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The development of modern biotechnology has brought fundamental changes to the life sciences through the application of molecular biology, genetic engineering, and bioinformatics techniques. This article aims to examine the transformation of life sciences through modern biotechnology approaches and its impact on health, agriculture, the environment, and industry. The method used is a descriptive-analytical literature review based on international journal sources and relevant scientific publications. The results of the study indicate that technologies such as CRISPR-Cas9 enable precise gene editing for genetic disease therapy, while the mRNA vaccine platforms developed by Pfizer and Moderna demonstrate the acceleration of biomedical innovation in response to the global pandemic. In the agricultural sector, genetic engineering increases crop productivity and resilience, while in the environmental sector, biotechnology supports environmentally friendly bioremediation processes. However, ethical and regulatory challenges remain important concerns in its implementation. In conclusion, modern biotechnology plays a key role as a catalyst for sustainable, innovation-driven transformation in the life sciences.

Processing plastic waste into fuel oil with pyrolysis technology. Research on plastic oil as an alternative fuel has been widely conducted. One of them is research on processing plastic bag waste into fuel oil with pyrolysis technology. In this study, a set of pyrolysis equipment was made by Lecturers and Students of UMNU Kebumen. The plastic waste used in this study was 1 kg of plastic bag. Meanwhile, the pyrolysis process used a temperature of 250oC-300oC. The fuel oil produced in the pyrolysis process of this study was approximately 400 ml. The average density of fuel oil from plastic bag waste was 0.733 gr/ml. The results of this density calculation are in the possibility of the density of gasoline, namely 0.710 gr/ml to 0.770 gr/ml. This pyrolysis process shows great potential for converting plastic waste into an environmentally friendly alternative energy source. Furthermore, the efficiency of this pyrolysis technology can be improved by adjusting the temperature and processing time, as well as by selecting a wider variety of plastic types. This technology has the potential to be applied more widely in plastic waste management within the community as a solution to reduce environmental pollution while generating renewable energy.

The danger of cadmium contamination in water sources remains a crucial environmental issue due to its persistent nature and high toxicity level, which poses serious risks to human health and ecosystems. Cadmium is a non-biodegradable heavy metal that can accumulate in living organisms over time. The presence of these toxic Cd²⁺ ions is known to trigger damage to vital organs such as the liver and kidneys; therefore, reducing their concentration in aquatic environments is of paramount importance for environmental protection and public health safety. Among various treatment methods, adsorption is considered one of the most effective and economical techniques for removing heavy metal ions from contaminated water. In this study, the capability of mesoporous silica MCM-48-NH₂ as an adsorbent for Cd²⁺ ions was systematically evaluated. The adsorption performance was examined by investigating several important parameters, including contact time, solution acidity level (pH), and initial Cd²⁺ concentration. Furthermore, the adsorption mechanism and interaction between Cd²⁺ ions and the adsorbent surface were analyzed using Langmuir and Freundlich isotherm models. The results demonstrate that the adsorption process of Cd²⁺ ions onto MCM-48-NH₂ tends to follow the Langmuir isotherm model, indicating monolayer adsorption behavior, with a maximum adsorption capacity of 0.66 mmol g⁻¹.

This study comprehensively evaluates the quality of biomass briquettes produced from blends of coconut shell charcoal and sengon sawdust, using both carbonized and non-carbonized materials. Composite briquettes were fabricated with varying compositions and characterized through proximate analysis, calorific value, density, and burning rate measurements to determine their suitability as solid fuel. The results indicate that adding non-carbonized sawdust increases volatile matter content and burning rate but reduces the calorific value of the briquettes. In contrast, incorporating up to 10% carbonized sawdust significantly improves the calorific value to 6119.2 cal/g, approaching that of pure coconut shell charcoal (6352.2 cal/g), while maintaining a relatively high burning rate. Briquettes containing carbonized sawdust also exhibit low ash content, below 3%, and moisture content under 8%, meeting standard solid fuel quality requirements. These findings demonstrate that a strategic combination of carbonized and non-carbonized materials can produce hybrid biomass briquettes with optimized thermal performance, providing a promising, sustainable, and environmentally friendly alternative fuel for domestic and industrial applications.

