bluuebunny/crossref_metadata_2025 huggingface dataset:
INNOVATIVE DEVELOPMENT OF THE TOURISM AND HOSPITALITY INDUSTRY WITHIN THE ESG CONCEPT
Authors: Морозова Н.С. , Морозов М.А.
Published: August 2024

Рассмотрены направления реализации ESG-принципов в индустрии туризма и гостеприимства. Отмечено, что в условиях геополитических изменений и санкционного давления российские предприятия продолжают разрабатывать и внедрять решения на основе ESG-инноваций. Уход с российского рынка международных гостиничных сетей и систем бронирования стал определенным драйвером, способствующим созданию российских инновационных решений в туризме и гостиничном бизнесе. Приведены примеры успешной ESG-трансформации предприятий гостеприимства, показаны наиболее типичные проблемы при внедрении ESG-принципов в деятельность предприятий индустрии туризма и гостеприимства.

The article discusses the directions for implementing ESG principles in the tourism and hospitality industry. It is shown that in the context of geopolitical changes and sanctions pressure, Russian enterprises continue to develop and implement solutions based on ESG innovations. The departure of international hotel chains and reservation systems from the Russian market has become a definite driver contributing to the creation of Russian innovative solutions in tourism and the hotel business. Examples of successful ESG transformation of hospitality enterprises are given, the most typical problems when introducing ESG principles into the activities of enterprises in the tourism and hospitality industry are shown.
LOCAL FACTORS AFFECTING THE FORMATION OF THE PERSONNEL MANAGEMENT MECHANISM OF AN INTERNATIONAL COMPANY
Authors: Тинькова Е.В. , Кузьминых А.С. , Тиньков С.А.
Published: August 2024

В статье рассматриваются локальные факторы, которые влияют на механизм управления персоналом в международной компании. Также представлены основные процессы управления персоналом организации и их компоненты на локальном и международном уровне. Основой создания механизма управления персоналом международной компании является мониторинг локальных практик управления отдельных стран, оценка факторов отличия, схожих практик. Механизм управления персоналом на локальном и международном уровне имеет ряд сходств и различий. В статье показано, какие возможности и риски могут возникнуть у компании при ориентации своих практик управления персоналом на один из уровней – локальный или международный.

The article examines local factors that influence the mechanism of personnel management in an international company. The main processes of personnel management of the organization and their components at the local and international level are also presented. A comparison of human resource management processes at the local and international levels is analyzed. The personnel management mechanism at the local and international levels has a number of similarities and differences. When forming a human resource management mechanism, it is necessary to examine the key characteristics and components of local human resource management practices, then examine international human resource management practices, identify their similarities and differences, and analyze what opportunities and risks a company may have when focusing its human resource management practices on one of the levels: local or international, all this is discussed in this article.
CONCEPTUAL APPROACH TO INNOVATION ACTIVITY IN INDUSTRIAL AND CIVIL ENGINEERING
Authors: Тюкавкин Н.М. , Карпушкин И.Д.
Published: August 2024

Промышленное и гражданское строительство по праву считаются локомотивом экономики. Состояние и развитие отрасли определяет устойчивое и эффективное функционирование социальной и производственной сфер государства. В настоящее время отрасль представляет не только строительство жилищной недвижимости и промышленных объектов, но и решает демографические вопросы, имеющиеся проблемы освоения заброшенных и пустующих земель. Одной из основных проблем промышленного и гражданского строительства является низкая техническая степень вооруженности организаций и значительный износ парка строительного оборудования, отражающих конкурентоспособность строительных организаций на рынке. В настоящей статье отмечены четыре главные проблемы, стоящие перед строительным комплексом: несоответствие ключевых направлений градостроительной политики запросам населения, дефицит земель для строительства, высокая стоимость жилья; низкий уровень государственного финансирования строительной сферы (17…20 %) и существенный уровень госрегулирования; наличие комплексной системы оценки затрат в строительной отрасли на основе стоимости полного жизненного цикла объектов строительства: по экспертным оценкам на изыскания и проектирование отводится 20 % затрат, а на эксплуатацию строительных объектов – 80 %; дефицит квалифицированного персонала. Решение данных проблем авторы предлагают организовать на основе инновационного подхода к организации строительной деятельности.

