Reviewerly/exHarmony huggingface dataset:
The prediction of land use and land cover change and its impact on soil erosion and sedimentation in the Musi Hydropower-Plant catchment area in Bengkulu Province

The Musi Hydropower-Plant catchment area is susceptible to soil erosion and sedimentation. Therefore, this research aimed to predict land use and land cover changes (LULCC) as well as their impact on soil erosion and sedimentation in the Musi Hydropower-Plant catchment area. The prediction of LULCC was calculated using Land Change Modeler module on IDRISI Terrset, while soil erosion and sedimentation were estimated with the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) models. The result showed that forest cover and paddy fields decreased significantly from 18.580 ha and 4.044 ha to 12.907 ha and 2.019 ha, respectively, in the periods of 1993 to 2019 and were predicted to reduce until 2032. Meanwhile, the built-up area and dry agricultural land increased from 818 ha and 2.116 ha in 1993 to 2.229 ha and 5.778 ha in 2019, which is expected to increase until 2032. The estimation of soil erosion rate also gave an increase from 75 t/ha/year to 113 t/ha/year, continuing to reach 122 t/ha/year until 2032. The escalation of soil erosion rate contributed to the change of sediment yield from 68.048 t/year in 1993 to 103.190 t/year in 2019,which is estimated to reach 111.028 t/year. These results are expected to be used by decision-makers and policymakers for the operation of the Musi Hydropower-Plant and the catchment area maintenance.
UPAYA PENINGKATAN HASIL BELAJAR IPS MELALUI APLIKASI QUIZIZZ

The low social studies learning outcomes of students in the learning process are due to the lack of use of instructional media that are appropriate to students' needs. The purpose of this study is to improve social studies learning outcomes for students using the quizizz application. This research belongs to the type of classroom action research conducted in two research cycles, where in each research cycle there are four stages consisting of planning, implementing, observing, and reflecting. The subjects involved in this study were fifth grade elementary school students, totaling 27 students. Data collection in the study was carried out using the test, observation, and interview methods with the research instrument in the form of social studies learning outcomes tests. The data obtained in the study were then analyzed using qualitative and quantitative descriptive analysis techniques. The indicator of success is determined by 90% of students whose learning outcomes pass KKM ≥75. The results of the study showed that the percentage of KKM completion in the pre-cycle was 52% to 63% in cycle I and 100% in cycle II. So it can be concluded that there is an increase in learning outcomes after using the quizizz application.&#x0D;
PENERAPAN MODEL PEMBELAJARAN PROJECT BASED LEARNING UNTUK MENINGKATKAN KONSEP PEMAHAMAN PESERTA DIDIK PADA MATERI SIMETRI PUTAR KELAS III DI SD N METESIH 01

Mathematics learning for class III at SD N Metesih 01 Jiwan Sub-District still uses a learning model that is not student-centered. Learning is done by lecture and assignment methods, which causes students to become passive in the learning process. This resulted in a lack of students' understanding of the material provided. It can be seen from the pre-cycle test that only 25% of students complete the KKM. This research is a classroom action research that aims to improve students' understanding by applying the Project Based Learning (PjBL) learning model to rotational symmetry material. The subjects of this study were class III SD N Metesih 01 with 12 students. The method used in collecting data is through tests and observations. Data analysis techniques using quantitative and qualitative analysis. Based on the results of data analysis, the results show that the use of the Project Based Learning (PjBL) learning model can improve student learning outcomes. Where it was obtained at the pre-cycle stage the percentage of completeness of students was only 25%, an increase of 66.67% in cycle I and in cycle II it experienced an even better increase of 91.67%.
PENERAPAN GERAKAN LITERASI MEMBACA BUKU DENGAN MODEL PEMBELAJARAN PJBL UNTUK MENINGKATKAN KOSAKATA BARU PADA MATA PELAJARAN BAHASA INDONESIA SISWA KELAS 4 SDN 1 NGEBEL

Learning Indonesian in Elementary Schools (SD) aims to improve students' ability to be able to communicate effectively, both orally and in writing. In learning Indonesian, there are four aspects of language skills that must be mastered by students, namely listening skills, speaking skills, reading skills, and writing skills. Reading is not just reciting written symbols, but what is more important is that readers must be able to understand various information or messages contained in the text they read. Listening and speaking skills are oral language skills while reading and writing skills are written language skills. The purpose of this study was to find out the results of implementing literacy movement with the pjbl model for Indonesian language lessons for class IV SDN1 Ngebel. The research method used is the PTK method, namely by carrying out the teacher's responsibilities as researchers. Through PTK the teacher examines the problems they face scientifically based on empirical evidence. PTK is research conducted by teachers in their own classes through self-reflection with the aim of improving their performance so that student learning outcomes increase. In accordance with this type of research, namely CAR, this research has several stages of implementation, namely planning, action, observation and reflection. The implementation of the actions in this study was planned in two cycles, each cycle consisting of two meetings. There are two data analysis techniques in this study, namely quantitative data analysis and qualitative data analysis. Based on the results of this study, the authors conclude that according to the findings of the research that has been done, it results that there is an increase in new vocabulary in the Indonesian language subject for fourth grade students at SDN1 Ngebel using the project-based learning model.
AUTOMATIC MODULATION CLASSIFICATION USING DEEP LEARNING POLAR FEATURE

