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Diagnosis of a disease is usually carried out in a laboratory test, where this test requires a lot of money and time. As time goes by in the world of health, there is one method that can be used to detect the level of condition in the body by recognizing the patterns that form on the iris of the eye or better known as Iridology. By using digital image processing, the disease diagnosis process using the iridology method can be carried out. The aim of this program is to identify diabetes milletus by processing digital images in the form of iris images using Matlab . The research data used comes from the open source website, namely Github , which consists of diabetic iris image data and normal iris image data. Data is taken in the form of digital images. In this research there are several stages, including pre-processing, feature extraction, and classification. The feature extraction process is based on statistical characteristics (contrast, correlation, energy, homogeneity) from the iris image, then classified using the K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) as a comparison algorithm. The results obtained from this research show that K-NN has better performance with accuracy values ​​of 84%, sensitivity 72%, and specificity 96%.    

This research use the Naive Bayes algorithm to classification of user reviews of the Sky Childern Of The Light application from the Google Play Store. The Sky Childern Of The Light application is a popular online game, because it offers a unique and immersive playing experience. This method was chosen because of its simplicity, speed, ease of interpretation, and suitability for high-dimensional data. The advantages of Naive Bayes are the accuracy and efficiency of calculations, fast results and presentation. The data collected was 1500 data with a classification ratio of 8:2 with an accuracy value of 87% using the Naïve Bayes algorithm. This method is very good at analyzing the sentiment of the Sky Children Of The Light application.      

This research aims to describe the use of language in community interactions at the Motabuik terminal, Atambua City. The use of language in question is the form and function of code switching and code mixing in the interaction of the Motabuik Terminal Community, Atambua City. The data referred to in this research is code switching and code mixing in the interactions of the Atambua City Terminal Community, in the form of speech between the Motabuik Terminal Community. The speech in question is in the form of a conversation containing words, phrases, clauses and sentences that have elements of code switching and code mixing. Data collection was carried out using listening, note-taking and introspection techniques. Data analysis techniques were carried out with all utterances that showed the occurrence of code switching and code mixing in interactions at the Motabuik Terminal, Atambua City which contained elements of code switching and code mixing. Data collection was carried out by listening, taking notes and introspection. Data analysis techniques were carried out with all utterances that showed the occurrence of code switching and code mixing in the interaction of the Motabuik Terminal community, Atambua City, identified and carded complete with their construction. Next, classification and categories of the overall data are carried out. The data was analyzed by selecting and sorting out the forms and functions of code switching and code mixing in the interactions of the Motabuik Terminal Community, Atambua City. After being analyzed and classified, the data is described and explained to find out the form and function of code switching and code mixing in the interaction of the Motabuik Terminal Community, Atambua City. The results of the research show that there are two types of code switching in the interaction discourse at the Motabuik Terminal, namely in the form of language transfer, includes code switching from Atambua language to Indonesian and code switching from Indonesian to Atambua language at the Motabuik terminal, namely internal code mixing in the form of Atambua language words and phrases and connecting Indonesian as a unifying language communication tool at the terminal, the code mixing function has aspects speech, to explain, provide information and respect passengers.

Understanding the concept of Justification by Faith is crucial for Christians, as it enables them to gauge their comprehension and application of this doctrine in their lives. Many Christians have yet to fully grasp this foundational aspect of their faith. This study aims to elucidate the true significance of Justification by Faith by comparing the perspectives of Paul and James, which at first glance, seem contradictory. This research employs a literature review approach, involving the identification, classification, and analysis of relevant literature on the topic of Justification by Faith according to Paul, James, and Calvin. The study involves a hermeneutical analysis of biblical texts, a comparison of theological perspectives, and a systematic organization of findings. Paul asserts that justification occurs through faith, independent of works, as an act of God's grace (Romans 3:28, 4:5). The law reveals human sinfulness and the need for divine justification through faith in Christ. James emphasizes that genuine faith is demonstrated through works (James 2:24). He argues that faith without works is dead and insists on the necessity of works as evidence of true faith. Calvin integrates both perspectives, emphasizing that justification by faith is inseparable from the process of regeneration by the Holy Spirit. He views faith as a gift from God that leads to good works, the fruit of genuine faith. Justification by Faith is an act of God that can only be achieved through His grace and the sacrifice of Jesus Christ (Romans 4:5; 5:6). Faith is rooted in the truth of God's revelation in Christ, culminating in belief in His crucifixion and resurrection. Both Paul and James agree that faith and works are essential in a genuine response to God, with good works being the inevitable result of true faith. This study underscores the interconnectedness of faith and works, aligning with Martin Luther's assertion that good works are the fruit of righteousness. The comprehensive understanding of Justification by Faith involves recognizing it as a divine act that provides hope for salvation through faith, a gift from God facilitated by the Holy Spirit (Ephesians 2:8; Galatians 5:22).  