Industrial and civil construction is rightfully considered the locomotive of the economy. The state and development of the industry determines the sustainable and efficient functioning of the social and production spheres of the state. Currently, the construction industry represents not only the construction of residential real estate and industrial facilities, but also solves demographic issues and existing problems of developing abandoned and empty lands. One of the main problems of industrial and civil construction is the low technical level of equipment of organizations and significant wear and tear of the construction equipment fleet, which reflect the competitiveness of construction organizations in the market. The authors note four main problems facing the state’s construction complex: inconsistency of key directions of urban planning policy with the needs of the population: shortage of land for construction, high cost of housing; low level of government funding for the construction sector – 17…20 % and a significant level of government regulation; the presence of a comprehensive cost assessment system in the construction industry based on the cost of the full life cycle of construction projects: according to expert estimates, 20 % of costs are allocated for surveys and design, and 80 % for the operation of construction projects; shortage of qualified personnel. The authors propose to organize the solution to these problems on the basis of an innovative approach to construction. The work proposes a conceptual innovative approach to organizing construction activities.
PREVENTING THE COMMISSION OF NEW CRIMES: ON THE ISSUE OF THE CONTENT OF THE PURPOSE OF PUNISHMENT
Authors: Бычков С.Н.
Published: August 2024

Статья посвящена анализу внутреннего содержания предупредительной цели наказания. Автор обращает внимание на различные подходы к определению цели предупреждения совершения новых преступлений. В уголовно-правовой доктрине доминирующей сегодня является точка зрения о том, что содержание предупредительной цели составляет как частная, так и общая превенция. На основе анализа законодательных формулировок ст. 2 и ст. 43 Уголовного кодекса РФ с учетом мнений специалистов автор статьи предпринимает попытку изменить существующий подход к оценке содержания рассматриваемой цели уголовного наказания. Результатом проведенного исследования является вывод, что предупреждение совершения новых преступлений, являясь целью уголовного наказания, направлено на реализацию частного предупреждения, а общее предупреждение является основным элементом предупредительной задачи уголовного закона.

The article is devoted to the analysis of the internal content of the preventive purpose of punishment. The author draws attention to various approaches to determining the essence of the goal of preventing the commission of new crimes. In the criminal legal doctrine, the dominant point of view today is that the content of the preventive goal is both private and general prevention. Based on an analysis of the legislative wording of Article 2 and Article 43 of the Criminal Code of the Russian Federation, taking into account the opinions of experts, the author of the article makes an attempt to change the existing approach to assessing the content of the considered purpose of criminal punishment. The result of the study is the conclusion that preventing the commission of new crimes being the purpose of criminal punishment is aimed at implementing private prevention, and general prevention is the main element of the preventive task of the criminal law.
PROBATION AS A LEGAL INSTITUTION IN THE SYSTEM OF MEASURES FOR SOCIAL ADAPTATION OF CONVICTED PERSONS
Authors: Давитадзе М.Д. , Саргсян Э.А.
Published: August 2024

В научной статье рассматривается особенности пробации как правового института в системе мер социальной адаптации осужденных. Это касается как зарубежного опыта, так и российской практики. В рамках данной работы было проанализировано положение Федерального закона от 6 февраля 2023 года № 10-ФЗ «О пробации в Российской Федерации» с учетом мнения представителей правоприменительной практики и научного сообщества в области реализации мер социальной адаптации осужденных.

This scientific article examines the essence and features of probation as a legal institution in the system of measures of social adaptation of convicts. This applies to both foreign experience and Russian practice. Within the framework of this work, the provision of Federal Law № 10-FZ dated February 6, 2023 “On Probation in the Russian Federation” was investigated and analyzed, taking into account the opinions of representatives of law enforcement practice and the scientific community in the field of implementing measures of social adaptation of convicts.
CONDITIONS THAT HAVE A SIGNIFICANT IMPACT ON THE MORAL AND PSYCHOLOGICAL CHARACTERISTICS OF FEMALE OFFENDERS
Authors: Маслов Е.Т.
Published: August 2024

Настоящая статья посвящена актуальной проблеме – изменению нравственных установок у женщин, что побуждает их совершать преступления. Увеличение женской преступности негативно сказывается на духовном развитии общества и его безопасности. В связи с этим представляется необходимым разработать новые меры уголовно-правового воздействия. Эффективное предупреждение возможно при качественном изучении личности виновного, его характеристик и среды, влияющей на него. Дано понятие личности, затронут процесс ее становления, рассмотрены факторы, оказывающие влияние на внутренний мир женщины-преступницы и формирующие у нее искаженное восприятие мира. Определены условия, влияющие на нравственно-психологические характеристики женщины, а также подчеркнута необходимость проведения правоохранительными органами предупредительной работы, направленной на нейтрализацию негативных условий.

