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This study aims to evaluate the effectiveness of training and mentoring programs for archive management targeting administrative staff at the Faculty of Islamic Studies, Universitas Muhammadiyah Lampung. A descriptive qualitative approach was employed, with data collected through observation, in-depth interviews, and documentation. Key informants included officials and administrative staff involved in archive management. The research identified supporting and inhibiting factors in program implementation and assessed its impact on staff competence enhancement. Findings revealed that the training positively contributed to improving participants' skills in archive classification, storage, and maintenance following national standards. Structural support and effective communication were identified as major supporting factors. However, challenges included limited facilities, low staff awareness, and a shortage of professional archivists. The study recommends sustainable mentoring, increased funding for facilities, and the adoption of digital archiving. These findings provide a foundation for developing more effective and adaptive training programs in higher education environments.

This work introduces the concept of neutrosophic AT-ideals in AT-algebra and presents some associated facts and theorems. The homomorphism of neutrosophic AT-ideals in AT-algebra is examined, and some findings pertaining to them under homomorphism are derived.

Febrile seizures are a common neurological condition occurring in children aged 6 months to 5 years, with an incidence rate of 2-5%. Prompt and appropriate management is crucial to prevent long-term developmental impacts. This study aims to analyze the relationship between the classification of febrile seizures and child development in the working area of the Barus Jahe Health Center, Barus Jahe District, Karo Regency, North Sumatra Province, in 2023. The research used an observational method with a qualitative descriptive design, collecting data through interviews and physical measurements from 10 children who experienced seizures. The results showed that 70% of the children who experienced seizures were older than 1 year, while 30% were under 1 year old. In terms of gender, 60% of the children were male and 40% were female. Regarding seizure classification, 70% of the children had simple seizures, and 30% had complex seizures. Concerning child development, 60% showed normal development, 20% showed questionable development, and 20% required further monitoring. This study highlights the importance of appropriate management to prevent developmental issues resulting from febrile seizures.

This study aims to describe: (1) the meaning of utterances containing conventional implicatures in the novel ‘The Years of The Voiceless’ by Okky Madasari, (2) define the function of utterances containing conventional implicatures in the novel ‘The Years of The Voiceless’ by Okky Madasari, and (3) describe the types of utterances containing conventional implicatures in the ‘The Years of The Voiceless’ by Okky Madasari based on Grice’s theory about implicature. The research method was descriptive qualitative method. The data in this study are sentences in utterances containing implicature. The data in this study were obtained by reading, recording and note-taking techniques. The findings are as follows. There are 8 forms of conventional implicature found. Based on the results of the classification that has been done, there are four intentions, 1 utterance of the intention of commanding, 1 utterance of the intention of pride, 1 utterance of the intention of giving information. Types of utterances that contain conventional implicatures in the novel 'The Years of The Voiceless'. Each implicature has a function. The function of conventional implicatures in the novel can be categorized into three functions, namely 1. implicatures that function as persuasive or persuade, 1 implicature that functions as directive, namely giving advice, 1 implicature that functions as expressive, namely showing hatred. Based on the results of the classification that has been done, there are two classifications, 1 type of declarative speech (statement), 1 type of imperative speech (command).

Some schools currently still use manual methods for assessment in determining the best teachers.  Giving grades to teachers is sometimes still subjective, so the risk of conflict is high due to inaccurate and non-transparent assessments, making it a problem for teachers in teaching. For this reason, an information system is needed to get accurate calculation results to get the best teacher ranking. In answering the problems found, the author developed a decision support system using the Simple Additive Weighting method to obtain accurate ranking results so that it can provide maximum decision results through faster, more accurate and precise classification and evaluation. And this research uses a prototype method as an addition to make system development easier. The results of calculations using the SAW method can determine the ranking for the best teachers. Based on a comparison between the pretest and posttest results, it was found that the conformity level was 60% for appropriate data from the 10 data used and 40% for inappropriate data.

Computer vision-based algorithms, especially Convolutional Neural Networks (CNN) and You Only Look Once (YOLO), have become the leading approaches in plant disease detection. CNN excels in extracting complex visual features for disease classification, while YOLO provides high-efficiency real-time object detection capabilities. Both algorithms have shown promising results in various studies, especially with controlled datasets. However, challenges remain in their application in real-world conditions, such as environmental diversity, overlapping symptoms, and poorly annotated data. Future research has the potential to optimize these algorithms through the development of lighter models, the use of transfer learning techniques, and multi-modal data integration. In addition, further exploration of a wider range of diseases, crops, and environmental conditions can expand the application of these algorithms. By leveraging these innovations, computer vision-based plant disease management can be improved to support sustainable precision agriculture.

