Based on Article 351 (Paragraph 1) of the Criminal Code on ordinary persecution where in this article persecution is divided into three parts, namely ordinary, mild and severe persecution as in the case that I took to make this thesis with case number 2/Pid.B/2019/PN. In this case, the panel of judges sentenced the perpetrator of persecution to 10 months imprisonment, while the sentence that should have been imposed on the perpetrator of ordinary persecution was 2 years and 8 months by considering various kinds of evidence and factors of persecution, This study aims to analyze cases of persecution that refer to Article 351 of the Criminal Code (KUHP) in Indonesia.  In this analysis, we identify the elements necessary to determine whether an act can be classified as maltreatment under Article 351 of the Criminal Code.  This research involves a case study and analysis of the Depok district court decision, as well as referring to applicable laws and regulations.  The results of this analysis may provide a better understanding of the application of Article 351 of the Criminal Code in cases of maltreatment and its implications for the criminal justice system in Indonesia.
 
Keywords: Maltreatment under Article 351 of the Penal Code

Vegetative propagation is an effective method for producing high quality plant seeds with similar characteristics to the parent plant. Techniques such as grafting and grafting are used to accelerate growth and increase plant resistance to pests and diseases. The advantages of this method compared to generative propagation include a faster fruiting period and consistency in the aroma and taste of the fruit. This research also highlights the importance of genetic compatibility between rootstock and scion and the use of phytohormones in the success of vegetative propagation techniques. The results show that both techniques can increase plant productivity, speed up fruiting time, and produce plants that are stronger and more adaptive to environmental conditions

Improving teacher competency is a key factor in improving the quality of education. One of the latest innovations in teacher professional development in Indonesia is the Merdeka Mengajar Platform (PMM), a digital-based platform designed to provide flexible access to independent training. This study aims to evaluate the effectiveness of PMM in improving teachers' pedagogical and professional competencies, especially in the context of educational transformation in the digital era. The method used is a literature study, by collecting and analyzing relevant literature from reputable academic sources. The results of the study indicate that PMM significantly improves teacher competency through interactive modules, online learning communities, and digital resources designed for learning personalization. Teachers who participate in training through PMM show better ability in implementing innovative learning strategies compared to those who do not use this platform or participate in traditional training. However, the effectiveness of PMM can be influenced by teachers' technological readiness and digital literacy, especially in areas with limited infrastructure. This study highlights the importance of supporting technology-based education policies to ensure that the benefits of PMM can be felt inclusively and sustainably. In conclusion, PMM is a promising solution to address the teacher training gap in Indonesia, but requires a strategic approach to improve its access and effectiveness. Further research is needed to evaluate the long-term empirical impact of this platform on teacher professional development and student learning quality.

This study aims to describe and analyze the movement structure of the Sepen Dentok Dance in the Kundor Flower Studio, Batu Penyu Village, Gantung District, East Belitung Regency. This research is a qualitative research. The object of the research is the Sepen Dentok Dance at the Kundor Flower Studio, Batu Penyu Village, Gantung District, East Belitung Regency. The types of data are primary data and secondary data. Supporting instruments used are stationery, and cameras. The steps of analyzing data are collecting data, describing the data and inferring the data. Data collection techniques are carried out through literature studies, observations, interviews, and documentation. The results of the study show that the relationship between the basic elements of the Sepen Penyok dance movement has basic elements related to the attitude and movement of the head, body, hands and feet. From the relationship system of the basic elements produce the forms of motifs. The hierarchical relationship system contained in the Sepen Penyok dance consists of 36 motifs, 15 phrases, 5 sentences, and 1 group. The Sepen Dentok Dance includes a syntagmatic relationship system, a relationship system that is like a link that cannot be separated or reversed between one and the other.

This study aims to analyze waste management in Indonesia from 2001 to 2022, and identify challenges and solutions that can be implemented to improve the effectiveness of waste management. Waste is a major problem affecting the environment and public health, with around 34.29% of waste in Indonesia not being managed properly. This research uses a descriptive approach by analyzing data related to policies, infrastructure, and community participation in waste management. The results showed that although there are regulations governing waste management, the main challenges faced are low public awareness and limited waste management infrastructure in many regions. The research also found that collaboration between the government, communities and the private sector is crucial in creating sustainable solutions. In addition, the development of environmentally friendly waste management technologies, such as Waste Power Plants (PLTSa), can be an innovative solution to reduce the negative impact of waste on the environment. This research suggests the need to strengthen regulations, increase public awareness, and develop more efficient and environmentally friendly waste management technologies.

