In Indonesia, tuberculosis is ranked third in terms of prevalence among countries with the highest tuberculosis burden. Radiological examination, such as X-rays or X-rays, is a method generally used to detect tuberculosis. Chest X-ray examination is one method used to detect tuberculosis. To achieve these goals, the research will combine two powerful data processing techniques. First, the K-Means algorithm will be used to group x-ray image data based on similar characteristics, making it easier to identify typical patterns from images infected with tuberculosis. The research results show the highest accuracy of 93% using data division with a ratio of 80 : 20 with parameter K = 1. These results show that the combined model of the two algorithms can be applied to identify tuberculosis in the lungs.
 
 

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.

Purpose: The objective of this study is to assess the usability of the Baraya Travel application, highlighting areas for enhancement to improve the online ticket booking experience for users. By concentrating on the Baraya Travel app as the research subject, the study intends to offer valuable insights for its developers. It emphasizes user feedback and recommendations to improve interface navigation, application speed, and the addition of new features, ultimately increasing convenience and ease of use. This study aims to provide an extensive examination of the app's performance, identifying precise areas where improvements can be made.
Methods: Using the Usability Testing methodology, particularly emphasizing the System Usability Scale (SUS), this study employs a quantitative research strategy. Surveys were administered to 409 Baraya Travel application users to gather primary data concerning usability aspects including user interface navigation, application responsiveness, and user convenience. The statistical analysis utilized both descriptive statistics, encompassing measures of central tendency and variability, to summarize the data, and potentially inferential tests like correlation analysis or t-tests, to investigate relationships and disparities between variables. The selection of statistical analysis aimed to offer a thorough interpretation of the data and extract meaningful insights into the usability of the Baraya Travel application. Furthermore, the utilization of the SUS evaluation metric provided a standardized measure to evaluate usability and facilitate comparisons across studies, ensuring consistency and reliability throughout the evaluation process.
Result: The examination of the SUS questionnaire indicated that users of the Baraya Travel application provided an average score of 75.26. This score suggests that the application demonstrates a commendable level of usability, earning a "Good" rating and a Letter Grade of "B". Nonetheless, there remains potential for enhancement to reach the desired standard of perfection. Users offered invaluable feedback and suggestions, highlighting the necessity for enhanced user interface navigation, improved application speed, and the implementation of additional features to augment convenience and user-friendliness.
Novelty: This research provides valuable insights for Baraya Travel application developers, emphasizing areas for improvement based on user feedback and suggestions. It contributes to the progression of online ticket booking systems, with the goal of enriching the overall travel experience for users. Highlighting the significance of user-friendly interfaces, efficient application performance, and convenient features is crucial for enhancing user satisfaction and fostering loyalty. Subsequent research endeavors could explore further factors influencing user satisfaction and loyalty in the context of digital travel applications.

Latar belakang pelaksanaan pengabdian masyarakat ini adalah rendahnya penggunaan bukti dalam praktik keperawatan serta urgensinya pengetahuan mengenai EBPN dalam memberikan pelayanan kesehatan oleh mahasiswa keperawatan. Pelatihan EBP berbasis problem solving adalah salah satu solusi dalam rangka transfer of knowledge mengenai penggunaan bukti yang mutakhir agar mahasiswa dapat meningkatkan efisiensi sumber daya serta memberikan asuhan keperawatan terbaik kepada pasien. Tujuan pengabdian masyarakat ini adalah untuk mengetahui perbedaan pengetahuan EBPN mahasiswa sebelum dan setelah diberikan pelatihan mengenai EBP berbasis problem solving. Metode yang digunakan adalah metode kuantitatif dengan teknik analisis uji komparatif (uji t) terhadap pengetahuan EBP mahasiswa sebelum dan sesudah diberikan pelatihan. Instrumen pengukuran yang diberikan adalah tes. Sampel dalam pengabdian masyarakat ini adalah mahasiswa keperawatan sebanyak 30 orang yang diambil secara purposive sampling. Hasil yang didapatkan adalah terdapat perbedaan pengetahuan EBPN mahasiswa keperawatan sebelum dan setelah diberikan pelatihan EBP berbasis problem solving. Kesimpulan dalam pelaksanaan kegiatan ini, bahwa pelatihan EBP berbasis problem solving terbukti efektif dan efisien serta dapat dipertimbangkan untuk meningkatkan pengetahuan EBPN mahasiswa.