The discharge of surfactant-laden wastewater from the rapidly expanding laundry industry poses significant environmental risks, especially in densely populated urban areas. While constructed wetlands (CWs) and Eco-Enzyme technology have shown promise for surfactant remediation, their standalone application requires long hydraulic retention times (HRTs), limiting practical implementation. This study evaluated the efficacy of a novel integrated system combining a subsurface constructed wetland (SSFCW) with fruit peel-derived Eco-Enzyme to treat synthetic laundry wastewater. Over a 6-day treatment period, the combined system achieved a remarkable surfactant removal efficiency of 99.63%, reducing the concentration from 225 mg/L to 0.835 mg/L—well below the regulatory threshold of 3 mg/L. The synergistic degradation mechanism involves enzymatic hydrolysis via Eco-Enzyme lipase and protease activity, complemented by microbial mineralization in the wetland rhizosphere. This system maintains optimal environmental conditions, with a stable pH of 6.85-7.32 and a temperature of 30.9-35.2°C, supporting robust biological activity. These findings demonstrate that the integrated Eco-Enzyme/SSFCW system overcomes the limitations of conventional HRT approaches, offering a highly efficient, sustainable, and practical decentralized wastewater treatment solution for the laundry industry.  

The increasing demand for paper has led to a shortage of wood-based raw materials, thus requiring environmentally friendly alternatives. This study aims to analyze the physical, mechanical, and morphological characteristics of paper made from cassava peels (Manihot esculenta Crantz) and oil palm fruit fibers (Elaeis guineensis Jacq.) as eco-friendly paper raw materials. The research method involved pulp preparation through delignification using 3.5% HNO₃ and 3% NaOH, bleaching with 10% H₂O₂, paper sheet formation with varying compositions (A = 70:30, B = 50:50, C = 30:70, D = 10:90), followed by grammage testing and tensile strain testing. The results showed that the grammage values ranged between 96.3–99.7 g/m², while tensile strain ranged between 2.6–4.4%. The study concluded that the best composition was found in sample C (30% cassava peel : 70% palm fiber) with a grammage of 98.5 g/m² and tensile strain of 3.2%. The implication of this study is that cassava peel and palm fiber waste have the potential to serve as alternative paper raw materials that meet the quality standards of A-grade printing paper (SNI 7274:2008), while also supporting the reduction of dependence on wood.

Low student achievement in mathematics, particularly in arithmetic sequences and series, has led to decreased learning motivation and participation. Therefore, innovative and engaging instructional strategies are required, one of which is the implementation of ice-breaking activities to reduce boredom and enhance students’ learning engagement. This study aimed to examine the effect of implementing ice breaking on the mathematics learning outcomes of tenth-grade students at SMK Kesehatan Samarinda in the topic of arithmetic sequences and series. This research employed an experimental method using a quasi-experimental design, involving two groups: an experimental group that received instruction incorporating ice breaking and a control group that did not receive such treatment. The total sample consisted of 60 students selected through purposive sampling. The research instrument was a learning achievement test administered as a pretest and posttest. Data were analyzed using a paired sample t-test to determine differences in learning outcomes between the two groups. The results revealed a significance value of 0.000 (< 0.05), indicating a significant difference in learning outcomes between the experimental and control groups. Therefore, it can be concluded that the implementation of ice breaking has a significant effect on improving students’ mathematics learning outcomes. This strategy creates a more active, interactive, and enjoyable learning environment, thereby facilitating students’ understanding of mathematical concepts, particularly arithmetic sequences and series.