Pneumonia is an acute inflammatory disease of lung tissue. It is usually caused by microorganisms such as bacteria, fungi and viruses. The young children are particularly vulnerable to this illness. Report in 2019 shows that pneumonia kills almost 2,000 children under the age of five every day worldwide and affects over 800,000 children under the age of five annually. Analyzing the chest X-ray results of the patient's body is one method of diagnosing pneumonia. Therefore, this research was done to deploy a deep learning to identify the healthy and pneumonia affected lungs from chest X-ray images in order to aid in the diagnosing process. This research was done by using 2000- chest X-ray dataset—of which 1500 pneumonia lung data and 500 normal lung data. The computer vision model YOLOv8 is used in this study. The accuracy results from the training process were 56.15% in the pneumonia class and 92.03% in the normal class. Wether in the testing process yielded an average value of 0.482 (48, 2%) for the pneumonia class and 0.675 (67,5%) for the normal class. From these results, there are promising possibilities for developing a pneumonia detection system using YOLO in the future.

Shallots are superior vegetable plant and contribute quite significantly to the development of the national economy. The price of shallots fluctuates almost every year. At certain times the price of shallots soars due to high demand while the supply in the market is insufficient. Therefore, an analysis is needed to see what phenomena significantly affect the increase in the price of shallots. The methods used in the study were LSTM, GRU and LR. The results of the analysis show that the LSTM algorithm gets a MAE value of 0.011072172783, MAPE 3.93678% and RMSE 0.03139695060, this error is the lowest compared to GRU getting MAE value is 0.01185741, MAPE 4.2282357% and RMSE 0.03122299395 and LR with MAE 0.0134737280395416, MAPE 5.45081% and RMSE is 0.0313332635305961, so LSTM is a suitable algorithm for predicting shallot data in Pati district.

Smart waste management and recycling have become critical issues in urban planning and environmental sustainability due to the increasing volume of waste generated by modern societies. In this study, we investigated the performance of Support Vector Machine (SVM) and Neural Network (NN) methods in an Arduino-based waste sorting system. Our testing phase revealed exceptional performance, with SVM achieving an accuracy of 92% and NN achieving 96%, alongside perfect precision, recall, and F1-score metrics. The consistent True Positive (TP) results across all waste categories underscored the system's capability to accurately direct waste into correspondingcolored bins. These findings highlight the significance of automated waste management systems in promoting effective waste sorting practices and facilitating sustainable recycling efforts. Moreover, advancements in technology and machine learning algorithms offer promising prospects for further enhancing the efficiency and scalability of such systems, thereby contributing to a cleaner and healthier environment for future generations. Future research in smart waste management could focus on exploring additional machine learning algorithms, advanced sensor technologies, and Internet of Things integration. Investigating alternative algorithms beyond SVM and NN, adopting advanced sensors like hyperspectral imaging or lidar, and incorporating IoT devices for real-time monitoring could enhance waste sorting accuracy and scalability.

Determination of the value of the guarantee to the Bank in the process of applying for Home Ownership Credit (KPR) submitted by prospective customers still refers to the provisions of the Financial Services Authority, where the assessment must follow the existing rules and be carried out by public appraisals or commonly called the Office of Public Appraisal Services (KJPP). Currently the analyst credit officer cannot validate the results of the assessment report from KJPP, so if an error occurs either intentionally or not by KJPP or appraisal parties continue to process according to the given value. In the event of default of payment by the customer due to the lower collateral value of the loan provided, the bank violates Bank Indonesia Regulation number 18/16/PBI/2016 concerning loan to value ratio. This study aims to apply the K-Means algorithm in grouping home sales so that it can be used for the calculation of the estimated value of house prices, and develop a prototype of the house price estimation information system. Data retrieval using crawling or scrapping techniques on the website makes it easier to fulfill data on a dataset. The result of this study is the data of home sales for kebon Jeruk area spread across the internet can be grouped into 3 clusters with the value of David Bouldin Index in duri Kepa sub area, which is 0.096, in South Kedoya sub area of 0.087, in North Kedoya sub area of 0.071, and Kelapa Dua sub area of 0.117. By combining clusterization results using K-Means methodology with land price calculation formula obtained land price estimation in sub area.
Keywords: K-Means, KPR, Data Scraping, KJPP, MAPPI

The heart disease is a prevalent and potentially fatal condition affecting individuals worldwide. In this study, we address the challenge of predicting the severity of heart disease using supervised learning techniques. Leveraging a dataset comprising various demographic and clinical attributes, we propose a solution that employs machine learning models to accurately predict the severity level of heart disease. Among the evaluated models, Random Forest emerges as the top performer, showcasing exceptional precision, recall, accuracy, and F1-score across all severity levels, with an overall accuracy of 98.8%. This highlights the robustness of the Random Forest model in accurately classifying instances across different severity levels. Following closely behind, the KNN algorithm demonstrates commendable performance, achieving an accuracy of 92% and showcasing competitive precision, recall, and F1-score values, particularly for higher severity levels. Despite its notable aspects, XGBoost ranks third among the evaluated models, with an accuracy of 90.4%. While XGBoost excels in certain aspects, such as recall for Level 3 severity, it falls short in overall performance compared to Random Forest and KNN. For future research, exploring ensemble methods that combine the strengths of different algorithms could yield even better classification results, providing avenues for further improvement in predicting the severity of heart disease