bluuebunny/crossref_metadata_2025 huggingface dataset:
Deteksi Kejadian Lalu Lintas pada Teks Twitter dengan Pendekatan Klasidikasi Multi-Label Berbasis Deep Learning
Authors: Atikah Luthfi , Purwitasari Diana , Suciati Nanik
Published: December 2022

Kemacetan merupakan salah satu kejadian yang sering terjadi di kota-kota besar. Hal ini dapat merugikan pengguna jalan, oleh karena itu perlu dilakukan pendeteksian kejadian lalu lintas. Saat ini, twitter digunakan sebagai sumber informasi untuk mendeteksi suatu kejadian. Namun, pengguna twitter cenderung membagikan beberapa informasi sekaligus, sehingga dalam satu tweet bisa memiliki lebih dari satu label. Pada penelitian ini dilakukan klasifikasi multi-label menggunakan 18.000 data dari akun twitter terverifikasi di Surabaya. Klasifikasi multi-label pada penelitian ini dilakukan untuk mengidentifikasi banyak situasi lalu lintas seperti kondisi cuaca, kecelakaan lalu lintas, kemacetan lalu lintas, lalu lintas padat, dan lalu lintas lancar. Klasifikasi dilakukan dengan menggunakan pendekatan deep learning (CNN dan LSTM) dan word embedding (word2vec dan fastText) dengan augmentasi dan non-augmentasi data. Eksperimen dilakukan dengan 3 skenario berbeda untuk melihat pengaruh data uji yang berbeda pada data latih yang sama. Selanjutnya dilakukan eksperimen untuk menguji pengaruh jumlah label terhadap klasifikasi multi-label pada data uji yang sama. Akurasi tertinggi pada non-augmentasi data adalah 0,75 dan pada augmentasi data adalah 0,95. Dari keseluruhan ujicoba akurasi tertinggi diperoleh dari kombinasi LSTM dan fastText.
Clustering Tingkat Risiko Klasifikasi Lapangan Usaha (KLU) Menggunakan Metode K-Means
Authors: Nugroho Jessica Rahmawati , Suprapto Yoyon K , Setijadi Eko
Published: December 2022

Pajak adalah sumber utama pendapatan negara. Karena itu, otoritas pajak di seluruh dunia, bertugas untuk mengurangi kesenjangan pajak (tax gap). Salah satu faktor yang menyebabkan adanya kesenjangan pajak adalah tingkat kepatuhan Wajib Pajak (WP). Dalam upaya meminimalisir ketidakpatuhan WP, Direktorat Jenderal Pajak (DJP) melakukan kegiatan pengawasan dan pemeriksaan terhadap WP. WP badan mempunyai kontribusi dominan terhadap penerimaan negara. Dari latar belakang tersebut, diambil pendekatan menggunakan metode clustering dengan algoritma K-Means untuk mengelompokkan Klasifikasi Lapangan Usaha (KLU) sesuai risiko tingkat ketidakpatuhan dan dampak fiskal bagi penerimaan yang dibagi menjadi tinggi, sedang, dan rendah. Hasil pengujian menunjukkan terdapat 9 KLU yang masuk ke dalam kuadran berwarna merah dengan tingkat ketidakpatuhan tinggi (variabel A) dan memiliki dampak fiskal yang tinggi (variabel B). Dengan validasi clustering menggunakan uji silhouette, diperoleh nilai 0,65 untuk variabel A dan 0,93 untuk variabel B. Informasi yang dihasilkan dari penelitian ini dapat dipergunakan untuk mendukung pengambilan keputusan dalam penentuan daftar KLU yang perlu diprioritaskan untuk dilakukan pemeriksaan dan pengawasan. AbstractTax are the main source of state revenue. Therefore, tax authorities around the world are in charged to reducing the tax gap. One of the factors that causes the tax gap is the level of taxpayer compliance. In order to minimising the risk of taxpayers non-compliance, Directorate General of Taxes (DGT) as a Tax Authorities need to supervising and inspecting taxpayers. Corporate taxpayers as one of the largest source of revenue, based on their business sector have a dominant contribution to state revenues. Thus, in this study, the researcher tries to implement the clustering method with the K-Means algorithm to grouping the business classifications (Klasifikasi Lapangan Usaha/KLU) according to the risk of non-compliance level and the fiscal impact on revenue which is divided into high, medium, and low. The results show that there are 9 KLUs that included into the red quadrant with a high level of non-compliance (variable A) and have a high fiscal impact (variable B). Clustering validation using the silhouette test, obtained values of variable A and variable B, respectively 0,65 and 0,93. The information provided from this study can be used to support decision making in determining the list of KLUs that need to be prioritized for supervising and inspecting.
Autoencoder untuk Sistem Prediksi Berat Lahir Bayi
Authors: Nugraha Fitra Septia , Pardede Hilman Ferdinandus
Published: December 2022