Law Number 31 of 2004 concerning Fisheries as amended by Law Number 45 of 2009 concerning Amendments to Law Number 31 of 2004 concerning Fisheries recognizes corporations as subjects of criminal acts in criminal acts in the fisheries sector, but the corporations are not held criminally responsible.This research is a normative legal research, using a statutory approach and a conceptual approach with literature study techniques or document studies. The processing of legal materials is carried out with several processes, namely the validity of legal materials, classification of legal materials, recording legal materials and analysis of legal materials. Analysis of legal materials is carried out in a normative qualitative juridical manner with descriptive and prescriptive analysis. The results of this study indicate  first, the regulation of corporate criminal liability in the current Fisheries Law still has shortcomings/weaknesses, namely it does not clearly formulate when a corporation is said to have committed a crime and based on the history of the development of corporations as subjects of criminal law, corporate criminal liability in the current Fisheries Law is still at stage II where corporations are recognized as perpetrators of criminal acts, but their criminal liability has not directly affected the corporation and only the corporate administrators are responsible. Second, the regulation of corporate criminal liability in the future can be seen in the Fisheries Bill which has answered the weaknesses/deficiencies in the Fisheries Law and has accommodated corporate criminal liability where corporations can be subject to principal or additional criminal sanctions for the crimes they commit.

This study aims to analyze the criminal law liability of the perpetrators in the case of corruption in the procurement of red onion seeds in Malaka Regency based on four decisions of the Kupang District Court: Decision of the KUPANG District Court Number 39 / Pid.Sus-TPK / 2023 / PN Kpg, Decision of the KUPANG District Court Number 40 / Pid.Sus-TPK / 2023 / PN Kpg, Decision of the KUPANG District Court Number 41 / Pid.Sus-TPK / 2023 / PN Kpg, and Decision of the KUPANG District Court Number 42 / Pid.Sus-TPK / 2023 / PN Kpg. This study uses a normative legal approach supported by empirical data through interviews with advocates, prosecutors, and judges. The results of the study indicate that the elements in Article 3 of Law Number 31 of 1999 in conjunction with Law Number 20 of 2001 has been cumulatively fulfilled in this case, namely the abuse of authority, state financial losses, and benefits for oneself or others. The application of the article to all defendants is considered legally valid, but in practice it does not fully reflect substantive justice because the roles and levels of involvement are not distinguished proportionally. The judge's decision and the prosecutor's indictment are considered consistent in normative aspects,but still leave challenges in terms of assessing the burden of proof and classification of criminal responsibility. This finding recommends the importance of evaluating the criminalization system, strengthening the capacity of procurement officials, and updating policies that distinguish between administrative errors and criminal acts of corruption.

Heart disease is one of the biggest causes of death in the world. This research examines the use of the Artificial Neural Network (ANN) algorithm to classify heart disease based on 303 medical data of heart disease patients obtained from the Kaggle dataset center. The data used includes medical parameters such as age, gender, blood pressure, cholesterol levels and other examination results. Various ANN architectures were tested to find the optimal configuration in terms of the number of hidden layers and neurons in each layer. Model performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. From the results of the performance measurement research, an accuracy rate of 97.06%, precision of 92.30%, recall of 92.30%, and F1-Score of 92.30% were obtained.

This descriptive research aims to shed light the general portrait of the utilization of EnglishScore as an international standardized test. It also intends to describe the test takers’ insights on the use of EnglishScore application. Eighty-two participants involved in this study. Questionnaire was employed to collect the data. The data were analyzed by Miles, et al Model consisting of collection, classification, description, and conclusion. The results imply that EnglishScore is an ideal and innovative English test and it is valid to use to measure the test takers’ English proficiency level since it is aligned with CEFR. Thus, it facilitates a better understanding and achievement of the test takers’ English proficiency.

This research focuses on developing a cayenne pepper quality detection system using the YOLO (You Only Look Once) V5 algorithm. The system is designed to address the limitations of manual post-harvest sorting by classifying cayenne peppers into three categories: “good” (ripe), “bad” (rotten or dry), and “raw” (green), based on their visual characteristics. A dataset consisting of 565 images was manually collected, labeled using Roboflow, and pre-processed through resizing and orientation standardization. Model training was conducted over 150 epochs, achieving high detection performance with a mean average precision (mAP) of 99.5%, precision of 99.6%, and recall of 99.9%. Real-time testing demonstrated the system’s capability to detect and classify cayenne peppers with exceptional accuracy. This research is expected to enhance the efficiency and accuracy of the cayenne pepper sorting process, while paving the way for the application of YOLO-based systems to other agricultural commodities. Further research is recommended to expand the dataset and optimize model parameters for improved system performance.