This article is devoted to the urgent problem of changing moral attitudes in women, which prompts them to commit crimes. The increase in female criminality has a negative impact on the spiritual development of society and its security, there-fore it seems necessary to develop new measures of criminal-legal impact. Effective prevention is possible with a qualitative study of the personality of the perpetrator, his characteristics and the environment affecting him. The concept of personality is given, namely the process of its formation is touched upon, the factors affecting the inner world of a female criminal and forming her distorted perception of the world are considered. The conditions affecting the moral and psychological characteristics of women are defined, and the need for law enforcement agencies to carry out preventive work aimed at neutralizing negative conditions is emphasized.
OWNERSHIP OF PUBLIC LAW COMPANIES
Authors: Приходько М.С.
Published: August 2024

В статье рассматриваются особенности правового режима имущества, закрепленного за публично-правовой компанией, которые заключаются в источниках его приобретения и порядке распоряжения им. Отдельно автором уделено внимание специфике обращения взыскания на имущество публично-правовой компании.

The article examines the features of the legal regime of property assigned to a public law company, which consist in the sources of its acquisition, the procedure for disposing of it. Special attention is paid to the specifics of foreclosure on the property of a public law company.
Industry Class Clustering of Software Expertise Competency at SMKN 2 Kraksaan Using Constrained K-Means Clustering Algorithm
Authors: Khairi Matlubul , Syafiih M. , Khairi Ahmad
Published: August 2024

Addressing the gap between school education and industry needs is a recurring concern, as many graduates struggle to enter the workforce due to lacking practical skills. Industry Classes aim to bridge this gap by preparing students with relevant skills and knowledge aligned with real-world industry demands. This study focuses on the application of Constrained K-Means Clustering to categorize students in the software engineering competency classes at SMKN 2 Kraksaan. This algorithm modifies traditional K-Means by utilizing Linear Programming Algorithm (LPA), ensuring each cluster meets predefined subject requirements. The research involves analyzing academic proficiency test data (TKDA) from 96 X-grade students, evaluating their abilities in analogy, series, figural, mathematical, and recall skills. Using a 3-cluster approach, each with 32 to 60 student capacity constraints, the study aims to optimize student distribution for effective learning outcomes. Evaluation through silhouette method yielded a score of 0.3199, indicating satisfactory separation between clusters with overlap to address. Cluster analysis revealed Cluster 2 as the most proficient, showcasing strengths in recall and series attributes critical for software engineering. These findings suggest that Constrained K-Means Clustering is effective in classifying students, highlighting Cluster 2 as optimal for software engineering competencies at SMKN 2 Kraksaan. Future research should focus on enhancing data quality, expanding sample size, and refining algorithms for improved clustering accuracy and effectiveness.
Disguising Text Using Caesar Cipher, Reverse Cipher and Least Significant Bit (LSB) Algorithms in Video
Authors: Siregar Manutur , Jinan Abwabul , Muhammad Raja Gunung Tar
Published: August 2024

