bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impelementasi Metode Tikrar Dan Sambung Ayat Dalam Meningkatkan Hafalan Al Quran Hadits pada Siswa Kelas XII MAN 2 Pesisir Selatan
Authors: Suhardi Suhardi , Harahap Lermian , Wahyudi Hakmi , Hidayat Hakmi 
Published: December 2023

Kegiatan menghafal satu atau beberapa kata merupakan pekerjaan yang cenderung sulit dari pada membaca, karena kegiatan tersebut berkaitan dengan ingatan (memory) bagaimana informasi disimpan dalam ingatan, dipertahankan setelah disajikan dan diungkap dikemudian hari, untuk mencapai tujuan menghafal yang baik dan melekat pada ingatan dibutuhkan sebuah metode pembelajaran yang tepat. Begitu pula dengan menghafal Al Quran membutuhkan metode yang tepat sehingga siswa lebih mudah menghafal ayat dan hadits pada bidang studi Al Quran hadits.Penelitian ini bertujuan untuk menganalisis impelementasi metode tikrar dan sambung ayat dalam menghafal ayat Al-Qur’an dan hadits pada mata pelajaran Al Quran Hadits di MAN 2 Pesisir Selatan. Jenis penelitian ini adalah penelitian quasi experimental dengan one group pretest posttest design. Penelitian ini tidak menggunakan kelas pembanding namun sudah menggunakan tes awal sehingga pengaruh pengimplemetasian metode tikrar dan sambung ayat dapat diketahui secara pasti. Teknik pengumpulan data menggunakan teknik Observasi, wawancara, dan dokumentasi. Analisis data menggunakan langkah reduksi data, penyajian data, dan verifikasi data. Verifikasi data dilakukan dengan teknik triangulasi yang membandingkan data dari tiga instrumen dan tiga sumber data yang berbeda. Hasil penelitian menunjukkan bahwa penerapan metode tikrar dan sambung ayat mampu meningkatkan hafalan siswa dalam pembelajaran Al Quran hadits. Tahapan yang dilakukan adalah proses menghafal secara berulang-ulang (tikrar) kemudian dilakukan setoran ayat secara bersama-sama dengan menggunakan metode sambung ayat, sehingga membantu konsentrasi dan daya ingat siswa TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack// This page is in Indonesian Translate to English AfrikaansAlbanianAmharicArabicArmenianAzerbaijaniBengaliBulgarianCatalanCroatianCzechDanishDutchEnglishEstonianFinnishFrenchGermanGreekGujaratiHaitian CreoleHebrewHindiHungarianIcelandicIndonesianItalianJapaneseKannadaKazakhKhmerKoreanKurdish (Kurmanji)LaoLatvianLithuanianMalagasyMalayMalayalamMalteseMaoriMarathiMyanmar (Burmese)NepaliNorwegianPashtoPersianPolishPortuguesePunjabiRomanianRussianSamoanSimplified ChineseSlovakSlovenianSpanishSwedishTamilTeluguThaiTraditional ChineseTurkishUkrainianUrduVietnameseWelsh Always translate Indonesian to EnglishPRO Never translate Indonesian Never translate ejournal.uin-suska.ac.id
Peningkatan Kontrol Diri Remaja Penggemar K-Pop Menggunakan Konseling Behavioral dengan Teknik Modeling: Studi Eksperimen
Authors: Kamelia Nola , Kasih Fitria , Nita Rahma Wira 
Published: December 2023