Salah satu ukuran terpenting saat awal persalinan adalah keakuratan prediksi berat lahir. Dengan menggunakan metode prediksi yang tepat, perkiraan ekstrim berat lahir bayi dapat dideteksi lebih atau kurang sehingga beberapa tindakan pencegahan dapat dilakukan sebelum persalinan. Di sisi lain, untuk meningkatkan akurasi suatu proses pembelajaran, dibutuhkan suatu prediksi yang akurat untuk masalah yang dihadapi atau dengan menggunakan gabungan beberapa metode. Penelitian bertujuan untuk prediksi berat lahir bayi menggunakan metode Deep Learning autoencoder untuk memprediksi berat lahir bayi. Salah satu tantangan dalam pengembangan sistem prediksi berat lahir bayi adalah datanya yang berdimensi tinggi. Teknik konvensional untuk feature reduction seperti principal component analysis (PCA), mengasumsikan orthogonality atau independensi antar komponen prinsipal nya. Dengan autoencoder, asumsi tersebut tidak ada. Sehingga autoencoder dapat memodelkan korelasi antar fitur. Dengan melakukan variasi parameter pada autoencoder, performa terbaik diperoleh adalah MSE 0.002, MAE 0.029, R2 0.991 dengan autoencoder dengan 4 lapisan hidden layer encoder dan decoder. Ini lebih baik dibandingkan PCA. AbstractOne of the most important measurements at the onset of labor is the accuracy of the prediction of birth weight. By using precise prediction methods extreme estimates of baby birth weight can be detected more or less so that some precautions can be taken before delivery. On the other hand, to improve the accuracy of a learning process, an accurate prediction is needed for the problem at hand or by using a combination of several methods. This study aims to predict baby birth weight using the Deep Learning autoencoder method to predict baby birth weight. One of the challenges in developing a predictive system for infant birth weight is the high dimensional data. Conventional techniques for feature reduction, such as principal component analysis (PCA), assume orthogonality or independence between the principal components. With an autoencoder, that assumption doesn't exist. So that the autoencoder can model the correlation between features. By varying the parameters of the autoencoder, the best performance is MSE 0.002, MAE 0.029, R2 0.991 with an autoencoder with 4 hidden layer encoder and decoder layers. This is better than PCA.
Implementasi Aplikasi Mobile Augmented Reality Untuk Pengenalan Materi Bangun Ruang
Authors: Widyantara I Made Oka , Wiharta Dewa Made , Widiadnyana Putu
Published: December 2022

Proses interaksi pembelajaran yang awalnya tatap muka sekarang beralih ke daring karena adanya pandemi COVID-19. Selama proses pembelajaran daring, siswa mengalami kesulitan dalam memahami konsep dan mengembangkan konsep sampai refleksi. Penelitian ini memiliki tujuan yaitu mengembangkan dan menerapkan media pembelajaran yang dapat menarik minat siswa dan membantu pemahaman materi bangun ruang. Pengembangan media pembelajaran menggunakan teknologi augmented reality sebagai aplikasi mobile untuk memvisualisasikan materi bangun ruang dalam bentuk 3D yang diproyeksikan pada smartphone. Model waterfall dipilih sebagai acuan dalam pengembangan aplikasi. Penerapan aplikasi augmented reality dalam pembelajaran daring menggunakan strategi REACT untuk memaksimalkan penggunaan aplikasi. Hasil penelitian ini yaitu aplikasi berbasis Android dengan menggunakan model waterfall dengan hasil valid melalui uji black box, hasil penilaian kuesioner untuk penggunaan aplikasi mendapatkan rata-rata 82.44% dengan indikator kategori ”Baik”.

 

Abstract

The learning interaction process that was originally face-to-face is now turning online due to the COVID-19 pandemic. During the online learning process, students have difficulty in understanding concepts and developing concepts until reflection. This research aims to develop and implement learning media that can attract students and help them understand the material of solid figures. The development of learning media uses augmented reality technology as a mobile application to visualize solid figure materials in 3D projected on smartphones. The waterfall model was chosen as a reference in application development. The adoption of augmented reality applications in online learning uses REACT strategies to maximize application usage. The results of this study are Android-based applications using waterfall models with valid results through black-box tests, questionnaire assessment results for application usage get an average of 82.44% with the category indicator "Good"

 
Pengaruh Word Affect Intensities Terhadap Deteksi Ulasan Palsu
Authors: Istanto Raga Saputra Heri , Bachtiar Fitra Abdurrachman , Ridok Achmad
Published: December 2022

Transaksi jual beli elektronik melalui internet terus berkembang dan menjadi populer, begitu pula dengan jumlah ulasan dari pelanggan yang meningkat pesat. Dengan banyaknya pemberi ulasan, terdapat kemungkinan seseorang menulis ulasan palsu yang disebut fake review untuk mempromosikan produk atau menjatuhkan produk kompetitor. Sangat penting untuk dapat mendeteksi ulasan palsu sehingga ulasan yang digunakan pelanggan sebagai pertimbangan untuk memilih produk atau jasa merupakan ulasan yang andal. Studi sebelumnya hanya menggunakan fitur sentimen yang terbatas pada objektivitas dan polaritas untuk melakukan deteksi ulasan palsu. Sedangkan studi yang lebih baru menunjukan adanya leksikon kosa kata berbasis emosi yang diberi nama word affect intensities yang terbukti mampu mengukur sentimen dengan lebih baik pada kalimat. Penelitian ini bermaksud untuk mengetahui apakah word affect intensities dapat menjadi faktor yang mempengaruhi hasil deteksi ulasan palsu. Penelitian dilakukan dengan memunculkan dua fitur baru berlandaskan word affect intensities berupa fitur kelompok emosi positif dan fitur kelompok emosi negatif. Fitur tersebut kemudian dikombinasikan dengan fitur pada penelitian sebelumnya dan dievaluasi menggunakan beberapa algoritme klasifikasi. Hasil penelitian menunjukan word affect intensities dapat menjadi faktor yang mempengaruh peningkatan akurasi deteksi ulasan palsu sebesar 2.1%. Abstract Electronic buying and selling transactions over the internet continue to grow and become popular, as well as the number of reviews from customers that is increasing rapidly. With so many reviewers, it is possible that someone wrote a fake review to promote a product or demote a competitor’s product. It is very important to be able to detect fake review so that the reviews customers use as a consideration for choosing a product or service are reliable reviews. Previous studies only used sentiment features that were limited to objectivity and polarity to detect fake review. Meanwhile, a more recent study shows that there is an emotion-based vocabulary lexicon called word affect intensities which are proven to be able to better measure sentiment in sentences. This study intends to determine whether word affect intensities can be a factor that affects the results of fake review detection. The research was conducted by bringing up two new features based on the word affect intensities in the form of positive emotion group features and negative emotion group features. These features are then combined with features in previous studies and evaluated using several classification algorithms. The results showed that word affect intensities can be a factor that affects the increased accuracy of fake review detection by 2.1%.
Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu
Authors: Awalina Aisyah , Bachtiar Fitra Abdurrachman , Utaminingrum Fitri
Published: December 2022