The automatic modulation classification of signals is of great importance in modern communications, especially on cognitive radio. Several methods have been used in this field, the most important of which is the classification of modulation automatically using Deep Learning, where the methods depend on the convolution neural network, which is one of the Deep Learning networks, achieved high accuracy in classifying the modulation, so the proposed network depends on the type of deep learning CNN consisting of four blocks, each block contains a set of symmetric and asymmetric filters. The network also contains Max Pool. In this paper, the features extracted in phase-squaring and polar have been combined for the input, which helps in extending the input, that is, an increase in the features inside the network. It also contributes to improving the accuracy of classifying the higher-order modulation through the Polar plane. The dataset RadioML 2018.01A was adopted, which is used in the most recent research, where 11 types of modulation normal-class: (FM, GMSK, QPSK, BPSK, 0QPSK, AM-SSB-SC, 4ASK, AM-DSB-SC, 16QAM, 8PSK,00K) were taken. A simulation of which can be found in Matlab 2021. The proposed network achieved 100% classification accuracy when the signal-to-noise ratio is greater or equal to 2 dB for 11 types of modulation. The results of the paper were compared with modern networks Baseline network, Visual Geometry Group network, and Residual Neural network. The comparison showed the superiority of the proposed network over these networks, as the proposed network achieved an accuracy equal to 100% at SNR 2 dB while BL achieved an accuracy equal to 72% at SNR 2 dB, RN, and VGG almost reach 93% at SNR 2 dB.
Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing

Since the discovery of COVID-19, the wearing of a face mask has been recognized as an effective means of curbing the spread of most infectious respiratory diseases. A face mask must completely enclose the lips and nose properly for effective prevention of the disease. Some people still refuse to wear the mask, either out of annoyance or difficulty, or they are just wearing it incorrectly, which diminishes the mask's effectiveness and renders it worthless. The deep learning models described in this research provide a mechanism for assessing whether a face mask is being worn correctly or incorrectly using images. For both training and testing, the suggested method makes use of MaskedFace-Net dataset that contains annotated photos of an individual's face with proper and improper masks. Threshold optimizations are applied to produce significant results of prediction when comparing ResNet50, MobileNetV2 and DenseNet121 models. It is observed that better performance can be achieved with having accuracy as the target evaluation metric and reaching accuracy levels of 97.6%, 99.0%, and 99.8% for ResNet50, DenseNet121, and MobileNetV2, respectively after threshold optimization. As an outcome, DenseNet121 outperformed the other evaluated models when accuracy, recall, and precision metrics were used to assess the testing set. The face mask categorization can be used to automatically monitor face masks in real-time in public locations like hospitals, airports, shopping complexes and congested spaces to verify compliance with the published guidelines by the higher authorities in a country, making the results valuable for future use.
A Meta-model for Key Performance Indicators in Higher Education

We propose a software solution for representing diverse sets of key performance indicators in higher education. Our solution addresses both the heterogeneity and the common structure of key performance indicators. To tackle the issue of heterogeneity, we employ metamodeling and propose a meta-model that is expressive and generic enough to represent any set of key performance indicators in higher education. The proposed meta-model is more abstract than any specific key performance indicators set, and the sets are considered as models, which are instances of the proposed metamodel. We address the heterogeneity in calculating the key performance indicators' values by representing them with mathematical formulas and utilizing an expression language that allows for their dynamic evaluation. We verified the solution by representing typical key performance indicator sets and developing a software application prototype that enables the creation, monitoring, and further development of key performance indicator sets. The verification confirms the wide applicability of our proposed solution.
Meningkatkan Hasil Belajar Akuntansi Melalui Model Team Games Tournament dengan Motivasi Sebagai Variabel Moderator

