Reviewerly/exHarmony huggingface dataset:
Applicability of Multispectral Images to Detect Soil Organic Carbon Content in Land Suitability Assessment: A Case of a Sugarcane Plantation

Soil organic carbon is important for sugarcane production as it plays a significant role in the development of soil aggregates and soil improvement in any agricultural soil, hence increasing soil health. Increasing soil organic matter encourages soil aggregation and slows the rate of organic matter breakdown. Soil aggregates act as nuclei for soil stabilization with time. It improves soil attributes by increasing organic carbon content in the soil and enhances the physical properties of the soil that favor water infiltration and retention. However, traditional methods of SOC determination like laboratory analyses are expensive and time-consuming. The objective of the study was to assess the applicability of multispectral images to determine SOC content in the surface soil. The research adopted two means to determine SOC of sugarcane. Under the first method, the lands were selected according to the sugarcane productivity in the study area, namely: low (35-54 t ha-1), medium (55-79 t ha-1), and high (80-100 t ha-1) productivity. Soil samples were collected up to 15cm depth. Walkley and Black Method was used for SOC determination in the laboratory. Simultaneously, multispectral images of each land were obtained using a drone platform. Multispectral images were then used to calculate Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), and Modified Secondary Soil Adjusted Vegetation Index (MSAVI2). These indices were used to predict SOC. The outcome was compared with SOC obtained from the laboratory analysis results. The results showed that the soil organic carbon (SOC) varied between 2.72 and 3.63% from the mean for the the highest and lowest productivity lands respectively. The results of regression analysis observed a moderate correlation between SOC and the BSI values (0.4828). Weak correlations were observed in MSAVI2 (0.0269) and NDVI (0.0858). Future research should focus on improving these indices for SOC determination with increasing sample quantity.
Komparasi Algoritma Neural Network dan K-Nearest Neighbor Dalam Mendeteksi Malware Android

The report from the State Cyber and Cryptography Agency (BSSN) recorded approximately 100 million cases of cyber attacks in Indonesia until April 2022, with ransomware and malware being the most commonly detected types of attacks. In this context, the increasingly sophisticated and hard-to-detect Android malware poses a serious threat, especially with successful penetrations into the Google Play Store. Therefore, early detection of Android malware is crucial. This research aims to compare the performance of two machine learning algorithms, Neural Network and K-Nearest Neighbors (KNN), in detecting malware on the Android platform. The dataset has been processed and divided into training and testing data. Both algorithms are trained using the training data, and their results are validated and evaluated. The research findings show that Neural Network achieves the best performance with an accuracy of 97%, precision of 97%, recall of 97%, and F1-score of 97%. Meanwhile, KNN performs slightly lower with an accuracy of 95%, precision of 96%, recall of 95%, and F1-score of 95%. In conclusion, Neural Network outperforms KNN in detecting Android malware based on accuracy and classification consistency. Further research suggestions involve the use of other algorithms, broader and more representative datasets, as well as the addition of features and parameter optimization. This research contributes to the development of accurate and effective solutions for detecting and identifying potentially harmful Android applications
Effect of knowledge management on intellectual capital

In the dynamic and competitive banking industry, knowledge management plays a vital role in enhancing intellectual capital, which is considered a valuable intangible asset for organizations. The purpose of this paper is to examine the effect of knowledge management (KM) on intellectual capital (IC). The study aims to provide a comprehensive understanding of how knowledge management initiatives affect the development, utilization, and enhancement of intellectual capital. The research method used is a quantitative approach, using data collection techniques with survey methods by distributing questionnaires. Data were garnered with questionnaires from 96 employees. To achieve this objective, a quantitative analysis was conducted using the Statistical Package for the Social Sciences (SPSS) as the analytical tool. Moreover, the regression analysis demonstrated that knowledge management practices significantly predict intellectual capital in Commercial Risk Group, Bank Mandiri. The study found that knowledge management processes, including knowledge socialization (KS), knowledge externalization (KE), and knowledge internalization (K1) were significant predictors of intellectual capital. These results emphasize the importance of fostering a culture of knowledge-sharing and continuous learning within the organization to enhance intellectual capital. In conclusion, this study provides empirical evidence of the positive impact of knowledge management on intellectual capital in Bank Mandiri.
The Determinants Of Economic Growth In North African Countries