Kemudahan untuk memperoleh informasi saat ini, telah sedikit membantu hidup kita. Seperti mencari ulasan untuk menimbang tempat atau barang yang akan dipilih. Beberapa orang memanfaatkan hal tersebut dengan membuat ulasan palsu untuk kepentingan mereka sendiri. Sehingga deteksi ulasan palsu sangat dibutuhkan. Model Transformer saat ini banyak diterapkan pada pemrosesan bahasa alami karena kinerja yang diperoleh nya sangat baik. Ada dua pendekatan yang dapat dilakukan dalam model Transformer yaitu pre-training dan fine-tuning. Penelitian sebelumnya telah banyak menggunakan fine-tuning dari model Transformer dikarenakan adanya kemudahan dalam pelatihan, waktu yang lebih sedikit, biaya dan kebutuhan lingkungan yang lebih rendah dibanding proses pre-training. Akan tetapi penelitian sebelumnya masih sedikit yang membandingkan model deep learning dengan fine-tuning yang khusus diterapkan pada deteksi ulasan palsu. Penelitian ini melakukan perbandingan model Transformer menggunakan pendekatan fine-tuning dengan metode deep learning yaitu CNN dengan berbagai pretrained word embedding untuk mengatasi deteksi ulasan palsu pada dataset Ott. Model RoBERTa mengungguli model Transformer dan deep learning dimana nilai akurasi 90,8%; precision 90%; recall 91,8% dan f1-score 90,8%. Namun dari segi waktu komputasi model pelatihan, DistilBERT memperoleh waktu komputasi terkecil yaitu dengan nilai 200,5 detik. Meskipun begitu, hasil yang diperoleh model Transformer maupun deep learning memiliki kinerja yang baik untuk deteksi ulasan palsu pada dataset Ott.AbstractThe ease of obtaining information today has helped our lives, like looking for reviews to weigh the place or item to choose. Some people take advantage of this by creating spam reviews for their benefit. So the detection of spam reviews is needed. Transformer models are currently widely applied to natural language processing because they have outstanding performance. Two approaches in the Transformer model is pre-training and fine-tuning. Previous studies have used a lot of fine-tuning due to the ease of training, less time, costs, and lower environmental requirements than the pre-training process. However, a few previous studies compare deep learning models with fine-tuning applied explicitly for detecting spam reviews. This study compares the Transformer model using a fine-tuning approach with a deep learning method, namely CNN, which uses various pre-trained word embedding to overcome the detection of false reviews in the Ott dataset. The result is RoBERTa model outperforms between Transformer and deep learning models, where the accuracy is 90.8%, precision is 90%, recall is 91.8%, and f1-score is 90.8%. Afterward, DistilBERT models obtained the shortest computation time with 200.5 seconds. However, the results obtained by both Transformer and deep learning models perform well to detect spam reviews in the Ott dataset.
Sistem Pendeteksi Banjir dan Badai Angin serta Monitoring Cuaca Berbasis Internet of Things
Authors: Pradirta Ida Bagus Made Lingga , Piarsa I Nyoman , Dharmaadi I Putu Arya
Published: December 2022

Curah hujan yang tinggi disertai badai angin biasanya terjadi saat musim pancaroba, hal tersebut dapat mempengaruhi aktivitas masyarakat. Perlu adanya teknologi yang dapat memberikan peringatan dini agar dapat meminimalisir dampak yang ditimbulkan, salah satunya yaitu Internet of Things yang merupakan teknologi yang memungkinkan manusia untuk mengontrol dan menjalankan tugas secara otomatis pada suatu objek yang terhubung melalui internet. Penelitian ini menciptakan sebuah prototipe alat dengan menggunakan konsep Internet of Things yang dapat memberikan peringatan dini dan monitoring ketinggian air ketika terjadi banjir, kecepatan angin, suhu, kelembaban, intensitas cahaya, dan hujan pada suatu tempat secara spesifik dan real time yang ditampilkan pada aplikasi mobile Android untuk user dan aplikasi web untuk admin. Perangkat yang digunakan yaitu seperti mikrokontroler Arduino Mega 2560, sensor ultrasonik, sensor anemometer, sensor suhu dan kelembaban, sensor intensitas cahaya, sensor hujan, buzzer, Liquid Crystal Display (LCD), modul Wi-Fi ESP8266, modul SIM800L V2, stepdown LM2596 DC-DC, modul GPS NEO-6M, baterai 18650, dan switch on off dengan 3 pin. Hasil pengujian pada prototipe alat menghasilkan pengujian yang cukup baik dan akurat yang terdiri dari pengujian keseluruhan sensor, kalibrasi setiap sensor, serta pengiriman data ke server, demikian pula pada pengujian perangkat lunak yang terdapat pada setiap fungsi aplikasi Android dan web dapat berjalan dengan optimal. AbstractHigh rainfall accompanied by windstorms usually occurs during the transition season, this can affect community activities. It is necessary to have technology that can provide early warning to minimize the impact, one of which is the Internet of Things which is a technology that allows humans to control and perform tasks automatically on an object that is connected via the internet. This research creates a prototype tool using the concept of the Internet of Things that can provide early warning and monitoring of water levels in the event of flooding, wind speed, temperature, humidity, light intensity, and rain in a specific and real time place displayed on an Android mobile application for users and web applications for admins. The devices used are Arduino Mega 2560 microcontroller, ultrasonic sensor, anemometer sensor, temperature and humidity sensor, light intensity sensor, rain sensor, buzzer, Liquid Crystal Display (LCD), ESP8266 Wi-Fi module, SIM800L V2 module, stepdown LM2596 DC-DC, NEO-6M GPS module, 18650 battery, and on off switch with 3 pins. The measurement results of the prototype tool produce a fairly good and accurate test consisting of testing the entire sensor, calibrating each sensor, and sending data to the server, as well as testing the software contained in every function of Android and web applications that can run optimally.
Studi Komparasi Naive Bayes, K-Nearest Neighbor, dan Random Forest untuk Prediksi Calon Mahasiswa yang Diterima atau Mundur
Authors: Sejati Puteri , Munawar Munawar , Pilliang Marzuki , Akbar Habibullah
Published: December 2022

