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The Community Partnership Program (PKM) aims to improve the capacity of teachers at Rara Elementary School in understanding and implementing the National Assessment as an educational evaluation tool. The problems faced by partners are limited understanding of the concept of National Assessment and technical skills in managing instruments, analyzing data, and following up on assessment results. The methods used in this activity are participatory-based socialization and technical training involving teachers as active participants. Activities are carried out through material delivery, interactive discussions, and hands-on practice using assessment tools. The results of the activity showed an increase in teachers' knowledge and skills in understanding the National Assessment indicators, developing learning strategies in line with the Pancasila learner profile, and optimizing the use of assessment results to improve the quality of learning in the classroom. Thus, this PKM activity contributed to improving the quality of learning at Rara Elementary School while supporting the achievement of national education standards.

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

Peat characteristics include low fertility, high acidity, and poor drainage because it comes from the weathering of organic matter under anaerobic conditions. However, peat has environmental benefits such as absorbing carbon, water buffers, and providing habitat for various unique flora and fauna. The use of peat as agricultural land is widely used by farmers in Pontianak City because it has a very large peatland area with a traditional agricultural system. However, traditional farming will have local knowledge (local wisdom) as a way to minimize damage to the peat ecosystem as an adaptation effort. This study aims to examine the adaptation of peatland management in traditional farming in Pontianak City. The method used in this study is qualitative descriptive and multiple linear regression analysis. The number of research samples was 68 farmers. The results of the study showed that traditional farmers have paid attention to the sustainability of their farming to minimize environmental damage. The results of the study showed that the adaptations carried out were making wells to maintain water availability, using short-lived vegetables that require shallow drainage of 20-30 cm and are adaptive to peat, providing intensive shade, planting trees on empty land, cleaning intensive irrigation, providing ameliorant ash, lime, chicken manure intensively, and arranging planting patterns with diversification. The determining factors for peatland management adaptation include age, land area, length of farming, participation of fire-conscious communities, and intensity of participation in outreach.

This research explores the role of human algorithm interaction mechanisms in enhancing trust, reliability, and user confidence in Decision Support Systems (DSS). Traditional DSS models often focus solely on algorithmic accuracy and performance, neglecting crucial factors such as transparency and user engagement, which are essential for building trust. By incorporating explainable AI (XAI) techniques like SHAP and LIME, real-time feedback mechanisms, and user-friendly interfaces, the study develops structured interaction models that improve the interpretability of AI-driven decisions. The results show that transparent decision-making processes and interactive features significantly enhance user trust, making DSS more reliable and easier to adopt. Users interacting with systems that provide clear, understandable explanations of decisions, along with real-time updates on the system’s confidence, reported higher levels of decision-making confidence, especially in high-stakes scenarios. These improvements lead to greater user engagement and adoption of the system in various domains, including healthcare and finance. The study also highlights the importance of balancing interpretability with efficiency in user interface design to ensure both trust and usability. The findings contribute to the design of more user-centric DSS that prioritize trust, interpretability, and cognitive factors, providing a framework for the successful integration of intelligent decision support systems in complex decision-making environments. Future research should focus on refining interaction models and exploring the broader applicability of these systems in different sectors.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

Recommender systems play a critical role in shaping user decisions across digital platforms; however, the increasing complexity of recommendation algorithms has raised serious concerns regarding transparency, trust, and accountability. This study focuses on enhancing the transparency of recommender systems by integrating Explainable Artificial Intelligence (XAI) techniques within a MovieLens-based recommendation framework. The primary problem addressed is the opacity of conventional recommendation models, which limits user understanding of why certain items are recommended and may reduce trust, perceived fairness, and system acceptance. Accordingly, the main objective of this research is to design and evaluate a hybrid explainable recommender system that balances predictive accuracy with human-understandable explanations. The proposed approach combines Matrix Factorization, feature-importance-aware neural networks, and knowledge graph embeddings to construct a robust recommendation model. To enhance explainability, multiple XAI strategies are integrated, including model-agnostic methods (LIME, SHAP, and CLIME), argumentation-based explanations, and context-aware personalized explanations. A comprehensive evaluation framework is employed, incorporating algorithmic metrics (accuracy, fidelity, robustness, counterfactual consistency, and fairness) alongside human-centered evaluations measuring trust, transparency, cognitive load, and perceived usefulness. Experimental results demonstrate that the knowledge graph–enhanced hybrid model achieves superior recommendation accuracy compared to baseline approaches. Moreover, context-aware explanations consistently outperform other methods in terms of fidelity, robustness, and user-perceived transparency, while argumentation-based explanations are found to be the most persuasive. CLIME offers a strong balance between technical stability and interpretability. The findings indicate that no single explainability technique is universally optimal; instead, hybrid and adaptive explanation strategies are most effective. In conclusion, this study confirms that human-centered, context-adaptive XAI significantly improves transparency and user trust in recommender systems, highlighting explainability as a fundamental component rather than an optional enhancement.

