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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 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.

This study aims to analyze the actualization of administrative service quality in managing school administration at MTs Darussalam Parigi Baru, Pondok Aren District, South Tangerang City, including service implementation, challenges encountered, and improvement efforts. This research employed a qualitative approach using a case study method through observation, interviews, and documentation involving the head of administration, administrative staff, teachers, and students. Data were analyzed through data reduction, data presentation, and conclusion drawing. The findings indicate that administrative services have been implemented reasonably well through the application of excellent service principles, including reliability, responsiveness, responsibility, and effective communication in correspondence and student administration services. However, several challenges remain, such as limited human resources, partially manual filing systems, inadequate administrative facilities, and the absence of well-established standard operating procedures (SOPs). Improvement efforts have been carried out through strengthening work coordination, enhancing staff discipline, and developing a more structured administrative system. This study concludes that the quality of administrative services plays a strategic role in supporting administrative effectiveness and improving the quality of educational services in madrasahs.

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.

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.

Indonesia’s Free Nutritious Meal Program (MBG) requires an efficient and adaptive supply chain system to ensure timely distribution, cost efficiency, and adequate nutritional delivery for a large number of beneficiaries. However, conventional supply chain approaches are generally static and unable to respond effectively to dynamic demand, supply uncertainty, and logistical constraints. This study proposes a Multi-Objective Reinforcement Learning (MORL) model to optimize the MBG supply chain by simultaneously considering distribution cost, delivery timeliness, service level, nutritional adequacy, and food waste reduction. The model is developed using a simulation-based environment representing real-world supply chain conditions, including demand variability, transportation limitations, and kitchen capacity constraints. The results show that the proposed approach achieves cost reductions of 15–22%, improves delivery timeliness by 18–25%, maintains a service level above 90%, increases nutritional fulfillment by 12–18%, and reduces food waste by 10–15% compared to baseline methods. Sensitivity analysis further demonstrates the robustness of the model, with minimal performance degradation under disruption scenarios. These findings indicate that Reinforcement Learning provides a scalable and adaptive solution for optimizing large-scale public food distribution systems. The proposed model contributes both theoretically by integrating multi-objective optimization within an RL framework and practically by supporting data-driven decision-making for improving the effectiveness of the MBG program in Indonesia.

Educational planning management is an essential process in managing educational institutions to achieve educational goals effectively and efficiently. One form of implementing educational planning management is through the preparation of the Madrasah Work Plan (RKM) and School Work Plan (RKS). This article aims to describe the concept of educational planning management through the preparation of RKM/RKS, their objectives, preparation processes, and implementation in schools and madrasahs. The research method used is library research with a qualitative descriptive approach by collecting data from books, scientific journals, government regulations, and other supporting documents. The results of the study indicate that RKM/RKS has a strategic role as a guideline for implementing educational programs, a basis for decision-making, an evaluation tool, and an instrument for improving the quality of education. The preparation of RKM/RKS is carried out systematically through team formation, school/madrasah self-evaluation, formulation of priority programs, preparation of medium-term and annual work plans, and budgeting plans. Proper implementation of RKM/RKS can improve the effectiveness of educational management, accountability, transparency, and the quality of educational services in schools and madrasahs.

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.

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.

Information technology services have an important role in supporting operational activities and digital services at PT Pertamina Patra Niaga Regional Sumbagsel through the MyPertamina application. However, several service disruptions are still encountered, such as server downtime, database errors, system bugs, login failures, and delays in transaction verification, which affect service quality and operational effectiveness. This study aims to analyze the maturity level of information technology services using the ITIL V4 framework, focusing on Incident Management and Problem Management practices. The research employed a mixed methods approach with descriptive analysis through observation, interviews, questionnaires, SWOT analysis, RACI mapping, maturity level assessment, and gap analysis. The results indicate that Incident Management obtained a maturity value of 3.09 and Problem Management obtained 2.91, both categorized at Level 3 (Defined Process). These findings show that service management processes have been implemented and documented properly, although several aspects still require improvement, particularly in monitoring, documentation, root cause analysis, and process evaluation. Therefore, the implementation of ITIL V4 practices is expected to improve the effectiveness, consistency, and quality of information technology service management within the organization.

