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The rapid use of smartphones and the internet in the modern era has brought significant impacts, especially in the spread of animal abuse content. In 2021, there were 5,480 animal abuse contents spread around the world, with 1,626 contents coming from Indonesia. The Art Director's role as the supervisor and director of art and visual elements in various creative projects, including public service announcements, is crucial. Art Directors are responsible for ensuring the aesthetic and creative vision of a project is in line with the objectives and target audience. This research aims to explain the importance of concept preparation in the creation of public service advertisements to readers or audiences. The general benefit of this study is to increase public understanding of the role of the Art Director as the conceptualizer behind public service advertisements. This study focuses on a public service announcement titled "Stop Animal Torture as a Means of Entertainment," which aims to remind people about the human rights of animals. Considering that Indonesia is the world's top producer of animal torture content, this ad calls for improving this bad data as it highlights the importance of the issue. With around 1,600 animal abuse content originating from Indonesia out of a total of 5,480 global content, this ad is an effort to reduce the negative impact on living beings and raise awareness of the issue.

Self-confidence is an important element in achieving success and happiness, and has a significant positive relationship with motivation to learn. However, many individuals face challenges in building their self-confidence due to social pressure and negative experiences. In this context, this study explores sources of motivation from a Quranic perspective, specifically through Surah At-Thaha verses 25-28, which describe Prophet Moses' plea to Allah for peace and ease in facing life's challenges. Tafsir Al-Misbah by Quraish Shihab provides the insight that self-confidence should be built on faith in Allah and recognition of one's potential. This study also highlights the importance of social support, appreciation, and a supportive environment in building self-confidence. Using a qualitative approach and literature review, the results show that self-confidence can be strengthened through spiritual connection, internal reinforcement, and recognition of personal weaknesses, which ultimately encourages individuals to express themselves and reach their full potential. This research is expected to inspire readers to apply Quranic values in their daily lives, in order to increase self-confidence and freedom of expression.

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

This research aims to design packaging and promotional media for the Lebaran edition of Tango Walut products with a focus on increasing consumer appeal in the competitive modern market. The design process includes in-depth analysis of products and markets,exploration of creative ideas, development of attractive designs, and implementation of effective marketing strategies. The resulting packaging design displays typical Eid visual elements with bright colors and illustrations that evoke the spirit of celebration. The marketing strategy implemented involves offline marketing and online marketing with social media campaigns, both of which are designed to increase consumer awareness, interest, desires and purchasing actions. The final result of this design is expected to increase the visibility and sales of Tango Walut products during the Eid period, as well as strengthen the brand's position in the FMCG market.

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.

Leadership of Joshua in the Bible, highlighting his role, method, results of discussion, and relevance in modern context. Joshua was chosen by God as the successor of Moses, leading the Israelites towards the land of Canaan with courage, steadfastness, and obedience to God's commands. The research method employed was a qualitative approach using literature review, exploring the leadership values of Joshua in the context of the Bible. Joshua's selection by God, preparation and strategies in conquering the Promised Land, and his obedience to God's commands were discussed. There are leadership principles embodied by Joshua, such as firmness, courage, good character, innovation, steadfastness, and moral principles, which are relevant for modern leaders in creating stable and innovative environments. These principles encourage integrity, courage, and the ability to overcome challenges with appropriate solutions, while maintaining focus on the main goals and building strong relationships with the team.

Analysis carried out after colonialism of what Jesus said about divorce in Matthew 19:1-9 shows how the text can be understood in the context of power and cultural domination. This research conducted qualitative research using a literature study approach. Researchers can use this research method to study, analyze and describe the meaning of Jesus' words regarding divorce through postcolonial analysis of Matthew 19:1–9 in Indonesian Christian understanding. This method considers how colonial beliefs and customs influenced the interpretation and application of Jesus' teachings on divorce. Jesus explained that divorce was not part of God's original plan, but was permitted by Moses because of the hardness of the human heart. Jesus then set a new, stricter standard: divorce was only permitted in cases of adultery.

The existence of God is something that humans have always questioned. Humans rely on reason and knowledge to find out about the existence of God, even though humans have never found certainty about the existence of God. So various human assumptions about God arose, that God never existed, and some people denied and denied the existence of God. Even though humans engage in various studies, it is impossible for humans to fully understand and know the existence of God, because God is transcendent and unlimited, while humans are immanent or limited. Therefore, in writing this article, the author looked at it from a philosophical perspective based on Christian faith to answer several questions: Is it true that God does not exist? Is God just a human imagination? Many people, especially certain groups who do not believe in the existence of God, think that anyone who believes in the existence of God is a fool. This article discusses theological concepts and Christian philosophical thought, using arguments used to prove the inseparable existence of God from the perspective of Christian faith. Philosophy has long been a means of understanding and exploring the existence of God in various Christian traditions. The author presents several philosophical arguments used by Christian theologians to support belief in the existence of God, and also discusses the contribution of thought leadership of Christian philosophers in forming views on the existence of God. From a philosophical perspective, this article offers a deeper understanding of how the Christian faith views the existence of God and how philosophy can play a role in strengthening those beliefs. Finally, this article provides an overview of how to study philosophy to understand and reflect on the existence of God, especially within the framework of Christianity.  

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.

Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing high-complexity environments. DQN simulations were conducted across four scenarios, allowing the agent to operate at speeds ranging from 60 to nearly 100 km/h. The simulations featured a variable number of vehicles/obstacles, ranging from 20 to 80, and each simulation had a duration of 40 seconds within the Highway-Env simulator. Based on the test results, the DQN method exhibited excellent performance, achieving the highest reward value in the first scenario, 35.6117 out of a maximum of 40, and a success rate of 90.075%.

Gender diversity and education level of management level is one from many issues that raised where companies are continually innovating and seeking the best ways to generate higher profit through years. Therefore this research aims to find evidence of whether there is an influence of gender diversity and education level on the increase of company’s profitability, which is studied through descriptive analysis by reviewing the literature from 15 studies obtained from google scholar and emerald insight. The result was found that the gender composition and the level of education do not have a direct impact on a company's profitability but rather depend on how the company can maximize them to get more benefits from the existency of woman on the companyy’s management level job.     Keywords:  Gender Diversity, Education level, and Profitability

Music genre classification is one part of the music recommendation process, which is a challenging job. This research proposes the classification of music genres using Bidirectional Long Short-Term Memory (BiLSTM) and Mel-Frequency Cepstral Coefficients (MFCC) extraction features. This method was tested on the GTZAN and ISMIR2004 datasets, specifically on the IS-MIR2004 dataset, a duration cutting operation was carried out, which was only taken from seconds 31 to 60 so that it had the same duration as GTZAN, namely 30 seconds. Preprocessing operations by removing silent parts and stretching are also performed at the preprocessing stage to obtain normalized input. Based on the test results, the performance of the proposed method is able to produce accuracy on testing data of 93.10% for GTZAN and 93.69% for the ISMIR2004 dataset.