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This research proposes a combination of Quantum Key Distribution (QKD) based on the BB84 protocol with Improved Logistic Map (ILM) to improve data transmission security. This method integrates quantum key formation from BB84 with ILM encryption. This combination creates an additional layer of security, where by default, the operation on BB84 is only XOR-substitution, with the addition of ILM creating a permutation operation on quantum keys. Experiments are measured with several quantum measurements such as Quantum Bit Error Rate (QBER), Polarization Error Rate (PER), Quantum Fidelity (QF), Eavesdropping Detection (ED), and Entanglement-based detection (EDB), as well as classical cryptographic analysis such as Bit Error Ratio (BER), Entropy, Histogram Analysis, and Normalized Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI). As a result, the proposed method obtained satisfactory results, especially perfect QF and BER, and EBD, which reached 0.999.

This research aims to determine the effectiveness of the audio-visual method for grade 3 elementary school students in understanding Old Testament character material, including the stories of Jacob, Joseph and Moses. This research uses a direct observation method with the aim of knowing the learning situations that occur, especially in understanding the story material of Old Testament characters. A teacher must provide interesting and fun learning so that children can draw conclusions from the learning. Audio-visual methods can expedite understanding And strengthen memory student so that on Finally expected student can understand learning specifically know channel story And message from story figure The Old Testament includes story Jacob , the story of Joseph, and the story of Moses.

By looking at the existing generations, it is very important to describe the great figures who were diligent in preparing generations, namely Moses educating Joshua in preparing the command of the line to go on a long journey, the same thing was done by Elijah by diligently educating professional cadres, namely Elisha, even the Lord Jesus too. He was active in educating his students and Paul was educating Timothy. This reminds us again that the Church should never neglect its obligations to young people because the risk is too dangerous, because the church will lose its strength, and even lose its young people. The study of science may give rise to doubt in the hearts of young people who are developing, because it seems like it is not in line with God's Word. True science will not conflict with the Bible, because scientific laws are from God and He is the one who inspired what is written in the Bible.  The theories put forward by scholars may conflict with each other, or may provide incorrect interpretations of the Bible. According to the educational dictionary, methods mean ways and procedures for carrying out an activity to achieve goals effectively.  And research aims to reveal phenomena in the field or in society so that they are clearly known and can be used as a basis for developing all kinds of needs for human life. rstabel Spearman with α = 5% and n = 9, then rstabel = 0.683 and from rstabel and rstabel it can be seen that rstabel ˂ rstabel is 0.658˂ 0.683 so it can be known for certain that Hi is rejected and Ho is accepted. This means there is no significant relationship between variable X (Counselor) and variable Y (student learning achievement). The regression coefficient is 0.482, meaning that each addition (because of the + sign) of one Counselor score will give an increase in score of 0.482. So, counselors can influence student learning achievement by 36.3%. The main foundation of the teachings disseminated to students by Christian Religious Education (PAK) teachers is the Bible, which is contained in II Timothy 3:16, so that teaching in the form of theory and practice must not deviate from this path. As a Christian Religious Education teacher, you must maintain that your teaching comes from God. With the aim of spreading the truth of God's Word (Matthew 28:19-20).

Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.

The focus of this paper is the theme of the Mission of Moses to Free the Israelites from Egyptian Slavery Exodus 6:1-12. The salvation of the Israelites is inseparable from the struggle of Moses who was blessed by God. But with God's word to Moses all problems were resolved with an outstretched hand. The purpose of this paper is to discover the location of God's role for the Israelites who were enslaved by Egypt at that time. The methodology used is the exegesis methodology. There are 6 analyzes used, namely analysis of Story, Structure, Syntax, Semantics, Context, and Theological Reflection. The six analyzes have been done on separate sheets and are summarized in this article. The findings obtained from this writing include: First, God's inclusion in Moses, who struggled with a lack of self-confidence. Second, God's inclusion appeared in Moses when Moses freed the Israelites from Egyptian slavery. Third, God always accompanies those who rely on God in their work. Thus Christians are invited to always rely on God in their life journey.  

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption

Diabetes Mellitus is a hazardous disease, and according to the World Health Organization (WHO), diabetes will be one of the main causes of death by 2030. One of the most popular diabetes datasets is PIMA Indians, and this dataset has been widely tested on various machine learning (ML) methods, even deep learning (DL). But on average, ML methods are not able to produce good accuracy. The quality of the dataset and features is the most influential thing in this case, so deeper investment is needed to examine this dataset. This research will analyze and compare the PIMA Indians and Abelvikas datasets using the Random Forest (RF) method. The two datasets are imbalanced, in fact, the Abelvikas dataset is more imbalanced and has a larger number of classes so it is be more complex. The RF was chosen because it is one of the ML methods that has the best results on various diabetes datasets. Based on the test results, very contrasting results were obtained on the two datasets. Abelvikas had accuracy, precision, and recall, reaching 100%, and PIMA Indians only achieved 75% for accuracy, 87% for precision, and 80% for the best recall. Testing was done with 3, 5, 7, 10, and 15 tree number parameters. Apart from that, it was also tested with k-fold validation to get valid results. This determines that the features in the Abelvikas dataset are much better because more complete glucose features support them.

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.

Convolutional neural network (CNN) is a deep learning (DL) model that has significantly contributed to medical systems because it is very useful in digital image processing. However, CNN has several limitations, such as being prone to overfitting, not being properly trained if there is data duplication, and can cause unwanted results if there is an imbalance in the amount of data in each class. Data augmentation techniques are used to overcome overfitting, eliminate data duplication, and random under sampling methods to balance the amount of data in each class, to overcome these problems. In addition, if the CNN model is not designed properly, the computation is less efficient. Research has proved that data augmentation can prevent or overcome overfitting, eliminating duplicate data can make the model more stable, and balancing the amount of data makes the model unbiased and easy to learn new data as evidenced through model evaluation and testing. The results also show that the custom convolutional neural network model is the best model compared to ResNet50 and VGG19 in terms of accuracy, precision, recall, F1-score, loss performance, and computation time efficiency

Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.