This study describes the use of the Team Games Tournament learning model with moderator variables of student learning motivation in accounting economics subjects. The purpose of this study was to evaluate differences in student learning outcomes using the Team Games Tournament Learning Model and conventional methods, including these differences in students with high and low motivation, as well as analyze the interaction between learning models, motivation, and student learning outcomes in economics subjects at Public High School 3 Kota Banjar. The method used in this study is the Quasi-Experimental method. The design used is a factorial experiment design. Data collection techniques are carried out using test and observation methods. The subject of the study was a grade XI student of the Department of Social Sciences, State High School 3 Banjar City. The results of the study 1) the Team Games Tournament learning model is more effective than learning using conventional methods; 2) there is no significant difference between the learning outcomes of students who have high motivation both in experimental and control classes with the learning outcomes of students who have low motivation in Economics lessons; 3) There is no significant interaction between learning methods, motivation, and student learning outcomes.
Implementasi Data Mining Pada Penjualan Pakaian dengan Algoritma FP-Growth

The amount of competition in the business world, especially in the sales industry, required developers to find a strategy that could increase sales and marketing of products sold, one of which was using clothing sales data with data mining. Data Mining was an iterative and interactive process to find new patterns or models that can be generalized for the future, valuable, and understandable in a massive database. HAS Stores in the arrangement of goods layout still place goods according to groups and types of goods, so that it has an impact on service and item search when consumers want to buy more than one item and are located far apart. Therefore, this study aims to apply the FP-Growth Algorithm to find out the most sold clothing sales at HAS Stores in Batam city. This study uses the Association Rule method by utilizing the FP-Growth Algorithm. This study aimed to apply the FP-Growth Algorithm to determine the most sold clothing sales at HAS Stores in Batam city. Through the mining process with the FP-Growth (Frequent Pattern Growth) algorithm, the types of clothes sold will be obtained, and how much inventory the store needs to provide the clothing stock. The results showed that the most sold clothing products were Gamis and Jilbab through the calculation of support 53,33% and confidence 100%. Regarding these results, marketing strategies can be focused on the product and set a layout that customers can easily see.
Flood Susceptibility Mapping in Gending District by Comparison Frequency Ratio and Weight of Evidence for Mitigation Strategy

Floods are natural disasters that occur all over the world. Gending District in Probolinggo Regency, East Java, is an area that often experiences floods and causes various losses. A flood susceptibility map needed to prepare appropriate mitigation actions. Choosing the right method will produce a more accurate flood susceptibility map. The research aims to make a flood susceptibility map in Gending District by comparing the Frequency Ratio (FR) and Weight of Evidence (WofE) methods and providing appropriate mitigation recommendations. Six data factors that cause flooding are used: slope, elevation, land use, normalized difference vegetation index (NDVI), curvature, and rainfall. The data obtained were processed using the FR and WofE methods, which were then validated using the Receiver Operating Characteristics (ROC) method. The validation value is calculated using the ROC chart's Area Under Curve (AUC). The higher the AUC value, the better. The study's results revealed that the correct method for making a flood susceptibility map in Gending District was FR with an AUC value of 92.8%, while the WofE method was 90.4%. The flood susceptibility map illustrates that 14% of the area is in very high and high flood-prone zones, 23% is in the moderate zone, and 63% is in the safe zone. The appropriate mitigation strategy based on the highest FR value is creating drainage networks, and green open spaces, normalizing rivers in residential areas, and implementing selective logging and reforestation programs. The results of this study are used to reduce the impact and risk of future flood disasters.
SAT Feature Analysis for Machine Learning Classification Tasks

The extraction of meaningful features from CNF instances is crucial to applying machine learning to SAT solving, enabling algorithm selection and configuration for solver portfolios and satisfiability classification. While many approaches have been proposed for feature extraction, their relevance to these tasks is unclear. Their applicability and comparison of the information extracted and the computational effort needed are complicated by the lack of working or updated implementations, negatively affecting reproducibility. In this paper, we analyse the performance of five sets of features presented in the literature on SAT/UNSAT and problem category classification over a dataset of 3000 instances across ten problem classes distributed equally between SAT and UNSAT. To increase reproducibility and encourage research in this area, we released a Python library containing an updated and clear implementation of structural, graph-based, statistical and probing features presented in the literature for SAT CNF instances; and we define a clear pipeline to compare feature sets in a given learning task robustly. We analysed which of the computed features are relevant for the specific task and the tradeoff they provide between accuracy and computational effort. The results of the analysis provide insights into which features mostly affect an instance's satisfiability and which can be used to identify the problem's type. These insights can be used to develop more effective solver portfolios and satisfiability classification algorithms.
On the Notion of Fixability of PDDL+ Plans [Extended Abstract]

PDDL+ is an expressive formalism that allows for the use of planning in hybrid discrete-continuous domains. To cope with unexpected situations, it is crucial for deployed planning-based systems to efficiently repair existing plans. In this paper, we revisit a recently proposed FIXABILITY framework for expressing and solving problems from validation to rescheduling of actions in PDDL+ plans.
Improving Students’ Writing Skill by Using Role, Audience, Format, Topic (R.A.F.T) Strategy