In communication, there is a process of transferring information from the sender to the recipient. The information sent must be the same as the information received. If there are differences, it means that there has been a data change process carried out by irresponsible parties. One technique for changing the content of information is man in the middle. The data changer will receive information from the sender, then change it and forward it to the recipient, so that the changed information appears to have come from the sender.To protect information, this can be done by utilizing the science of cryptography and steganography which aims to protect information by changing it to another form or by inserting the information into other media. In this research, to protect information the Caesar Cipher Algorithm is used, this algorithm will change the letters in plaintext to another letter (ciphertext) by using an alphabetical shift according to the number in the form of the key used, namely > 1 and < 26, then the Reverse Cipher algorithm is carried out, namely changing the position of the letters of the plaintext from the first order to the last order and so on. The encrypted information will then be inserted into a video using the Steganography Algorithm, namely Least Significant Bit (LSB). Before being inserted, the video will first be converted into several image frames, then in one frame the information will be inserted. This can be done because the frame is a collection of RGB arrays which have values 0-255 or 0 and 1. So the insertion is done in bit form. Frames containing information will then be converted back into a video.On the receiving side, the video will be converted into a frame, next is the process of retrieving the information that was previously inserted. The information that has been taken is then reversed in order and then shifted using the Caesar chipper algorithm according to the key used by the sender, then the first letter of each word is changed to capital, so that the information sent is the same as that received. The implication of this research is that it is a way to combine cryptography with steganography as an information security technique.
Implementation of Data Mining for Speech Recognition Classification of Sundanese Dialect Using KNN Method with MFCC Feature Extraction
Authors: Shandy Ery , Anshor Abdul Halim , Ardiatma Dodit
Published: August 2024

The importance of preservation and development of speech recognition technology for regional languages such as Sundanese, which have unique phonetic characteristics. Regional language speech recognition can assist in the development of local, educational, and cultural preservation applications to implement and evaluate the effectiveness of the combination of MFCC and KNN methods in classifying Sundanese dialect speech recognition. Methods used include trait extraction with MFCC, which converts voice data into numerical representations based on frequency characteristics, and classification with KNN, which groups data based on similarity to train data. The Dataset used consisted of speech recordings of Western and Southern Sundanese dialects. The results showed that the k-Nearest Neighbors (KNN) method can classify Sundanese dialect speech recognition with an accuracy of 80.00%, showing good ability in distinguishing "Western" and "southern" dialects. Mel-Frequency Cepstral Coefficients (MFCC) proved to be very effective in extracting sound features, helping KNN achieve low error rates. The combination of MFCC and KNN proved effective for speech recognition classification of Sundanese dialects, providing satisfactory results with high accuracy.
Performance Comparison of CART And KNN Algorithms for Analyzing Early Predictions of Mental Health
Authors: Anggraeni Eling Sekar , Fitriya Maharani Lulu Amnah , Riyanti Desi , Aji Ranggi Praharaningtyas , Subarkah Pungkas
Published: August 2024

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life.  This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data.  The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.
Implementation of The Apriori Algorithm in Managing Stock Items at Drl.Rumahan Retail
Authors: Satria Permana Muhammad Safri , Widodo Edy , HadiKristanto Wahyu
Published: August 2024

Drl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence.
Analysis of Product Sales in the Application of Data Mining with Naive Bayes Classification
Authors: Zahri M. Hannata , Sunge Aswan S. , Zy Ahmad Turmudi
Published: August 2024

H&F Shoe Store is a privately owned Micro, Small, and Medium Enterprises retail store that sells merchandise. The owner serves customers directly and also acts as a cashier. In this store, the business owner is less aware of what types or categories of products are most in demand by customers, making sales operations less than optimal. Because of this, special expertise is needed to handle the problems in the retail store, namely data mining or Data Mining with the aim of digging up information related to sales problems, in this case the author will use the Classification method with the Naive Bayes algorithm. In this study, the author uses secondary data obtained from sales notebooks and re-collected into Microsoft Excel according to research needs. The data that has been collected on the software is 121 data which have 10 attributes, namely “Nama Produk”, “Size Produk”, “Kategori Produk”, “Jenis Produk”, “Gender Produk”, “Merek Produk”, “Stok Awal”, “Stok Terjual”, “Stok Sisa”, and “Penjualan”. The Naive Bayes Classifier method has successfully produced good results in classifying sales on a type or category of marketed products, the results obtained are in the form of product sales analysis and Naive Bayes model evaluation values. The results of the model evaluation values on the Confusion Matrix obtained are accuracy of 86.11%, recall of 84.62% and precision of 84.62%.
Apriori Algorithm to Predict Availability of Beauty Products
Authors: Hasibuan Maria Hikmah , Fakhriza M.
Published: August 2024

