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This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

Acne (Acne vulgaris) is one of the most common skin problems, affecting 80–85% of adolescents aged 15–18 years, with some cases persisting into adulthood. One of the associated bacteria is Staphylococcus epidermidis, a normal skin flora that can become an opportunistic pathogen. Bay leaves (Syzygium polyanthum) are known to contain secondary metabolites such as alkaloids, flavonoids, tannins, saponins, and essential oils with antibacterial activity. This study aimed to formulate ethanol extract of bay leaves into ointments with concentrations of 10%, 20%, and 30%, test their antibacterial activity against Staphylococcus epidermidis using the disc diffusion method, and evaluate their physical quality (organoleptic properties, homogeneity, pH, adhesion, and spreadability). Results showed that the ethanol extract of bay leaves contained alkaloids, flavonoids, saponins, and tannins. The ointments met topical quality standards with pH 5.8–6.9, adhesion time 36–41 seconds, and spreadability 6.9–8.1 cm. Antibacterial activity increased with higher concentrations: 7.8 mm (10%), 12 mm (20%), and 15 mm (30%), although still lower than the positive control (clindamycin, 34 mm). In conclusion, ethanol extract of bay leaves has potential as an active ingredient in topical anti-acne ointments, but further studies with higher concentrations, in vivo testing, and optimized formulations are needed to achieve efficacy comparable to synthetic antibiotics.  

Excessive alcohol consumption, including traditional alcoholic beverages such as Cap Tikus  (alcohol content ±45%) from North Sulawesi, can induce liver damage characterized by steatosis, inflammation, and necrosis. Leilem leaf (Clerodendrum minahassae) extract, rich in phenolic and flavonoid compounds, has potential hepatoprotective effects due to its antioxidant properties. To evaluate the effect of leilem leaf extract on the histopathological features of the liver in Wistar rats (Rattus norvegicus) induced by Cap Tikus alcoholic beverage. This experimental study used a post-test only control group design with 24 male Wistar rats divided into four groups: normal control, negative control (Cap Tikus 2.16 mL/day), treatment I (leilem extract 150 mg/kgBW + Cap Tikus), and treatment II (leilem extract 300 mg/kgBW + Cap Tikus). Treatments were administered orally for 14 days. Liver tissue was processed for histopathological examination using hematoxylin-eosin staining and observed under a light microscope. The negative control group showed significant steatosis and inflammatory cell infiltration. Treatment groups exhibited reduced steatosis and inflammation, along with increased hepatocyte regeneration. The higher dose (300 mg/kgBW) demonstrated greater hepatoprotective effects and more prominent regeneration compared to the lower dose (150 mg/kgBW). No necrosis or fibrosis was observed in any group. Leilem leaf extract exerts a hepatoprotective effect by suppressing inflammatory responses and enhancing hepatocyte regeneration in Wistar rats induced with Cap Tikus. The highest protective efficacy was observed with leilem leaf extract at 300 mg/kgBW.

Inflammation is a physiological response to tissue injury, infection, or harmful stimuli, characterized by redness, swelling, heat, and pain. However, excessive or chronic inflammation may lead to tissue damage and degenerative diseases. Long-term use of non-steroidal anti-inflammatory drugs is associated with gastrointestinal and cardiovascular side effects, highlighting the need for safer natural alternatives. Taro leaves (Colocasia esculenta) are traditionally used to treat swelling and wounds and contain bioactive compounds such as flavonoids, alkaloids, tannins, saponins, and triterpenoids with potential anti-inflammatory effects. This study aimed to evaluate the anti-inflammatory activity of ethanol extract of taro leaves and determine the most effective dose. An experimental study was conducted using male white mice (Mus musculus) divided into five groups: negative control (Na-CMC), positive control (sodium diclofenac), and three treatment groups receiving extract doses of 25, 50, and 75 mg/kg BW. Inflammation was induced by 1% carrageenan injection. The extract significantly reduced inflammation, with the highest inhibition (84.13%) observed at 75 mg/kg BW, comparable to diclofenac.  