This study analyzes the use of deep learning, primarily Convolutional Neural Networks (CNN), to categorize various types of citrus fruits. The study attempts to create an automated system that can accurately categorize citrus fruit kinds using image processing techniques. The collection contains 40 photos of four different citrus fruit types: pomelo, mandarin orange, kaffir lime, and lime. The methodology entails gathering photos, preprocessing them to improve quality, and then training a CNN model to classify the fruit varieties. The results show a high accuracy rate of 95% in classifying fruit types, demonstrating that the CNN model is effective for this task. The findings indicate that increasing the dataset and including other fruit species could significantly boost the system's accuracy.

Land cover is defined as the physical and biological cover of the earth's surface, both those formed naturally such as swamps, hills and rivers and those formed by man-made means such as rice fields, gardens, forests and buildings. As technology develops, conventional methods of satellite image processing are starting to be abandoned. This is because conventional methods require quite a long time to process satellite image data. The presence of Google Earth Engine (GEE), which is a cloud computing-based platform, makes it easier for users to process satellite image data boldly and for free. This research aims to classify satellite image land cover in the Trenggalek Regency area, East Java. The level of accuracy in this study uses a confusion matrix. The accuracy test results show a value of 90.23%.

This study aims to explore the diversity of syllable types and develop a comprehensive classification framework. Analysis was conducted on the syllable structures of various languages to understand both common patterns and language-specific variations in syllable formation. Linguistic data from diverse languages were collected and analyzed to identify phonotactic patterns, morphological interactions, prosodic features, and phonetic realizations of syllables. Findings indicate a wide range of syllable structures, ranging from simple CV to complex structures with multiple consonants and vowels. There are also significant interactions between syllable structure and morphological processes, as well as prosodic features. The results of this study are integrated into a comprehensive classification framework that encompasses various phonological, morphological, and phonetic dimensions of syllables. Theoretical and practical implications of the research are discussed, along with recommendations for future research.

Inheritance is wealth which can be a collection of assets and liabilities from the heir which is transferred to the heirs. In inheritance law, a principle applies, namely that if an heir dies then by law his rights and obligations immediately pass to his heirs. The division of inherited assets can have legal impacts in society which occurred in the Sikka Krowe Tribe community, specifically in Klotong Hamlet, Bura Bekor Village, Bola District, Sikka Regency between disputed heirs. The problem formulation of this research is 1. What are the reasons for the struggle between heirs? 2. What is the process for resolving property disputes between heirs? And 3. How is the distribution of assets resulting from struggles between heirs viewed from the customary law of the Sikka Krowe tribe, Bola District, Sikka Regency? This research is empirical legal research. The data sources in this research are primary and secondary data sources. Data management and analysis techniques are editing, classification and description, then the data obtained, both primary and secondary data, will be processed and analyzed by researchers based on the problem formulation. Based on the research results, the reason why there is a struggle between heirs in inherited land disputes is that the family does not know the exact status of ownership of the inherited land and the factor of poor communication between the two parties. Settlement of disputes or disputes Done through utun omok (gathering to find a way out) includes: presenting family parties, deliberation or mediation (kula babong), decision making, settlement and returning decisions to the parties in dispute. Distribution of land inheritance using a patrilineal kinship system, namely distribution that is directly given to sons as the main heirs and gets more inheritance.

