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Permanent tooth loss is a common oral health problem experienced by patients. This condition can be caused by pathological conditions such as tooth decay, leading to tooth extraction. Rehabilitation treatment for partial posterior tooth loss aims to restore masticatory function, maintain occlusal relationships and periodontal health, and improve aesthetics. A 34-year-old man came with a complaint that his lower right posterior tooth had been extracted and wanted a permanent dentures to replace his missing tooth. The intraoral and periapical radiograph examination revealed that 46 was missing, and 47 had enamel caries. The diagnosis for 46 was determined to be partial edentulous. The treatment plan was a PFM fixed-fixed bridge with a sanitary pontic for 46, a rigid connector, and a full crown retainer for 45 and 47. In this case, the choice of PFM bridge is based on several considerations, i.e., high chewing loads, relatively low cost, and high long-term success. The PFM bridge has been the primary choice for posterior tooth rehabilitation, due to its durability and cost-effectiveness.

Inflammation is a biological response that occurs as a defense mechanism of the body against tissue damage, infection, or exposure to harmful stimuli. Excessive or prolonged inflammation can lead to various chronic diseases and negatively affect overall health. Natural compounds derived from medicinal plants have gained attention as alternative anti-inflammatory agents due to their relatively lower side effects compared to synthetic drugs. Jatropha curcas is a plant traditionally used in herbal medicine and is known to contain various bioactive compounds. This study aims to examine the effect of Jatropha curcas leaf extract on inflammatory responses. The research method used was an experimental laboratory approach with extract preparation through maceration techniques. The anti-inflammatory effect was evaluated based on changes in inflammatory indicators observed during the treatment process. The results showed that Jatropha curcas leaf extract demonstrated potential anti-inflammatory activity, which was indicated by a reduction in inflammatory signs. The presence of secondary metabolites such as flavonoids, tannins, and saponins is suspected to contribute to this effect. These findings suggest that Jatropha curcas leaf extract has promising potential as a natural anti-inflammatory agent. Further research is recommended to explore dosage optimization and toxicity levels for safe therapeutic use.  

This study aims to examine the activity of ginger (Zingiber officinale) on metabolism and digestion through a laboratory experimental approach. Metabolism and the digestive system play a crucial role in maintaining physiological balance, and disturbances in these systems can affect nutrient utilization and overall health. Ginger has long been used as a traditional herbal remedy and is known to contain bioactive compounds such as gingerol, shogaol, and zingerone, which are believed to support digestive and metabolic functions. The research was conducted using ginger extract prepared through an extraction process under controlled laboratory conditions. Observations focused on changes in metabolic responses and digestive activity following the administration of ginger extract. The study employed a descriptive experimental design to provide an objective overview of ginger’s biological activity. The results indicated a gradual improvement in digestive activity and metabolic responses after treatment with ginger extract. These changes suggest that ginger extract has the potential to enhance digestive efficiency and support metabolic processes. The findings provide scientific support for the traditional use of ginger as a natural ingredient in maintaining metabolic and digestive health and may serve as a preliminary reference for further research using quantitative and clinical approaches.

The increasing consumption of packaged food products in Indonesia reflects modern lifestyle changes but simultaneously raises public health concerns related to high calorie, sugar, and fat intake. Nutritional information presented on food labels consists of multiple interrelated variables, making it difficult to identify dominant nutritional factors that characterize packaged food products. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of nutritional data and to map the nutritional characteristics of packaged food products in Indonesia. The research employs a quantitative exploratory approach using secondary data obtained from nutrition facts labels of 1,651 packaged food products. Seven nutritional variables were initially analyzed, namely total energy, protein, total fat, total carbohydrates, sugar, sodium, and dietary fiber. Data preprocessing included data cleaning, Z-score standardization, and iterative variable selection based on the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity to ensure sampling adequacy and sufficient correlation among variables. Variables with low sampling adequacy and perfect multicollinearity were eliminated, resulting in five variables retained for the final PCA model. Principal components were extracted using the eigenvalue greater than one criterion and confirmed through a scree plot, followed by Varimax rotation to enhance interpretability. The results indicate the formation of two principal components explaining approximately 69.7% of the total variance. The first component represents energy density and macronutrient richness, while the second component reflects carbohydrate-related characteristics, particularly the contrasting pattern between sugar and dietary fiber. Biplot visualization further illustrates product distribution based on these components. The findings demonstrate that PCA effectively simplifies complex nutritional information and provides a clear nutritional mapping of packaged food products, offering practical insights for consumers, producers, and policymakers in supporting healthier food choices in Indonesia.

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.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

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.

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.  

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.

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.

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 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.

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.