Atherosclerosis, as the leading cause of cardiovascular disease, can be triggered by a high-fat diet that causes hyperlipidemia. Fern leaves (Diplazium esculentum) contain various bioactive compounds (alkaloids, phenols, flavonoids, terpenoids) with antioxidant and anti-inflammatory potential, which are suspected to inhibit the formation of atherosclerotic lesions. This study aims to determine the effect of administering fern leaf extract on the histopathological features of the aorta in Wistar rats induced by a high-fat diet. This laboratory experimental study used 25 male Wistar rats divided into five groups: (A) normal control, (B) negative control (high-fat diet), (C) positive control (high-fat diet + simvastatin), (D) treatment I (high-fat diet + fern leaf extract 600 mg/kgBW), and (E) treatment II (high-fat diet + fern leaf extract 1200 mg/kgBW). The treatment was administered for 21 days. On the 22nd day, the aorta was taken and processed into histopathological preparations using Hematoxylin-Eosin (H&E) staining. The number of foam cells was counted at 400x magnification. Data were analyzed using the One-Way ANOVA test followed by the Tukey test. There were differences in the number of foam cells between groups. The post-hoc test showed a significant difference between group B (high-fat diet) and all other groups (p<0.001). There was no significant difference between group E (extract 1200 mg/kgBW) and group C (simvastatin) (p=0.783), nor between group D (extract 600 mg/kgBW) and group C (p=0.075). Fern leaf extract (Diplazium esculentum) is able to reduce the number of foam cells in the aorta of Wistar rats induced by a high-fat diet.

A high-fat diet can cause hyperlipidemia, which is one of the main risk factors for atherosclerosis. Papaya flowers (Carica papaya L.) contain antioxidants that have the potential to inhibit the development of atherosclerotic lesions. This study aims to determine the effect of papaya flower extract on the histopathological features of the aorta of Wistar rats induced by a high-fat diet. The study design is a posttest only-trial group design. The study sample consisted of 25 male Wistar rats divided into 5 groups with treatment for 21 days. Group A received no treatment. Group B was given high-fat diet consisting of 3 mL of pork fat and 2 g of duck egg yolk. Group C was given high-fat diet and simvastatin 0.18 mg/200 gBW. Groups D and E were given high-fat diet and papaya flower extract 125 mg/kgBW and 250 mg/kgBW, respectively. The parameter used is the histopathological description of the aorta in the form of foam cell formation. The results showed that there were few foam cell in group A. Group B had many foam cells in the tunica intima and media of aorta. Group C, D and E showed a reduction in the number of foam cells. The administration of papaya flower extract (Carica papaya L.) was effective in inhibiting foam cell formation in the aorta of Wistar rats induced by a high-fat diet, with a better effect seen at a dose of 250 mg/kgBW, which was almost comparable to simvastatin.

This study aims to analyze how the integration of traditional knowledge and digital knowledge shapes organizational strategies in Micro, Small, and Medium Enterprises (MSMEs) in responding to an increasingly competitive business environment. A quantitative approach was employed by applying variance-based Structural Equation Modeling (SEM) using SmartPLS 3 software to 100 MSME respondents. The research model was developed by incorporating three main latent constructs, namely digital knowledge, traditional knowledge, and organizational strategy, which were measured using several indicators relevant to the MSME context. The analysis results indicate that all research instruments meet good reliability and validity criteria, with outer loading values ranging from 0.719 to 0.902, Average Variance Extracted (AVE) values above 0.64, and composite reliability exceeding 0.87. Structural model testing reveals that digital knowledge has a strong and significant effect on organizational strategy (β = 0.799), while traditional knowledge shows a very small effect (β = 0.024). The research model explains 66.4% of the variance in organizational strategy. These findings confirm that digital transformation is the primary factor in shaping competitive strategies among MSMEs, while traditional knowledge continues to serve as a foundation for local context and cultural values. Therefore, the implementation of a hybrid management approach is considered effective in assisting MSMEs in formulating more adaptive, innovative, and sustainable strategies.

Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.