This study aims to analyze the implementation of good governance principles in digital population administration services in Medan Marelan Subdistrict from the perspective of Administrative Law. The background of this research is based on the importance of digital transformation in public services through the Electronic-Based Government System (SPBE) to enhance transparency, accountability, effectiveness, and efficiency. However, in practice, several obstacles are still encountered, such as limited infrastructure, network disruptions, and limited human resources. This research employs an empirical legal research method with both empirical and statutory approaches. Data were collected through field research, including interviews with subdistrict officials and the community, as well as library research involving laws, regulations, and relevant literature. The data were analyzed qualitatively to examine the conformity between the implementation of services and the principles of good governance and general principles of good administration (AUPB). The results show that the implementation of good governance principles in digital population services in Medan Marelan Subdistrict has not been fully optimal. This is indicated by challenges in information transparency, system effectiveness, and limitations in human resources and infrastructure. Nevertheless, digital services have provided convenience for the community in accessing population administration services. In conclusion, improvements in human resources quality, technological infrastructure, and the strengthening of supervision and evaluation mechanisms are necessary to achieve public services that align with good governance principles.

This study aims to evaluate the management system of the Community Service Program (Kuliah Kerja Nyata/KKN) implemented by the Institute for Research and Community Service (LPPM) from the perspective of student participants as internal actors. KKN is a form of community engagement program that requires an effective management system to ensure its objectives are achieved optimally. This research employs a qualitative approach with a descriptive-exploratory design. Data were collected through Focus Group Discussions (FGDs), participant observation, and document analysis, then analyzed using the interactive model proposed by Miles et al. The findings indicate that the KKN management system still faces several challenges, including administrative irregularities, weak coordination and communication among stakeholders, limited information system support, and insufficient technical readiness during implementation. In addition, students provide critical reflections on their roles and experiences throughout the program. The problems identified are not only caused by institutional factors but also by interactions among actors within the KKN implementation system. This study highlights the importance of strengthening system integration, improving the quality of coordination among related parties, and developing digital-based information systems. These efforts are expected to enhance the effectiveness of KKN implementation and improve overall program governance in a sustainable manner.

Parental violence against children constitutes a serious violation of their human rights, as guaranteed by Article 5 of the 1948 Universal Declaration of Human Rights and Article 19 of the 1989 Convention on the Rights of the Child. It is also contrary to Indonesia’s family law system, as set out in Law No. 35 of 2014 on Child Protection. This study aims to analyse the forms of such violence and formulate a holistic, restorative strategy to restore children’s human rights. Using a normative-empirical approach involving the analysis of legal documents, court rulings and 2023 data from the National Commission on Violence Against Women (Komnas Perempuan), which recorded 45,000 cases annually (60% of which involved parents as perpetrators), it was found that effective strategies include medical and psychological rehabilitation via P2TP2A and TF-CBT, and restorative justice through mediation under Supreme Court Regulation (Perma) No. 4 of 2019. Socio-economic support, such as the PKH programme, is also crucial. The main obstacles are patriarchal culture and a regulatory vacuum regarding the KHI. The conclusion emphasises the need for cross-sectoral commitment and makes recommendations including the introduction of mandatory parental counselling, prioritisation of court mediation, sub-district recovery units and large-scale public awareness campaigns. This study contributes to the strengthening of child protection within Indonesian family law.

Dredger operations in water-based mining areas have a high occupational risk because they involve a combination of wet environments, mechanical equipment, and electrical systems. The presence of electrical panels, power cables, terminal connections, electric motors, inverters/drives, and protection components on the Dredger requires a sound understanding of Occupational Health and Safety, particularly electrical safety. Water splashes, wet walking surfaces, high humidity, and improper procedures during inspection or cleaning may increase the risk of short circuits, protection trips, and electric shock. Therefore, structured basic safety training is required so that operators are able to identify electrical hazards and implement safe working procedures. This activity aimed to improve operators’ understanding of hazardous electrical components, initial safety briefing procedures, measurement practice and safe handling during trips or short-circuit indications, and the correct and safe use of water pumps for unit cleaning. The activity was carried out through field observation, technical briefing, direct demonstration on the electrical panel and work area, simple measurement practice, and safe cleaning practice. The results indicate that field documentation can serve as an effective learning medium to strengthen operators’ understanding of electrical hazard sources on the Dredger, the application of simple lock out-tag out procedures, the control of wet areas, and the limitation of water spray direction during cleaning activities. Overall, the activity shows that improved compliance with safety procedures may reduce the risk of short circuits and electric shock during operation and maintenance.