Attention to spices and flavorings among the younger generation is still low. The strategy that can be used to overcome this problem is a programmed and computerized arrangement of spices and flavorings using Convolutional Neural Network (CNN) calculations. In this exploration there are 300 images of spices and flavors which will be characterized into 3 classifications. Namely ginseng, ginger and galangal. Information in each classification is divided into two, namely preparation information and testing information with a proportion of 80%: 20%. The CNN model used in computerized grouping of spice and flavor images is a model with 2 convolutional layers, where the first convolutional layer has 10 channels and the second convolutional layer has 20 channels. Each channel has a 3x3 portion frame. The channel size in the pooling layer is 3x3 and the number of neurons in the secret layer is 10. The actuation capability in the convolutional layer and secret layer is tanh, and the actuation capability in the result layer is softmax. In this model, the accuracy of preparation information is 0.9875 and the loss value is 0.0769. The precision of the test data is 0.85 and the loss value is 0.4773. Meanwhile, testing new information with 3 images for each classification resulted in an accuracy of 88.89%.

Chronic diseases in humans are very difficult to detect visually, for example glaucoma, hypertension, diabetes, and others. So it takes a lot of time for further medical examination by visiting a health center or hospital. Therefore, this research aims to find a solution combining medical and computer science to classify quickly and precisely. Classifying eye images requires good features and characteristics so that disease images can be classified. This research uses the Deep Learning method, namely Convolutional Neural Network with MobileNet-V3 architecture which can extract features from large resolution images very well. This research resulted in accurate classification of images of chronic diseases Normal, Diabetes, Glucoma, Cataract, Age related macular degeneration, Hypertension, Pathalogical Myopia. uses the MobileNet-V3 architecture, with transfer learning reaching 81%, and loss only 0.4913.

On August 14, 2023, Indonesia had approximately 228 million social media users, a number that is expected to continue growing to reach 267 million by 2028. Social media can be used to spread both positive and negative information, and one of the various negative effects is cyberbullying. Consequently, much research is conducted in the field of machine learning to develop sentiment analysis. One crucial step in sentiment analysis is word weighting. The two most common word weighting methods are TF-IDF and Word2Vec. These methods can be compared to determine which one produces better classification results, allowing cyberbullying sentiments on social media to be detected more accurately. Based on nine test scenarios, the final results showed that TF-IDF performed better than Word2Vec in this study, with an accuracy of 84%.    

The development of image processing technology today can create systems that are able to effectively recognize digital images, one of which is in the field of agriculture for plant disease identification. Citrus plants experience a decrease in productivity due to pathogen attacks on leaves such as Black Spot, Cancer, and CVDP so that disease identification is needed. The classification method that can be used to classify images is the K-Nearest Neighbor (K-NN) algorithm because it is simple and has high accuracy in image management. This study aims to implement and determine the performance of the K-NN algorithm in identifying citrus plant diseases based on leaf images. This research uses a dataset from the Kaggle website of 1,096 images. There are 12 research scenarios using the comparison between test data and training data as much as 4, namely (90% training data + 10% test data, 80% training data + 20% test data, 70% training data + 30% test data, 60% training data + 40% test data) and testing with 3 random state values (42, 32, 22). The results showed that the K-NN algorithm is very effective in identifying citrus plant diseases with the highest accuracy value in the 90% training data scenario and 10% test data with a value of K = 2 which is 98.5%.

This research explores the classification of brass instrument sounds using Convolutional Neural Network (CNN) combined with Mel-Frequency Cepstrum Coefficient (MFCC) feature extraction. This research aims to improve the accuracy of brass instrument sound recognition by utilizing CNN's ability to process audio data. Through experiments conducted with different audio durations and variations in CNN model architecture, this study evaluates the impact of dataset separation and model design on classification performance. The results show that dataset duration and CNN model architecture significantly affect classification accuracy, with the highest accuracy achieved in the scenario using 30 seconds of audio duration with an accuracy value of 84%. In addition, experiments varying the number of convolution layers in the CNN model show that the selection of the model architecture plays an important role in classification performance. Overall, this research contributes to advancing the field of audio classification by providing insight into the optimal dataset duration and model architecture for wind instrument speech recognition using CNNs.

Advertising as one of the forms of communication has an important role in introducing a product that is goods or services to the society. And advertising is one of the parts of the discourse, which is called the advertising discourses. This study aims to describe the assumption analysis in the Takaful Ikhlas 2024 publicity discourse. As for the method used in this research, it is qualitative descriptive. The source of the data used is data collected using the technique of logging and logging. The researchers took a video of the Takaful Ikhlas 2024 publicity, and then recorded all the announcements for classification and analysis. The subject of this study is Takaful Ikhlas 2024. Data analysis process using data reduction, data display (data presentation), conclusion withdrawal (verification). The results of the study found that there are six types of assumptions found: 1) factual assumption (following the vocabulary that can be considered as a fact), 2) non-factual (assuming not true), 3) structural (constantly and conventionally that the part of the structure has been assumed to be true), 4) lexical (interpreting not expressed as understood), 5) existential (associating the existence of an existence), and 6) contractual (not just untrue but the opposite of the truth or not of the fact).    