Penelitian ini bertujuan untuk mendapatkan model prediksi terbaik dari data Penerimaan Mahasiswa Baru tahun 2014 hingga 2019 dengan membandingkan Naive Bayes, K-Nearest Neighbor, dan Random Forest. Penelitian ini menggunakan metode klasifikasi untuk memprediksi calon mahasiswa. Mereka diterima atau  mundur. Dalam penelitian ini digunakan 19.603 data latih dan 4.901 data uji. Hasil penelitian menunjukkan bahwa algoritma Random Forest adalah yang terbaik dengan akurasi 73,61%, dibandingkan dengan K-Nearest Neighbor dengan akurasi 72,08%, dan Naive Bayes dengan akurasi 70,47%. Disimpulkan juga bahwa optimasi model dengan teknik Hyperparameter menghasilkan nilai akurasi yang lebih baik. Hasil penelitian ini dapat digunakan untuk mendukung bagian pemasaran dalam meminimalisir jumlah calon mahasiswa yang mengundurkan diri. AbstractThis study aimed to obtain the best predictive model from New Student Admissions data for 2014 to 2019 by comparing Naive Bayes, K-Nearest Neighbor, and Random Forest. This study used the classification method to predict prospective students. They are accepted or withdrawn. In this study, 19,603 training data and 4,901 test data were used. The results showed that the Random Forest algorithm was the best with an accuracy of 73.61%, compared to K-Nearest Neighbor with an accuracy of 72.08%, and Naive Bayes with an accuracy of 70.47%. It is also concluded that optimizing the model with the Hyperparameter technique produces better accuracy values. This study's results can be used to support the marketing department in minimizing the number of withdrawn prospective students.
Perbandingan Arsitektur Convolutional Neural Network Pada Klasifikasi Pneumonia, COVID-19, Lung Opacity, dan Normal Menggunakan Citra Sinar-X Thoraks
Authors: Setiawan Agung Wahyu
Published: December 2022

Covid-19 telah mewabah sejak awal Tahun 2020. Meskipun terjadi penurunan jumlah kasus penderita Covid-19, namun masih terdapat beberapa kasus baru karena terjadi mutasi virus. Selain Covid-19, prevalensi pneumonia juga masih tinggi. Oleh karena itu, perlu dilakukan klasifikasi Covid-19 dengan pneumonia meskipun pascapandemi. Salah satu cara yang digunakan untuk mendeteksi Covid-19 dan pneumonia adalah menggunakan citra sinar-X dada. Pada studi ini dilakukan tidak hanya Covid-19 dan pneumonia, tetapi juga lung opacity dan normal. Beberapa tahun terakhir, marak digunakan pendekatan klasifikasi berbasis kecerdasan buatan. Beberapa studi telah dilakukan dengan menggunakan pendekatan deep learning berbasis arsitektur CNN. Pada studi ini, klasifikasi keempat kelas di atas dilakukan dengan menggunakan data yang lebih banyak, yaitu 21.165 citra sinar-X dada. Selain itu, dilakukan perbandingan kinerja sembilan arsitektur CNN, yaitu Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2, dan ResNet152v2. Sebagai tambahan, studi ini juga membandingkan kinerja dua pengoptimasi, yaitu Adam dan SGD untuk masing-masing arsitektur CNN. Kinerja tertinggi diperoleh dengan menggunakan arsitektur CNN berbasis ResNet50v1 dan pengoptimasi Adam dengan nilai rerata akurasi pelatihan, validasi, dan pengujian mencapai 92,22 ± 0,25 %. AbstractCovid-19 has been a global pandemic since the beginning of 2020. Although there has been a decrease in the number of cases of Covid-19, however, there are still some new cases due to virus mutation. Besides Covid-19, the prevalence of pneumonia is still high. Therefore, it is necessary to classify Covid-19 and pneumonia not only during a pandemic but also post-pandemic. One of the methods is using chest X-ray images. In this study, not only Covid-19 and pneumonia but also lung opacity and normal were carried out. In recent years, artificial intelligence-based classification approaches have been widely used. Several studies have been conducted using a deep learning approach based on Convolutional Neural Networks (CNN) architecture. This study aims to classify the four classes using 21,165 chest X-ray images. In addition, a comparison of the performance of nine CNN architectures was perfomed, i.e. Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2 and ResNet152v2. In addition, this study also compares the performance of two optimizers, i.e. Adam and Stochastic Gradient Descent (SGD) for each CNN architecture. The highest performance was obtained using ResNet50v1 and Adam optimizer with the average value of training, validation, and testing accuracy of 92.22 ± 0.25 %.
Implemetasi Komputasi Akar Kuadrat Resolusi Tinggi pada Field Programmable Gate Array (FPGA)
Authors: Irfan Muhammad , Setiawan Hendra
Published: December 2022

