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This research is motivated by the phenomenon of decreasing number of customers at Emdee Easy Beauty Clinic Batam, which indicates the need for an evaluation of marketing strategies. The purpose of this study is to analyze the influence of price, promotion, brand image, and product quality on consumer satisfaction. The research method used is quantitative associative with a sample of 91 respondents taken using simple random sampling technique. Data were collected through questionnaires and analyzed using multiple linear regression. The results showed that simultaneously, price, promotion, brand image, and product quality had a significant effect on consumer satisfaction (F count 14.111 > F table 2.47). Partially, the variables of price, promotion, and brand image have a positive and significant influence, but product quality does not have a significant effect on consumer satisfaction in this study. The implication of these findings is that clinic management needs to prioritize competitive pricing strategies, strengthen promotional activities, and maintain a positive brand image to sustain and increase customer satisfaction.

This study is motivated by the increasing competition in the Muslim fashion industry, which encourages SMEs to develop effective marketing strategies to build brand image. The purpose of this study is to analyze the implementation of the marketing mix (4P) strategy in strengthening the brand image of Bell.Scraft SMEs. This research employs a qualitative approach using a case study design. Data were collected through interviews, observations, and documentation, and then analyzed using the data analysis techniques of Miles, Huberman, and Saldana, which include data condensation, data display, and conclusion drawing. The results indicate that the product aspect is carried out through continuous trend research and market needs analysis, the pricing aspect applies a combination of cost-based and value-based approaches, the distribution aspect utilizes a multichannel strategy through resellers, social media, and marketplaces, while the promotion aspect focuses on trust-based digital strategies through authentic content and influencers. Overall, the implementation of the marketing mix (4P) supports the strengthening of Bell.Scraft’s brand image amid market competition. The implications of this study suggest that the use of integrated and digital-based marketing strategies can enhance the competitiveness of SMEs and build long-term relationships with consumers.

This study aims to analyze the extent of the effect of honey bee pollen supplementation on increasing appetite and body weight in anemic children. The type of research used in this study is an experimental study with a pre- and post-test one-group design. The research sample consisted of 52 anemic fourth- and fifth-grade elementary school students. The average total food intake (Yellow Rice) showed a significant result (P=0.000), increasing from 241.40±94.334 g to 354.94±122.57 g after the final measurement of ad libitum appetite. The average body weight also showed a significant result (P=0.00), with a mean of 23.59±5.29 kg at pre-test and rising to 24.65±5.58 kg at post-test. The Z-Score calculation for weight-for-age based on the WHO NCHS reference showed a non-significant result: P=0.0180 (0.01±0.04) at pre-test and 0.03±0.08 at post-test. Based on the 24-hour recall analysis over two days, a significant result was obtained (P=0.000), with total energy intake of elementary school students increasing from an average of 1,594±420.17 kcal before intervention to 2,070.2±491.77 kcal after two months of intervention. After statistical testing (Paired T-Test), a P-value of 0.00 (<0.05) was obtained. It can be concluded that the administration of honey bee pollen for 8 weeks at a dose of 25 grams per day significantly increased appetite and body weight in anemic elementary school children.

The purpose of this study is to examine how online promotion, product innovation, and brand image affect product competitiveness in Batam City's flower bucket equipment industry (CV Acmeteam). This study employs a quantitative methodology, gathering information from 100 respondents via questionnaires using Simple Random Sampling with Slovin formula from a population of 135 active customers. Data were analyzed through validity tests, reliability tests (Cronbach's Alpha = 0.750), classical assumption tests (normality, multicollinearity, heteroscedasticity), multiple linear regression, partial t-test, simultaneous F-test, and coefficient of determination (R²). Results show that all questionnaire items are valid and reliable. The normality test (Asymp. Sig. = 0.866 > 0.05) indicates normally distributed data, and no multicollinearity (VIF < 10) or heteroscedasticity (Sig. > 0.05) was detected. Regression analysis reveals that online promotion (β = 0.521, Sig. = 0.041), product innovation (β = 0.668, Sig. = 0.033), and brand image (β = 0.458, Sig. = 0.049) each significantly influence product competitiveness. Simultaneously, all three variables are significant (F = 9.876, Sig. = 0.000) and explain 44.9% (R² = 0.449) of product competitiveness variance. Product innovation is the strongest predictor. This study concludes that strong brand image, continuous product innovation, and effective digital marketing are essential pillars for enhancing product competitiveness. Business actors must optimize these three elements to compete effectively in an increasingly open creative industry market.

