Reviewerly/exHarmony huggingface dataset:
COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA

Survival analysis is a statistical procedure in analyzing data with the response variable is time until an event occurs (time-to-event). In the last few years, many classification approaches have been developed in machine learning, but only a few considered the presence of time-to-event variable. Random Survival Forest and Survival Support Vector Machine are machine learning approach which is a nonparametric classification method when dealing with large data and a response variable of survival time. Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. The data used in this study is generated data in the form of right-censored survival data. This study uses the RandomForestSRC and SurvivalSVM packages on R software. This study aimed to compare the performance of the Survival Support Vector Machine and Random Survival Forest methods using simulation studies. Simulation results on right-censored survival data using binary predictor variables scenario indicate that the Survival Support Vector Machine (SSVM) method with Radial Basic Function Kernel (RBF Kernel) has the best model performance on data with small volumes, whereas when the data volume becomes larger, the method that has the best performance is Survival Support Vector Machine using Additive Kernel. Meanwhile, Random Survival Forest is a method that has the best performance for all conditions in mixed predictor variables scenario. Method, proportion of censored data and size of data are factors that affect the model performance.
STUDY TIME CLASSIFICATION OF MATHEMATICS AND INFORMATION TECHNOLOGY DEPARTMENT OF KALIMANTAN INSTITUTE OF TECHNOLOGY USING NAÏVE BAYES ALGORITHM

Institut Teknologi Kalimantan (ITK) is one of the state universities in Indonesia which has 5 majors, one of them is the Department of Mathematics and Information Technology (JMTI). JMTI has six study programs, and only three study programs have graduates, namely Mathematics, Information Systems, and Informatics. Every year the number of new students continues to grow, but this is not proportional to the number of graduates, because some students study for more than 8 semesters. Because of this, the quality of study programs being poor. In this research, a model was built that could classify student study timeliness, using the naïve Bayes algorithm. The data used is data from JMTI student graduates from the 2013 to 2019 batch. The 2013 to 2018 batch data will be training data and validation data, while the 2019 batch data will be testing data. This research compare accuracy and F1-score naïve Bayes algorithm without correlation and with correlation. The best model obtained from training data is a model with variables that have gone through a correlation test, namely 70:30, 80:20, and 90:10. The attributes selected after the correlation test, namely, IP Tahap Bersama, GPA, Final GPA, Length of Study (Semester), dan Graduation GPA (Category), yield results for accuracy and an F1-score of 1.
KESALAHAN MORFOLOGI DALAM TULISAN YANG DIUNGGAH DI INSTAGRAM @RIARICIS1795

The majority of Indonesian people choose to use social media such as Instagram to channel their thoughts and feelings in the form of videos, and photos, then affixed with writing to describe the activities carried out. In this study, the researcher analyzed the morphological errors contained in the writing uploaded on Instagram @riaricis1795. This account was taken as the object of the data because it is included in top 10 the most popular celebrities who reached 34 million followers. Besides that, Ria Ricis is a role model for the young generation in Indonesia nowadays. This research aims to know how many mistakes were made by one of the country's celebrities when writing 'captions' on her personal Instagram page. The research method used is a descriptive analysis approach by observing and recording. Then, the data taken is limited to 30 uploads from a total of 560 that have been uploaded by @riaricis1795. The results of the analysis of the data found that there are 102 errors which include errors in the reduction of prefixes, and suffixes, omission of letters, use of uppercase and lowercase letters, improper reduplication words, separation and connection of words, and errors in abbreviating words.
CLASSIFICATION OF STUDENT GRADUATION STATUS USING XGBOOST ALGORITHM

College is an optional final stage in formal education. At this time, universities are required to have good quality by utilizing all the resources they have. Therefore, efforts are needed to help the faculty and study program make policies and decisions. One of the efforts that can be made is to classify student graduation status as early as possible to increase the number of students graduating on time. Thus, a classification algorithm is needed to avoid overfitting and produce good accuracy. The purpose of this study was to classified the student graduation status of the Statistics Untan Study Program using the XGBoost algorithm. XGBoost is an ensemble algorithm obtained through the development of gradient boosting. XGBoost has several features that can be used to prevent overfitting, but it can only process numerical data. Therefore, 140 numerical data were adjusted using the dummy technique in this study. The resulting XGBoost classification model is optimal at the number of rounds is 3 and the number of folds is 5. Based on the performance evaluation of the XGBoost algorithm, an accuracy of 75,00%, precision of 88,89%, sensitivity of 76,19% and specificity of 71,43% were obtained. Thus, the performance of the XGBoost algorithm is classified as good.
ANALYSIS OF NEW CHAOTIC MAP AND PERFORMANCE EVALUATION IN ITS APPLICATION TO DIGITAL COLOR IMAGE ENCRYPTION