Economic growth is one of the most important indicators used in developing and evaluating the economic activity of developing countries, including North African countries, but economic growth faces several major economic challenges such as FDI, unemployment rate, inflation and exchange rate. This study used quantitative research, and data were collected by documentation method with secondary data since 2001-2020, Based on the results of the analysis by panel data regression, it was found that FDI is statistically significant, while it has a positive sign, which indicates that it has a positive impact on economic growth, If FDI increase by1%, economic growth will increase by 0.684%, and there is no impact of inflation and unemployment rate on economic growth, and the exchange rate has a positive and significant impact on economic growth, as each increase leads by 1% in the exchange rate to a increase in economic growth by 0.420%, in future research other variables affecting economic growth, such as the volume of exports, import tariffs, taxes or government regulations.
Key Frame Extraction Based on Real-Time Person Availability Using YOLO

Keyframe extraction plays a crucial role in summarizing lengthy videos, particularly in the context of surveillance footage with a fixed field of view that records events over extended periods. The process of manually reviewing such videos can be time-consuming and challenging to extract essential information effectively. To address this issue, a study was conducted to evaluate four distinct methods for keyframe extraction, with the aim of determining the most suitable approach for creating a people-based dataset. The four methods assessed in the study were absolute difference, entropy, optical flow, and object detection-based video summarization using the YOLO (You Only Look Once) algorithm. Each method offers a unique approach to identify keyframes that encapsulate critical instances within the video footage. Among all the four evaluated methods, the object detection-based video summarization approach stood out as particularly promising. This method employed the YOLO algorithm, which utilizes advanced object detection techniques to identify and track people within the video frames. With this approach only fewest numbers of frames are extracted but still capturing all the relevant instances featuring people. The results of this study suggest that object detection-based video summarization using the YOLO algorithm is a highly effective method for keyframe extraction in surveillance videos. By significantly reducing the number of frames while preserving all relevant instances, this approach offers a time-efficient solution for reviewing and analyzing extensive video footage, ultimately facilitating the creation of a people-based dataset for further research and applications in various domains.
User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations.
Flipped Classroom Learning Model With Kahoot Media: the Effectiveness to Affect Students Motivation and Learning Outcomes in Biology Subject

The problem of low motivation and student learning outcomes in Biology after the Covid-19 pandemic is still found at SMAN 1 Payakumbuh District. In this study a flipped classroom type hybrid learning model was used combined with the use of the kahoot! game application. The research was conducted using a quasi-experimental quantitative approach with a one shot case study design in class XI MIPA 1 students totaling 32 people. Data were collected using motivational questionnaires and objective questions to measure student learning outcomes. The results of the study show the use of the flipped classroom type hybrid learning model combined with the use of the kahoot! effective effect on increasing motivation and student learning outcomes in Biology subject, besides that the use of this model also has a positive effect on student independence in learning.
ANALISIS KINERJA STREAMING VIDEO MENGGUNAKAN MOBILE ADHOC NETWORK

Mobile Ad Hoc Network is a Network made of several wireless mobile host that made a temporary Network anytime anywhere. Each node connected to the network will act as host as well as a router, there of each node must be willing to send a Package to the next node. Because of this, a routing Algorithm is needed to arrange the Mobile Ad Hoc Network test.&#x0D; This write up describes a network test that consists of 3 (three) test models or scenarios using the video conference technology. The parameter that will be calculated is the quality of service which will then be compared with the increase in the number of node.
A Survey on Design, Applications and Limitations of Computational Intelligence

Computational Intelligence (CI) is a branch of Artificial Intelligence (AI), which deals with the designing and enhancement of intelligent models with the ability to process and assess big data. The segment of CI has developed significantly over the past few decades due to the enhancement of AI and soft computing approaches, techniques, and tools, which envision the status of intelligence embedded in reality observation. This research contribution provides a critical survey of CI designs and its different applications. This research provides a description of the major methods, techniques and concepts in the field of CI, including smart system designs, CI types, and practical applications in different fields. The research also presents an analysis of li mitations and challenges of CI, and provides insight into the results, effects and future research. The main purpose of this study is to provide a detailed understanding of CI applications and design; making is a vital resource for practitioners, and researchers in the field of AI.
AQUA: A BLOCKCHAIN BASED MULTI-WINNER E-VOTING SYSTEM

