Reviewerly/exHarmony huggingface dataset:
Model Prototype of a Solar Tracking System Supplying Electrical Power for Sensors Used in a Natural Disaster Monitoring System

Sun is the universe main source of energy. This energy can be converted to useful electrical energy by the utilization of solar panels or photovoltaic (PV) during the daytime with the presence of suitable sun irradiation level. It is important that during the energy conversion process, suitable methods are employed to extract as much energy as possible from the PV panels. Some issues affecting PV performance are the accuracy of the panel’s orientation and tilt angle with respect to the sun position. This causes PV to operate with lower efficiency, thus generating low output power compared to its rated capacity. One of the approaches to this issue is to constantly maintain a perpendicular profile of PV panels towards the sun direction as to capture the sun energy as much as possible, thus increases the PV efficiency. To do so, the conversion system needs a tracking mechanism to help the solar panel to constantly follow the sun’s direction, enhancing the achievement of higher PV efficiency. For this reason, a model prototype has been designed and developed which consists of a dual-axis solar tracking system, with two axis movement direction from east to west and north to south by utilizing the Light Dependent Resistors (LDRs) sensors. This tracking system is developed using Arduino UNO as the microcontroller that allows the PV panel to move optimally towards the high intensity of sunlight via four LDRs and two servo motors. To analyze the PV tracker’s performance, a monitoring system is implemented using ThingSpeak platform as the Internet of Things (IoT) platform interfacing with the controller Node MCU ESP8266. This monitoring system records the data of the PV energy parameters from the sensors’ output employed in this prototype. To assess the system efficacy, the dual-axis tracking system is compared with the single-axis tracking system. The result showed that the dual-axis tracker has efficiencies of 68.46% which is higher compared to that’s of the single-axis tracker. Implementation of the real-time monitoring system has shown a practical and handy way to analyze and monitor the solar tracking system’s performance.
IMPLEMENTASI E-COMMERCE MENGGUNAKAN LOW CODE PROGRAMMING PADA SAM’S FOODIE BERBASIS RESPONSIVE WEB

ABSTRACTTechnology continues to develop from time to time. With increasingly sophisticated technology, various information can be obtained easily. One of the visible developments is there’s responsive web that automatically will adjust itself with the device used. The use of the website is not only for getting and uploading information but also used in the business world, which known as E-Commerce. There are many programming languages that can be used in making a website and many developers tend to apply a high code programming in their making. Even though there are new ways to create websites that are easier and faster by implementing low code programming. The design of the website that is made includes the system design described in the UML Diagram. The resulting website is a website in the form of simple E-Commerce that can be opened not only via a computer but also from a mobile device. The aim of this research is to be able to make an E-Commerce website by using the implementation of low code programming so that business needs both in terms of owners and users can be fulfilled.Keywords: Website, E-Commerce, Low Code Programming, Responsive Web
PENINGKATAN KETERAMPILAN BAHASA INGGRIS MASYARAKAT PEGUNUNGAN di DESA PEMEPEK

The importance of soft skills for the younger generation is the basis for the preparation of this Community Partnership Program (PKM). The focus of this community service activity is to increase the interest in learning English for the people in highlanders based on environmental/nature folklore books and to increase the soft skills of Karang Taruna partners in Pemepek Village, Pringgarata District. The specific objective of this PKM is to increase interest in learning and improve English skills (soft skills). Some of the methods used in the Community Partnership Program (PKM) are question and answer and discussion methods to identify knowledge and supporting technology. The lecture method is used during the learning process and theoretical understanding of English. The results of community service that has been trained show that learning English based on nature-themed storybooks is easier to understand by the students being taught. By using this method, there is a pretty good increase for students. Apart from that, other results obtained were that youth and children absorbed English learning material more quickly and this activity was also a starting point for other activities.
An Intelligent Approach to Code-Driven Test Execution

In the constantly evolving world of software development, it is crucial to have effective testing methodologies in order to ensure the strength and reliability of applications. This scholarly article presents a new and intelligent approach to test execution that is driven by code and utilizes machine learning to greatly improve adaptability and accuracy in testing processes. Traditional testing methods often struggle to handle changes in code, resulting in less than optimal test execution. Our proposed method utilizes machine learning techniques to predict the impact of code modifications on test results, allowing for a more precise test execution strategy. We have demonstrated significant improvements in test execution efficiency, reducing unnecessary tests and speeding up feedback cycles. The following discussion examines these findings, addresses potential limitations, and suggests future areas for improvement and expansion. Notably, our methodology explains how Git commits aid in updating features, and how the machine learning model predicts the updated feature names. This predicted feature name is then integrated into BehaviorDriven Development (BDD) test selection and execution using standard BDD frameworks. By seamlessly incorporating machine learning into the testing process, developers can achieve greater precision and effectiveness, making significant progress in overcoming challenges posed by changes in code in modern development environments.
3D Convolution for Proactive Défense Against Localized Adversary Attacks