Blasting and crushing are essential stages in the limestone mining process; however, both stages may contribute to material volume loss due to technical factors and geological conditions. This study aims to analyze the blasted volume, crushed volume, and the amount of volume loss occurring throughout these processes. The research utilizes primary data including blasting geometry, blasting patterns, crushing production, and secondary data such as regional geology and equipment specifications. Based on 15 blasting activities conducted from October to December 2024, the total blasted volume reached 71,691 tons with an average powder factor of 0.23 kg/m³. Meanwhile, the total volume produced from secondary crushing was 71,575 tons. The comparison indicates volume loss influenced by suboptimal fragmentation, rock characteristics, work efficiency of the crushing unit, and operational constraints in the field. The results of this study are expected to serve as a reference for optimizing blasting design and crushing operations to minimize volume loss and improve overall mining productivity.

Carbonate rocks are one of the most important geological resources with a significant role in various industrial sectors, particularly as the main raw material in cement, lime, and construction industries. This study aims to review the characteristics, quality, and industrial potential of three major carbonate rock types—limestone, dolomite, and marble—based on relevant scientific literature. The research employed a literature review method using a qualitative-descriptive approach through analysis and synthesis of previous studies. Data were collected from scientific publications addressing the mineralogical, chemical, and physical aspects of carbonate rocks and their implications for industrial performance. The findings indicate that the quality of carbonate rocks is largely determined by mineral composition (calcite and dolomite), chemical purity (CaO and MgO), and the presence of impurities such as silica, alumina, and iron oxides. High-purity limestone rich in calcite is identified as the most efficient raw material for cement and lime industries due to its stable processing behavior and lower energy requirements. Conversely, dolomite with high magnesium content requires process adjustments to prevent excessive MgO formation during calcination. Marble, both in natural and waste powder form, shows promising potential as a supplementary material in sustainable cement production. Overall, this study emphasizes that understanding the mineralogical and chemical variations of carbonate rocks is essential for optimizing their processing strategies and promoting sustainable industrial utilization 

The development of information technology (IT) has made a significant contribution to the field of education, including at the junior high school level. This study aims to examine the implementation of information technology in the learning process of seventh-grade students at SMP Katolik St. Aloysius. The research method applied was descriptive qualitative, employing observation, teacher interviews, and student questionnaires. The findings indicate that the use of IT tools such as computers, LCD projectors, and online learning applications, particularly Google Classroom and WhatsApp Groups, has enhanced student learning motivation, facilitated easier access to learning materials, and strengthened interaction between teachers and students. Moreover, technology integration enables teachers to deliver more engaging and varied learning experiences, encouraging students to participate more actively in the classroom. Nevertheless, several challenges were identified, including limited school infrastructure, varying levels of digital literacy among students, and unequal internet access. These obstacles may hinder the effectiveness of IT implementation in the learning process. Therefore, continuous mentoring strategies, infrastructure improvement, and digital literacy training for both students and teachers are necessary. In conclusion, the implementation of information technology at SMP Katolik St. Aloysius has had a significant positive impact on the learning process but requires sustainable support and improvements to achieve optimal outcomes