Mechanical Neck pain memungkinkan terjadinya gangguan fungsional yang kebanyakan disebabkan karena postur dan mekanik. Sebuah riset epidemologi menyatakan sekitar 27%-48%  populasi pekerja yang mengalami keterbatasan aktivitas akibat mechanical neck pain. Sekitar 20 % masyarakat di Wilayah Kecamatan Biringkanaya Kota Makassar memiliki pekerjaan sebagai pekerja kantoran. Berdasarkan hasil observasi pekerja di Wilayah Kecamatan Biringkanaya Kota Makassar banyak yang mengalami keluhan dan leher. Hal ini di sebabkan para karyawan dalam melakukan pekerjaannya duduk dalam waktu yang cukup lama dengan postur yang tidak ergonomis. Dalam kegiatan pengabdian ini, ada beberapa kegiatan yang dilaksanakan antara lain: Pemberian edukasi tentang  resiko, gejala, dampak, cara pencegahan dan pemberian self mobilisasi pada kondisi mechanical neck pain. Pemberian Pelatihan tentang pemeriksaan dan self mobilisasi serta demontrasi praktis yang melibatkan kader puskemas dalam melakukan self mobilisasi yang benar pada kondisi mechanical neck pain. Berdasarkan hasil pre test dan post test terhadap kader puskesmas tentang pemahaman pencegahan dan pengobatan mechanical neck pain terjadi peningkatan pemahaman setelah mengikuti pengabdian masyarakat edukasi dan pelatihan self mobilisasi pada penurunan nyeri dan perbaikan aktivitas fungsional penderita mechanical neck pain

Dengue Hemorrhagic Fever (DHF) is an infectious disease that is influenced by the environment and people's behavior. In Jambi City, Dengue Fever is still a public health problem. This research aims to distribute dengue hemorrhagic fever (DBD) incidence in Jambi City. This research uses Global Moran's analysis and Local Indicator of Spatial Association (LISA) to determine the Hot Spot and Cold Spot areas for dengue cases in Jambi City in 2018 -2022 with a sub-district analysis unit of 62 sub-districts. During 5 years of observation, the results showed that the distribution pattern of dengue fever cases was in the form of a clustered pattern (I>E[I]) in 2018-2022. The research results on the distribution of dengue fever cases from 2018-2022 showed that the lowest cases were in 2021, namely 132 cases, while the highest cases were in 2019, 698 cases. The results of this study are expected to provide an overview of the distribution of dengue fever incidence in Jambi City in 2018 - 2022 so that it becomes a recommendation material for the Jambi City Health Office or the Jambi City Government in an overview of environmental factors related to dengue fever incidence in Jambi City to prevent various diseases due to poor environmental sanitation in Jambi City. Knowing the distribution pattern of dengue fever can determine regional priorities in implementing dengue fever prevention and control intervention programs in Jambi City.

Investment is a tool to earn income in the future. With so much uncertainty in generating income, it encourages someone to do something that will benefit themselves in the future. So that those who invest in the right way will feel calm about facing the lives of themselves and their families in the future. For investors, this research involved 54 companies and 11 companies were selected as samples through purposive sampling. The data analysis tools used in this research are classical assumption testing, multiple linear regression analysis, correlation analysis, coefficient of determination analysis, and hypothesis testing. The results of this research indicate that partially the Liquidity, Activity Ratio and Profitability variables have a significant effect on share prices. Leverage has no significant effect on stock prices. Simultaneously, the ratio variables Liquidity, Leverage, Activity and Profitability have a significant influence on Share Prices of 88.2% and the remaining 11.8% is influenced by other variables.

Lampung, a province located at the southern end of Sumatra Island, serves as a gateway for many migrants from different regions in Indonesia. However, some of the locals and tourists still face difficulties in finding the central location for souvenirs due to the  vast number of shops available. The purpose of this research is to assist tourists in finding the shortest distance to the nearest souvenir location using the Floyd Warshall algorithm. The software development method employed in this study is the Prototype, which consists of four stages, namely Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, Deployment Delivery & Feedback. In the Rapid Modeling stage, the Floyd Warshall algorithm is used to determine the shortest distance for users to find the souvenir location. The result of this research is an android-based application that can provide the    nearest souvenir location with the shortest distance around the user.

Sufism should not be interpreted as an activity solely focused on withdrawing from the world. Instead, Sufism should be utilized as a motivation for every Muslim to seek worldly wealth. However, this worldly wealth should ultimately be used to accumulate good deeds for the hereafter. Prophetic Intelligence is a Sufi study that can be used to integrate the interests of both the worldly and the hereafter. The practice of Sufism in Prophetic Intelligence refers to the example set by the Prophet Muhammad (PBUH). On one hand, he was the Messenger of Allah carrying out duties for the hereafter, and on the other hand, he was a merchant demonstrating how to be a successful entrepreneur in the world. The example of the Prophet Muhammad (PBUH) serves as a model of entrepreneurial ethics.
 

In the current era of technological advancement, deep learning has become a widely discussed and utilized topic, particularly in image classification, object detection, and natural language processing. A significant development in deep learning is the Convolutional Neural Network (CNN), which is enhanced with various optimizations such as Adam, RMSProp, and SGD. This thesis implements the Inception v3 architecture for the deep learning model, utilizing these three optimization methods to classify malaria disease. The study aims to evaluate performance and determine the best optimization based on classification accuracy. The results indicate that the SGD optimization with a learning rate of 0.001 achieved an accuracy of 94%, RMSProp with learning rates of 0.001 and 0.0001 achieved an accuracy of 96%, and Adam with learning rates of 0.001 and 0.0001 achieved an accuracy of 95%.