State-based conservation in Indonesia often faces institutional failure and social resistance, resulting in continued deforestation. Conversely, Indigenous conservation models in West Java, specifically the Leuweung Larangan (forbidden forest), demonstrate significant ecological resilience. However, the legal standing of these customary practices within Islamic jurisprudence remains under-theorized, creating a dichotomy between Adat (custom) and Sharia. This study proposes a juridical reconstruction of the Sundanese taboo mechanism (Pamali) as Jarimah Ta’zir (discretionary sanction) to strengthen environmental law enforcement. Utilizing a socio-legal approach and ecological hermeneutics, this research analyzes Yusuf al-Qardhawi’s concept of Fiqh al-Bi’ah and Seyyed Hossein Nasr’s sacred science, alongside relevant empirical data on Indigenous forest governance. The findings demonstrate that Leuweung Larangan structurally manifests as Hima Syar’i (sacred protected zone). Operationally, Pamali functions not merely as a cultural myth, but as a preventive legal instrument (Sadd al-Dzari’ah) where environmental violations constitute religious criminal acts. This creates a "Sundanese Eco-theology" model that establishes a double-layered compliance system—social sanctions and theological liability—proving more effective for carbon preservation than state regulations alone. The study recommends integrating this model into the Green Constitution framework to resolve tenurial conflicts and enhance climate resilience.

This study is entitled “The Ecofeminist Movement of Mollo Indigenous Women in Rejecting Marble Mining (study: Rejection of Marble Mining in Fatumnasi Village, South Central Timor Regency)” which aims to understand and analyze the ecofeminist movement carried out by Mollo indigenous women in rejecting marble mining activities in Fatumnasi Village. The presence of marble mining in the Mollo indigenous area has posed a threat to Environmental sustainability, water sources, and cultural values that have long been the identity of the community. Through a descriptive qualitative approach, this study describes the role and form of resistance of Mollo indigenous women based on the ecological relationship between women and nature. Data were obtained through in-diepah interviews, field observations, and documentation of the head of Fatumnasi Village, traditional women’s figures, religious figures, community leaders, and youth leaders. The results of the study indicate that the movement to reject marble mining is not merely a form of protest against environmental damage, but also a form of ecofeminist awareness that emphasizes that women’s bodies and the body of nature are an inseparable whole. This movement is expressed through various acts of resistance such as traditional rituals, weaving, demonstrations, and customary deliberations, each carrying symbolic meaning about the harmony between humans and nature. Based on Françoise d’Eaubonne’s theory of ecofeminism, the Mollo women’s movement reflects critical awareness toward patriarchal and capitalist systems that exploit both women and the environment. Thus, it can be concluded that the ecofeminist movement of Mollo indigenous women in rejecting marble mining is a form of women’s struggle to maintain environmental sustainability and maintain cultural identity through loclah wisdom practices.Ecofeminism, Mollo Indigenous Women, Marble Mining, Fatumnasi Village, Environment

This study aimed to analyze the implementation of policies prohibiting illegal fishing practices involving the use of explosives, poisons, and electric shock devices in Sanga-Sanga District, Kutai Kartanegara Regency. Although regulations and extension activities related to the prohibition have been established, illegal fishing practices continue to occur as of 2025. The findings reveal that some fishermen still use poison and electric shock due to economic reasons and the perception that these methods yield greater catches. However, such actions cause losses to other fishermen by reducing the quality of the catch and damaging the aquatic ecosystem. The community is aware of these violations but is reluctant to report them due to fear of threats from the perpetrators. Efforts by fisheries extension officers to establish community-based surveillance groups (Pokmaswas) have not been optimal due to low public participation. Collaboration between provincial fisheries supervisors and the local marine police (Polairud) has resulted in joint patrols that successfully apprehended two perpetrators of illegal fishing, yet law enforcement has not produced a deterrent effect. Moreover, the program to replace destructive fishing gear with environmentally friendly alternatives for fishermen willing to transition has not been realized due to budget constraints. Based on these findings, it is necessary to strengthen integrated surveillance, provide legal protection for whistleblowers, accelerate the implementation of sustainable fishing gear programs, and enhance public awareness of legal and ecological issues.

This study aims to analyze the perceptions of students from various majors regarding the urgency of legal protection for the Napoleon wrasse (Cheilinus undulatus), a rare and endangered marine species that plays a crucial role in maintaining the balance of coral reef ecosystems. This fish faces serious pressure due to overexploitation and illegal trade, thus requiring special attention from a legal and conservation perspective. This study used a qualitative descriptive approach with purposive sampling technique, involving students from marine, fisheries, environmental, and legal departments. Primary data were collected through a questionnaire designed to elicit respondents' insights, level of understanding, and recommendations regarding the protection of this species. The data obtained were then analyzed using thematic analysis to identify patterns of perception and key ideas. The results showed that the majority of students considered legal protection for the Napoleon wrasse to be very urgent. They advocated for strengthening regulations, stricter law enforcement, increased surveillance in water areas, education for coastal communities, development of conservation and cultivation programs, and strengthening international cooperation. The implications of this study emphasize the importance of a comprehensive and collaborative protection strategy to support the sustainability of marine resources.

