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PT. Semen Indonesia Logistik, as a logistics service provider company, plays a strategic role in supporting Indonesia's economic dynamics. This research aims to determine the extent to which responsibility accounting is implemented as a tool to assess the performance of cost center managers at PT. Semen Indonesia Logistik in supporting the achievement of company objectives. Responsibility accounting is one of the most important components of overall control in a company. This research uses a qualitative descriptive method with a case study approach. The analysis results show that PT. Semen Indonesia Logistik has implemented responsibility accounting optimally. This can be seen from the responsibility accounting requirements that have been implemented, including the organizational structure that has described the authority and responsibility of each level of manager in the company. The company has carried out budget preparation by classifying controllable and uncontrollable costs, account code classification, and responsibility reports. The implementation of responsibility accounting plays an important role in determining responsibility centers, making it easier for managers to evaluate their performance according to established responsibilities. The performance assessment standard at PT. Semen Indonesia Logistik uses Key Performance Indicators (KPI) that must be achieved within one period.

The research used is the library research method. The primary data sources in this study are hadiths about wearing sandals when praying, including the Sahih Bukhari Book, the Sahih Muslim Book, the Sunan Abu Daud Book, the Sunan Tirmidzi Book, the Musnad Ahmad bin Hambal, etc. The results of the studyHadith of wearing sandals when praying In this classification, the hadiths have the same wording and meaning, namely the statement of the Prophet praying using sandals. Hadith of not wearing sandals when praying In this classification of hadith, the Prophet took off his sandals during congregational prayers when the conquest of the city of Mecca and the actions of the Prophet were followed by his companions, and the reason the Prophet took off his sandals was because the Angel Gabriel told him that there was dirt on the Prophet's sandals. And it is explained in the sound of the hadith from another path, by rubbing the sandals on the ground in an effort to clean the sandals from dirt. And when taking off sandals it is advisable to prioritize the comfort of others. In terms of wording, the Prophet stated that he prayed with sandals and sometimes not. As for the prophet's editorial to differentiate (differentiate) between Muslims and Jews, the Prophet ordered to wear sandals when praying. In historical, sociological and anthropological context. The space and time when the Messenger of Allah performed or recited the hadith of wearing sandals when praying, in the early period of Islam there were not so many mosque buildings built and still had pedestals, so the Messenger of Allah gave concessions (rukhsah) on the face of this earth to create a mosque that could be used as a place of prayer. So it is possible to wear a khuf or na'lun when praying with the condition that it must be pure from the major and minor hadas. If we look at modern times, mosques have been built with ceramic or floor coverings.

This research aims to analyze the summation of blood cells so that it becomes a reference for pharmacy students in their learning and becomes the foundation for further study of pathology, pharmacology, and therapeutic mechanisms that they can use in professional practice. This study uses qualitative research, primary data sources and secondary data sources generated from literature studies, data types in the form of qualitative, data collection techniques in the form of tracing previous research, data analysis techniques in the form of qualitative descriptive.  Blood is one of the organs in the human body that is very vital. Blood is a unit consisting of blood cells and blood plasma. This analysis is carried out to obtain information about the sum of blood cells in the human body, hospitals or laboratories conduct blood analysis with excellent accuracy but the shortcomings lie in the slow process and the accuracy becomes non-standardized because it depends on the level of expertise/experience of the operator and also the level of fatigue. Efforts to overcome the length of time and for the purpose of initial diagnosis can use image processing techniques based on blood cell morphology. Along with the development of technology, there are currently several researchers who are conducting various studies in the application of computerized methods used in the classification of white blood cell types. The main function of red blood cells is to carry oxygen from the lungs and distribute it throughout the body. The analysis of human blood is very important, especially related to the diagnosis of a disease. Based on the results of this study, it shows how many red blood cells are present in the body and can be applied to detect a disease. For pharmacy students, an understanding of the sum of blood cells is necessary to develop knowledge and will be useful in clinical practice.

Coffee is an agricultural product highly favored by the majority of the Indonesian population. Coffee comes in various types, each with a distinct aroma. Currently, differentiating coffee types relies on conventional methods, using the human nose, which is shubjective and dependent on individual conditions. This approach is less effective. To address this, researcher have developed a machine learning based system using Arduino Mega 2560 as the microcontroller, gas sensor MQ4, MQ7, MQ135, as the aroma detection tools, and the Linear Discriminant Analysis (LDA) algorithm as the classification method. The results of this system can classify 5 types of coffee with an accuracy rate of 90%.