This study aims to analyze the role of local Micro, Small, and Medium Enterprises (MSMEs) in utilizing lemongrass (Cymbopogon citratus) as the main ingredient for mosquito repellent products and its contribution to improving the economy of the community in Timbang Jaya Village. Lemongrass contains essential oils that function as a natural insect repellent, making it a potential raw material for value-added products such as mosquito repellent sprays, aromatherapy candles, and herbal lotions. This research employed a descriptive qualitative method with data collected through interviews, observations, and literature studies. The findings indicate that local MSMEs play a significant role in the production process, product innovation, and marketing of lemongrass-based products. Besides generating additional income, this development also creates new job opportunities and supports the village’s economic independence. However, several challenges remain, including limited capital, lack of processing technology skills, and competition with chemical-based products. The recommended solutions include capacity building through training and mentoring, better access to funding, and strengthening digital marketing strategies. Therefore, the development of mosquito repellent products from lemongrass by MSMEs not only contributes to the community’s economic growth but also provides an environmentally friendly alternative beneficial for public health.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Conflicts in transmigration areas are generally multidimensional and influenced by social, economic, land, and institutional factors. This study aims to identify the forms and distribution of conflicts in three districts of the transmigration area, namely Momi Waren District, Ransiki District, and Oransbari District, as well as to formulate a smart system-based conflict resolution approach through the use of spatial data, local institutions, and local wisdom-based settlement practices. Based on field mapping, four main categories of conflict were identified: 1) Land conflicts occur throughout the transmigration sites in the form of claims to transmigration land that has not been handed over to transmigrants because the compensation price is below normal. In addition, there is no ATR BPN office in South Manokwari Regency, one of whose functions is community empowerment and conflict resolution. 2) Economic conflicts occur because transmigrants are registered and recorded in the population registry, making it easy for them to access capital. Several economic activities in agriculture and transportation services are dominated by transmigrants, causing economic jealousy. 3) Social conflicts occur when the distribution of social assistance is uneven and the excessive use of illegally sold alcoholic beverages causes social unrest. 4) Institutional conflicts occur when civil servants, police, and military personnel are recruited, and not all indigenous Papuans who are nominated can be accommodated, requiring the involvement of tribal councils to formulate recommendations for recruitment that prioritize indigenous Papuans. The root causes of the conflict were analyzed using a root cause analysis approach that covered unclear land boundaries, unequal economic access, weak coordination between institutions, and low social trust due to differences in interests between groups. This study utilizes best practices from the Tribal Council, the South Manokwari Regency Transmigration and Manpower Office, the Religious Harmony Forum, and the Social Services Office as the basis for developing smart maps for an early warning system for conflicts. The results of the study formulate a Smart Conflict Resolution System framework consisting of three main components: (1) participatory spatial mapping of conflicts and key actors, (2) integration of institutional databases and social-customary mediation channels, and (3) design of smart maps as a mitigation and decision-making tool in transmigration areas. This system is expected to strengthen collaborative governance, prevent conflict escalation, and realize inclusive and sustainable management of transmigration areas

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

News texts are a systematic way of conveying information that contains declarative and imperative sentences used to convey meaning directly and demonstrate the persuasive value of the news. The research goals are to analyze imperative sentences to understand how commands, requests, prohibitions, or invitations are conveyed so that they can influence the actions or responses of readers, as well as to analyze declarative sentences, which play an important role in constructing discourse structure and indicating the writer's position on the topic being discussed. This study was conducted using a qualitative descriptive method. This method was chosen because it can describe what is seen, heard, felt, and asked. The researcher considers qualitative descriptive research to accurately describe a situation without manipulating variables.  This research concludes that imperative sentences elicit responses in the form of agreement and action, or rejection, either directly or indirectly. Meanwhile, declarative sentences serve as a guide or basis for understanding the context the speaker wishes to convey and can also be used to reinforce previously conveyed information to avoid ambiguity and misunderstanding when examining the meaning. This research is expected to be useful in linguistic studies, more specifically in syntactic material that discusses declarative and imperative sentences in news texts and political discourses.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