Iron deficiency anemia (IDA) is a condition of insufficient iron that lowers hemoglobin levels and disrupts erythrocyte formation. This condition commonly occurs in children and can weaken the immune system, thereby increasing the risk of recurrent respiratory tract infections such as bronchiolitis, pneumonia, and Acute Respiratory Tract Infections (ARTI). Children with IDA are also at risk of worsening clinical conditions. This study employed a literature review method with a descriptive-analytical approach. A total of 10 journals (2020–2026) from Google Scholar, PubMed, ResearchGate, Elsevier, and the National Center for Biotechnology Information (NCBI) were analyzed using the Population, Intervention, Comparison, Outcome, Study Design (PICOS) framework and synthesized narratively. The results of the review indicate that iron deficiency anemia is significantly associated with an increased risk of recurrent respiratory tract infections in children. Children with IDA have a 2–5 times higher risk of developing bronchiolitis and pneumonia, and up to 10 times greater susceptibility to recurrent ARTI. This condition is also associated with increased disease severity, characterized by low hemoglobin levels, mean corpuscular volume (MCV), and serum iron, as well as elevated total iron-binding capacity (TIBC). Iron supplementation has been proven effective in reducing the incidence of recurrent infections. In conclusion, iron deficiency anemia is associated with an increased risk and severity of recurrent respiratory tract infections in children. Early detection and iron supplementation are essential to reduce morbidity and prevent infection recurrence.

Poor soil quality and inefficient nutrient utilization remain major constraints in tomato (Solanum lycopersicum L.) cultivation, often resulting in reduced plant growth, lower productivity, and suboptimal fruit quality. This study aims to evaluate the growth performance of rose tomatoes through the integration of open-field cultivation and hydroponic planting systems. The research methods included land preparation using the Pythagorean technique to ensure proper spacing and planting layout, the application of organic fertilizers to improve soil fertility, and the transition to a hydroponic drip irrigation system using rockwool as the growing medium during weeks 1 to 7 of plant development. Plant growth parameters, including plant height, leaf development, stem vigor, and overall plant health, were observed throughout the cultivation period. The results indicated that the combination of appropriate soil management practices and precise nutrient delivery in the hydroponic system significantly enhanced nutrient uptake and supported healthy plant growth. Furthermore, the drip irrigation system helped optimize water use efficiency while reducing nutrient losses. This integrated cultivation approach demonstrated its potential as an effective solution for maintaining crop quality and productivity, particularly in areas with limited land resources. Overall, the findings suggest that integrated management practices can provide a practical, efficient, and sustainable strategy for farmers seeking to improve horticultural production in modern agricultural systems.

This study examines the controversy surrounding the granting of investigative authority over electoral crimes to the Election Supervisory Body (Bawaslu) within the framework of the Integrated Law Enforcement Center (Sentra Gakkumdu). The research problem focuses on whether investigative authority should be directly assigned to Bawaslu and the main factors contributing to the suboptimal enforcement of electoral law in Indonesia. The study aims to analyze the effectiveness of the institutional design of Sentra Gakkumdu and to formulate an ideal model for electoral law enforcement in Indonesia. This research employs a normative legal method with statutory, conceptual, and comparative approaches. The analysis is strengthened through bibliometric mapping using the VOSviewer application to examine the development of studies on electoral law enforcement and institutional design, as well as by comparing Indonesia’s electoral law enforcement system with those of Norway, New Zealand, and Sweden. The findings indicate that the primary issue does not lie in the absence of investigative authority within Bawaslu, but rather in the limited investigation timeframe, weak interinstitutional coordination, and inflexible regulations.

The increase in activity and security threats on Ubuntu server causes the volume of system logs to become very large and difficult to analyze manually. This condition potentially leads administrators to experience delays in detecting abnormal activities, such as repeated login attempts and web access patterns related to online gambling promotions. Therefore, this research aims to develop a machine learning-based Early Warning System capable of automatically detecting anomalous activities. The system is developed using the Python programming language and runs on an Ubuntu server by utilizing authentication logs and web access logs as the main data sources. The anomaly detection model is trained using normal activity data collected directly from the Ubuntu server logs to learn standard system behavior patterns. During the operational phase, the system reads server logs in real-time, extracts activity features, and analyzes them using the Isolation Forest algorithm. Activities detected as anomalies trigger alert notifications via Telegram to the administrator without performing automatic blocking. The results show that the system is able to provide early warnings for suspicious activities, thereby helping to improve server security more effectively.