Komputasi akar kuadrat diperlukan pada beberapa proses pengendalian, diantaranya untuk Direct Torque Control (DTC) pada sistem penggerak motor yang membutuhkan proses perhitungan yang sangat cepat. Field Programmable Gate Array (FPGA) merupakan salah satu perangkat yang dapat digunakan untuk implementasi komputasi yang memerlukan kecepatan dan presisi tinggi. Penerapan komputasi akar kuadrat pada FPGA menggunakan metode digit by digit non-restoring dengan beberapa modifikasi agar memiliki hasil perhitungan dengan nilai error yang kecil. Sistem tersebut diimplementasikan menggunakan 32-bit input dan 16-bit output. Proses perhitungan melibatkan Finite State machine (FSM) untuk menghemat resource yang diperlukan. Proses verifikasi sistem dilakukan dalam dua tahap, yaitu verifikasi fungsional dengan aplikasi ModelSim-Altera dan verifikasi hardware menggunakan modul FPGA Cylcone IV EP4CE6E228N. Hasil verifikasi menunjukkan bahwa hasil perhitungan akar kuadrat memiliki resolusi sampai dengan 0,0039. Selain itu, sistem ini membutuhkan 157 Logic Elements dan 120 register dengan kecepatan clock tertinggi yang dicapainya adalah 205 MHz untuk input 32 bit AbstractSquare root computing is required in several control processes, such as for Direct Torque Control (DTC) on motor drive systems that require a very fast calculation process. Field Programmable Gate Array (FPGA) is one of the devices that recommended for high speed and precision computation. The implementation of the square root on the FPGA uses the digit-by-digit non-restoring method with some modifications to get a high precision of computation result. The system is implemented using 32-bit input and 16-bit output. The calculation process involves a Finite State machine (FSM) to minimize computation resources. The system verification process is carried out in two stages, i.e. functional verification using the ModelSim-Altera and hardware verification using the FPGA Cylcone IV EP4CE6E228N. The verification shows that the result of the square root calculation has a resolution of up to 0.0039. In addition, the system requires 157 Logic Elements and 120 registers with the highest clock speed can achieves 205 MHz for 32-bit input.
Penerapan Algoritma Fuzzy Mamdani untuk Memberikan Saran yang Optimal dalam Pengambilan Keputusan pada Permainan KArtu Monster
Authors: Perdana Muhammad Rizky , Sundawaijaya Andika , Nuraminah Ahlijati
Published: December 2022

Permainan kartu monster Yu-Gi-Oh merupakan permainan strategi yang dilakukan bergiliran antar pemain dengan mengeluarkan kartu yang tepat untuk menyerang atau bertahan dengan tujuan mengurangi poin kehidupan musuh dan melindungi poin kehidupan pemain. Dalam menentukan kartu yang tepat dalam permainan, diperlukan informasi yang akurat pada suatu kondisi permainan. Banyak pemain salah mengambil langkah dalam permainan, sehingga kesempatan kalah menjadi lebih besar. Untuk itu diperlukan sebuah sistem saran yang mampu membantu pemain dalam menentukan kartu yang sesuai untuk digunakan dalam permainan tersebut. Sistem saran akan memberikan masukan kepada pemain dalam menentukan kartu yang tepat dan optimal dalam suatu langkah. Salah satu algoritma yang mampu memetakan dan menentukan keputusan yang dapat menjadi sebuah sistem saran adalah fuzzy dengan inferensi Mamdani. Pada penelitian ini, pemilihan saran kartu menggunakan algoritma Fuzzy Mamdani dilakukan berulang kali sebanyak 15 kali menggunakan data kartu Yu-Gi-Oh yang sudah diolah. Hasil akurasi dari model pemilihan saran kartu yang dirancang sebesar 0,733 yang artinya cukup baik. Dari hasil tersebut rekomendasi pengembangan penelitian antara lain dengan menambah kartu pada dataset seperti beberapa tipe kartu yang berbeda dan menambahkan atau menggunakan metode yang berbeda serta analisis penambahan input pada variabel Fuzzy untuk menambah akurasi sistem dalam memilih saran kartu yang lebih optimal.AbstractThe Yu-Gi-Oh monster card game is a strategy game that takes turns between players by issuing the right cards to attack or defend with the aim of reducing enemy life points and protecting the player's own life points. In determining the right card to use in the game, the right information is needed in a game condition. Many players take the wrong steps in the game, so the chance of lose is greater. It need a suggestion system that is able to assist players in determining the appropriate cards to be used in the game. The suggestion system will provide input to players in determining the right and optimal card in a move. Algorithm that is able to map and determine decisions that can become a suggestion system is fuzzy with Mamdani inference. In this study, the selection of card suggestions using the Fuzzy Mamdani algorithm was repeated 15 times using processed Yu-Gi-Oh card data. The results of the accuracy of the proposed card selection model are 0.733, which means it is quite good. From these results, recommendations for further research include adding cards to the dataset such as several different card types and adding or using different methods and analysis of adding input to fuzzy variables to increase system accuracy in choosing more optimal card suggestions.
موانع الزواج عند اليهود والنصارى (دراسة مقارنة )
Authors: Omar Saad Fathallah , Einad Nahar Omar Ali
Published: December 2022

ما من زواج يعقد ويبرم في جميع الاديان الا بعد التأكد من عدم وجود مانع يحول دون ابرامه   وقد تكون هذه الموانع عامة عند جميع الطوائف , وقد تكون خاصة عند بعض الطوائف, وقد تكون موانع مانعة :وهي التي تجعل الزواج محرما من جهة الدين, وعلى الرغم من تحريمها لكن العقد صحيح ولا تؤثر ذلك في صحة عقد الزواج اذا تم رغم وجودها ,مثل ,النذر البسيط ,مانع التبني ,مانع اختلاف المذهب واما الموانع المبطلة :هي التي تجعل العقد باطل فلا ينتج عنه اي اثار.
الارجاء الأكاديمي لدى طلبة الجامعة
Authors: Al-Sumaidaie Namir Ibrahim , Daham Marwan Ayash
Published: December 2022