Penelitian ini dilatarbelakangi dengan adanya permasalahan tentang kontrol diri remaja yang rendah penggemar K-Pop. Tujuannya untuk mendeskripsikan : 1.) Gambaran control diri remaja penggemar K-pop sebelum dilakukan konseling behavioral dengan menggunakan teknik modeling. 2.) Gambaran kontrol diri remaja penggemar K-pop setelah dilakukan konseling behavioral dengan menggunakan teknik modeling. 3.) Efektifitas konseling behavioral dengan menggunakan teknik modeling untuk meningkatkan kontrol diri remaja penggemar K-pop. Jenis penelitian ini adalah eksperimen. Populasi pada penelitian ini adalah peserta didik SMA PGRI 2 Padang yang berjumlah 10 orang. Pengambilan sampel menggunakan teknik purposive sampling yaitu pada peserta didik dengan jumlah 3 orang. Alat pengumpulan data berupa angket, kemudian di analisis dengan menggunakan persentase untuk efektifitas pelaksanaan pelayanan konseling behavioral dengan menggunakan teknik modeling untuk meningkatkan kontrol diri remaja penggemar K-Pop. Hasil penelitian mengungkapkan bahwa : 1.) Gambaran kontrol diri remaja penggemar K-Pop sebelum diberikan konseling behavioral dengan menggunakan teknik modeling, berada pada kategori rendah sebanyak 100%. 2.) Gambaran kontrol diri remaja penggemar K-Pop setelah diberikan konseling behavioral dengan menggunakan teknik modeling terlihat dari hasil analisis berada pada kategori tinggi sebanyak 100%. 3.) Terdapat efektifitas pelaksanaan konseling behavioral dengan menggunakan teknik modeling untuk meningkatkan kontrol diri remaja penggemar K-Pop. Berdasarkan hasil penelitian ini direkomendasikan kepada guru BK untuk dapat meningkatkan pelaksanaan konseling behavioral dengan menggunakan teknik modeling ke depannya This page is in Frisian Translate to English AfrikaansAlbanianAmharicArabicArmenianAzerbaijaniBengaliBulgarianCatalanCroatianCzechDanishDutchEnglishEstonianFinnishFrenchGermanGreekGujaratiHaitian CreoleHebrewHindiHungarianIcelandicIndonesianItalianJapaneseKannadaKazakhKhmerKoreanKurdish (Kurmanji)LaoLatvianLithuanianMalagasyMalayMalayalamMalteseMaoriMarathiMyanmar (Burmese)NepaliNorwegianPashtoPersianPolishPortuguesePunjabiRomanianRussianSamoanSimplified ChineseSlovakSlovenianSpanishSwedishTamilTeluguThaiTraditional ChineseTurkishUkrainianUrduVietnameseWelsh Always translate Frisian to EnglishPRO Never translate Frisian Never translate ejournal.uin-suska.ac.id
Hubungan antara Gender dengan Kesiapan Belajar tatap muka pada Masa Covid-19 di Sekolah Menengah Atas Negeri 12 Pekanbaru
Authors: Lubis Nur Kholilah , Zatrahadi Muhammad Fahli 
Published: December 2023

Penelitian ini bertujuan untuk mengetahui hubungan antara gender dengan kesiapan belajar tatap muka pada masa Covid-19 di Sekolah Menengah Atas Negeri 12 Pekanbaru. Jenis penelitian ini adalah korelasi. Penarikan sampel menggunakan teknik quota sampling dari kelas X IPA 1 dan X IPS 4 yang berjumlah 64 orang. Data dikumpulkan menggunakan angket dan dokumentasi. Teknik analisis data yang digunakan dalam penelitian ini adalah analisis korelasi pearson Product Moment dengan bantuan program SPSS Versi 23 for windows. Hasil menunjukan adanya hubungan yang positif antara gender dengan kesiapan belajar dengan nilai koefisien korelasi yaitu sebesar 0,386 dengan df 62 pada tabel nilai “r” Product Moment taraf signifikan 5% yaitu 0,246. Hal ini menunjukkan bahwa 0,386 > 0,246 maka hipotesis alternatif diterima dan hipotesis nihil ditolak. Hal ini berarti terdapat hubungan yang signifikan antara gender dengan kesiapan belajar tatap muka pada masa Covid-19 di Sekolah Menengah Atas Negeri 12 Pekanbaru. Koefisien korelasi 0,386 dikonsultasikan dengan tabel interpretasi “r” product moment terletak diantara 0,200–0,400 yaitu tergolong pada tingkat interpretasi lemah atau rendah. Kualitas kesiapan belajar dipengaruhi oleh gender sebesar 14% sedangkan sisanya 86% ditentukan oleh faktor lain.
A case study – The occurrence of pests in stands of newly introduced energy crops in large-scale pilot experiments in conditions of Central Europe
Authors: Tóth Štefan , Tancik Ján , Šoltysová Božena , Porvaz Pavol 
Published: December 2023