Conducting fair and secure elections has been the heart of democracy since its birth in ancient Greece. To be more precise, the act of voting has been an essential and significant component of human societies, particularly for government and municipal elections. Nonetheless, the conventional method of casting a ballot by physically visiting a voting booth raises concerns regarding the security of the process, as there have been reported incidents of tampering. To tackle these issues, we propose an online voting system that makes use of Blockchain technology. By utilizing encryption and hashing techniques, the Blockchain system guarantees the security of each vote. In our proposed system, a smart contract is triggered when an election is scheduled, thereby facilitating a secure and efficient voting process. The need to modernize voting systems has become more urgent due to the increasing frequency and volume of voters. Despite these challenges, security and fairness should not be compromised. Therefore, modernizing voting systems in terms of security is essential to ensure the integrity of elections, so that voters can have confidence in the electoral process. To address this challenge, we introduce Aqua, a distributed system that implements a multi-winner approval-based voting scheme, following the AV rule, on the Ethereum blockchain. Aqua supports both English and Greek languages for multi-winner elections with committee sizes up to three members. It is user-friendly, cost-effective, and accessible from any device with an Internet connection. Aqua voting is based on the AV rule, but it can be readily modified to accommodate any multi-winner voting scheme with any committee size.
Analysis of the Impact of Erroneous Land Measurement Results on Construction Project Implementation (Study Case of the Integrated Sports Area Development for South Kalimantan Province)

Development problems in Indonesia are divided into two major parts, namely the problem of development objects and the problem of subjects who will carry out development. Problems originating from the subject of development are in terms of land acquisition. Thus, land acquisition efforts for these purposes need to be handled properly and carried out by taking into account the role of land in human life and the principle of respect for legal rights to land. The Public Works and Spatial Planning Office of South Kalimantan Province has planned the development area of the government center in Banjarbaru City. One of them is the design of a more representative sports area. In 2022 and 2023, the land acquisition of the Integrated Sports Area Phase I and II has been carried out together with the implementation team of the South Kalimantan Provincial BPN Office. In connection with this achievement, this research is important because the land acquisition process is related to project productivity which causes the construction of the Integrated Sports Area to be hampered. This research focuses on the variable factors of the stages and technical implementation of land acquisition (Inventory and identification of land tenure, ownership, use, and utilization) and internal factors (Verification and improvement of land parcel maps and / or nominative lists by the Chief Land Acquisition Executive if there are objections to the results of inventory and identification), namely the results of land measurement. Analysis of measurement error results by finding the amount of linear shift to determine the standard deviation of comparison of measurement results of two different tools. The results of the questionnaire will be tested for validity and reliability to determine the level of importance of the measurement results from respondents followed by Relative Importance Index (RII) Analysis to find out what dominant impacts affect the Construction project, these two methods are taken into consideration in determining mitigation efforts for land measurement inaccuracies on the physical development process. The impact of KOT land measurement errors on delays in physical development plans is obtained dominant factors based on the rank of RII values, namely the compensation value (X1.2), development project costs (X2.4), design changes (X2.3), plant growth and affected buildings (X1.3), development permits (X2.5), land certification (settlement) (X1.4), development process (X2.1), smooth land acquisition (X1.1) while delays (X2.2) are below 75% of the RII value so they are not included as priority factors to be mitigated.
Investigating the influencing factors on trust and professional skepticism in the relationship between the auditor and the client firms’ managers

The current study investigates how interpersonal trust between the auditor and the client firms’ managers affects the auditor's professional skepticism in Iraq. In the qualitative part of the research, 20 auditors and client firms’ managers were interviewed. In the qualitative section, the interviews conducted were analyzed using MAXQDA software. The statistical population includes two groups: all auditors, 1735 participants until the end of 2022, and all senior managers and managers of public companies, private companies, and other organizations. The statistical sample size is 314. The data collection instrument was Aschauer et al.'s (2017) questionnaire, which was analyzed using PLS3 software. The findings support the idea that there is a positive correlation between the degree of skepticism towards the auditing profession and the degree to which corporate managers and auditors are trusted. Perceived trust between auditors and managers and skepticism of auditors' professions are not significantly correlated with the length of the auditor's contact with the client. The relationship between auditors' and managers' trust in them and their skepticism of the auditing profession is not also significantly impacted by the supply of non-audit services.
CRRESS - Session 7 - Working in the Public’s Interest: Publishing Research Replication Packages

