emotional abilities. This study aims to analyze the influence of effective communication through a play approach in early childhood based on the results of previous research. The method used in this study is a quantitative method by conducting observations, research, and observations on effective communication and a play approach in early childhood education. Data collection techniques were carried out through documentation and literature searches from journals, articles, and relevant scientific references. Data analysis techniques used quantitative descriptive analysis by comparing and summarizing the results of previous research. The results show that the play approach has a significant influence on improving children's effective communication skills, such as the courage to speak, the ability to express opinions, social interaction, cooperation, and self-confidence. In addition, play activities have been proven to be able to create a fun learning atmosphere so that children are more active in interacting with peers and teachers. Thus, the play approach can be an effective strategy in developing the communication skills of children aged 5–6 years.

Communication within the family, especially between parents and children, is an important foundation for the development of a child's personality, emotions, and social skills. The purpose of this writing is to identify the types of parental communication styles, analyze the factors that influence them, examine their impact on child development, and provide practical recommendations to improve the effectiveness of communication patterns within the family. The method used in writing this paper is a literature study. The discussion results show that there are three main types of parental communication styles: permissive, authoritarian, and democratic. The permissive style tends to give the child unlimited freedom, the authoritarian style emphasizes control and obedience, while the democratic style encourages openness and mutual respect. The choice of communication style is influenced by various factors such as educational background, culture, past experiences, and the socio-economic conditions of parents. Each communication style has a different impact on the emotional, social, and cognitive development of children. The democratic style is considered the most effective in optimally supporting child development. This article provides recommendations to parents to increase knowledge about dialogical-based parenting, practice assertive communication skills, give children space to express their opinions, and build a family culture that is open and mutually respectful.

This study aims to map the trends, integration forms, effectiveness, and contribution of ethnomathematics in elementary school mathematics learning toward national cultural preservation. A systematic literature review (SLR) approach following the PRISMA 2020 protocol was employed. Searches across four academic databases (Scopus, Google Scholar, CrossRef, and Semantic Scholar) yielded 1,176 articles, from which 30 articles met the inclusion criteria. Results indicate that ethnomathematics research in elementary schools has increased significantly during 2023–2026, particularly following the implementation of Kurikulum Merdeka. The three most dominant forms of integration are digital modules and media (53.3%), traditional games (33.3%), and ethnomathematics-based worksheets (13.3%). Geometry and measurement is the most researched mathematics topic (50.0%), and conceptual understanding shows the most significant improvement (40.0%). Beyond improving mathematics achievement, ethnomathematics integration contributes to cultural preservation through cultural contextualization in learning (60.0%), strengthening cultural identity and values (26.7%), and active local cultural preservation (10.0%). The study recommends teacher training, curriculum policy strengthening, cross-sector collaboration, and further research to sustain ethnomathematics implementation in Indonesian elementary schools.

TPS 3R is a community-based waste management approach aimed at reducing waste generation through reduce, reuse, and recycle activities. However, the operational sustainability of TPS 3R still faces several challenges,including  waste processing effectiveness, and community participation. This study aims to analyze the community’s Willingness to Pay (WTP) for waste management fees and to formulate priority recommendations for improving the performance of TPS 3R Banjar Sugihan using the Quality Function Deployment (QFD) method. The research was conducted in Banjar Sugihan Village, Tandes District, Surabaya City, involving 563 household respondents. WTP analysis was carried out using the bidding game method, while QFD analysis was conducted through the preparation of the House of Quality (HoQ). The results showed that the community’s WTP ranged from IDR 16,000 to IDR 19,000 per month. If applied to all 3,758 households in Banjar Sugihan Village, the potential retribution revenue is estimated to reach IDR 60,128,000–IDR 71,402,000 per month. The QFD analysis indicated that the priority strategies for improving TPS 3R performance include enhancing infrastructure facilities, optimizing waste sorting and processing, increasing waste processing capacity, strengthening human resource capacity, and implementing a WTP-based retribution system. Therefore, the implementation of a WTP-based retribution system and priority strategies derived from QFD analysis are expected to support the sustainability of waste management at TPS 3R Banjar Sugihan.

In the Education 4.0 era, schools face unprecedented challenges in managing teacher performance effectively. Traditional top-down performance management systems have proven inadequate for fostering continuous professional growth, intrinsic motivation, and adaptive teaching competencies required in increasingly technology-driven learning environments. This study aims to develop, validate, and assess the effectiveness of a School-Based Performance Management (SBPM) model designed to enhance teacher effectiveness across cognitive, affective, and pedagogical dimensions. Using a Research and Development (R&D) design following the ADDIE framework, the study was conducted in three phases: needs analysis, model development, and model validation. Participants included 12 school principals, 186 teachers, and 8 education experts from 24 public secondary schools in [Province, Country]. Validation by experts yielded a content validity index (CVI) of 0.91. Implementation resulted in statistically significant improvements in teacher effectiveness scores (t = 8.74, p < 0.001, Cohen's d = 1.23), digital pedagogy competency (mean increase = 22.4%), and student learning outcomes (mean improvement = 17.8%). The SBPM model provides a contextually responsive, evidence-based framework that empowers schools to manage teacher performance collaboratively, fostering professional accountability and sustainable instructional quality in Education 4.0.