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Background. Public transportation of vehicles coming in and going out from the Passo Transit Terminal, along with its location close to the market, can influence the levels of CO and HC in the exhaust gases from the vehicles. This can affect the air quality around the area. This is because the intercity buses use diesel fuel, some of them are old, and others have been operating for about 10 years with only 4 to 5 months before their last maintenance. Because of this, the researcher wants to study the levels of CO and HC in the exhaust gas emissions from the vehicles. Research Objective. To check the air quality of the exhaust gas emissions from intercity buses at the Passo Transit Terminal. Method. This study is a descriptive research that includes laboratory tests conducted at the HipperKes Laboratory Center. The population of this study includes 20 intercity buses, with a sample of 2 buses selected based on inclusion and exclusion criteria. Results and Discussion. The results of the CO levels show that bus 1 has 1.77% and bus 2 has 0.22%, which exceed the standard quality level of 0.5% as stated in the Regulation of the Minister of Environment and Forestry Number 8 of 2023. Conclusion and Suggestions. The parameters measured do not meet the required exhaust emission quality standards. The community and passengers are encouraged to increase awareness and use personal protective equipment such as masks

The rapid development of information technology has had a significant impact on various aspects of life, including the decision-making process that requires precision and objectivity. One of the problems often faced is product selection with various complex criteria, for example in selecting a quality drum. Drums as a percussion instrument have many variations in terms of sound quality, material, design, price, and durability, so a system is needed that can assist users in making a more rational choice. This study aims to develop a website-based Decision Support System (DSS) using the Weighted Product (WP) method to provide recommendations for drum selection. The WP method was chosen because it is able to perform calculations by considering the weight of each criterion so that the recommendation results are more objective. The system implementation is carried out through the design of a user-friendly web-based interface, making it easier for users to access, compare alternatives, and obtain recommendations without having to perform manual assessments that are time-consuming and potentially subjective. System testing was carried out by comparing the results of the system's recommendations with expert assessments, which obtained an accuracy level of 95%. These results indicate that the developed system has a high level of reliability and is suitable for use as an aid in decision-making in selecting quality drums. With this system, it is hoped that users, both beginners and professionals, will find it easier to choose drums that suit their needs and preferences.

The role of Islamic Religious Education (PAI) teachers in shaping students' religious moderation is highly needed. The very diverse religious diversity in public schools is a challenge for PAI teachers compared to religious-based schools. The purpose of this study is to identify PAI learning based on Problem Based Learning (PBL) to improve students' moderate attitudes and national insight which includes; planning, implementation, evaluation, supporting and inhibiting factors as well as the impact of problem-based PAI learning to shape students' moderate attitudes and national insight. This study uses a qualitative approach using the case study method. Data collection techniques are carried out through interviews, observations and documentation analysis. The data analysis technique uses the Miles and Huberman analysis technique using the ATLAS.ti analysis tool. The results of the study found that: First, PBL learning planning is carried out by; setting learning objectives, compiling learning steps and learning evaluation. Second, implementation is carried out by; orientation stage, guiding stage, developing stage and conclusion stage. Third, evaluation is carried out by; holistic, diagnostic, formative and summative evaluation stages. Fourth, supporting factors include; student involvement, high teacher creativity, student collaborative learning, program collaboration, contextualization of issues in learning, support for infrastructure and school culture. Inhibiting factors include; difficulty in determining relevant problems, time availability, lack of teacher understanding, student diversity, difficulty generating ideas, social and cultural barriers, and resistance to change. Fifth, a moderate attitude is demonstrated by; tolerance, openness, respect for diversity, good ethics in interactions, wasathiyah, social harmony, critical and objective.

This study aims to evaluate user sentiment toward the Liputan6.com application available on the Google Play Store. In the digital era, user reviews serve as a significant indicator in assessing the quality of an application. However, the inconsistency between rating scores and review content renders manual analysis less objective. To address this issue, a machine learning approach was adopted by comparing two algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). A total of 2,500 reviews were collected through a web scraping process and automatically labeled based on the rating (positive if ≥ 3, negative if < 3). The data preprocessing stages included cleaning, case folding, tokenizing, stopword removal, and token filtering. Subsequently, word weighting was carried out using the TF-IDF method, followed by classification using 10-Fold Cross Validation in RapidMiner. The evaluation results indicate that, in the positive class, NB demonstrated superior precision (89.47%), whereas SVM achieved higher recall (98.94%) and F1-score (90.96%). In the negative class, SVM performed better in terms of precision (66.15%), while NB attained higher recall (65.65%) and F1-score (36.34%). Further evaluation based on AUC and accuracy positioned SVM in the good category (AUC 0.842; accuracy 83.82%), while NB was categorized as fail (AUC 0.505; accuracy 60.87%). Overall, SVM is considered to be more effective than NB.

Human action recognition involves recognizing and classifying actions performed by humans. It has many applications, including sports, healthcare, and surveillance. Challenges such as a limited number of classes of activities and variations within inter and intra-class groups lead to high misclassification rates in some of the intelligent systems developed. Existing studies focused mainly on using public datasets with little focus on real-life action datasets, with limited research on HAR for military obstacle-crossing activities.  This paper focuses on recognizing human actions in an obstacle-crossing competition video sequence where multiple participants are performing different obstacle-crossing activities. This study proposes a feature descriptor approach that combines a Histogram of Oriented Gradient and Region Descriptors (HOGReG) for human action recognition in a military obstacle crossing competition. The dataset was captured during military trainees’ obstacle-crossing exercises at a military training institution to achieve this objective. Images were segmented into background and foreground using a Grabcut-based segmentation algorithm, and thereafter, features were extracted and used for classification. The features were extracted using a Histogram of Oriented Gradient (HOG) and region descriptors from segmented images. The extracted features are presented to a neural network classifier for classification and evaluation. The experimental results recorded 63.8%, 82.6%, and 86.4% recognition accuracies using the region descriptors HOG and HOGReG, respectively. The region descriptor gave a training time of 5.6048 seconds, while HOG and HOGReG reported 32.233 and 31.975 seconds, respectively. The outcome shows how effectively the suggested model performed.