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.

Biology learning, particularly the topic of the classification of living organisms, is often considered difficult and boring by some students. To address this challenge, a more engaging and interactive approach is needed. One effective solution is to utilize game-based technology, such as Wordwall, which allows students to actively learn through various educational games. This study aims to enhance student engagement in learning the classification of living organisms using interactive Wordwall games. Through a community service method, this activity involved teachers and students from several schools to integrate Wordwall as a learning tool. The interactive games provided by Wordwall include various types of activities, such as puzzles, quizzes, and matching games, designed to help students better understand the concepts of classification of living organisms. The implementation of this program is expected to improve student motivation, involvement, and understanding of the material. Furthermore, it is hoped that the use of this technology will provide a more enjoyable learning experience and increase the effectiveness of classroom learning. The evaluation conducted shows a significant improvement in student engagement, as well as a better understanding of the material compared to traditional teaching methods.

It has become an axiom that parents are responsible for their children's education. Parents are the first teachers for children. It is appropriate that since waiting for the birth of a child, even before marriage, prospective parents have planned how to raise and educate their children. The aim of this research is to describe the responsibilities of parents in raising children at an early age based on al-Maragh's interpretation of the Al-Qur'an verse 9 of Surah An-Nisa. This research uses a qualitative research method with a library approach, the main source of information is obtained from reading materials from books, magazines, articles and interpretations of the al-Maragh hadith which contain the thoughts of Imam al-Maragh. Data analysis was carried out through the following stages: data processing (unification), data classification, data interpretation. This research produced several results. First, the Al-Qur'an, Surah Al-Nisa verse 9, contains a warning to every parent that they will be worried and afraid if in the future they have to leave their child in a weak and helpless state. Second, parents are responsible for educating young children. The areas of education that are the responsibility of parents are physical and spiritual education, religious education, and character education.

This study aims to develop a web-based malware detection system using Convolutional Neural Network (CNN) utilizing the IoT23 dataset. Malware is malicious software that can exploit security vulnerabilities in computer systems, steal data, and degrade performance. The implementation of this detection system involves CNN, capable of extracting important features from both visual and textual data, applied to malware classification. The IoT23 dataset comprises 23 scenarios of IoT network traffic, including traffic from malware-infected devices. The study results show that the developed web application can detect malware attacks with accuracy, precision, recall, and F1-score of 99% on separate data scenarios. This CNN-based detection system has proven effective in identifying and classifying malware attacks, contributing to the enhancement of network and device security.  

The aim of this research is to classify Indonesia's trade balance data using the SVM (Support Vector Machine) method with two features, namely Gross Regional Domestic Product (X1) and Wholesale Price Index (X2). Classification is carried out by comparing two types of kernels, namely polynomial kernels and RBF (Radial Basis Function) kernels. Equality Hyperplaneobtained from the polynomial kernel is: . The Hyperplane equation obtained from the RBF kernel is:  Experimental results show that classification with polynomial kernels provides better performance than RBF kernels. This can be seen in the evaluation results which show that the Polynomial kernel has an average model goodness of 75.93% and for the RBF kernel the average model goodness is 74.07%. Leave One Out cross validation (LOOCV) simulation for training data obtained an average accuracy of 76.67% for the polynomial kernel and 66.67% for the RBF kernel. This shows that in this classification context, kernel polynomials are more effective in separating data classes.

This article discusses the integration of business information systems with machine learning technology to enhance decision-making efficiency and accuracy. The objective of this research is to implement machine learning algorithms into business information systems to automatically process and analyze data, predict business trends, and provide more intelligent recommendations. The methods involve collecting historical data from business information systems, applying machine learning algorithms such as regression, classification, or clustering, and integrating the analysis results into the BI platform. The outcome is an enhanced capability of business information systems to optimize supply chains, identify consumer behavior patterns, and optimize marketing strategies based on more accurate predictions. Integrating machine learning technology with business information systems proves to be an effective strategy for improving a company's competitiveness in the current digital era.