This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), it significantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100)and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.
Communication Signals Modulations Classification based on Neural Network Algorithms

This paper aims to find an automatic solution for the modulation’s classification of different types of radio signals by relying on Artificial Intelligence. This project is part of a long process of Communications Intelligence looking for an automatic solution to demodulate, decode and decipher communication signals. Our work therefore consisted in the choice of the database needed for supervised deep learning, the evaluation of existing techniques on raw communication signals, and the proposal of a solution based on deep learning networks allowing to classify the types of modulation with an optimal ratio (computation time / accuracy). We first carried out a research work on the existing models of automatic classification in order to use them as a reference. We consequently proposed an ensemble learning approach based on tuned ResNet and Transformer Neural Network that is efficient at extracting multi- scale features from the raw I/Q sequence data and also considers the challenge of predicting in low Signal Noise Ratio (SNR) conditions. In the end, we delivered an architecture that is easy to handle and apply to communication signals. This solution has an optimal and robust architecture that automatically determines the type of modulation with an accuracy up to 95%
Deep Learning based Zero Watermarking for Authentication of Medical Records

The security of digital images is crucial since they often contain sensitive and confidential data. Unauthorized access to this data could result in severe penalties for the parties involved. Despite the availability of highly secure algorithms, security remains a significant concern due to the rapid emergence of new technologies that can breach it. Thus the proposed work implements a technique that makes the confidential data inaccessible to intruders. Hence fragile type of data hiding technique is used where even with the slightest tampering to the image by an attacker, the information i.e. watermark image is completely destroyed, hence preventing it from unauthorized access. Also, a hybrid transform including DTCWT and NSST is used to fuse two medical images to form a more sophisticated output image, which serves as the final watermark. Further, the zero watermarking model is implemented using the ResNet 50 DL model for more precise results and extraction of feature maps. Embedding the actual image in the carrier image could make the watermarking detectable especially when it is fragile, hence Zero Watermarking overcomes this also by virtual embedding. Moreover, the algorithm employs the avalanche effect of SHA512 for highly secure authentication, further strengthening the security of the system. Overall, the proposed method is an effective way to ensure the security of digital images with confidential data.
Klasifikasi Tingkat Keparahan Korban Kecelakaan Lalu Lintas Di Kota Samarinda Menggunakan Algoritma K-Nearest Neighbor dan Naive Bayes

Classification is the process of evaluating data objects to be included in a particular class from a number of available classes. The K-Nearest Neighbor algorithm is one of the algorithms used to classify an object against a new object based on its K nearest neighbors. Naive Bayes is a classification of data using probability based on the Bayes theorem with strong independence assumptions. This study aims to compare the accuracy of the classification results on traffic accident victim data in Samarinda City using the K-Nearest Neighbor algorithm and the Naive Bayes algorithm. The data used is data on the severity of traffic accident victims in Samarinda City from 2020 to 2021 with death and non-death classes and uses 6 independent variables, namely age, gender, victim's role, victim's vehicle, road status, and condition weather. The measurement of accuracy in classifying the K-Nearest Neighbor algorithm and the Naive Bayes algorithm uses a classification performance matrix. Based on the results of the study, the accuracy of the classification results of the K-Nearest Neighbor algorithm was obtained at 75.86%, while the Naive Bayes algorithm obtained an accuracy rate of 79.31%. From the results of this analysis, it can be concluded that the Naive Bayes algorithm works better than the K-Nearest Neighbor algorithm in classifying the severity of traffic accident victims in Samarinda City.
College Teachers’ Involvement in the Promotion of Information Literacy