This study aims to describe the management of the Keceme Drinking Water Supply System SPAM as an effort to overcome drought in Waru Hamlet, Girisekar Village, Panggang District, Gunungkidul Regency, Special Region of Yogyakarta. The drought problem in the area has been going on for a long time, thus demanding a sustainable solution to meet the community's clean water needs. This study uses a qualitative approach with data collection techniques through observation, in-depth interviews with SPAM managers and the community, and documentation. Data analysis was carried out with reference to George R. Terry's management theory which includes four management functions, namely planning, organizing, actuating, and controlling. The results show that the existence of the Keceme SPAM greatly helps the community in obtaining access to clean water, although various obstacles still exist. The main obstacles include planning that is mostly not documented in writing, limited number and capacity of human resources in management, technical problems in the form of lime deposits that disrupt water flow, and high operational and maintenance costs of the network. However, the existence of social capital in the form of a spirit of mutual cooperation among the community, as well as policy support and facilities from the village government, are important factors driving the sustainability of SPAM management. This research is expected to provide practical contributions as a reference for other regions facing similar challenges, particularly in optimizing the use of local water sources as a sustainable drought mitigation strategy.

This study examines the reclamation plan of PT. Z for the 2023–2027 period with the aim of assessing the technical feasibility, ecological effectiveness, and alignment with national reclamation regulations. A descriptive-quantitative approach was employed through field observations, analysis of geomorphological conditions, evaluation of topsoil management, assessment of revegetation performance, and review of key environmental documents. The planned land disturbance reaches 138.53 ha, with reclamation focused on the disposal and topsoil areas covering 29.67 ha, as the mining pit remains active. Reclamation activities include land recontouring, topsoil spreading, erosion control, establishment of Legume Cover Crops (LCC), and planting of local species such as Rukam. The results indicate that the implemented strategies effectively enhance soil stability, improve vegetation cover, and support ecosystem recovery. Furthermore, the reclamation program meets the key performance criteria set by the Ministry of Energy and Mineral Resources, with vegetation cover exceeding 90% and stable slope conditions. Challenges remain, particularly related to sedimentation management and the absence of pit reclamation during the active mining phase. Overall, the study concludes that PT. Z has developed and executed a reclamation plan that reflects good mining practices and environmental stewardship, contributing to the long-term sustainability of post-mining land use.

Citrus hystrix DC., commonly known as kaffir lime, is a citrus plant widely distributed in Southeast Asia including Indonesia, Thailand, India, and China. Its leaves are traditionally used as food flavoring and herbal medicine due to their distinctive aroma and therapeutic potential. This study was conducted to identify the presence of secondary metabolite compounds in kaffir lime leaves through qualitative phytochemical screening. Fresh leaves were extracted and tested using standard phytochemical methods with specific reagents to detect alkaloids, flavonoids, phenolics, tannins, saponins, steroids, and terpenoids. The screening process was carried out through colorimetric reactions that indicate the presence of specific metabolite groups. The results showed that kaffir lime leaves positively contain several classes of secondary metabolites, including alkaloids (brownish-black precipitate with Bouchardat reagent), flavonoids (red color with Mg and concentrated HCl), saponins (stable foam formation after shaking with HCl), steroids (green color with acetic anhydride), terpenoids (reddish-purple color with concentrated H2SO4), and tannins (greenish-black color with FeCl3). The findings confirm that kaffir lime leaves are rich in bioactive compounds that may contribute to pharmacological activities such as antimicrobial, antioxidant, anti-inflammatory, and therapeutic properties. These results support the traditional use of kaffir lime leaves as natural medicine and highlight their potential as a source of natural bioactive compounds for further pharmaceutical and nutraceutical development. This study provides preliminary scientific evidence and encourages more detailed quantitative and isolation studies on the active components of Citrus hystrix.

In the automotive manufacturing industry, efficiency in quality control is a crucial factor to ensure consistent product quality. Conventional Quality Assurance (QA) processes using manual record-keeping often face challenges such as delayed reporting, human errors, and difficulty in tracking historical data. This study aims to design and implement a QA performance dashboard based on digital forms at PT Dharma Polimetal, Tbk, to enhance efficiency in production quality control. The research methodology includes direct field observation, collection of production and QA data, mapping of QA process flows, interactive dashboard interface design, and system trial implementation. The designed dashboard focuses on four main aspects: QA Incoming, QC Line, QC Gate, and Customer Handling, each containing measurable performance indicators and quality parameters. Initial implementation results indicate significant improvements in QA process monitoring, faster reporting of inspection results, and easier real-time data access for both production teams and management. The system enables early detection of potential quality issues, supports rapid decision-making, and facilitates internal and external audits. Moreover, the use of digital forms within the dashboard enhances data accuracy, minimizes human error, and creates structured historical records for long-term analysis. This study provides a tangible contribution to the digitalization of QA systems, strengthening sustainable quality control practices in the automotive industry, thereby ensuring consistent productivity and product quality.