In this research a new chaotic map which is a modification from composition of MS map and Improved logistics map is proposed. New map’s chaotic behavior is proven by the bifurcation diagram and Lyapunov exponent. This map will be used in chaos-based cryptography as a keystream generator and then it will be processed in the encryption and decryption algorithms through XOR operations. The results of the encryption and decryption processes were evaluated by several tests such as key sensitivity analysis, histogram analysis, correlation analysis, and image quality analysis. All the tests are doing to evaluate the performance new chaotic map in encryption of digital color image. Based on the results of several tests, a conclusion can be drawn that the encryption and decryption process is successful and difficult to attack with various kinds of attacks. The key that built from new chaotic map has a good sensitivity.
Application of the Empirical Mode Decomposition Method to Noise Reduction Using Seismic Event Data Recorded at Kirkuk Seismological Station.

This study aims to use the Empirical Mode Decomposition for reducing the noise of passive seismic signals recorded across seismic event recorded at Kirkuk station which is one of the Iraqi Meteorological Organization and Seismology data, where human activities in Iraq's populous cities significantly impact the seismic recordings at Iraqi Meteorological Organization and Seismology stations sites. In this paper, we present an algorithm based on the Empirical Mode Decomposition. It is a data driven method used as a propelling tool for analyzing and decomposing non-stationary and non-linear data without any previous assumptions. Empirical Mode Decomposition generates a finite and often small number of Intrinsic Mode Functions. The decomposition is based on the direct extraction of the energy related to various intrinsic time scales, an important parameter of the system. Empirical Mode Decomposition technique is applied to the available seismic records to reduce the noise level in the records, which helps in the interpretation of the records.
A Preliminary Study Investigating the Acquisition of valid qEEG Data While Wearing a Virtual Reality (VR) Headset

The use of virtual reality (VR) therapy is being utilized and promoted for a wide range of treatment applications. Yet, the majority of clinical evidence that supports the efficacy of VR treatment has been established utilizing reports of subjective outcome variables, such as rating scales or a reduction of symptoms reported by the patient. Instead, the present study supports the use of quantitative electroencephalography (qEEG) as a more precise and objective method for assessing treatment efficacy involving the use of VR-based treatments. Although a few studies have attempted to establish physiological evidence from qEEG recordings to strengthen the efficacy of pre-post treatment effects for VR-based treatments, these attempts have been based upon very small sample sizes or case studies. Therefore, to the best of our knowledge, prior studies have failed to uniformly account for ingenuine treatment effects that could arise from merely wearing a VR headset while acquiring qEEG. The current preliminary study sought to systematically measure any potential confounding effects that wearing a VR headset could produce by measuring and comparing the baseline qEEG recordings for the eyes-open, resting condition (staring at a dot) with and without the VR headset for 28 participants. The present results revealed very minimal significant differences between the two conditions when analyzed collectively and no significant differences for the male participants. The implications of these findings are discussed and provide preliminary support for confidently reporting qEEG efficacy data involving the use of a VR headset. Additionally, the current study is believed to have successfully established a valid and standardized approach for reliably obtaining active or real-time qEEG data while wearing a VR headset in order to confidently report the physiological effects of VR immersion on electrical brain activity.
Modeling Metadata and Data from Censuses and Surveys with Graph Databases

Relational database users are switching to non-relational databases because non-relational databases are better able to handle dynamic data storage. One of the institutions that require dynamic data storage is Statistics Indonesia (BPS). Currently, data storage for census and survey activities at BPS is done using a relational database, although there are metadata changes in each activity. Accommodating metadata changes in each activity requires one database, which creates problems when retrieving some raw data. There is an opportunity for convenience if the data collected is stored in a non-relational database, one of which is a graph database. This research discusses the modeling of metadata and data from censuses and surveys at BPS using a graph database. Then we implement the Neo4j DBMS and compare the proposed model with the relational model in the Microsoft SQL Server DBMS. Then, a comparison of the features and characteristics of each DBMS is done, and finally, performance testing is done with Apache JMeter. Modeling has been able to handle dynamic data structure changes, but Neo4j's performance is still lagging behind Microsoft SQL Server.
K-Means Algorithm Implementation for Project Health Clustering

Indonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company revenue and productivity. On the other hand, there are some risks that need to be considered for every project when it is about to start. Project data is recorded from start to finish so that the project's progress and improvements can be monitored and analyzed. As the project runs, the project team at one of Indonesia's telecommunication companies, which is responsible for the processes leading to project success, requires a project health category. Therefore, this study is conducted to develop a clustering project health process, which is included in a type of unsupervised learning that runs on unlabeled data. One of the clustering algorithms is K-Means, which groups data based on similar criteria. Researchers also use dimensionality reduction with the principal component analysis (PCA) method to determine its impact on the clustering process with the K-Means algorithm. From this study, the researcher obtained three groups or project health categories, consisting of groups 0, 1, and 2. The evaluation results with the Calinski-Harabasz index showed that the K-Means model in the PCA dimensionality reduction data performed better than the standard K-Means model with a Calinski-Harabasz index value of 55633,12776405707, which is higher than 25914,578262576793.
TATA KELOLA REKAM MEDIS BERBASIS TEKNOLOGI INFORMASI DALAM MONITORING KUNJUNGAN PASIEN IGD DENGAN MENGGUNAKAN METODE AGILE

This study aims to simplify and expedite the processing of patient data, as well as the preparation of reports for officers. In addition, it aims to design an information system for emergency room patient visits to process patient data more quickly and effectively. This study employs a qualitative method with a descriptive approach. Data collection techniques include observation, interviews, and supplemented by literature study. The development method follows agile principles. The problems identified in this study are as follows: There is no information system for emergency patient visits, and the patient visit data for emergency departments are still being recorded using Microsoft Excel. With the implementation of this information system, officers can process patient data quickly and easily, as the computerized system is more organized and better integrated than before.
PENERAPAN METODE SNMP (SIMPLE NETWORK MANAGEMENT PROTOCOL) DALAM OPTIMALISASI KINERJA JARINGAN KOMPUTER STUDI KASUS PADA IDN BOARDING SCHOOL

This study aims to analyze and optimize the performance of the IDN Boarding School computer network using the SNMP (Simple Network Management Protocol) method. Computer network infrastructure in schools is an essential aspect that plays a key role in supporting the learning and management process. With rapid growth in usage and increasing network complexity, network performance often becomes a challenge. This research involved collecting data using SNMP to monitor and analyze network performance at IDN Boarding School, including measuring bandwidth usage, latency, and data transfer rates. The collected data is then analyzed in depth to identify weaknesses in the network that could potentially lead to unsatisfactory performance. Once the problem is identified, this study will recommend repair and optimization solutions including selecting appropriate hardware, optimal configuration, and implementing effective network management measures. SNMP methods will be used to track the implementation of innovative solutions and measure their impact on network performance. We hope that the results of this study will provide valuable information to IDN Boarding Schools on strategies to improve and optimize their networks. By implementing SNMP, schools can increase network resource efficiency, reduce network interference, and increase user satisfaction by up to 35%.
OPTIMALISASI KINERJA JARINGAN VPN DENGAN METODE DMVPN

DMVPN (Dynamic Multipoint Virtual Private Network) is a VPN technology that can simplify the configuration process and improve network efficiency. The purpose of this research was conducted as a response to the obstacles experienced by companies in VPN network performance that did not meet expectations and reduced high operational and infrastructure costs related to the performance of the current VPN network. In addition, to determine the extent to which the use of DMVPN can improve the performance of VPN networks in companies. The method used is data collection through observation and interviews, and analysis of the data that has been collected. The results showed that the use of DMVPN can increase the efficiency of the VPN network in the company by reducing traffic load and speeding up the data transmission process. Therefore, DMVPN technology is highly recommended for companies that require complex VPN networks and need optimal network performance. The results of this research are expected to provide a better understanding of the potential and effectiveness of using DMVPN in optimizing the performance of VPN networks in companies. It is expected that the implementation of DMVPN can increase data transfer speed, reduce latency, and increase data security in VPN networks. In addition, it is also expected that the use of DMVPN can reduce the operational and infrastructure costs associated with VPN networks, so that companies can achieve higher efficiency
Stock Price Prediction Using Formula From Data Scale Analysis Extrapolation Method

Public awareness about stocks is increasing every year. This needs to be balanced with advances in science to help investors make more measurable and systematic decisions. The purpose of this study is to apply Extrapolation of Data Scale Analysis (DSA) in predicting stock prices. The DSA Extrapolation method will produce a new formula called JIC-FLY 3 which is used to process big data in predicting prices at a certain time. The population in this study is the share price of the telecommunications sub-sector with EXCL (PT XL Axiata Tbk.) as a sample. The error value measurement method used is the Mean Absolute Percentage Error (MAPE). The result of this study is that the DSA 19 extrapolation method using the JIC-FLY 3 formula is able to predict the share price of the telecommunication subsector with the best error giving the smallest error value of 0.193%.
ANALISIS KESALAHAN BERBAHASA DALAM TATARAN SEMANTIK PADA POSTINGAN AKUN INSTAGRAM @demauinsurakarta