Intelligence is an individual's ability to comprehend, connect, and solve a variety of problems. This study reviews the concept of intelligence from various perspectives, including definitions, classifications of intelligence, and the factors that influence it. This study also discusses multiple intelligences such as intellectual quotient (IQ), emotional quotient (EQ), and spiritual quotient (SQ), as well as various other forms of intelligence based on Howard Gardner's theory. Heredity and environment are presented as the main determinants of individual intelligence development. This study aims to understand the complexity of human intelligence and how intelligence can be applied in learning, especially in Islamic religious education.

This study aims to classify the level of satisfaction of Dasawisma cadres with the Carik application in West Semper Village by utilizing the Naive Bayes method. Data was obtained through questionnaires, which were compiled based on three main aspects: ease of use, speed of access, and the usefulness of applications in supporting cadre tasks. After the data is collected, a pre-processing and labeling process is carried out, where the level of satisfaction of respondents is categorized into two classes, namely "satisfied" and "dissatisfied". The Naive Bayes algorithm is applied to predict satisfaction classes based on questionnaire answers. The results of the analysis show that the Naive Bayes method is able to perform classification with sufficient accuracy, so that it can be used as an evaluation tool and decision support in the development of the carik application. This method can also help the management understand user perceptions and improve the system based on objective and routine data in line with the needs of field cadres

Pox disease is an infectious disease that attacks the immune system. This disease is often considered mild because it is usually harmless and does not cause death. With the lack of counseling and treatment of this disease, lacks an understanding of pox disease, the types and the treatment . Some of the types of pox disease are Monkeypox, Chickenpox, and Cowpox. Cowpox can cause complications such as keratitis and corneal melting due to persistent erosion. To avoid complications of pox disease arising, CNN algorithm is considered capable of classifying the type of pox disease. Therefore, this study was conducted using 2 CNN methods used, namely: ResNet50 and VGG16. Because the distinguishing features of pox disease classification involve a mixture of color, shape and texture, CNN algorithm approaches including ResNet50 and VGG16 were tried. The ResNet50 model showed quite good results with an average accuracy of 76% but VGG16 had better accuracy with a value of 93%. The ResNet50 model showed good results with an average accuracy of 76% but VGG16 had better accuracy with a value of 93%. Using further evaluation, VGG16 has superior values with good precision, recall, and F1-score  for each class. This proves that VGG16 is a superior model for classifying pox disease types. The ResNet50 model is good at identifying three classes, namely, Chickenpox, Cowpox, and Healthy compared to the Monkeypox class because that class has 25% recall value and 40% F1-score  value. Similarly, the VGG16 model where the monkeypox class still has a recall value of 76%. This shows the potential of using artificial intelligence technology to classify smallpox disease.  

This study uses a descriptive qualitative method, with a systematic literature review approach. Data collection techniques are based on literature studies. With this, the collection of research data using literature studies through literature studies from various appropriate reference sources from journals, books, and internet sources to conduct analysis and classification of the issues or topics being studied. This study uses evaluation theory, because it focuses on the extent to which the policy of Law No. 13 of 2003 has been achieved regarding the protection of labor rights in Banten Province. The results of the study related to the Evaluation of Law No. 13 of 2003 Regarding the Protection of Labor Rights in Banten Province show that it has not been running well and is also not optimal. Where there are still many obstacles that occur in implementing the Law as a legal basis for meeting the needs of the workforce as the purpose of the implementation of Law No. 13 of 2003.

This study aims to analyze the fulfillment of National Health Insurance Rights (JKN) for workers who experience Termination of Employment (PHK) at PT. FI and evaluate the regulation of JKN rights after layoffs that provide justice for workers. The method used is normative legal research with a qualitative approach, which includes the selection and classification of legal materials as well as normative analysis of relevant regulations. Based on field findings, this study shows that there are administrative errors in reporting the status of layoffs that cause delays in fulfilling health insurance rights for laid-off workers. In addition, this study also found that even though the post-layoff JKN regulation has been regulated in the law, improper implementation can hinder the granting of rights fairly. This study recommends simplifying reporting procedures, increasing supervision by BPJS Kesehatan, and further education for companies to avoid administrative errors. Thus, this study contributes to the understanding of the importance of social justice in regulating health insurance for laid-off workers.