This research ains to fimd out how much physical work environment and compensation affect employee job satisfaction at PT Putra Jaya Curug, both individually and together. The method used is quantitative, with all 62 employee as the research sample. Data was proccesed usimg SPSS 25 with simple linear regression, multiple limear regression, correlation tests, coefficient of determination, t-tests, and f-tests. The results show that the physical work environment has a significant effect on job satisfaction (t-value 3,038 > t-table 2.000; sig. 0.000 < 0.05). Compensation also has a significant effect (t-value 9,271 > t-table 2.000; sig. 0.000 < 0.05). Together, both factors significantly affect employee job satisfaction (F-value 128.675 > F-table 3.150; sig. 0.000 < 0.05). The R Square value of 0.813 means that the physical work environment and compensation contribute 81.3% to job satisfaction, while the renaining 18.7% is imfluenced by other factors mot studied

This study investigates the influence of job satisfaction on teacher performance at Omba Tana Rara Public School, a rural educational institution located in a geographically remote area with distinct contextual challenges. Despite the crucial role of teachers in shaping educational outcomes, limited research has explored how job satisfaction contributes to teacher performance in such isolated settings. Employing a quantitative approach, the research surveyed 32 teachers using a validated Likert-scale questionnaire designed to measure key dimensions of job satisfaction, including recognition, professional development, remuneration, interpersonal relationships, and work environment. Data analysis was conducted using simple linear regression to determine the statistical relationship between job satisfaction and teacher performance. The results revealed that job satisfaction has a significant and positive impact on teacher performance (β = 0.67; p < 0.01), indicating that increases in satisfaction levels are associated with higher levels of effectiveness and engagement among teachers. Among the satisfaction factors examined, recognition from leadership, opportunities for professional growth, and supportive working conditions were identified as dominant contributors to improved performance. The findings of this study contribute to filling the research gap concerning human resource issues in remote schools and offer practical insights for stakeholders in education. In particular, the study emphasizes the importance of designing context-specific strategies that prioritize teacher welfare as a means of boosting instructional quality and learning outcomes. It recommends policy interventions focused on enhancing intrinsic and extrinsic satisfaction elements through improved school management practices. By highlighting the correlation between teacher satisfaction and performance, especially in rural and under-resourced areas, this research underscores the need for holistic human resource development in education. It advocates for a welfare-centered approach in managing and supporting teachers to ensure sustainable improvements in school effectiveness.

Fever and cough are diseases that often attack humans. Fever and cough are early signs of infection of a disease that attacks the body. This qualitative study was initiated to identify the types of plants, organs used, and local wisdom inherent in the traditional medicine practices of the people of East Java Province in dealing with fever and cough. Data collection was carried out through literature survey methods and journal reviews. Data sourced from articles in the Google Scholar, Google, and ResearchGate databases were then analyzed using a qualitative descriptive approach.The results of the study showed that there were 9 species of plants used for fever and cough medicine, including turmeric (Curcuma domestica), lime (Citrus X aurantiifolia), fennel (Foeniculum vulgare), starfruit (Averrhoa bilimbi L), kencur (Kaempferia galanga), mengkudu (Morinda citrifolia), katuk (Sauropus androgynus (L.) Merr), tapakliman (Elephantopus scaber), lemongrass (Adropogon nardus). The plant has certain content that can cure fever and cough. Fruit, leaves, rhizomes, and roots are parts of the plant organs that are often used by the community.