Information literacy is an important skill student must gain, and college teachers are major contributor to this independent lifelong learning and enhance students' library engagement. This study attempted to establish that college teachers’ involvement in the promotion of information influenced students' library engagement. This assumption was verified in one of the higher educational institutions (HEIs) in the Southern part of Bukidnon, Philippines. A concurrent nested mixed method research design using one modified survey questionnaire on the constructs was the main primary source of the quantitative and qualitative data. Three hundred twenty-eight (328) students from one of the HEIs participated and assessed their respective college teachers' involvement in promoting information literacy. Participants were college students scheduled to get their modules from their teachers during the school year 2021-2022. Descriptive statistics, and regression analysis used and complemented with qualitative data from students. The study revealed that teachers' involvement is a significantly influences student engagement in the library. This study hopes to contribute to the scanty research in the Philippines that focused on the involvement of college teachers in promoting
Nowcasting of Chili Pepper (Capsicum frutescens L.) Prices in East Java Province Using Multi-Layer Perceptron Method

The aims of study is to predict the price of chili pepper at the provincial level in East Java by looking for the best input variable from three types of input variables, price of chili pepper at the regency and city levels, natural factors, and word search index on Google Trends as an approach to the causes of chili pepper price fluctuations. The Multi-Layer Perceptron method, accompanied by a search for the best combination of model parameters is selected to get the model with the best nowcasting ability. The result shows that the best model for nowcasting is characterized by: the input variable is price of chili pepper at the regency and city levels with three hidden layers and 32, 45, and 51 neurons in each hidden layer, maximum iteration is 200 iterations, maximum iteration when the model not increase in performance for applying early stopping is 20 iterations, non-linear activation used is RELU (Rectified Linear Unit), and optimization function used is ADAM optimizer. The accuracy of nowcasting in this study is highly accurated with MAPE smaller than 10%.
Comparison Of Kernel Support Vector Machine In Stroke Risk Classification (Case Study:IFLS data)

Stroke s a disability main source and main disability source to lost years of disability-adjusted life. Currently the information technology development, especially the field of machine learning has an important role in early warning of various diseases, such as strokes. One of the methods used for stroke classifying is Support Vector Machine (SVM). In this study, we aim to compare several kernel functions in SVM such as linear, radial basis function(RBF), polynomial, and sigmoid for classifying stroke risk. We determine the best kernel based on accuracy, sensitivity, and specificity values. The result of this study shows that linear kernel function gives the best performance in classifying with values of classification accuracy 99.0%, specificity 100.0%, ,and sensitivity 97.0%. Those scores are the highest scores among the other kernel , that means the linear kernel function is the best method for classifying strokes risk.
Pojok Statistik Virtual Improvement: Development of Online Consultation and Scientific Articles Modules

With a commitment to improving statistics literacy in Indonesia, BPS Statistics Indonesia built a Pojok Statistik. Pojok Statistik is a collaborative service between BPS Statistics Indonesia and Universities initiated to answer the needs of academics and students for statistics. Due to the effects of the pandemic and the increasing interest from students, a virtual version of Pojok Statistik (Pojok Statistik Virtual) was built to meet all the needs of the offline version. However, the features are still limited and do not represent the criteria of the Pojok Statistik Offline. From the results of interviews with the Pojok Statistik Team, several plans exist to improve the Pojok Statistik Virtual, including adding online consultation and scientific articles modules. Therefore, this study aims to add online consulting service features and scientific articles modules to the Pojok Statistik Virtual. The system development process uses the Prototyping Methodology. System evaluation is conducted by black-box testing and usability testing using the USE Questionnaire. The evaluation results show that all functions in both modules are running and functioning properly. The usability test results using the USE Questionnaire reveal that these two features are feasible or usable to support the statistical needs of academics and students.
Clustering Village Development in West Java Province on the Condition of Developing Village Strata Using K-Means Algorithm

Villages are important units in the socio-economic structure of a country, and an in-depth understanding of the factors that influence village growth and welfare is essential. To facilitate the local government in handling the equitable distribution of village needs, it is necessary to cluster or group villages. The purpose of this research is to assist the government in clustering certain villages into several clusters, making it easier to monitor and procure village needs within the West Java Provincial government. Clustering is done using the K-Means algorithm. The application of the K-Means Algorithm by determining the Cluster value is 96. The results showed that each cluster has its own membership number. Cluster 0 consists of 8 villages, Cluster 1 consists of 12 villages, and Cluster 2 consists of 8 villages. Furthermore, in testing the performance of the K-Means algorithm by dividing into 96 clusters, the Davies Bouldin Index value is -0.996. From the results of data processing and analysis, it is necessary to provide assistance and procurement of village needs for villages with low clusters by the West Java provincial government
INVESTIGATION OF USE OF SALIENCE FOR EFFECTIVE NARRATIVE PLANNING