Abstract
The objective of this paper is to document the occurrence of pests recorded in a large-scale pilot field experiment, where a unique set of newly-introduced herbaceous energy crops was tested under conditions of eastern Slovakia. The set consisted of Elymus elongatus, Secale cereanum, Silphium perfoliatum, Galega orientalis, Sida hermaphrodita, Panicum virgatum, Amaranthus spp., Panicum miliaceum, Sorghum sudanense, and Sorghum bicolor. A total of 14 cultivars were tested on 4 sites during 4 crop years (2016 – 2020). At least 17 species of pests were recorded in total, as some of the species may represent the identity of the couple or several relative ones, which was rare only as usually a fower common species. The occurred pests list consists of Anisoplia lata Erichson, Agriotes lineatus L., Capreolus capreolus L., Deroceras agreste L., Oulema gallaeciana Heyden, Lepisma saccharinum L., Lepus europaeus Pallas, Meloe proscarabaeus L., Metcalfa pruinosa Say, Microtus arvalis Pallas, Philaenus spumarius L., Psylliodes chrysocephala L., Rhopalosiphum padi L., stemborers as any insect larva or arthropod boring into plant stem, Sus scrofa L., Aleyrodes proletella L., Tettigonia viridissima L., and Trialeurodes vaporariorum Westwood. Overall, the occurrence of pests was regular and scattered, and damages caused by them were negligible usually, but a total destruction of the crop stand even cultivar-specific was recorded too. Most of the meaningful pests are primarily field hunting animals and only secondarily commonly occurring insect species, which for further observations are desirable in order to capture the tendency of important pests’ overpopulation, confirming respectively. Although even rare finding of the single caterpillar of Xylena exsoleta exsoleta L. was recorded in a buffer zone, within the experiment no newly introduced pest species was recorded.
Confusion and Countermeasures of College Students’ Career Guidance Work Based on Deep Learning Models
Authors: Ji Liya 
Published: December 2023

Abstract
In this paper, we identify teaching signals and employment factors by designing a college student employment guidance work model. The deep learning model is used to identify the given feature vectors, find the word sequence with the highest probability among them, generate the probability of the corresponding acoustic feature vectors, and model the college students’ employment guidance work model to model and calculate them. The teaching signal feature distribution is used to create the description, and the output probability is adjusted to it. The number of college graduates in 2020 will be 6.3 million, an increase of 190,000 compared to last year, and the initial employment rate is 91.07%. The deep learning model can effectively identify college students’ employment confusion, propose effective countermeasures and improve the employment rate.
Study on the Role of Big Data Technology in Promoting the Transformation of Financial Accounting in the Digital Economy Era
Authors: Zhao Jinjiang 
Published: December 2023

Abstract
This paper examines big data technology through the use of big data clustering algorithms and examines the factors that influence the transformation of financial accounting in the digital economy. Separate the clustering center to rectify it, finish accurate clustering of the original data set, and normalize the data. The data center point results are output after dividing the data set, and the final clustering results of the data set are obtained according to the similarity coefficient through the continuous iterative execution process. The results show that the comprehensive scores after the transformation of financial accounting have all risen sharply, and the score of the work content indicator in financial risk avoidance has risen from 4 points before the transformation to 9.5 points. It shows that big data technology can effectively promote the transformation of financial accounting in the digital economy era.
Analyzing the Integration Interpretation of Civics and Family Poetry Culture in University Language Programs Based on Deep Learning Models
Authors: Cai Liping 
Published: December 2023