This session brings various perspectives together on how research institutions think about publishing replication packages themselves, i.e., not a journal or generalist repository. Panelists come from a university with a specialized, university-centred data repository; from a Federal Reserve Bank with an active researcher community, and from a non-profit (non-academic) research institution. Each faces the requirements of varied internal researchers, external visibility, and differing audiences. The panelists can all speak to how a research institution makes decision about the degree of transparency, and how much of that to do with internal resources.
APLIKASI CANVA SEBAGAI MEDIA PEMBELAJARAN SEJARAH DI ABAD 21

The purpose of this study is to examine related to sophisticated, imaginative and inventive digital-based learning media through recorded learning involving the Canva application in history learning so that it can create an innovative atmosphere in the learning process.The research method used is descriptive qualitative with data collection techniques using documentation techniques and literature research analysis techniques to arrive at the stage of drawing a conclusion.The results of the study show that the use of the Canva application in learning history is an interesting and creative learning medium in accordance with the development of 21st century schools, especially in all parts of the use of innovation in everyday life. Thus, the final conclusion in writing this article explains that the Canva application is very easy and practical to access in learning activities. The application of the Canva application in history learning is the right solution to assist educators in creating computerized learning media in the 21st century so that the delivery of learning materials can be more easily accepted by students.
Relevance classification on service desk texts using Natural Language Processing

Service desk systems have a vast and rich base of information, consisting of the history of calls made, which can and should be used as a reference base for subsequent calls. Common search tools, such as keyword searches, prove to be unfeasible in large datasets, in addition to being able to bring results not necessarily related to the problem. “State-of-the-art” techniques exist, but they require high computational and operational costs for their training and use. In this sense, the purpose of this work is to investigate the sensitivity of machine learning algorithms in finding the characteristic defined here as “relevance”: the characteristic of texts with knowledge that can be reused. The motivation is that non-relevant texts can be removed in advance from the database, allowing complex algorithms to be employed in a more condensed database, reducing computational costs. Tests were performed with several combinations between the TF-IDF vectorizer and the word embedding Doc2Vec and the classic classifiers Naive-Bayes, Adaptive Boosting, Random Forest, Stochastic Gradient Descent, Logistic Regression, Support Vector Machine and Light Gradient Boosting Machine, and the classifier TextConvoNet, an architecture based on Convolutional Neural Networks. The TextConvoNet classifier presented the best results, with metrics close to 0.93, showing that the concept is detectable and that the technique is viable for removing non-relevant texts from a database.
Application of The Think Talk Write (TTW) Method Oriented Towards Self-Efficacy of Students To Improve Indonesian Language Learning Outcomes

This research aims to found out: (1) the students’ Indonesian grade use Think Talk Write (TTW) better compared than the students’ Indonesian grade use Cooperative Learning method, (2) the students’ Indonesian grade by high self efficacy development compared with moderate self efficacy development, the students’ Indonesian grade by high self efficacy development compared with low self efficacy development, and the students’ Indonesian grade by moderate self efficacy development compared with low self efficacy development, (3) is there an interaction between Think Talk Write (TTW) method and Conventional learning method about the students’ self efficacy development on the increase of Indonesian’s grade . The type of this research is quasi experimental with design factorial of 2 x 3. The population in this research were all students in the third grade of SD Negeri Mekarasih of Cihampelas Subdistrict of Bandung Barat Regency selected by chosen without random assignment into III B as the experiment class and III A as control class. The data of questionnaires result and tests of Indonesian are analyzed by using the test of analyze two way anova with Microsoft Excel 2016. Base on the founded data analyze, the research points are identified as follows: First, the test result of datas mean there are differences between Think Talk Write (TTW) method and Cooperative Learning method about the Indonesian’s grade, then the decision is learning Indonesian using Think Talk Write (TTW) method gives better influence than learning Indonesian using Conventional learning method. Second, the test result of datas give explanation that there are differences comprehention between students’ self efficacy categories development and Indonesian’s grade. This is because the tests result show that there are the differences Indonesian learning ability between students’ high self efficacy, students’ moderate self efficacy, and students’ low self efficacy. The students’ high self efficacy development has understanding ability of Indonesian better than students’ moderate self efficacy developments. Third, the test result of datas give explanation that there is no interaction between Cooperative Learning method and students’ self efficacy categories development about the Indonesian’s grade.