Fruit flies remain a persistent threat to horticultural production, especially in tropical regions where seasonal shifts can significantly affect their population dynamics. This study investigated the species composition, diversity, and environmental drivers of fruit fly populations infesting Capsicum annuum and Solanum lycopersicum over a one-year period in Batu City, Indonesia. Using weekly sampling across four seasonal phases—dry-to-rainy transition, rainy season, rainy-to-dry transition, and dry season—fruit fly specimens were collected, reared, and identified. Environmental variables, including temperature, humidity, rainfall, light intensity, and pesticide application frequency, were monitored and analyzed in relation to fruit fly abundance using Principal Component Analysis (PCA) and multiple linear regression. A total of four species were recorded: Bactrocera carambolae, Bactrocera dorsalis, Atherigona sp, and Silba sp, with the latter representing a new regional record for East Java. Population peaks occurred during the rainy season and its transitional periods, while significant declines were observed during the dry months. Regression models revealed that temperature and humidity supported population growth, whereas pesticide use had a consistently suppressive effect—particularly for B. dorsalis and Atherigona sp. However, the models explained only 20–50% of the variation, suggesting that additional ecological factors may be at play. These findings deepen our understanding of fruit fly ecology in tropical systems and highlight the importance of integrating climatic and agronomic data in pest management strategies. The discovery of Silba sp. further enriches current biogeographical knowledge and signals the need for adaptive, interdisciplinary approaches to sustainable pest control.

Indonesia’s coastline, particularly Kendal Regency in Central Java, faces critical mangrove degradation due to land-use changes and rising sea levels. This study aims to asses the condition and erosion impacting Kendal's mangrove ecosystems to inform future conservation strategies. Using remote sensing technology, multi-spatial and multi-temporal imagery from 2005–2023 was analyzed and validated with field observations to evaluate mangrove species composition, structure, and land changes. The mangrove area increased by 52% in three locations, with the Bodri River delta (Pidodo Kulon and Pidodo Wetan villages) showing the most significant growth at 76.69 hectares. Ngebum Beach (Mororejo) had the largest proportional increase, reaching 185%. The ecosystems are moderately diverse, containing at least 11 mangrove species, though Rhizophora dominates due to extensive planting efforts. Despite stable conditions, these ecosystems face threats from deforestation for aquaculture, the primary driver of land erosion, compounded by rising sea levels. Although mangrove areas have expanded significantly, ongoing challenges include erosion, anthropogenic pressures, and limited species diversity. Future strategies must involve multi-stakeholder collaboration to implement sustainable practices, promote species diversity, develop erosion mitigation models, and conduct awareness campaigns to ensure the resilience of Kendal’s mangrove ecosystems.

The increasing use of plastic materials in daily life has led to a rise in plastic waste, contributing significantly to environmental pollution. This study aims to analyze the characteristics of insects that contribute to the eco-friendly degradation of plastic waste and to evaluate the rate of plastic waste degradation and the waste reduction index (WRI) achieved through insect larvae. The research uses a Randomized Factorial Design, investigating plastics such as styrofoam blocks, low-density polyethylene, polyurethane, polystyrene, and a control. The parameters examined include insect characteristics, larval length and weight, degradation rate, and WRI. Both Zophobas atratus and Tenebrio molitor exhibit the potential for the bioconversion of plastic waste. The weight of T. molitor larvae ranges from 0.07-0.09g, while Z. atratus larvae weigh between 0.51 and 0.60g. The WRI is directly proportional to the reduction rate, with higher reduction rates corresponding to higher WRI values. The results showed that the control using Z. atratus and T. molitor larvae achieved the highest WRI values among all treatments. Among the plastic types studied, styrofoam blocks showed the highest WRI value, indicating that a feeding rate of 1g of plastic per larva per day is optimal for efficiently reducing plastic waste. This supports circular economy initiatives for companies developing insect-based proteins for food, feed, and natural products while advancing sustainable development goals.

Emotion recognition in Virtual Reality (VR) has become increasingly relevant for enhancing immersive user experiences and enabling emotionally responsive interactions. Traditional approaches that rely on facial expressions or vocal cues often face limitations in VR environments due to occlusion by head-mounted displays and restricted audio inputs. This study aims to develop an emotion recognition model based on body gestures using a hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The CNN component extracts spatial features from skeletal data, while the LSTM processes the temporal dynamics of the gestures. The proposed model was trained and evaluated using a benchmark VR gesture-emotion dataset annotated with five distinct emotional states: happy, sad, angry, neutral, and surprised. Experimental results show that the CNN-LSTM model achieved an overall accuracy of 89.4%, with precision and recall scores of 88.7% and 87.9%, respectively. These findings demonstrate the model’s ability to generalize across various gesture patterns with high reliability. The integration of spatial and temporal features proves effective in capturing subtle emotional expressions conveyed through movement. The contribution of this research lies in offering a robust and non-intrusive method for emotion detection tailored to immersive VR settings. The model opens potential applications in virtual therapy, training simulations, and affective gaming, where real-time emotional feedback can significantly enhance system adaptiveness and user engagement. Future work will explore real-time implementation, multimodal sensor fusion, and advanced architectures, such as attention mechanisms for further performance improvements