Abstract
In this paper, firstly, we start from the imagery in the poem, excavate the resources of the ideology and politics in the university language poetry, and put forward the feasible path of the integration of the ideology and politics of the university language course and the poetry culture of the family training. The research on poetry culture in university language is conducted using a deep learning model. Based on the deep learning model, keywords in the poetry text are extracted and extended, taking into account the issue of word splitting and lexical annotation. The sequence coding of word vectors is needed so as to construct a poetry generation model based on RNNPG, and then the research and analysis on the concept of ideology and politics of the poetry culture in the university language are carried out. The results show that in terms of model performance, the RNNPG model is 1.17 overall, and the overall generation effect is more ideal compared to other single discriminator generation models. The acceptance level of sophomores is generally above 0.29, which is better than that of freshmen, and the teaching of ancient poems under the perspective of “curriculum politics” is especially necessary to improve their cultural taste and aesthetic interest. This study is conducive to the personalized development of students and the formation of correct concepts.
Interdisciplinary Talent Cultivation Model for Theatre Performance Based on Big Data Analysis
Authors: Zhang Cong 
Published: December 2023

Abstract
The first part of this paper examines the issues of interprofessional talent cultivation for theatrical performances and develops a model of interprofessional talent cultivation for theatrical performances based on the competency model. Secondly, to evaluate the effectiveness of the talent cultivation model, a quality evaluation index system is constructed. Then, the index weights are calculated by using the ordinal relationship analysis method in big data analysis, and the object element topology law is introduced to comprehensively evaluate the quality of talent cultivation. Finally, an empirical analysis was conducted to verify the effectiveness of the talent cultivation model and the evaluation system. The results show that the quality of professional teaching has the greatest influence on the cultivation of interdisciplinary talents in theatrical performance, with a weight of 0.3508, and the comprehensive correlation can effectively realize the ranking of the cultivation level of professional talents in theatrical performance. This shows that big data analysis in theater performance inter-specialty talent cultivation can provide effective data support for the development of university disciplines and help universities innovate and reform the theater performance specialty.
The Path of Integrating Curriculum Civics into Advertising Design Teaching in Higher Vocational Colleges and Universities Based on Big Data Analysis
Authors: Tao Yinghong 
Published: December 2023

Abstract
In order to cultivate advertising design applied talents, this paper firstly combs through the status quo of the current teaching system of advertising design and designs the model of advertising design teaching system under the concept of curriculum ideology and political education. Secondly, on the basis of the classical division clustering algorithm, by adding a fuzzy set, fuzzy clustering and fuzzy center of mass and explaining the importance of distance, a weighted fuzzy clustering teaching quality evaluation model based on big data analysis is designed. In the end, the teaching model was employed to evaluate the clustering of teaching quality by accumulating educational data. The percentage of excellent teaching quality in the clustering results is about 0.35, the smallest average percentage is failing and average, which are 0.12 and 0.11, respectively, and the highest average weight is the teacher’s professional knowledge and the student’s learning achievement, which are 0.12 and 0.125, respectively.
An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering
Authors: Sun Hong 
Published: December 2023

Abstract
This paper first analyzes the path of network ideological education to boost student management and constructs a student management platform that integrates network ideological education using big data technology. Secondly, K-means clustering analysis is used to mine students’ campus behaviors, and the DPC algorithm combined with the improved AdaBoost algorithm is used to detect students’ academic anomalies so as to help schools better realize student management. Finally, performance testing and application analysis were conducted to verify the effectiveness of the student management platform that integrates online ideological education. The results show that when the number of concurrent users is 5000, the CPU occupancy rate and response time of this platform are 10.34% and 0.52 s. It is able to analyze and detect abnormalities in students’ campus behaviors and academic situations and encourage universities to analyze students’ situations through visual reports. This shows that the student management platform integrating network ideological education in the era of big data can promote the level of student management in colleges and universities.
Innovation and Practice of Educational Management Path Building in Colleges and Universities in the Context of Big Data
Authors: Zhai Hong 
Published: December 2023

Abstract
In this paper, a digital education management information system (e-MIS) is constructed based on big data, which realizes the comprehensive processing of education administration and school management data. Through the fuzzy comprehensive evaluation model, the e-MIS system was evaluated and optimized. Then, the e-MIS system was practically applied to the fields of teaching effectiveness evaluation, teachers’ work assessment, scholarship evaluation and dormitory management. Finally, the significant impact of the e-MIS system on the enhancement aspects of new student enrollment education was empirically verified. The results show that the average score of freshmen’s school rules and discipline education is 4.35, learning culture education is 4.53, mental health education is 4.41, and life guidance education is 4.31, all of which are in the middle-upper level, as calculated by the e-EMIS evaluation model. This paper is a positive exploration of improving the efficiency of student education and management in local colleges and universities and promoting the overall development of students.
The Influence of Traditional Opera Culture on the Development of Ethnic Vocal Music Art under Deep Learning Modeling
Authors: Hou Juncai 
Published: December 2023