يعدُ الارجاء الأكاديمي ظاهرةً يعاني منها كثير من الطلبة، وهذا ما دعى إلى الوقوف على طبيعة واشكال هذه الظاهرة ومعرفة اسبابها والعوامل التي تؤثر عليها، ، ولأنها تركز على فئة مستهدفة باعتبارها ركيزة أساسية في المجتمع حيث تقع عليها الكثير من المسؤوليات والمهمات التي تهم مستقبل الوطن , وقد هدفت الدراسة إلى الكشف عن مستوى الارجاء الأكاديمي لدى طلبة الجامعة فضلا عن التعرف على الفروق بين الطلبة فــــــــــي الارجــــاء الأكاديمي وفقا لمتغير التخصص الدراسي (علمي- إنساني )تألفت عينة الدراسة من (300) طالبٍ وطالبة , وتم تطبيق  مقياس الارجاء الأكاديمي من إعداد الباحث وقد أسفرت نتائج الدراسة عن إنه طلبة الجامعة أظهروا مستوى مرتفع من الارجاء الأكاديمي, وكذلك لا وجود لفروق بين طلبة الجامعة في مستوى الارجاء الأكاديمي وفق متغير التخصص (علمي – انساني ) , وفي ضوء نتائح الدراسة أوصى الباحث ببعض التوصيات والمقترحات.
اثر تدريس الجغرافية على وفق استراتيجية المنظمات المعرفية التخطيطية في تحصيل طلاب المرحلة الاعدادية في مادة الجغرافية
Authors: Aswad Sajid Ahmed
Published: December 2022

يهدفُ البحث إلى تعرف اثر استراتيجية المنظمات المعرفية التخطيطية في تحصيل مادة الجغرافية الطبيعية لدى طلاب الصف الخامس الاعدادي ، استخدم الباحث التصميم التجريبي ذو الضبط الجزئي و الاختبار البعدي ،اختار الباحث الشعبة (أ) لتمثل المجموعة التجريبية التي تدرس على وفق استراتيجية المنظمات المعرفية التخطيطية ، ومثلت الشعبة (ب) المجموعة الضابطة التي تدرس بالطريقة التقليدية، بلغ عدد الطلاب (60) طالب بواقع (30) في كل مجموعة. تم مكافأة المجموعتين في المتغيرات: العمر الزمني، الذكاء، المعرفة السابقة . اعد الباحث أداة البحث الاختبار التحصيلي ، وتم التحقق من الصدق الاختبار و ثباته ، ولاستخراج النتائج استخدم الباحث  اختبار  (T-test)  لمعالجة البيانات  احصائيا ، وأظهرت النتائج تفوق المجموعة التجريبية التي درست على وفق  استراتيجية المنظمات المعرفية التخطيطية في التحصيل ، و أوصى الباحث باستخدام  استراتيجية المنظمات المعرفية التخطيطية في التدريس.
Being Homeless at Home: The Role of the Society and Family in the Growth and Development of the child in Toni Morrison’s The Bluest Eye
Authors: Musir Zainab Abdul-Karim
Published: December 2022

The focus of this research is the family influences on children in the way they think about themselves as adults. This effect shapes children’s personalities and as a result influences their identities. In Toni Morrison's The Bluest Eye the Society and Pecola’s family seem to tell her that beauty brings love and since she is ugly she remains unloved and unworthy. The research answers questions like, what are the effects of the society on the psychology of a child. What are the effects of the family, what breakdowns occur in the children’s lives when they live in a loveless family? What are the effects of being homeless in your own home and among your own family and people?
The Well-Being of Older Adults in the Philippines: Application of the Years of Good Life
Authors:   Asian Demographic Research Institute, Shanghai University, Shanghai, China, Vicerra Paolo Miguel Manalang
Published: December 2022

The population structure of the Philippines is young, but it is expected to age in the coming years. It is important to understand the well-being of older people to prepare policies that can ensure an adequate quality of life. Understanding the heterogeneity of a population is needed to guarantee that individual development is equitable. Years of Good Life (YoGL) is a novel indicator of well-being comprised of measures of good physical and cognitive health, being out of poverty, and being satisfied with life. Various measures were tested to determine which indicators best fit the stated dimensions of YoGL. Using the 2018 Longitudinal Study on Ageing and Health in the Philippines, observations show men aged 60 have a life expectancy of about 17 years, and YoGL was 14 years. Women of the same age have a life expectancy of 19 years, while the YOGL is 16 years. Among men and women aged 70 years, the YoGL was 7.9 and 8.6 years, respectively. Analyses show that for both ages and sexes, physical health was the dimension that decreased the number of good years. This study demonstrates the applicability of the YoGL methodology in investigating the well-being of subpopulations.
Pelatihan Penggunaan Microsoft Office Sebagai Penunjang Keberhasilan Bisnis UMKM di Desa Nangka Kecamatan Kadugede
Authors: Syamfithriani Tri Septiar , Mirantika Nita , Herlina Elin
Published: December 2022

Di era Society 5.0 para pelaku usaha dituntut untuk adaptif, bergerak cepat menghadapi segala perubahan yang tidak dapat terprediksi, para pelaku usaha dituntut untuk dapat menyelesaikan segala tantangan dan permasalahan social dengan memanfaatkan segela jenis bentuk inovasi teknologi informasi. Untuk mempersiapkan hal demikian maka dibutuhkan literasi teknologi kepada pengguna dan pelaku usaha agar mampu menggunakan aplikasi teknologi, mampu mengakses dan mampu mengevaluasi informasi dengan baik. Desa Nangka merupakan bagian dari Kecamatan Kadugede Kabupaten Kuningan dikepalai oleh Kepala Desa Bapak Sukmana, S.T., terdiri dari 2 Dusun, dimana di Desa Nangka sebagian besar mata pencaharian masyarakatnya adalah bercocok tanam, 90% warganya menanam sayuran di pekarangan, adapun jenis sayur yang ditanam adalah, cabai, bawang, bayam, kangkung, sawi,  untuk nantinya ditampung di bank sayur desa yang akan dijual ke konsumen. Pengelolaan  administrasi di Bank Sayur tersebut dilakukan oleh ibu-ibu UMKM dimana pengelolaan manajemen perkantoran ini dibutuhkan sebagai kegiatan administrasi yang dilakukan sehari-hari seperti mencatat, mengawasi dan mengontrol. PkM ini mempunyai tujuan untuk memberikan pelatihan, meningkatkan keterampilan mitra dalam memanfaatakan perangkat teknologi informasi sehingga penyelenggaraan administrasi penunjang bisnis berjalan dengan tertib. PkM ini dilaksanakan selama 3 hari, dengan cara memberikan pelatihan administrasi perkantoran menggunakan Microsoft Word, Excel dan powerpoint. Pelatihan ini mendapat antusiasme dari mitra dan respon sangat baik, sehingga hasil dari pelatihan ini adalah meningkatnya keterampilan mitra untuk memanfaatkan teknologi informasi.
PSYCHOLOGICAL AND PEDAGOGICAL ASPECTS OF TEACHING FOREIGN LANGUAGES INDIVIDUALIZATION BY MEANS OF ARTIFICIAL INTELLIGENCE
Authors:   Lomonosov Moscow State University, Moscow, Russia, Avramenko A.P.
Published: December 2022