Low-light image enhancement plays a crucial role in fields such as surveillance, photography, and medical imaging, where inadequate lighting significantly reduces image quality, leading to loss of detail and increased noise. Traditional enhancement methods, such as histogram equalization and Retinex, struggle to preserve fine details and often amplify noise, limiting their effectiveness in real-world applications. To address these issues, this study proposes a Generative Adversarial Networks (GANs)-based model to enhance low-light images in real-time while maintaining high visual fidelity. The model aims to improve contrast, reduce noise, and retain image structure more effectively than conventional methods. The proposed GAN model is trained using the LOL and SID datasets and evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Experimental results show that the method achieves a PSNR of 28.4 dB and SSIM of 0.91, outperforming histogram equalization (PSNR: 18.5 dB, SSIM: 0.65) and Retinex (PSNR: 20.3 dB, SSIM: 0.72). Although the model operates in real-time, its inference time of 35.6 ms per image suggests further optimization to support edge computing applications. This study demonstrates that GAN-based enhancement significantly improves low-light images by preserving structural integrity while reducing noise. Future research should focus on optimizing the model for faster processing, experimenting with larger and more diverse datasets, and integrating the system into real-world applications such as automated surveillance and smart camera technologies.

The use of digital media as a medium to communicate social messages is increasingly widespread, including in environmental conservation campaigns. One of the platforms used is YouTube, which allows messages to be delivered through a visual and narrative approach. However, not all content produced is able to present emotional impacts and environmental awareness effectively. The strategy for delivering messages is an important factor, especially through literary and visual expressions that can build emotional closeness with the audience. The Rekam Nusantara YouTube channel is an interesting example to study because it combines literary and visual elements in conveying the message of an environmental cleanliness campaign. This study aims to analyze the forms of literary and visual expressions used in the channel and how effective they are in building audience awareness. This study uses a qualitative approach with a descriptive method, which systematically describes the characteristics of the content based on the data analyzed. Data were obtained through literature studies and analysis of video content published by the Rekam Nusantara channel. The results of the study show that this channel utilizes literary elements in the form of poetic narratives and a cinematic visual approach to move the emotions of the audience. However, the effectiveness of the campaign can still be improved by adjusting the theme to the local context and a more specific audience profile. These findings underscore the importance of a relevant and touching narrative-based content strategy to optimize the role of digital media in social and environmental campai.

This study investigates the role of motivation and self-efficacy in English language learning among fourth-semester students majoring in English at IAIN Takengon. Employing a quantitative method, data were collected from a limited population of five students using a validated Likert-scale questionnaire designed to assess levels of motivation and self-efficacy across key language skills. The results revealed that while students demonstrated high levels of motivation—driven by academic and career goals, as well as personal inspirations—their self-efficacy levels varied, with some showing a lack of confidence in their language abilities. This finding suggests a discrepancy between students’ motivational drive and their self-perceived competence. The study recommends the integration of mentoring programs, engaging classroom strategies, and exposure to authentic language environments to enhance learners’ self-efficacy. These findings contribute to a deeper understanding of affective factors in second language acquisition and provide practical recommendations for language instructors and institutions.

Jesus Christ as a role model for PAK teachers so that the role and competence of Christian Religious Education (PAK) teachers. Jesus, as the Great Teacher, provides a learning model centered on love, patience, wisdom, and real-life examples. By understanding and applying the principles of Jesus' leadership and teaching, PAK teachers can be more effective in guiding students, both in spiritual and moral aspects. This research uses a qualitative method with a descriptive-analytical approach. Data were collected through literature study of theological literature, Christian education, and analysis of Jesus' teachings and actions in the Bible. The results showed that the example of Jesus has a significant impact in shaping the character of PAK teachers, especially in the aspects of integrity, servant leadership, and the ability to educate with love and wisdom. The conclusion of this study confirms that PAK teachers who imitate Jesus can be agents of transformation in the world of education, forming a generation that is not only intellectually intelligent, but also has a strong Christian character.

This study aims to determine the role of pastoral care in motivating the congregation to attend Sunday services at GKPI Simangaronsang Resort Doloksanggul. Data were collected through semi-structured interviews and analyzed descriptively qualitatively. The lack of congregation motivation is caused by faith that has not grown, personal problems, economic problems, and conflicts between congregations and with church leaders. The results of the study indicate that the role of pastors and elders in motivating the congregation has not been maximized. Pastoral care such as visits and coaching of the congregation is very important to build motivation and strengthen faith and the relationship between the congregation and the church. Therefore, pastoral care is a vital aspect in church life and the motivation of congregational worship.

Basketball is a dynamic team sport that emphasizes hand coordination, requiring players to score by shooting the ball into the opponent’s hoop. The game involves five players per team and demands mastery of several fundamental technical skills, including throwing and catching, dribbling, shooting, pivoting, and lay-up techniques. One of the highest individual achievements in basketball is being named the Most Valuable Player (MVP), a title awarded to athletes who demonstrate exceptional performance during a game or tournament. This study aims to analyze the determining factors behind an athlete achieving MVP status, specifically focusing on LeBron James from the United States basketball team during the 2024 Paris Olympics final match against France. The research employs a descriptive method with data collection through document analysis, particularly using official match statistics. The assessment includes both physical performance metrics—such as points, rebounds, assists, blocks, steals, turnovers, and fouls—and psychological aspects, such as mental toughness during competition. Statistical data from the match showed that LeBron James recorded 14 points, 6 rebounds, 1 block, 10 assists, 2 steals, 5 turnovers, and 3 fouls. His shooting efficiency was also high, with a 60% field goal percentage (6/10), 62.5% on two-point shots (5/8), 50% on three-point shots (1/2), and 100% on free throws (1/1). The findings indicate that high contributions in both offensive and defensive aspects significantly influence the MVP designation. This study concludes that MVP recognition is strongly supported by measurable statistical performance and recommends that coaches integrate performance analysis technology to maximize athlete potential based on individual needs.