This study introduces the Apriori algorithm in beauty product availability prediction system as a solution to enhance stock prediction accuracy and mitigate inventory risks in the beauty industry. By applying data mining technology, specifically the Apriori algorithm, Kazana Kosmetik aims to gain insights into consumer purchasing patterns to optimize operations. The research analyzes transaction data to identify key buying patterns and improve stock management strategies. The results reveal seven main purchasing patterns with an average confidence value of 0.414, offering valuable guidance for Kazana Kosmetik in inventory control and marketing tactics. By leveraging data mining techniques, companies like Kazana Kosmetik can streamline sales strategies and enhance customer satisfaction. This research underscores the effectiveness of the Apriori algorithm in predicting beauty product availability and its potential to revolutionize operational efficiency in the cosmetics market.
Sentiment Analysis of Reviews of Tourist Attractions in the Lake Toba Area Using the Naïve Bayes Method
Authors: Wiranti Yuke , Nasution Yusuf Ramadhan
Published: August 2024

Lake Toba is one of the tourism destinations in Indonesia which is the main destination for domestic and foreign tourists. However, the natural beauty of Lake Toba is not enough to develop quality tourist destinations, so analysis needs to be carried out in order to develop tourist destinations that suit tourist needs with the aim of improving the economy from tourism, especially at Lake Toba. One aspect that must be analyzed is comments from tourists who have visited Lake Toba via various platforms. This is very influential for potential future tourists to have a reference for Lake Toba tourism. The analysis process can be carried out by analyzing comments using the Naive Bayes method so that managers of the Lake Toba tourist destination can improve tourist attractions and provide tourist satisfaction and develop various tourism innovations to meet various tourist needs in a sustainable manner. The results of the sentiment analysis of Lake Toba tourist reviews using Naive Bayes detected 31 positive labels, 378 neutral labels and 7 negative labels with an accuracy result of 77.49% from 1260 data, where training data was 1008 and test data was 252 data.
Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays
Authors: Udayana I Putu Agus Eka Darma , Indrawan I Gusti Agung , Prawira I Made Karang Satria
Published: August 2024

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.
Sentiment Analysis of Starlink on Twitter Using Support Vector Machine Algorithm
Authors: Sardin Sardin , Nugroho Agung , Kurniadi Nanang Tedi
Published: August 2024

Indonesia faces unique challenges in the provision of internet services. Cable and fiber optic infrastructure is often difficult and expensive to implement in many areas. Based on data from the Asosiasi Penyelenggara Jasa Internet Indonesia (APJII), by 2024 internet users will reach 221.5 million. Starlink, Elon Musk's satellite-based internet service through his SpaceX company, offers an innovative solution to fill the void in Indonesia's telecommunications infrastructure. However, Starlink's presence has raised concerns among local service providers, particularly regarding potential market disruption and existing regulations. Starlink could also pose a potential threat to Indonesia's security and sovereignty. Starlink has been a hot topic on various social media platforms, including Twitter. Twitter is a very popular social media platform with millions of active users who often share their opinions in real-time. The number of public responses in assessing the presence of Starlink in Indonesia, became a reference for a sentiment analysis. The Support Vector Machine algorithm is used to classify opinions into positive and negative categories. Based on testing that has been done using the Cross Validation technique with a K-Fold value with a total of 1976 tweets data. The results show that 1112 tweets contain positive sentiment and 864 tweets contain negative sentiment. This shows that 56.3% of people agree with the presence of starlink in Indonesia. While from the use of the Support Vertor Machine algorithm, the Accruracy value is 76.22%, Precision is 77.48%, and Recall is 81.38%.
Design and Development of a Mobile Application for Marriage Counseling and Divorce Mediation at KUA Medan Tuntungan
Authors: Satria Satria , Alda Muhamad
Published: August 2024

Marriage counseling is an effort that can be aimed at preparing prospective couples to establish a more serious relationship. Good communication involvement between the two prospective couples is one factor in building a solid foundation in marriage. Life after marriage has a big role and responsibility. Divorce can occur due to lack of readiness between the two prospective couples, so marriage counseling is very important so that prospective brides and grooms can prepare carefully for the relationship that will be carried out in the future. Therefore, this study builds a marriage counseling mobile application aimed at understanding the roles and responsibilities of marriage. This study itself uses a qualitative method by collecting, analyzing and reviewing valid data and information regarding marriage counseling for prospective couples. The waterfall method is used in this study as a system design where the waterfall is considered adaptable in the development of a marriage counseling mobile application accompanied by Unified Modeling Language (UML) modeling.
Literature Review Application of YOLO Algorithm for Detection and Tracking
Authors: Imanuel Feri , Waruwu Seven Kriston , Linardy Alvin , Husein Amir Mahmud
Published: August 2024