Steganography is a method to hide confidential messages in digital media so that they are not detected by unauthorized parties. Unlike cryptography which protects the content of messages through encryption, steganography hides the message itself. One popular technique is the Least Significant Bit (LSB), which replaces the least important bit on the pixel with a secret message bit. However, conventional LSB methods such as 1-bit or 3-bit have limitations due to the compromise between insertion capacity and visual quality of the media. This study proposes an LSB-based video steganography method with an adaptive multi-bit embedding approach. This technique determines the number and position of bits that are dynamically inserted based on the local brightness and texture levels of each video frame, with Laplacian operators used to analyze both high and low textured areas. The process includes frame and audio extraction, frame-by-frame embedding, inserted video reconstruction, and decoding using video cover references. The evaluation was carried out quantitatively using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, as well as qualitatively through visual comparison. The results showed that the adaptive multi-bit method was able to maintain visual quality with a PSNR of 45.23 dB and SSIM of 0.9424, and increased the insertion capacity by up to 2–3 times compared to the 1-bit adaptive method. Thus, this approach effectively balances imperceptibility and insertion capacity on dynamic video steganography systems.  

The extract of Carica papaya L. leaves contains phytochemical compounds such as flavonoids, alkaloids, phenolics, and saponins that play a role in biological activities, including antioxidants, anti-inflammatory, analgesic, and modulation of the nervous system. This study aims to evaluate the effects of fresh papaya leaf extract on the central nervous system (CNS) and autonomic nervous system based on varying extract concentrations. The study used test animals with treatment groups consisting of negative control, positive control, and extracts at concentrations of 4%, 8%, and 16%. Parameters observed included parasympathomimetic (PSM), parasympatholytic (PSL), sympathomimetic (SM), sympatholytic (SL), analeptic effects (ANA), CNS stimulation (SSSP), CNS depression (DSSP), and muscle relaxation (RO). The results showed that the 16% concentration produced the highest PSM activity (46.82%) and dominant CNS stimulation (38.59%), while DSSP and RO decreased at higher concentrations. This phenomenon indicates that increasing the extract concentration does not always enhance CNS depression, but rather leads to stimulation. These findings support the potential of papaya leaf extract as a neuroprotective and multifunctional agent. Further research is needed to confirm its molecular mechanisms and clinical effectiveness as a phytotherapy candidate.

Extrinsic skin aging or photoaging occurs due to repeated and prolonged exposure to ultraviolet (UVA) and ultraviolet B (UVB). Photoaging prevention can be done, one of which is the use of sunscreen. This literature review aims to review the role of sunscreen in photoaging prevention using a variety of existing literature. Sunscreen plays an important role in preventing skin damage caused by sunlight, especially in preventing and repairing skin damage due to sun exposure (photoaging). The addition of antioxidants such as vitamin C, vitamin E, vitamin A, melatonin, polyphenols, Polypodium leucotomos (PLE) extract as well as enzymes such as photoliase and T4N5 has been shown to increase protection against UV, visible light, and infrared-A rays and help repair skin damage. The use of colored sunscreen is recommended for protection from visible light. In addition, education on how to use sunscreen properly, such as reapplying every two to three hours and selecting the appropriate SPF and PA, also plays a big role in maximizing the effectiveness of skin protection from photoaging.

The production of breast milk is essential for promoting optimal growth and developmental outcomes in infants throughout the first six months of life. Nevertheless, several studies have indicated that some breastfeeding mothers face challenges in producing adequate milk, often influenced by nutritional status, psychological conditions, and environmental support. The katuk plant (Sauropus androgynus), a well-known local herb, has long been recognized for its potential as a natural galactagogue that can stimulate breast milk production. The innovation of processing katuk leaf extract into biscuits offers a more practical, convenient, and potentially acceptable alternative for daily consumption among the community. The present study aimed to examine the impact of katuk leaf biscuits on breast milk production among breastfeeding mothers within the jurisdiction of Kuta Baro Health Center, Aceh Besar District. A pre-experimental design utilizing a one-group pretest–posttest method was implemented, involving 20 breastfeeding mothers who fulfilled the established inclusion criteria. Data collection was carried out through the administration of questionnaires and by measuring breast milk volume using a calibrated measuring cup.The study findings indicated that before the intervention, most participants had relatively low breast milk production, averaging 69.1 ml. After a seven-day intervention, the mean breast milk volume showed a significant increase to 246.5 ml, with a p-value of 0.000 (p < 0.05), the findings indicate that katuk leaf biscuits exert a significant influence on increasing breast milk production. Overall, this study underscores the potential of locally sourced, herbal-based food innovations as an effective and sustainable approach to enhancing breast milk production and supporting the success of exclusive breastfeeding programs.