In this research the author took a study in the field of syntax. The syntactic survey in this research focuses on the types of transitive verb clauses in popular articles by Muh. Syahrul Padli in the April 2024 edition of the medium. This research aims to classify and analyze transitive verb clauses. This research is conducted with a synchronic approach that seeks to describe and provide the types and structures of clauses presented descriptively and qualitatively. The purpose of this study is to provide insight into the classification of verb clauses and provide examples of their classification in the description text in “Popular Articles by Muh. Syahrul Padli on Medium Masa Media April 2024 Edition”. Data collection in this research uses note-taking and listening/reading techniques. Data analysis techniques include data reduction, analysis, interpretation, and triangulation after which a conclusion is drawn. The data presentation technique is informal data presentation. From the data analysis, it can be concluded that some form of verb clauses in description texts, namely: transitive verb clauses. This article can be useful for students because it gives insight into the structured division of verb clauses and also the use of verb clauses in the article. 

This study aims to find out about . What are the legal provisions for determining suspects as pretrial objects in criminal law and b What are the legal implications for re-determination of suspects after being canceled by a pretrial decision. The data collection method in this study was carried out by literature study, which is a way of collecting data by searching and studying library materials (literature, research results, scientific magazines, scientific bulletins, scientific journals, etc.). Legal materials are collected through inventory procedures and identification of laws and regulations, as well as classification and systematization of legal materials according to research problems. Therefore, the data collection technique used in this study was a literature study. The results of the study show that pretrial is a new thing in the life of law enforcement in Indonesia, has a goal to be upheld and protected, namely protecting the rights of suspects at the level of investigation and prosecution or preventing the arbitrariness of law enforcement officials against someone who is undergoing a legal process and To guarantee the protection of human rights and for law enforcers to carry out their duties consistently, the Criminal Procedure Code establishes a new institution called pretrial. Seeing the contents of the decision as referred to in Article 82 paragraph (3) of the Criminal Procedure Code, that there are 3 (three) types of implementation of pretrial decisions: committing certain acts, paying an amount of money and providing rehabilitation. The legal basis for pretrial authority in deciding the suspect's determination is found in the Constitutional Court (MK) decision No. 21/PUU-XII/2014 which states that the determination of the status of a suspect is included in the object of pretrial and also in its ruling that Article 77 of the Criminal Procedure Code is contrary to the 1945 Constitution and does not have binding legal force as long as the pretrial is not interpreted including determination of suspects, searches , and confiscation. Meanwhile, the background to the addition of pretrial authority in deciding the determination of the suspect is that the determination of the suspect's status is the end result of the investigation activities carried out which are none other than the object of pretrial. Criminal acts after the status of the determination of the suspect is declared invalid in the pretrial decision are basically still valid. Examination in pretrial cases is only in the administrative area of the investigative process. Often the Pretrial decision which cancels the determination of the suspect by the investigator, is based.

This study aims to analyze the classification of climate types in Bandung City, West Java based on rainfall data using the Schmidt-Ferguson method. Rainfall is an important factor that will be analyzed for the Schmidt-Ferguson method. The Schmidt-Ferguson method classifies climates into four types, namely dry, semi-dry, semi-humid, and humid. This method will compare the number of dry months and the number of wet months. One of the reasons for choosing this method is its ability to group climates based on monthly rainfall patterns accumulated over a period of 10 years. The rainfall data used was taken from the local meteorological station on the Central Statistics Agency website during the period 2013-2023. The results of the analysis show that Bandung City has climate characteristics that are influenced by geographical and topographic factors, with a climate classification dominated by the wet tropical climate type. This study is expected to provide useful information for environmental planning and water resource management in the area.