Modern educational standards and the specifics of the teaching foreign languages require individualization. Today we are witnessing the transition from digitalization to atomization of personalized learning path by means of the artificial intelligence mechanism, namely the introduction of neural networks into electronic and mobile applications. As for the results of the study, the theoretical significance includes clarifications to the definition and mutual integration of the concepts of a personalized learning path along with learning style and cognitive style. Practical significance, in its turn, includes, on the one hand, a proposal for the distribution of task formats according to the learning styles of students; on the other hand, methodological recommendations for a teacher of foreign languages on the use of questionnaires to determine the type of motivation. Thus, the study at the level of cognitive and educational psychology generalizes the psychological and pedagogical aspects of personalized learning path, which can be taken into account when automating the selection of learning materials and tasks by means of artificial intelligence.
ANTHROPOLOGICAL CONNOTATIONS OF EDUCATING A PROSECUTOR’S COMMUNICATIVE CULTURE
Authors:   Orenburg State University, Orenburg, Russia, Panov A.A. , Frolov O.V. ,   Orenburg State University, Orenburg, Russia
Published: December 2022

The research presents a critical analysis of the prosecutor’s practice of communicative interaction in the space of professional culture. The sociocultural nature of prosecutorial communications is explained. The “anthropological errors” are comprehended (J. Milbank) as a result of an unformed educational system that does not ensure the effectiveness of communicative culture in reality, characterized by a “performative failure of subjectivity” (Y. Ananka, H. Kirschbaum). The meaningful connection of the key concepts is revealed: culture, communicative culture, education, etc. It is established that the educated communicative culture is a conceptual tool for developing behavioral strategies that ensure constellation in the “empire of law and order”. The purpose of the article is to consider the anthropological aspect of the problem of educating the communicative culture of a prosecutor’s employee. The axiological analysis of the traditional communicative culture of the prosecutor’s employee was carried out; the following methods were used: the method of scientific observation, the analysis of communicative behavior in the light of the theory of axiological communication; an interview determining the degree of compliance of the interaction of prosecutor’s employees with the norms of institutional communication. It is established that the communicative processes accompanying the prosecutor’s activity are the axiogenic and anthropological basis for the formation and development of the educational system.
FEATURES OF COMMUNICATION IN THE ELECTRONIC INFORMATION AND EDUCATIONAL ENVIRONMENT IN THE CONDITIONS OF DISTANCE LEARNING
Authors:   Orenburg State Medical University, Orenburg, Russian Federation, Fomina M.V. , Mikhailova E.A. ,   Orenburg State Medical University, Orenburg, Russian Federation, Zherebyateva O.A. ,   Orenburg State Medical University, Orenburg, Russian Federation, Sokolova O.Ya. ,   Orenburg State Medical University, Orenburg, Russian Federation, Fateeva T.A. ,   Orenburg State Medical University, Orenburg, Russian Federation, Borisov S.D. ,   Orenburg State Medical University, Orenburg, Russian Federation, Aznabaeva L.M. ,   Orenburg State Medical University, Orenburg, Russian Federation, Bibartseva E.V. ,   Orenburg State Medical University, Orenburg, Russian Federation, Liskova E.V. ,   Orenburg State Medical University, Orenburg, Russian Federation, Lyashenko I.E. ,   Orenburg State Medical University, Orenburg, Russian Federation
Published: December 2022

The field of higher education can be called one of those that the COVID-19 pandemic has had the most impact on a global scale. The changes affected, first of all, the format of communication. The aim of the study was to study the preferences of students in the use of social networks, instant messengers for communication, platforms for posting content, video conferencing services for online lectures and consultations during the periods of May-June and November-December 2020. The study showed that in the period May-June 2020, respondents preferred WhatsApp in 50% of cases, email 42.8%, video conferencing services Zoom Meetings 3.6%, Google Meet web application 3.6%. That testified to the role of mobile means of communication in the educational process. The second survey, which took place in November-December 2020, showed a trend of using Zoom Meetings at 50%, email at 28.6%, WhatsApp at 7.2% of cases. According to the respondents, Zoom Meetings proved to be the most convenient tool (72.4%), which, according to users, has a functional interface (75.8%), the ability to share a screen (89.6%), an audio recording and video recording service of a conference ( 96.5%. In turn, Skype was considered convenient for video conferencing by only 13.8% of respondents, along with this, a big minus was noted — there was no possibility of screen sharing. Thus, it is difficult to overestimate the impact of the pandemic on the transition of higher educational institutions to a remote form of work, which radically changed communication in the electronic information and educational environment of universities and posed the problem for the teaching staff in an extremely short time to master digital tools for implementing the educational process using distance learning technologies.
TRAINING ENGINEERING UNIVERSITY STUDENTS FOR PARTICIPATION IN ACADEMIC MOBILITY PROGRAM
Authors:   Kazan National Research Technological University, Kazan, Russia, Kraysman N.V.
Published: December 2022