The writing skills of students in class VIII A of SMP N 2 Jujuhan Bungo are still low. Evaluation of student assessment is categorized as low because of a number of aspects, namely students have difficulty understanding ideas in writing, preparation in understanding ideas in writing, inadequate vocabulary, inadequate use of learning methods applied by teachers in class. For this reason, this research was conducted in developing student competencies in writing by applying the RAFT method. This study used class action research (PTK) with the subject of the study as many as 25 students of class VII A of SMPN 2 Jujuhan Bungo. The study went through two cycles that were divided into five meetings. This research was supported by informants collected in the form of writing assignments, writing tests, interviews, and field recordings. The results of the study based on field notes of cycle I and cycle II showed that the average value of students' writing ability increased from cycle I to 66.08 increased to 74.24 in cycle II. This research resulted in the improvement of students' writing skills through the RAFT method which was influenced by several aspects, namely material, media, learning activities, classroom management, teacher approaches and teacher strategies.
Metacognitive, Cognitive, and Socio-Affective Strategies Used By English First Language Students in Academic Listening Course

Listening has an essential role in the language learning process. The success or failure of language learning is influenced by how students use the strategies. However, sometimes students still find it difficult to choose appropriate listening strategies. Therefore, listening strategies are crucial for the learning process in English as a foreign language context. This study aimed to investigate the learning strategies used by the English Language Education Program students in the Academic Listening course. The study was conducted in the first semester, 2022/2023 academic year. By using qualitative methods, the researchers employed close-ended and open-ended questionnaires and semi-structured interview protocols to collect the data. The participants were thirty seven students of batch 2021 who have taken the Academic Listening course. The findings showed that the students used metacognitive, cognitive, and socio-affective strategies. In metacognitive, they tended to make plans before study, paid attention to the speakers, and learned from previous mistakes. Students also used dictionaries and glossaries to define the meaning of unfamiliar words, took notes, and summarized cognitive strategies. Then, they tended to relax and practiced English with others in socio-affective strategies. The study hopefully can increase Academic Listening students' awareness of listening strategies that can help them during the learning process. Moreover, it is hoped to be useful for lecturers who want to teach Academic Listening and support students’ listening strategies.
A Noise-aware Deep Learning Model for Automatic Modulation Recognition in Radar Signals

Automatic waveform recognition has become an important task in radar systems and spread spectrum communications. Identifying the modulation of received signals helps to recognize different invader transmitters. In this paper, a noise aware model is proposed to recognize the modulation type based on time-frequency characteristics. To this end, Choi-Williams representation is used to obtain spatial 2D pattern of received signal. After that, a deep model is constructed to make signal clear from noise and extract robust and discriminative features from time-frequency pattern, based on auto-encoder and Convolutional Neural Networks (CNN). In order to reduce the effect of noise and adversarial disorders, a new database of different modulation patterns with different AWGN noises and fading Rayleigh channel is created which helps model to avoid the effects of noise on modulation recognition. Our database contains radar modulations such as Barker, LFM, Costas and Frank code which are known as frequently used modulations on wireless communication. Infact, the main novelty of this work is designing this database and proposing noise-aware model. Experimental results demonstrate that the proposed model achieves superior performance for automatic classification recognition with 99.24% of accuracy in noisy medium with minimum SNR of -5dB while the accuracy is 97.90% in SNR of -5dB and f=15 Hz of Doppler frequency. Our model outperforms 5.54% in negative and 0.4% in positive SNRs (even though with less SNR).
FORMS OF THE DIFFERENTIATION OF WORK WITH TEXTS IN PRIMARY SCHOOLS

This article justifies the importance and relevance of the use of differentiated work with texts starting with primary school. Results of the analysis of scientific and methodological literature, modern teaching practice on the reflection of the issue of using various forms and kinds of differentiation are described. It is underlined that differentiation of learning is based on discovering and reckoning with the individual characteristics of students and their differences. Individualization of teaching is interpreted as the highest stage of differentiation. Described ways and kinds of differentiation are the most adequate for the effective differentiated work with texts. Such criteria as the level of a student’s autonomy in completing tasks and the complexity of an educational work are at the heart of this classification. Varying levels of autonomy in educational work are ensured by joint completion of tasks, independent work, playing the role of a consultant. The difficulty of tasks is provided by various ways of formulating tasks and the variation in their content. One more regulator of the tasks’ difficulty is the volume and the level of difficulty of texts. As a result of effectiveness checks of the use of aforementioned forms and ways of differentiation of primary school students with educational texts significant positive effect was found.