Pakpak Bharat Gambir Tea MSMEs are products extracted from the young leaves of the Gambir plant. Not many people know about Gambir tea and its benefits or functions. Gambir tea products can only be found in online marketplaces, such as Tokopedia, Shopee, Lazada, Mbizmarket.co.id, and Indotrading, where Gambir tea producers are registered with the Pakpak Bharat Regency. One local exporter, Agro Lestari, sells "Gambier Leaf Tea" packaged in boxes with aluminum foil with 25 sachets. Offline sales in Pakpak Bharat Regency are still lacking, both in traditional markets, modern markets such as Indomaret, shops or stalls. Pakpak Bharat Gambir Tea, especially Gambir Sondel leaf tea, has been marketed to several regions in Indonesia, through wholesalers, trading partners and online resellers and social media such as TikTok, Facebook and Instagram, but the content does not provide an explanation of what Gambir tea is, its benefits or functions. The problem is:(1)What types of marketing strategies are effective? (2)How to design online and offline marketing? Objectives:(1)Help the process of understanding effective marketing strategies. (2)Help how to design online and offline marketing. The implementation of the lecture method is accompanied by questions and answers, demonstrations and practice. Gambir Pakpak Bharat tea UMKM carries out effective marketing strategies through market segmentation, targeted markets and fulfilled according to needs. The distribution system is through direct sales at outlets, delivery applications integrated with third-party applications such as GoFood or GrabFood, and online marketplaces as well as marketing communications using social media. and collaborating with local influencers

PT. Hamparan Mulya operates in the coal mining industry and applies an open-pit extraction system. In this mining method, managing surface water particularly rainwater is essential to ensure smooth operations. An effective drainage system is required to prevent runoff water from contaminating nearby rivers, lakes, and surrounding ecosystems. One practical approach used in mining operations is the construction of settling ponds, which function as treatment units for water collected in the sump before it is released into natural waterways. This study aims to identify the appropriate storage capacity for the settling pond and sump by analyzing rainfall data using the Log Pearson Type III method. The analysis produced a design rainfall value of 507.16 mm/day and a rainfall intensity of 56.94 mm/hour. With a catchment area of 14 km², the resulting runoff discharge reaches 30,782.16 m³/hour. Based on these parameters, the settling pond must be engineered to accommodate a total discharge of 30,782.16 m³/hour.

Indonesia is the world’s second largest contributor of plastic waste entering the oceans, where it degrades into microplastics smaller than 1 micron. The use of bioplastics is therefore essential to mitigate environmental pollution. One promising alternative is starch–cellulose blend bioplastic derived from empty fruit bunches (EFB) of oil palm. Indonesia produces about 56.35 million tons of EFB annually, containing approximately 40% cellulose, which makes it a potential raw material for bioplastic production, although commercialization has not yet been realized. Environmentally, converting EFB into bioplastics within the palm oil value chain represents a sustainable waste recycling strategy that transforms solid residues into higher-value products. In this design, soda cooking technology is employed to extract cellulose from EFB, utilizing about 16% of waste from a crude palm oil (CPO) mill with a capacity of 30 tons of fresh fruit bunches (FFB) per hour. The starch–cellulose blend bioplastic pellets are formulated using gelatinized cassava starch as the polymer matrix, glycerol as a plasticizer, polyvinyl alcohol (PVA) as a stabilizer, and cellulose as a filler. The designed production capacity of the EFB-based starch–cellulose bioplastic plant is 16,500 tons per year. This bioplastic industry is expected to contribute positively to achieving eight Sustainable Development Goals (SDGs), including Goals 1, 8, 9, 12, 13, 14, 15, and 17, thereby supporting Indonesia’s commitment to sustainable industrial development and a cleaner environment.

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.