Named Entity Recognition (NER) is a crucial natural language processing task that extracts and classifies named entities from unstructured text into predefined categories. While existing NER methods have shown success in general domains, they often face significant challenges when applied to specialized contexts like Filipino cultural and historical texts. These challenges stem from the unique linguistic features, and diverse naming conventions. This research introduces an enhanced rule-based NER approach that specifically addresses these challenges. At its core, the system utilizes curated Corpus of Historical Filipino and Philippine English (COHFIE), which serves as both training and evaluation data. This research presents an enhanced rule-based approach for NER using a Corpus of Historical Filipino and Philippine English (COHFIE) building on pattern-learning methods, incorporating character and token features, and by using positive and negative example sets. To enrich the classification process, we used the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM), a cultural heritage framework, to provide a more nuanced categorization of entities based on their historical and cultural significance. Tested across existing Filipino based models (calamanCy and RoBERTa Tagalog), the enhanced model shows improvement on identifying entities related to Filipino culture (CUL) and history terms (PER, ORG, LOC).

Diabetes is one of the diseases that poses a significant global health challenge, with a considerable impact on quality of life and mortality rates. This study examines the use of the Support Vector Machine (SVM) algorithm for diabetes classification through a literature review. SVM was chosen due to its ability to handle imbalanced and complex data. The aim of this study is to assess the effectiveness of SVM compared to other machine learning methods in detecting diabetes. The results of the literature review indicate that SVM achieves higher accuracy than other methods such as Naïve Bayes and Decision Tree, with some studies showing accuracy above 90%. This study is expected to provide deeper insights into the development of machine learning-based diagnostic systems for diabetes.

This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

Education is the main foundation for the progress of society and individuals, Basic education is very important to build students' knowledge and skills, especially in the field of science. Conventional methods, such as the use of illustrations and textbooks, are often not interesting and interactive. Digital technology presents new opportunities in learning innovation, including by utilizing augmented reality (AR). This study aims to analyze and evaluate the use of interactive media based on augmented reality (AR) in learning the topic of animal classification based on their type of food. The method used is a literature study by reviewing various scientific sources, including journals, articles, and relevant research reports in the last five years. AR-based interactive media offers an innovative and immersive learning experience, allowing students to interact directly with virtual objects in the form of three-dimensional (3D) visualizations. The results of the study show that the use of AR can improve students' understanding of the material, increase engagement in learning, and provide a more interesting and contextual learning experience. In addition, AR media is considered effective in helping students identify animal characteristics based on eating habits, such as herbivores, carnivores, and omnivores. This study concludes that the integration of AR in science learning at the elementary school level has great potential to improve learning effectiveness.

Education is the main foundation for the progress of society and individuals, Basic education is very important to build students' knowledge and skills, especially in the field of science. Conventional methods, such as the use of illustrations and textbooks, are often not interesting and interactive. Digital technology presents new opportunities in learning innovation, including by utilizing augmented reality (AR). This study aims to analyze and evaluate the use of interactive media based on augmented reality (AR) in learning the topic of animal classification based on their type of food. The method used is a literature study by reviewing various scientific sources, including journals, articles, and relevant research reports in the last five years. AR-based interactive media offers an innovative and immersive learning experience, allowing students to interact directly with virtual objects in the form of three-dimensional (3D) visualizations. The results of the study show that the use of AR can improve students' understanding of the material, increase engagement in learning, and provide a more interesting and contextual learning experience. In addition, AR media is considered effective in helping students identify animal characteristics based on eating habits, such as herbivores, carnivores, and omnivores. This study concludes that the integration of AR in science learning at the elementary school level has great potential to improve learning effectiveness.

This study aims to determine the use of language variations in terms of speakers (sociolect) in Semester VII B Students of the Indonesian Language and Literature Education Study Program, Faculty of Teacher Training and Education, Muhammadiyah University of Bengkulu, Academic Year 2022/2023. Based on these objectives, the method used in this study uses descriptive qualitative research. Data was collected through data obtained from student chat screenshots. The data collection technique in this thesis is a documentation technique in the form of screenshots. Data analysis techniques in this thesis are Data Identification, Data Classification, Data Analysis, Data Interpretation. Based on the results of the study, it can be concluded that theoretically there are 8 language variations based on the speaker's aspect (sociolect), namely 1. Acrolek, 2. Basilek, 3. Vulgar, 4. Slang, 5. Colloquial, 6. Jargon, 7. Argot, and 8. Ken. But based on research data, the type of use of language variations based on the speaker's point of view (siolect) used by WhatsApp Group members of students VII B of Indonesian Language and Literature Education Study Program Batch 2019 Muhammadiyah University of Bengkulu, obtained 114 data on 5 types of use of language variations, namely: 1. Acrolect found 7 data, 2. Basilek found 53 data 3. Slang found 28 data 4. Colloquial found 5 data and 5. Jargon found 21 data. Language variations that are often used by semester VII B students are basilek or regional language variations, because the group members come from different regions and many varieties of regional languages ​​are used.