A paragraph is the smallest unit in written discourse which has an important role in building text cohesion and coherence. However, a lack of understandiag of the structure and function of paragraphs often becomes an obstacle in writing, especially in the context of learning Indonesian at the high school level. This research aims to analyze the structure and function of paragraphs in various types of text, such as narrative, descriptive and expository, and identify patterns that support communication effectiveness. This research uses a qualitative method with a content analysis approach. Data were obtained from five Indonesian high school level textbooks chosen randomly. The analysis focuses on paragraph structure (subject, predicate, object) and characteristics of text types. The research instrument includes guidelines for paragraph structure analysis, classification of text types, and criteria for effective paragraphs. The research results show that each type of text has a unique paragraph structure and function pattern, in accordance with its communication objectives. Narrative paragraphs support the storyline, descriptive create images, and expository convey factual information logically. In conclusion, understanding the structure and function of paragraphs can improve students' writing skills. This research recommends strengthening learning strategies that focus on analyzing paragraph structure and text variety to improve students' writing skills effectively.

Skin cancer (SC) is a highly serious kind of cancer that, if not addressed swiftly, might result in the patient’s demise. Early detection of this condition allows for more effective therapy and prevents disease development. Deep Learning (DL) approaches may be used as an effective and efficient tool for SC detection (SCD). Several DL-based algorithms for automated SCD have been reported. However, more efficient models are needed to improve accuracy. As a result, this paper introduces a new strategy for SCD based on Grey Wolf optimization (GWO) methodologies and CNN. The proposed methodology has four stages: preprocessing, segmentation, feature extraction, and classification. The proposed method utilizes a Convolutional Neural Network (CNN) to extract features from Regions of Interest (ROIs). CNN is employed for feature categorization, whereas the GWO approach enhances accuracy by refining edge detection and segmentation. This technique utilizes a probabilistic model to accelerate the convergence of the GWO algorithm. Employing the GWO model to optimize the structure and weight vectors of CNNs can enhance diagnostic accuracy by a minimum of 5%, based on evaluation outcomes. The application of the proposed strategy and its performance comparison with other methods indicate that the proposed method with GWO predicted SC with an average accuracy of 95.11% and without GWO an Accuracy of 92.66%, respectively, enhancing accuracy by a minimum of 2.5% when we train our model with GWO.

This study aims to analyze the errors in word usage in the children’s fabels “Seekor Kancil yang Selalu Ingat Tuhan” by Atisah, with a focus on how these errors impact the educational value of the story. The fabels, intended for elementary school readers, carries significant moral lessons but presents linguistic challenges due to certain errors in vocabulary, grammar, and sentence structure. These errors include the use of technical terms, uncommon words, and overly formal expressions that may hinder comprehension for young readers. Using a descriptive quantitative method, the research collects data through documentation and error classification based on linguistic theories in morphology, syntax, and semantics. The findings reveal that while the fabels employs imaginative storytelling to convey moral lessons, some linguistic inconsistencies, such as redundant phrases, inappropriate vocabulary, and lack of contextual explanations, reduce its effectiveness. Recommendations include simplifying language, ensuring alignment with children’s cognitive levels, and providing contextual definitions for complex terms. These efforts aim to enhance the readability and educational value of children’s literature while fostering linguistic development.

This study enhances Jiang et al.'s compression-based classification algorithm by addressing its limitations in detecting semantic similarities between text documents. The proposed improvements focus on unigram extraction and optimized concatenation, eliminating reliance on entire document compression. By compressing extracted unigrams, the algorithm mitigates sliding window limitations inherent to gzip, improving compression efficiency and similarity detection. The optimized concatenation strategy replaces direct concatenation with the union of unigrams, reducing redundancy and enhancing the accuracy of Normalized Compression Distance (NCD) calculations. Experimental results across datasets of varying sizes and complexities demonstrate an average accuracy improvement of 5.73%, with gains of up to 11% on datasets containing longer documents. Notably, these improvements are more pronounced in datasets with high-label diversity and complex text structures. The methodology achieves these results while maintaining computational efficiency, making it suitable for resource-constrained environments. This study provides a robust, scalable solution for text classification, emphasizing lightweight preprocessing techniques to achieve efficient compression, which in turn enables more accurate classification.