Citra Jalatarangga is a karawitan composition that is contextually inspired by the duality of freshwater and seawater springs found at Geger Beach. The duality embodied in this idea serves as the fundamental concept and is subsequently implemented in the formulation and structuring of Balinese karawitan performance techniques. This paper discusses the process of creating the work, the formal structure of the composition, and the aesthetic values contained within it. The karawitan composition Citra Jalatarangga employs three theoretical frameworks as analytical tools to describe the duality of the different water sources. Transformation theory is used to determine the form and structure of the composition, creativity theory serves as a reference in the creative process, and postmodern aesthetic theory functions as a guideline for applying aesthetic nuances to the work. The creative method used in the creation of Citra Jalatarangga is the Panca Sthiti Ngawi Sani method, which consists of five stages: the inspiration stage (ngawirasa), the exploration stage (ngawacak), the conception stage (ngarencana), the execution stage (ngawangun), and the production stage (ngebah). This work utilizes several instruments from the Gamelan Semara Dana as its expressive medium and is structured into three sections, each representing the duality of the water sources through different formulations and performance techniques. In addition, instrument arrangement, costume design, and lighting design also function as supporting aspects in the presentation of the work, which was staged at Wantilan Banjar Sawangan, Desa Adat Peminge.

Cocoa is recalcitrant seed which it has the high water content and the deterioration goes quickly. The distribution of cocoa seed need longer time and it will decrease the quality of cocoa seed and cocoa seedling. The research aimed to determine of effectiveness seed soaking with Beauveria bassiana after storage during the distribution of seed. This research was conducted in Indonesian Cofee and Cocoa Research Institute. The research was conduct using randomized complete block design with a factorial arrangement. First  factor was the  storage period  of two days (L2), three days (3 hari), four days (L4), five days (L5), six (L6) and seven days (L7). The second factor was seed soaking with Beauveria bassiana (B1) and seed soaking with fungicide (B0). The result showed that storage periode had significant effect on  plant height and stem diameter. Seed soaking with Beauveria bassiana showed significant effect on plant height. The interaction between storage periode and seed soaking with Beauveria bassiana had no significant effect on the height of seedling, stem diameter and leaf number of seedling.

The advancement of technology has made social media a strategic platform for companies, including PLN Icon Plus, to build corporate image and reputation through public interaction and the promotion of Corporate Social and Environmental Responsibility (TJSL) programs. This study aims to identify and analyze effective social media management strategies and measure their effectiveness in improving the company's image. Employing a qualitative approach, data was collected through in-depth interviews, observation, and content analysis on the PLN Icon Plus platform, conducted from August 19 to December 20, 2025. The findings indicate that the implemented strategies, which include rapid interaction, the use of engaging visuals (especially reels focusing on TJSL), collaboration with national news media, and publication of achievements, are proven effective. Instagram insights analysis showed a significant increase in content views, reach, and interaction from August to November 2025. The study concludes that proper social media management is essential for building a positive image, with implications for the need for continuous content innovation, active audience interaction, and routine evaluation to achieve future business goals.

Stress among early adolescent girls in junior high school is an increasing health concern due to academic demands, social pressure, digital exposure, and menstrual related discomforts. Complementary interventions such as deep-breathing techniques offer a simple method that can be implemented in school settings to rapidly reduce stress. This study aimed to examine the effect of deep-breathing techniques on menstrual related stress among ninth-grade female students at SMPN 2 Rambutan, Banyuasin Regency.  A pre-experimental one-group pretest–posttest design was used. The sample consisted of 60 students selected from the ninth-grade population. Stress levels were measured using the Perceived Stress Scale (PSS-10) focusing on stress during menstruation before and after a single deep-breathing session. Data were analyzed using the Wilcoxon test. The results showed a significant reduction in stress scores after the intervention (p < 0.001). The proportion of students in the high-stress category decreased from 20% to 6.7%, while those in the low-stress category increased from 10% to 30%. The mean stress score decreased from 21.35 ± 4.82 to 15.42 ± 4.11. This study concludes that deep-breathing techniques are effective in reducing menstrual-related stress in female students within a single session and can be recommended as a complementary strategy that is easy to implement in schools for adolescent menstrual stress management.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

