Christian Religious Education management has a very important impact in fostering children's character in the family. It not only focuses on the formation of a child's religious beliefs, but also plays an important role in the formation of moral values and character. In the family environment, the management of Christian religious education implies several important components: teaching, training, modeling, monitoring the child's personal development, and creating a solid foundation for the formation of the child's character. This can be done through various methods, such as teaching the Bible, praying together, telling inspiring stories based on the truth of God's Word, participating in family and church worship activities. It also helps to encourage children to face various challenges and difficult situations experienced in daily life with a strong faith. Children can also make the right decisions when facing pressure, temptation, and even difficult circumstances.
The overall role of Christian religious education management in the family has a huge impact on helping to develop children's character. This not only affects the child's behavior in the family environment, but also has an impact on social relationships and interactions with the wider community. Therefore, it is imperative for parents to be more diligent in implementing Christian education management to help their children grow into individuals who have the morality and responsibility of the Christian faith.

The Omah Sawah community focuses on environment-based education for children aged 7-13 years. One of the activities is a weekly school held at the Omah Sawah Pavilion every weekend. Activities include environmental observation, flora-fauna identification, yoga, natural batik dyeing introduction, and writing. The limited skills of facilitators, consisting of teenage alumni of Omah Sawah, in innovating learning, and the lack of supporting facilities at Omah Sawah pose challenges. One alternative to enhance learning innovation and instructor skills is by introducing and studying Photovoice. This community service began with preparation, including permission requests and field surveys, followed by curriculum development training using the Photovoice method through lectures and practice. Evaluation was carried out through pre-test and post-test methods. The results showed an increase in participants' knowledge of the Photovoice method and its use in environmental education.

Farmer awareness in Cottage Sunan Gunung Jati Islamic Boarding School Ba'alawi Gunungpati Semarang city for improve quality and income in the group breeder . There are breeders in that environment just make feed cattle in a way traditional without use machine , then create a livestock group to be able to process it quality feed . The technological transformation developed is improve feed quality cattle using manufacturing technology machine enumerator grass . Machine manufacturing technology enumerator grass feed cattle For increase income, How breeder get tool cutter grass in accordance with need feed and energy efficiency . Machine manufacturing enumerator grass is a new innovative technology for downstreaming products resulting from the UNNES team's research. Service Team This much needed to support energy efficiency and upgrades coffee quality . Machine manufacturing technology enumerator grass , able to increase income breeders , improve feed quality and energy efficiency . Machine manufacturing enumerator grass is a new innovative technology for downstreaming products resulting from research by the Community Service team UNNES. During the activity, mentoring and monitoring and evaluation were carried out to measure target achievement.

Scarcity fertilizer subsidies and high costs non- subsidized fertilizer cause The Gunung Makmur Farmers Group is taking different alternatives, namely? make fertilizer liquid organic in a way  collective with utilise materials that are around? they. See potential dirt livestock that has not been maximized and only used part small. Activities training making fertilizer has facilitated by Lazismu Kendal, the group farmer follow training making fertilizer liquid organic with facilitator practitioner agriculture Healthy from PT. Independent Earth Workshop. Aspect which become problem is related making fertilizer that is still use manual way, not yet use machine so that the production process is long and requires lots of energy, and conditions facility supporters who have not adequate. Activities devotion community that is done started from stage First that is preparation, doing licensing and survey field. Stage second that is activity devotion public through socialization about agriculture regenerative with method lectures, training making fertilizer organic through method practice directly. Stage third that is stage evaluation activity through method pre-test post-test. Evaluation results show that existence improvement knowledge farmer to agriculture regenerative and manufacturing fertilizer liquid. Farmers also participate participate in a way active in the process of activity discussion.

Segmentation of pneumonia areas on chest X-rays is essential to improve the accuracy of recognition tasks and subsequent diagnosis. The capabilities of deep learning techniques, U-Net, SegNet, and DeepLabV3, are assessed to achieve these purposes. Using transfer learning, these models were adapted to pneumonia-specific datasets. The evaluation focuses on Intersection over Union (IoU) and accuracy metrics. Results show that DeepLabV3 outperforms U-Net and SegNet, achieving 84.4% accuracy and 81% IoU. U-Net achieves 80.3% accuracy and 68% IoU, while SegNet achieves 81.0% accuracy and 70% IoU. These findings highlight the potential of transfer learning models to automate the segmentation of pneumonia-affected regions, thereby facilitating timely and accurate medical intervention.