The relevance of the paper is due to the importance of students’ academic mobility as an efficient tool in human capital accumulation, and ensuring the future competitiveness of specialists in the international labor market. The purpose of this paper is to identify the importance of students’ academic mobility, its main criteria, as well as to show the implementation of programs for preparing students for academic mobility at KNRTU. The paper demonstrates the programs for preparing students for academic mobility Psychology of Professional Success and Academic Mobility Training for Studying in French Universities. Every year since 2014, KNTRU has implemented the Preparation for Academic Mobility to French Universities program. The program was created in order to prepare students for academic mobility, acquaint them with training in French universities, i.e. improve and deepen their knowledge of the French language and acquaint them with the French system of higher education. Also, to prepare students for academic mobility, the Psychology of Professional Success program was implemented. This program was successfully completed by 39 KNITU students. The goal of the program Psychology of Professional Success is professional and psychological training of students, increasing the level and readiness for academic mobility, for effective professional activity, forming a general idea of the psychology of human behaviour in various life situations, the development of professional orientation and personality psychology. The result of these programs is that thanks to the new knowledge and skills gained, KNRTU students will be more successful and competitive in their professional activities both at the Russian and world levels.
FEATURES OF SELF-ORGANIZATION OF PEDAGOGICAL UNIVERSITY STUDENTS
Authors:   Omsk Humanitarian Academy, Omsk, Russia, Lopanova E.V. , Savina N.V. ,   Omsk Humanitarian Academy, Omsk, Russia
Published: December 2022

When studying the development of self-organization of students — future teachers, its importance is high in the current conditions of an unstable world, both for the student and for the teacher. The transition of world development from a complex and ambiguous world (VUCA-world) to a nonlinear and incomprehensible world (BANI-world) requires a change in the person himself. Complex types of activities, including educational and pedagogical, do not leave their subject a choice to be organized or not. Self-organization of the individual acts as the ability to independently form internal ordered stable structures in response to the challenges of the new BANI world. The concept of self-organization does not have a single, accepted definition, and is considered at various levels, from different sides, by various scientific directions. From the standpoint of a technical approach (scientific organization of labor, time management), the results of a survey of students of a pedagogical university are presented. The factors that complicate the educational activities of students are identified; factors that hinder the development of self-organization competence, as well as factors that help reduce stress in the learning process and perform educational activities at the required level. It is shown that there are significant differences between full-time and part-time students in some personal factors that prevent students from being organized. The ways of further research of the problem field of self-organization development in the educational process of the university through the definition of methodology, including axiological grounds, are proposed.
METHODS OF DIGITAL TRANSFORMATION OF THE PROCESS OF FOREIGN LANGUAGE TEACHING AT SCHOOL
Authors:   Orenburg State Pedagogical University, Orenburg, Russia, Yezhova T. V. , Stukolova E. A. ,   Orenburg State Pedagogical University, Orenburg, Russia
Published: December 2022

The paper deals with the relevance of the problem of revising the existing methods, programs, tools for foreign language teaching, taking into account the irreversible process affecting all spheres of human activity, all layers and levels of public life — the digital transformation process. The modification of traditional and innovative methods of work in the field of a foreign language teaching is supported by the idea about the integrative qualities of the educational system as a whole — its adaptability, flexibility, internal predisposition to change following the changes in the society in which it exists. The authors revealed the main features of the digital transformation of the education process, presented the stages of this process, created an algorithm for the foreign language teaching transforming using the example of a chosen educational technology, highlighting the effective and accessible information technologies to apply to a lesson of a foreign language. The study identified key aspects of the implementation and development of an algorithm for applying the methods of digital transformation of the process of foreign language teaching at school through the analysis of the theoretical aspects of the digital transformation of the learning process; guidelines development for the implementation of this process at the lesson of English. The algorithm for introducing and applying digital transformation techniques in the process of foreign language teaching is presented with a sequence of actions in a project on the topic “Mother Nature”. Based on the results of the approbation, it was concluded that for the full and successful implementation of the methods of digital transformation of education it is necessary to adhere to a consistent and strategically targeted course, taking into account the levels of digital transformations that are universal for most social spheres.
SELF-DETERMINATION AS A STARTING POINT IN THE DESIGN OF INTRA-SCHOOL CHANGES
Authors:   Leningrad Regional Institute of Education Development, St. Petersburg, Russia, Kamenskiy A.M.
Published: December 2022

The study deals with the issues of the development of an educational organization in combination with the development of the individualities of the subjects of educational activity. There are conditions conducive to the most effective implementation of this activity. The project approach contributes to the harmonization of the interests and aspirations of the individual, directing them to the needs of the development of the team. Such long-term projects are being implemented on the basis of the supporting educational institution of Lyceum No. 590 in St. Petersburg. For members of the teaching staff, it is necessary to create conditions conducive to their professional and personal self-determination.
COGNITIVE INDEPENDENCE AS A PEDAGOGICAL PHENOMENON
Authors:   Orenburg State Pedagogical University, Orenburg, Russia, Koroteeva A.S. , Chelpachenko T.V. ,   Orenburg State Pedagogical University, Orenburg, Russia
Published: December 2022

The methodological foundations of the organization of the learning process at the present stage are undergoing significant changes. This is due to the introduction of new standards and digital technologies into the educational process. In this regard, the phenomenon of “cognitive independence of students” is also undergoing transformation. Schoolchildren and students today have new digital tools in their hands, they do not need to search for new information bit by bit, painstakingly and carefully. There is a need to develop a new approach to understanding the traditional understanding of the phenomenon of “cognitive independence”. The article presents an analysis of the concept of “cognitive independence” from the point of view of the genesis of the problem under consideration. The main characteristics and signs of cognitive independence, factors influencing its formation and development are revealed. The results of an intermediate experiment on the study of cognitive independence in the student environment are given.