Village entrepreneurship is an initiative designed to stimulate regional economic growth by accelerating the development of rural areas, which have long been perceived as slow, less productive, and lagging behind urban centers. The creation of entrepreneurial villages offers a concrete strategy to enhance community productivity because it encourages all components of the village government, residents, and local institutions to participate in structured and collaborative entrepreneurial activities. Such villages are expected to generate new economic opportunities, strengthen local industries, and increase community independence. However, initial observations from this Community Service program show that partner villages still face significant barriers, including limited land area, geographical remoteness, and inadequate human and natural resources. Many peripheral villages have not yet utilized digital technology effectively, making it difficult to expand markets or access broader economic networks. In addition, challenges related to financial access, limited talent development, socio-cultural constraints, and weak market infrastructure continue to hinder economic progress. These conditions demonstrate the urgent need for strategic assistance, capacity-building programs, and stronger support systems to help villages transform into sustainable entrepreneurial ecosystems capable of contributing to long-term regional development.

Pengujian perangkat lunak merupakan tahap penting dalam memastikan kualitas dan keandalan sistem sebelum digunakan secara luas. Salah satu metode yang umum digunakan adalah black box testing, yaitu pengujian yang berfokus pada fungsionalitas perangkat lunak tanpa memperhatikan struktur internal kode. Dalam penelitian ini, metode black box testing diterapkan dengan menggunakan teknik state transition untuk menguji sistem appointment klinik berbasis web “Citra Medica”. Pengujian dilakukan dengan memetakan alur status pengguna melalui diagram transisi, kemudian membuat skenario uji untuk setiap perubahan keadaan. Hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan spesifikasi, dengan tingkat keberhasilan pengujian mencapai 100% pada setiap skenario transisi. Metode ini terbukti efektif untuk mendeteksi kesalahan logika dan memastikan kestabilan sistem selama perpindahan status pengguna.

Penelitian ini mengkaji identitas visual merek Pia Saronde—salah satu produk kuliner khas Gorontalo—dengan menggunakan pendekatan semiotika yang berlandaskan teori Roland Barthes. Sebagai salah satu UMKM yang bergerak di sektor pangan, merek ini sangat bergantung pada peran logo untuk membentuk persepsi konsumen, memperkuat posisi merek, serta membedakannya dari produk lain dalam persaingan pasar. Penelitian ini bertujuan menelaah struktur visual yang membentuk logo, menginterpretasikan makna denotatif dan konotatif yang muncul, serta menilai sejauh mana logo tersebut mampu merepresentasikan nilai-nilai budaya lokal. Penelitian ini menggunakan metode deskriptif kualitatif dengan memadukan pengamatan visual terhadap logo dan kajian pustaka yang relevan. Hasil analisis menunjukkan bahwa kombinasi warna kuning dan coklat pada logo mampu menimbulkan kesan hangat, manis, dan menggugah selera, sedangkan bentuk tipografi yang melengkung menghadirkan citra tradisional, akrab, dan bernuansa rumahan. Selain itu, penggunaan nama “Saronde” menciptakan hubungan simbolis dengan Pulau Saronde yang terkenal, sehingga memperkuat keterikatan merek terhadap identitas daerah. Secara keseluruhan, temuan penelitian ini menunjukkan bahwa logo tersebut berhasil mengomunikasikan keterkaitan budaya dan citra kuliner yang diusung produk. Meskipun demikian, penelitian ini merekomendasikan adanya pengembangan pada beberapa elemen simbolik dan peningkatan adaptasi visual untuk kebutuhan media digital guna memperkuat daya saing merek.