Gender inequality in terms of safety in public spaces is a crucial issue in Indonesia. This research background is driven by the high rate of violence against women, reaching 330,079 cases in 2024, and reinforced by a patriarchal culture that perpetuates gender hierarchy. This study uses a qualitative method with a literature study approach to analyze the root causes and impacts of gender inequality on women's sense of security in public spaces. The results indicate that the disparity manifests in various dimensions, such as women-unfriendly urban infrastructure (e.g., inadequate lighting), security threats in urban areas, and the high rate of gender-based violence where 80.1% of the victims are women. The discussion reveals that this inequality impacts not only physical safety but also psychological well-being, economic security, and access to public spaces. This research concludes that a multidimensional approach encompassing infrastructure improvement, strengthening legal protection, changing social norms, and economic empowerment is required to create an inclusive, equitable, and safe public environment for all genders.

This study examines how children and their relationship with nature are represented through the character of Antonio in the film Encanto (2021). Antonio is portrayed as a child who can communicate with animals, reflecting innocence, empathy, and a spiritual closeness to nature. This study aims to describe how children's relationship with nature is depicted in the film and to interpret the symbolic meaning behind these interactions. The method used in this study is a descriptive, qualitative approach that draws on theories of child development, symbolism, and children's imagination. The results of this study show that among children, Antonio reflects a harmonious, loving, and open attitude towards other life forms. Animals in the film serve as moral and emotional symbols that reflect the nature of children: the jaguar symbolizes courage, the toucan symbolizes honesty and cheerfulness. In contrast, the capybara symbolizes peace of mind. This study shows that children's films can be a tool for building ecological awareness and social empathy through imagination. The implications of this study underscore the importance of strengthening empathy and environmental balance in children's education through literature and film.

The objective of this study is to investigate several facets of the role that educators play in the verbal abuse and bullying prevention of students at SMP Negeri 1 Tanjung Raja in Tanjung Raja District, Ogan Ilir Regency. The qualitative descriptive method is being used in this study. The researchers conduct observations and interviews as part of their data collection methods. There were seven informants in total for this investigation. This study is being conducted at Tanjung Raja sub-district, Jalan Sultan Mahmud Badaruddin II, in the Ogan Ilir Regency. The study's findings indicate that educators have a positive impact in educating students about bullying and how to protect themselves from various risks. In addition to this, the role of the teacher and the curriculum in the school is crucial in providing a safe and nurturing environment for the development of the students. It is anticipated that the research findings will be able to make a contribution to more effective education reform in SMP Negeri 1 Tanjung Raja and other schools.  

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

This study presents an ethical framework for learning analytics aimed at addressing key challenges related to the collection and use of student data in higher education. Learning analytics, a powerful tool for improving student outcomes and institutional decision-making, has raised ethical concerns regarding data privacy, transparency, fairness, and equity. The proposed framework integrates four core principles: data privacy, informed consent, transparency, and fairness, ensuring that institutions use learning analytics responsibly while safeguarding student rights. A central feature of the framework is its focus on promoting equitable decision-making, minimizing bias, and preventing the reinforcement of existing inequalities in algorithmic and data-driven decisions. The framework also emphasizes the importance of continuous ethical oversight, holding institutions accountable for ethical data use and adapting practices as technology evolves. The study concludes that the framework offers a comprehensive solution to the ethical challenges in learning analytics, providing institutions with a practical guide to embedding ethical principles in data practices. Additionally, the research discusses its potential to foster fairness, equity, and transparency in decision-making processes. Future research is recommended to refine the framework and explore its application across various educational contexts, ensuring responsible and inclusive use of learning analytics.