This paper explores the integration of salience modeling into narrative planning to enhance the generation of coherent and engaging stories for interactive experiences. Salience indices and a salience-based cost function were incorporated to improve the efficiency and effectiveness of narrative planning algorithms. The research demonstrated that solutions with lower salience costs were more prevalent, thereby highlighting the potential for optimizing narrative planning by prioritizing such solutions. Moreover, the study emphasized the importance of accurately defining the relationship between actions for salience representation. In conclusion, the integration of salience modeling in narrative planning streamlined the process, resulting in improved storytelling experiences and better utilization of computational resources.
Pengembangan Media Pembelajaran Interaktif Swishmax Berbasis Android pada Mata Pelajaran SBdP Siswa Kelas III SDN Kalibata 07 Jakarta Selatan

This research has two objectives to be developed, namely knowing the design and development of Swishmax media in learning arts and culture and crafts (SBdP) for class III students at SDN Kalibata 07 South Jakarta, knowing the feasibility and effectiveness of Swishmax media in learning arts and culture and crafts ( SBdP) for class III students at SDN Kalibata 07 South Jakarta. In developing this media, a research method was used, namely Research and Development (R&amp;D) with the Dick and Carrey research model which consists of 10 research stages. The results of the research carried out by the researcher obtained a percentage of 90% by material expert validators, a percentage of 90% from the language expert validator test, a percentage of 80% from the media expert validator test. The feasibility test results in the small group were obtained with a percentage result of 82%, the feasibility test results in the large group were obtained with a percentage result of 92%.
EFEKTIVITAS PEMBELAJARAN DISCOVERY LEARNING BERBASIS DIGITAL UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA PESERTA DIDIK

Along with advances in science and technology, educational institutions are required to play an active role in improving the quality and quality of education optimally to balance advances in science and technology as well as increase the competitiveness of graduates to face intense competition and challenges in the world of work. Based on the results of initial observations of learning at SDN 1 Rawalaut, the learning process is still monotonous using only blackboards, printed books, and worksheets as learning media. Learning that tends to be monotonous certainly has a very significant effect on the development of student learning in the classroom which causes student learning outcomes to decrease. This study aims to determine the effectiveness of digital-based Discovery Learning learning to improve mathematics learning outcomes for students at SDN 1 Rawalaut. The method used in this study is the SLR (Systematic Literature Review) method. Data collection is carried out using documentation and reviewing all articles related to the discovery learning model. Based on the results of the research been carried out in the table of research results, explains that the effectiveness of learning media contributes to the teaching and learning process in the classroom, with the presence of digital media, of course, learning can be creative and fun and of course can improve student learning outcomes, especially at SDN 1 Rawalaut.
SIGACT News Complexity Theory Column 119

After a short break, the complexity theory column is back! In this issue, Sevag Gharibian introduces us to the multifaceted nature of Quantum NP and its relation to Snow White (yes, it all makes sense if you read the column). Thanks a lot to Sevag for his dedication and thoroughness and the effort he put into his fantastic contribution.
Analisis Literasi Kesehatan dalam Buku Pelajaran Bahasa Indonesia Kelas XI SMA

This study aims to describe the presentation and percentage of health literacy aspects in textbooks Indonesian grade XI high school. This research is qualitative descriptive research using content analysis methods. The data used is health literacy content in textbooks Indonesian grade XI high school. The data collection techniques used in this study were listening and note-taking techniques. Furthermore, the data analysis method used is a matching method using the Determining Element Sorting (PUP) technique with semantic and referential sorting methods. The data validity technique used is triangulation of theory with confidence levels through persistence and observation techniques. The results of the analysis showed that aspects of health literacy presented in textbooks Indonesian grade XI SMA consisted of aspects of food and drink, physical activity, physical violence/injury, sexual behavior, mental health, protective factors, environmental hygiene, hair hygiene, cigarette consumption, and illegal drug consumption. The lowest health literacy presentation is found in e-books Indonesian grade XI SMA (published by the Ministry of Education and Culture); That's 35% (enough). Next, health literacy in the "Pride of My Indonesian Nation" from the Triad is expressed in percentage 30% (sufficient). While the "Indonesian" of Yrama Widya is calculated at 36% (sufficient). The highest presentation of health literacy aspects in textbooks is the protective factor aspect with a percentage of 37% which is included in the sufficient category of the overall data