A vehicle tracking system is a computer program that utilizes devices to monitor the position, movement and condition of a vehicle or fleet of vehicles. Multi-vehicle tracking on highways has significant research interest and practical value in building intelligent transportation systems. Nevertheless, traffic road video frames consist of various complex backgrounds and objects. Detection and tracking are very challenging because foreground to background switching occurs frequently. One-stage algorithm approaches such as YOLO and its various variants have been proven to be accurate for detecting vehicles. Meanwhile, the SORT, DeepSORT, ByteTrack and other algorithms can be combined in YOLO. The aim of this study is to highlight existing research on the application of YOLO and its variants in detecting and tracking vehicles, especially in traffic management. The journals used are limited to 2019 – 2024 and the journal sources consist of Hindawi, IEEE, MDPI, Research Gate, Science Direct, and Springer. Based on the research that has been reviewed, the YOLO variant algorithm approach has been successfully applied in the field of vehicle monitoring to support smart cities. In addition, many new model combinations and improvements have been proposed, proving that this algorithm has a big influence in the field of computer vision.
YOLO-Based Vehicle Detection: Literature Review
Authors: Kosasi Tommy , Sihombing Zein Adian Laban , Husein Amir Mahmud
Published: August 2024

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.
Evaluation of Information Technology Service Devices At High Schools in Kendal Regency With ITIL 4.0
Authors: Herlinudinkhaji Didin , Purnamasari Dewi , Erwanti Nindita
Published: August 2024

Schools as educational institutions with information technology services in the form of New Student Admission Systems, e-report systems, and other information technology services require devices supported by good service. Information technology devices must be appropriately managed to conduct business processes according to business goals. With good service management, the business value offered to customers (students) will increase. ITIL V4 contains information technology service management guidelines that contain best practices for information technology service management, including attention to customer experience, value streams, and support for current digital transformation. This research measures information technology equipment services at high schools in Kendal Regency. This research uses indicators adapted to schools with ITIL V4: Key Performance Indicators (KPI) and Critical Success Factors (CSF). The results of this research show that the performance of the hardware needs to be improved, and the value of the maturity model for each service device is still not good.
Implementing Preference Selection Index for Optimal Employee Ranking in Organizational Decision-Making
Authors: Wijanarko Rony , Nugroho Fifto , Islam Khoirul
Published: August 2024

The rapid development of information technology has affected various aspects of life, including in the world of work. This research aims to apply the Preference Selection Index (PSI) method in determining the best employees at Bina Karya Utama Company. The assessment is based on four main criteria: Attendance, Tardiness, Overtime, and Length of Service. Data is obtained through observation and interviews, then processed using the PSI method which involves the normalization process and the calculation of preference values. The results showed that employees with alternative code A8 obtained the highest score, followed by A5 and A9. The PSI method proved to be effective in helping companies make objective and fair decisions, as well as motivating employees to improve their performance. This research concludes that a PSI-based decision support system can improve transparency and fairness in employee evaluation at Bina Karya Utama Company.
Optimizing Decision-Making for Aid Allocation in Underdeveloped Regions Using the MOORA Method
Authors: Wijaya Vera , Nugroho Fifto , Kraugusteeliana Kraugusteeliana
Published: August 2024