Abstract
In this paper, according to the characteristics and development trend of traditional opera, based on the deep learning model, we use a convolutional neural network to extract the opera features, combined with an SVM classifier to construct a CNN-SVM classification model. For the two classification algorithm models of logistic regression and deep confidence network, combined with two types of feature parameters of timbre and melody, six groups of experiments are designed to extract the time-frequency features of traditional opera. The CNN-SVM classification model is used to categorize the emotion of traditional opera, which aims to divide the musical features by multi-feature selection. Analyze the timbre feature parameter MFCC to investigate the impact of traditional opera timbre on ethnic vocal singing. For the logistic regression model, the coefficient of MFCCs is 0.5588, and the classification accuracy is only 0.5301 when the feature parameters are selected as melodic features, i.e., gene frequency, resonance peak, and band energy, and 0.6228 when the feature parameters are selected as a combination of timbral and melodic features. The diversity of the traditional opera timbres contributes to the development of ethnic vocal art with the trend of inclusiveness.
Construction of a vocal singing style repository based on a deep learning model
Authors: Kang Shaohua 
Published: December 2023

Abstract
In this paper, we first use the short-time Fourier transform method to extract statistical features in the frequency domain of vocal music. The extracted features are fused using D − S -evidence theory. The fused vocal features are inputted into the improved deep learning network to construct a vocal singing style classification model. Secondly, the requirements of vocal music resources according to the classification of song styles are constructed for the vocal singing resource library system. Finally, the vocal music resource library system undergoes testing in all directions to ensure it meets both functional and performance requirements. The results show that under the respective optimal threads of the vocal music resource library, the number of DM7 network reads and writes remains between 200 and 300 kb, and the random read performance of HBase reaches 8340 TPS, indicating that the resource library provides users with a fast and convenient way to retrieve multidimensional resources. This paper provides a long-term reference for the preservation and use of vocal singing resources.
Innovative Development of College Students’ Civic Education Based on Multiple Data Chain Networks
Authors: Liu Qilin , Zhang Ye 
Published: December 2023

Abstract
This paper logically explores the overall form of ideological and political education system construction in colleges and universities, constructs the logical rationale of system cognition and development, and explores the operation process of system dynamic balance. The ideological and political education system is constructed through the comprehensive planning of multiple data chains, and the demand for teaching tasks and communication delays is solved through optimized planning. Maximizing the utilization of teaching resources is achieved by optimizing the deployment of teaching resources. Finally, the Civics teaching mode of multi-data chain integrated planning was evaluated and tested through experiments. The results show that the three dimensions of the pre-test of the experimental group are 4.29, 4.21 and 4.41, respectively, which are improved to 5.60, 5.83 and 4.99, with a significant improvement effect. This paper effectively explores the development of Civics teaching, realizes all-round education, and achieves the fundamental purpose of cultivating morality.
Research on the Integration Path of College Vocal Music Teaching and Traditional Music Culture Based on Deep Learning
Authors: Wang Jing 
Published: December 2023

Abstract
In this paper, we first start from extracting music features and analyze the extraction methods for time-domain, frequency-domain, and cepstrum-domain features of music. Next, the logistic regression model is used to recognize music and deal with two-class and multi-class classification problems. To extract music and text features, a CNN-SVM-based classification model is proposed using a deep learning teaching practice structure. Finally, feature selection experiments are carried out on the extracted raw feature set using various feature selection algorithms to optimize the feature set and reduce classifier computation. The results show that the average number of feature items selected using the improved correlation coefficient method is 64, and the number of feature items is mostly distributed between 58 and 72. The data verifies that the improved correlation coefficient method proposed can effectively extract the features of traditional music, improve the accuracy of traditional music classification, and thus promote the development of the fusion of traditional music culture and vocal music teaching in colleges and universities.