This research is motivated by the use of the Indonesian language where there are still many errors found, especially at the semantic level. The purpose of this study is to find out language errors at the semantic level contained in the @demauinsurakarta account. This study uses a qualitative descriptive method approach. The data source in this study is the account caption @demauinsurakarta. This study uses data analysis methods in the form of references from the Kamus Besar Bahasa Indonesia (KBBI) and Pedoman Umum Ejaan Bahasa Indonesia (PUEBI). The results of this study found semantic level errors in the field of hypercorrection found 7 errors, pleonasm found 8 errors, inappropriate word choice or diction found 3 errors and word selection which resulted in ambiguity meaning found 3 errors.
KESALAHAN GRAMATIKAL BAHASA INGGRIS PADA PENJABARAN GRAFIK DALAM MATA KULIAH BAHASA INGGRIS 1

In learning English, the learners make some common mistakes in applying the correct grammar. One of the mistakes is the error in explaining graphs in English. This competency is highly needed to be applied in many aspects, especially for vocational university students. For example, explaining a chart in English is somehow mandatory in composing a budget or presenting a project to a client. Thus, this study is aimed to identify the types of grammatical errors typically done by the learners. By applying the descriptive qualitative methodology, the 30 data used in this study are taken from the sample of students’ writing in explaining a chart in English. The steps taken are done by doing data categorization based on Dulay’s surface strategy taxonomy which mentions four types of errors: addition, omission, misformation, and misordering. The results show that omission is the type of error mostly done by learners with 34,78%, followed by addition with 31,88% occurrence, and the least error found in the data is misorder with 10,14%. It is suggested that students should be equipped with basic Grammar understanding in explaining a graph in English, so this kind of error will be minimized. This study is also expected to give insight to lecturers in composing the General English syllabus that emphasizes the student’s understanding of basic Grammar.
AN ANALYSIS OF THE LINGUISTIC COMPETENCE OF MORPHOLOGY TEACHING ON YOUTUBE VIDEO SERIES

Linguistic competence has had a significant influence on English language teaching. Linguistic competence is largely concerned with a notion shared by language teachers and learners when using language knowledge in real performance. English teachers are expected to have this competency. The present study aimed to examine linguistic competence in morphology teaching, which was demonstrated by an English teacher. The morphology teaching was sourced from a series of YouTube videos which were broadcast by a teacher on a Youtube channel. This study was carried out by analyzing the linguistic competence of the teacher in each video by analogizing the teacher’s knowledge with the theory from the linguistics book written by some experts. The selected books which were used to analyze were: “How English Works, A Linguistic Introduction” (Curzan &amp; Adams, 2012); “Linguistics, An Introduction” (Radford et al., 2009); and “Contemporary Linguistic Analysis, An Introduction” (O’Grady &amp; Archibald, 2016). The study’s findings revealed that the teacher had sufficient linguistic competence while delivering the course. The findings also assisted in recognizing that linguistic competence is a significant part of the analysis in terms of how a teacher may remain competent while providing the course. In short, linguistics is considered valuable because it may increase teacher’s awareness of language, making the teacher more competent and, hence, a better language teacher.
Automated poetry scoring using BERT with multi-scale poetry representation

Automated poetry scoring is an emerging task in automated text scoring, which is receiving increasing attention in AI for education. Poetry is distinct from other text in its complexity and specialty in language feature moreover, poems are usually rated from multiple criteria besides the overall impression. However, few existing methods to the best of our knowledge have considered a tailored text representation model for encoding poetry. Moreover, the lack of large poetry corpus and extensive labelled data is another major constraint to construct an effective poetry scoring model. To address such problems, we proposed BERT-based models with multi-scale poetry representation. In addition, we employ multiple losses and R-Drop strategy to align the distribution of manual and model scoring and mitigate the tendency of consistent score in poems. Experiment results demonstrate that our model with multi-scale poetry representation stands out when comparing with single-scale representation model.
Detection of computationally-intensive functions in a medical image segmentation algorithm based on an active contour model

Image segmentation is one of the most critical operations performed on medical images. These operations require developing optimisation strategies to reduce runtime. Profiling methods can assess algorithm's performance concerning the overall cost of runtime, memory access, and performance bottlenecks. Therefore, we propose an approach for detecting computationally intensive functions in a competent medical image segmentation algorithm based on an active contour model. Our approach applies performance analysis tools commonly available in traditional computer operating systems, requiring no new setup or developing new performance-measuring techniques. The overall cost of execution time, memory accesses, and performance bottlenecks are measured in execution time. In conclusion, a call graph visualisation can suggest to users a quick graphical overview of the execution time of their codes and, therefore, guarantee the shortest possible learning curve by the community of researchers from medical image processing and analysis.