The allocation of assistance for the Family Hope Program is a process that requires precision to ensure that assistance is given to those most in need. This research develops a Decision Support System (DSS)  using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method for optimizing the selection of beneficiaries in disadvantaged villages which includes criteria used including education, toddlers, pregnant women, disabilities, elderly, income, employment, number of dependents, and house size. Each criterion is normalized and given a weight according to its level of importance. The results show that alternative A2 has the highest optimization value with Yi of 0.254, followed by A8 (0.208) and A5 (0.204). In contrast, alternatives A3 (0.029) and A10 (0.035) have the lowest optimization value. Matrix normalization and criteria weights show the significant influence of the criteria of education, pregnant women, elderly, income, number of dependents, and house size in the selection process. The implementation of DSS with the MOORA method is proven to increase efficiency and accuracy in the selection process of Family Hope Program beneficiaries, reduce subjective errors, and ensure assistance is channeled to those who really need it. Therefore, the MOORA method is recommended as an effective tool to optimize social assistance allocation, increase transparency, and reduce bias in decision-making.
Sentiment Analysis of Oppenheimer Movie Reviews: Naïve Bayes Algorithm for Public Opinion
Authors: Noviansyah Berliana , Effendi Muhammad Makmun , Achmad Yudianto
Published: August 2024

The development of information and communication technology has revolutionized the way people consume and engage with media, particularly in the realm of film. Online platforms such as Netflix, Amazon Prime Video, and YouTube have transformed movie consumption habits, providing a vast array of options for viewers to explore and enjoy. A crucial aspect of this digital landscape is the proliferation of movie reviews, which serve as valuable guides for users seeking to discover films aligned with their preferences. However, the abundance of reviews, often varying in quality and objectivity, necessitates tools capable of effectively processing and understanding these textual data. This research delves into sentiment classification of Oppenheimer movie reviews, utilizing the Naive Bayes algorithm to categorize reviews into positive, negative, and neutral sentiments. The dataset comprising audience reviews and numerical ratings undergoes preprocessing using the TF-IDF method to facilitate numerical representation. Subsequently, the Naïve Bayes algorithm is trained on this processed data to accurately classify sentiments. The model demonstrates exceptional performance, achieving an accuracy rate of 97.45% in distinguishing between positive, negative, and neutral sentiments within Oppenheimer movie reviews. This study underscores the efficacy of the Naive Bayes algorithm in sentiment classification and emphasizes the significance of employing techniques like TF-IDF for enhancing sentiment analysis in the domain of movie reviews.
Comparative Analysis of Naïve Bayes and K-Nearest Neighbor (KNN) Algorithms in Stroke Classification
Authors: Iswara Ida Bagus Ary Indra , Anandita Ida Bagus Gede , Dahul Maria
Published: August 2024

Stroke, also known as cerebrovascular, is a type of Non-Communicable Disease (NCD). The symptoms of this disease arise due to a blockage (ischemic) or rupture (hemorrhagic) of a blood vessel that disrupts blood flow to the brain. This condition causes a lack of oxygen and nutrients to brain cells, resulting in damage and potentially death. This research aims to compare the use of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms in classifying stroke diseases. The research process involves data collection, data validation, data preprocessing, data reading, data transformation, data splitting, model implementation, classification evaluation, application of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms, and comparative analysis of results. The variables used in this study include: gender, age, hypertension, heart disease, ever married, work type, residence type, avg glucose level, bmi, smoking status, stroke. Sugar, BMI, Smoking Status, Stroke. Based on the experiments conducted, it was found that the Naive Bayes algorithm achieved an average accuracy rate of 91.67%, while the K-Nearest Neighbor (K-NN) algorithm achieved an average accuracy rate of 95.59%. Therefore, it can be concluded that the K-Nearest Neighbor (K-NN) algorithm has a higher average accuracy rate than the Naive Bayes algorithm, with a percentage difference in accuracy of 3.92%.
Product Layout Analysis Based on Consumer Purchasing Patterns Using Apriori Algorithm
Authors: Radhitya Made Leo , Widiantari Ni Komang Mira , Asana Made Dwi Putra , Wijaya Bagus Kusuma , Sudipa I Gede Iwan
Published: August 2024

In every self-service store, it is certain to have a sales transaction data, where the data will continue to grow every day. But in self-service stores the data is only a record of sales at the store. Whereas transaction data can be used as information on how consumer purchasing patterns when shopping at the store, but not all supermarkets know this. So this research aims to find information on these purchasing patterns, where to do this research using the apriori algorithm which is part of the association technique which is also part of data mining, where in its application it will calculate the support value, confindence value and will be tested using the lift ratio. And after the calculation is carried out, optimization will be carried out using the high utility itemset mining variable which will calculate the highest profit value on the product, so that based on the calculation, the final result is obtained with a support value of 85%, a confidence value of 86%, a lift ratio test of 1.01 and the high utility gets the highest result of Rp. 567,000.
Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method
Authors: Anggraeni Dwi Puspita
Published: August 2024

Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data.  This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.
Reverse Engineering for Static Analysis of Android Malware in Instant Messaging Apps
Authors: Adnyana I Gede Adnyana , Nugraha Putu Gede Surya Cipta , Nugroho Bagus Rahmat Adin
Published: August 2024

Malware poses a significant threat to Android devices due to their high prevalence and vulnerability to attacks. Analyzing malware on these devices is crucial given the persistent and sophisticated threats targeting Android users. Static analysis of Android malware is a key approach used to detect malicious software without executing the application. This method involves meticulously examining the application's source code or binaries to identify signs of suspicious or harmful activities. The research methodology consists of three stages. The first stage involves collecting malware samples spread through instant messaging applications. The second stage employs reverse engineering, where APK files are decompiled to extract their contents. Following this, a static analysis is conducted, focusing on the AndroidManifest.xml file and the source code to identify the behavior and potential threats posed by the malware. The static analysis results revealed that Android malware often requests sensitive permissions to access personal data, such as receiving, reading, and sending SMS, as well as accessing location and contacts. Further analysis uncovered that after acquiring this data, the malware transmits it to the Telegram API via authenticated HTTP requests using specific tokens and chat_ids. These findings highlight that the permissions requested by the malware are designed to clandestinely collect and export personal data, posing a severe threat to the privacy and security of Android users.
Effect of Compensation, Discipline and Motivation on Employee Performance: The Case of PT Jaya Masawan Putera Sejahtera (JM Group) Palembang
Authors: Desmaliani Marni , Rahayu Sri , Trihandayani Zaleha
Published: August 2024

This research, with practical implications, aims to determine the effect of compensation, work discipline, and work motivation on the performance of employees of PT Jaya Masawan Putera Sejahtera (JM Group) Palembang. The objects of this research are PT Jaya Masawan Putera Sejahtera (JM Group) Palembang employees. The sampling technique used the cluster sampling method with 315 employees. The research method used is associative research using primary data obtained from the results of questionnaire answers from 176 respondents. The analysis technique used is Multiple Linear Regression. This research shows that compensation, work discipline, and motivation not only significantly influence the performance of PT Jaya Masawan Putera Sejahtera (JM Group) employees but also have a positive influence. Partial testing shows a positive and significant influence of compensation, work discipline, and motivation on the performance of PT Jaya Masawan Putera Sejahtera (JM Group) Palembang employees, providing practical insights for effective human resource management.
HSHEP: An Optimization-Based Code Smell Refactoring Sequencing Technique
Authors: Maini Ritika , Kaur Navdeep , Kaur Amandeep
Published: August 2024

The process of refactoring enhances software quality by modifying its design composition while preserving its core framework. However, addressing code smells without appropriate prioritization can be ineffective. Code smells significantly increase maintenance costs and obstruct system evolution. Refactoring sequencing techniques mitigate these issues by improving a system’s internal structure without altering its external behavior. In large-scale systems, the sheer number of code smells can be overwhelming, and not all can be automatically resolved. Hence, prioritizing code smells based on criteria such as risk and importance is essential. This paper introduces a novel hybrid approach utilizing the hybrid spotted hyena and emperor penguin (HSHEP) optimization-based algorithm. This approach aims to optimize the sequence of code smell bugs by incorporating maintainer opinions and requirements, thereby maximizing the resolution of critical code smells. Unlike existing technologies, the HSHEP algorithm combines the strengths of two optimization strategies, offering a unique and innovative solution to refactoring challenges. To validate the effectiveness of the proposed method, it was applied to various large-scale open-source systems, analyzing five different types of code smells. Results demonstrated a significant improvement in maintenance efficiency and system evolution, confirming the superior performance and practical applicability of the HSHEP-based approach.
 
Received: 22 April 2024 | Revised: 13 June 2024 | Accepted: 30 June 2024
 
Conflicts of Interest
The authors declare that they have no conflicts of interest in this work.
 
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
 
Author Contribution Statement
Ritika Maini: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Navdeep Kaur: Supervision. Amandeep Kaur: Project administration.