The 2024 Indonesian Presidential Election marked the fifth general election in the country, aimed at electing a new President and Vice President for the 2024–2029 term. Candidates competed to succeed the outgoing president, who had served two constitutional terms. A key aspect of this election was the candidate debates, where each candidate presented their vision, allowing the public to assess their policies. These debates were broadcast on platforms like YouTube, giving the public a space to comment. However, analyzing YouTube comments presents challenges due to the volume of data, language diversity, and informal expressions. Sentiment analysis, crucial for understanding public opinion, uses algorithms such as Naïve Bayes, which is based on Bayes' Theorem and assumes feature independence. Naïve Bayes is widely used in text analysis for its speed and simplicity. When applied to YouTube comments from the 2024 debates, the algorithm demonstrated its effectiveness, especially with a balanced dataset through random oversampling. It achieved 85.155% accuracy, high precision, recall, and an AUC of 96.8% on an 80:20 data split. Its fast classification time (0.000998 seconds) makes it suitable for real-time sentiment analysis, validating its use for political events. Future applications may incorporate advanced techniques like BERT for more sophisticated analysis.

Building fires can significantly degrade the strength and integrity of steel structures, so post-incident evaluation is crucial to ensure building safety and feasibility. This study aims to evaluate the condition of the steel tower structure after the fire through a visual inspection method. A total of 35 structural elements were examined, including columns, beams, and bracing, to identify damage caused by heat exposure. The inspection results showed that 6 elements (17%) were in the category of Acceptable, 8 elements (23%) Needs Attention, 5 elements (14%) Not Acceptable, and 1 element (3%) Not Applicable because they had been removed. Steel columns generally remain upright without deformation, but suffer damage to the protective layer (coating). In contrast, most blocks lose their protective layers, are directly exposed to fire, show early signs of corrosion, and some suffer severe damage such as flange tears and cuts. These findings confirm the importance of systematic documentation and classification of element conditions as the basis for technical decision-making for structural improvement. Visual inspection proved effective as an initial step in the evaluation process, providing a relevant initial picture of the extent of damage and the need for intervention. This study recommends follow-up in the form of advanced structural analysis and material testing to ensure the feasibility of reusing the affected steel elements.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

Activities that have tasks and functions according to their provisions require fixed assets, which are an important component in supporting their operations. Indonesia uses Government Accounting Standard Statement (PSAP) Number 07 on fixed asset accounting as the asset treatment system. Every government agency that reports on its accounting treatment, including recognition, valuation, presentation, and disclosure, must use this standard statement. In addition, as a task support unit for the Tarakan City Tourism Office, the Tourism Office also provides financial reports. The purpose of this study is to determine whether this office has presented financial statements in accordance with PSAP No. 07. This research uses a qualitative descriptive approach with data collection through interviews, observation, and documentation. The results of this study indicate that the asset components of PSAP No. 07, namely asset classification, recognition, measurement, valuation, expenditure, depreciation, termination and disposal, and disclosure have met the principles of PSAP No. 07 on fixed asset accounting.

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

This study aims to utilize QGIS as a spatial analysis tool to map the distribution of divorces based on economic factors and disputes in Surakarta City during the 2020–2023 period. The data used includes spatial data in the form of Surakarta City's administrative map in shapefile format and non-spatial data comprising the number of divorces obtained from BPS Surakarta. Non-spatial data were integrated into spatial data using the "join attribute" feature in QGIS. The analysis process was conducted using classification methods to identify areas with the highest divorce density. The findings reveal that divorces due to economic factors are concentrated in low-income areas, such as Banjarsari and Jebres, while divorces caused by disputes exhibit a more evenly distributed pattern. The thematic maps were then exported into GeoJSON format for implementation on an interactive website accessible to the public and policymakers. This study contributes to the utilization of GIS technology in supporting data-driven decision-making.