Digital Pathology Image Analysis (DPIA) is one of the areas where deep learning (DL) techniques offer modern, cutting-edge functionality. Convolutional Neural Network (CNN) technology outperforms the competition in classification, segmentation, and detection tasks while being just one of numerous DL techniques. Classification, segmentation, and detection methods can often be used to address DPIA concerns. Some difficulties can also be resolved using pre- and post-processing techniques. However, other CNN models have been investigated for use in addressing DPIA-related issues. Furthermore, the research seeks to explore how susceptible the model is to adversarial attacks and suggest strategies to counteract them. To predict ischemic strokes caused by blood clots, the authors of this study developed CNN with a pixel brightness transformation (PBT) technique for image enhancement and developed several approaches of image augmentation techniques to increase and provide the learning model with more diverse features. Also, adversarial training was integrated into CNN models to train the model with perturbed data in order to assess the impact of adversarial noise at different stages of training. Several metrics, including precision, F1-score, accuracy, and recall, are utilized to assess the experiments' effectiveness. The research findings indicate that employing transfer learning with a deep learning model achieved an accuracy of up to 97% using the ReLU activation function. Also, data augmentation helps improve the accuracy of the model.

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.

In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer effective and minimally invasive alternatives. However, the complexity of patient response demands more sophisticated analytical strategies for accurate outcome prediction. This research focuses on analyzing the effect of preprocessing in various machine learning models on the prediction performance of cryotherapy and immunotherapy. The preprocessing techniques analyzed are advanced feature engineering and Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links as resampling techniques and their combination. Various classifiers, including support vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), XGBoost, and Bidirectional Gated Recurrent Unit (BiGRU), were tested. The findings of this study show that preprocessing methods can significantly improve model performance, especially in the XGBoost model. Random Forest also gets the same results as XGBoost, but it can also work better without significant preprocessing. The best results were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790, respectively, for accuracy, recall, specificity, precision, and f1 on the Immunotherapy dataset, while on the Cryotherapy dataset, respectively, they were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790. This study confirms the potential of customized preprocessing and machine learning models to provide deep insights into treatment dynamics, ultimately improving the quality of diagnosis.

This research was conducted to improve learning outcomes on number sorting material using numerical jersey media in grade II SDN 02 Rowoyoso. This study uses a class action research method that uses II cycles in which each cycle consists of planning, implementing, observing, and reflecting activities. The data collection techniques used are observation, tests, and documentation. The results of classroom action research found that numerical jersey media can improve student learning outcomes. In the first cycle student learning outcomes were 55.55% (10 students who completed) and 44.44% (8 students who were not completed). While in cycle II student learning outcomes increased to 86.66% (13 students who completed) and 27.77% (5 students who have not completed). So it can be concluded that students experience improved learning outcomes and achieve the minimum completeness criteria determined.

Education is an activity that helps people talk about what they already know in order to broaden their understanding of life's fundamentals. The foundations or principles that guide one's life, particularly those derived from religious ideals, are crucial for governing and enhancing the system of life. Based on observations made by the author at Takhassus Al-Qur'an Middle School, it is stated that there is a lack of student focus in learning and students' abilities vary in receiving learning. However, this does not rule out the possibility that students can stimulate learning more quickly. This can be done by training students' thinking through the rote method because the more the brain gets used to it, the more it will be trained and of course it will increase students' intelligence. The level of cognitive intelligence is divided into 3, namely: level of memory and understanding, level of application, level of analysis, evaluation and creativity. By using the rote method so that the level of cognitive development is observed in memory and understanding.Based on the findings of the author's research, the following conclusions can be drawn: 1) The memorization method for teaching PAI at Takhassus Al-Qur'an Middle School is in the good category, the level of cognitive intelligence of Takhassus Al-Qur'an Middle School students is known to be in the good category and level Student memorization based on memorization scores in PAI learning subjects is in the very good category. 2) hypothesis testing based on the t value, the predetermined value of tcount > ttable, namely 2.867 > 1.69389, while based on the significance value of 0.008 < 0.05, it is concluded that the rote method variable (X) has an effect on the cognitive intelligence variable (Y). simple linear regression (11.151 + 0.363 determination is 0.215, which means it has a positive influence of 21.5% and 78.5% is influenced by other factors that are not yet known by researchers. 3) Supporting factors in the memorization method for students' cognitive intelligence at Takhassus Al-Qur'an Middle School, namely using Mr. Abdurrahman al Asy'ari's method which is practiced in the tahfidz class using a special Al-Qur'an, level of perseverance, consistency in training, environment learning, and student concentration during the learning process.