Early mobilization is an important component of postpartum midwifery care to prevent complications, accelerate physical recovery, and improve maternal comfort after delivery. However, not all mothers are able to perform early mobilization optimally due to fatigue, pain, and decreased energy after delivery. Sukari date palm juice (Extractum Phoenix dactylifera) is known to contain simple carbohydrates, minerals, and bioactive compounds that have the potential to increase energy and accelerate maternal recovery. This study aims to analyze the effect of consuming Sukari date palm juice on accelerating early mobilization of postpartum mothers from day 1 to day 3 in the working area of ​​the Sikui Community Health Center (UPT). The study used a quasi-experimental design with a pretest–posttest control group approach. The study sample consisted of 30 postpartum mothers divided into an intervention group (n=15) and a control group (n=15). Early mobilization ability was assessed based on the time the mother was able to sit, stand, and walk. The results showed that postpartum mothers in the intervention group experienced a significant acceleration in early mobilization compared to the control group, particularly in walking ability, with a time difference of up to 8–12 hours faster. The results of the Mann–Whitney statistical test showed a significant effect of date palm juice consumption on the acceleration of early mobilization (p < 0.001). Thus, Sukari date palm juice has been proven to be effective in accelerating early mobilization and can be recommended as a supporting nutritional intervention in postpartum midwifery care.

Lemongrass (Cymbopogon citratus) is a plant known to contain various bioactive compounds with potential antioxidant properties as well as xanthine oxidase (XO) inhibitory activity. This study aimed to evaluate the phytochemical content, antioxidant activity, and XO inhibitory potential of ethanol extracts from the leaves, stems, and their combination. Phytochemical screening revealed that all extracts contained alkaloids, flavonoids, and terpenoids, while phenolic compounds were detected only in the leaf extract and the combined leaf–stem extract. Antioxidant activity assays demonstrated that all extracts exhibited very strong antioxidant activity, with IC₅₀ values below 50 ppm, indicating a significant potential to scavenge free radicals. In the XO inhibition assay at a concentration of 200 ppm, the ethanol extract of lemongrass stems showed the highest inhibitory activity at 81.37%, followed by the leaf extract at 48.08% and the combined leaf–stem extract at 33.65%. Overall, these findings suggest that the ethanol extract of lemongrass stems is the most promising natural source of antioxidants and has the greatest ability to inhibit xanthine oxidase activity, indicating its potential development as a functional ingredient for health applications.

This study aims to analyze and synthesize research findings related to the integration of Buddhist values in the development of interactive learning media. The research employed a qualitative approach using a systematic literature review method on fifty selected articles published between 2018 and 2025. Data were collected through a structured process of identification, screening, and extraction from primary sources consisting of accredited national journals and reputable international journals. The data were analyzed using thematic and comparative synthesis techniques to identify patterns, effectiveness, and research gaps. The results show that interactive learning media based on Buddhist values can enhance students’ motivation, moral understanding, and engagement through the use of technologies such as gamification, educational animation, augmented reality, and mobile applications. The effectiveness of these media is strongly influenced by the alignment between Buddhist ethical principles, instructional design, and the cultural context of learning. Furthermore, the study reveals that the successful integration of spiritual values into digital media depends on educators’ readiness, digital literacy, and technological infrastructure support.

The rapid development of digital technology has increased the intensity of social media use among adolescents, particularly Instagram, leading to a decline in social interaction, low emotional regulation, and an increased tendency toward addictive behavior. Within the context of guidance and counseling, art possesses therapeutic potential, enabling students to express themselves and manage digital behaviors more adaptively. This study is a literature review analyzing seven selected scientific articles regarding the utilization of art in group counseling services. The analysis was conducted through data extraction of article characteristics, including research design, instruments, findings, and implications. The results indicate that art plays a significant role in supporting group dynamics, enhancing emotional regulation, and providing alternative activities capable of diverting students' attention from excessive Instagram use. Structured art activities also facilitate the development of self-awareness and self-control, thereby reducing the frequency of Instagram usage. These findings suggest that art-assisted group counseling is an effective and relevant strategy for guidance and counseling teachers to implement in addressing digital challenges within the school environment.