The geomorphological condition of Fakfak Regency is dominated by limestone. It makes ground water difficult to access. The clean water crisis in Fakfak Regency requires people to look for alternative sources of clean water such as rainwater. However, during the dry season, people will have great difficulty with clean water. Cloudfisher or fog catcher is a technique for collecting fog that have condensed into water. Cloudfisher's ability to catch fog or dew and produce water varies depending on the situation with an average of around 2 liters – 10 liters/m2/day. The ability of this technology to produce water depends on the net material used, the size of the net, the direction and model of the net, and the location of the net placement. In this research, the potential cloudfisher to produce water in Fakfak Regency will be examined, with the ultimate aim of seeing how effectively this technology is applied in Fakfak Regency in terms of the amount of water produced. The method used in this research is a field study by reviewing locations in Fakfak Regency with the altitude, fog conditions, temperature and humidity that are most suitable for the application of cloudfisher technology. The fog catcher was installed using a standard research model using 65% and 85% cover nets. Each fog catcher material was installed for 1 month and the amount of water captured by the tool was checked every morning at 06.30 am. The location of the fog catcher is based on several factors including altitude, air humidity, fog frequency, temperature and wind speed. The average water volume obtained in the 65% cover net was 59.6 ml while in the 85% cover net it was 26.24 ml. Factors that influence the amount of water obtained are net material, net cover, fishing area, equipment placement, and weather. The fog catcher (cloudfisher) in Fakfak Regency has the potential to be used but still requires further research regarding the design and materials used so that more water can be obtained and can be used by local communities as an alternative source of clean water

Because it is used to establish the product's selling price, the cost of manufacturing is a crucial figure. The cost of labor, raw materials, and manufacturing overhead must all be taken into account when compiling the cost of manufactured goods. Giving the wrong selling price is the issue Micro, Small, and Medium-Sized Businesses are facing. The full costing conduct is one of the techniques required to determine the cost of things produced in an accurate manner. Qualitative data is used in this kind of study. The cost of commodities produced at the Ayy Slime company in Kediri City is the focus of this study. The owner of the Ayy Slime company was directly interviewed for this study. According to the study's findings, the Ayy Slime company did not calculate the cost of goods produced correctly. Because of the presence of several factor, including fixed overhead costs that include uncalculated building and equipment depreciation costs, have an impact on the selling price calculation.

Bahan pangan pada umumnya sangat sensitif dan mudah mengalami penurunan kualitas karena faktor lingkungan, kimia, biokimia, mikrobiologi, oksigen, air, cahaya, dan temperatur. Penelitian tentang pelapisan produk pangan dengan edible coating/film telah banyak dilakukan dan terbukti dapat memperpanjang masa simpan dan memperbaiki kualitas produk. Materi polimer untuk edible coating/film yang paling aman, potensial, dan sudah banyak diteliti adalah bahan organik terbarukan. Smart edible packaging merupakan inovasi kemasan pangan yang menggabungkan fungsi proteksi produk dengan kemampuan mendeteksi perubahan mutu secara visual. Penggunaan bahan dasar alami seperti konjac, kitosan, alginat, dan pati memungkinkan pembentukan film yang stabil, fleksibel, dan aman dikonsumsi. Penambahan bahan aktif berupa antosianin dari bunga rosella dan bunga telang, serta biofiller dari tepung cangkang telur, mampu meningkatkan ketahanan mekanik film serta memberikan indikator perubahan mutu melalui perubahan warna. Smart edible packaging ini berpotensi untuk memperpanjang masa simpan produk, meningkatkan keamanan pangan, serta menjadi solusi pengemasan yang lebih ramah lingkungan. Optimalisasi formulasi dan pengujian lebih lanjut diperlukan untuk memperluas penerapannya dalam berbagai produk pangan.

This research is an experimental study on porous concrete and the use of fly ash as a binder for concrete to analyze the effect of compressive strength and porosity. The research method used in this study is the experimental method which is a research method used to find the effect of certain treatments on concrete. In this research, the concrete mix design uses a ratio of gravel and geopolymer paste as a binder, namely 4: 1 and uses a molarity ratio of 10M with differences in grading of coarse aggregate using sieves number 4, 1/2 ", and 3/8". The optimum compressive strength value was obtained in mix design 1 using sieve gradation no.4 which was 4.25 MPa at 28 days old. While the results of the highest porosity value were found in mix design 1 which was 7.15% at 28 days old