scholarly journals Analisis Metode Cosine Similarity Pada Aplikasi Ujian Online Otomatis (Studi Kasus JTI POLINEMA)

2021 ◽  
Vol 8 (2) ◽  
pp. 343
Author(s):  
Eka Larasati Amalia ◽  
Angelita Justien Jumadi ◽  
Irsyad Arif Mashudi ◽  
Dimas Wahyu Wibowo
Keyword(s):  
Cosine Similarity ◽  
Term Frequency ◽  
Long Time ◽  
E Learning ◽  
Exam System ◽  
Similarity Method ◽  
Learning Concept ◽  
Word Frequencies ◽  
F Measure

<p>Dalam konsep <em>e-learning</em> pelaksanaan ujian dilakukan secara online salah satunya ujian esai. Ujian esai online merupakan ujian yang menggunakan metode online dan mewajibkan siswa menjawab dengan kalimat mereka sendiri. Namun, dalam ujian esai online ini memerlukan waktu yang lama untuk mengoreksi jawaban jika dikerjakan secara manual. Agar tidak memakan banyak waktu untuk mengoreksi jawaban siswa maka dalam sistem terdapat penilaian kemiripan jawaban untuk penilaian. Pada penelitian ini dilakukan pembuatan sistem ujian esai online dengan penilaian kemiripan jawaban menggunakan metode <em>Cosine Similarity</em> dan persamaan <em>Term Frequency</em> (TF) untuk menyamakan frekuensi setiap kata yang terdapat dalam kalimat. Suatu faktor yang menentukan bobot kata berdasarkan pada jumlah frekuensi kata dalam sebuah dokumen disebut dengan<em> Term Frequency</em>. Untuk pengujian akurasi metode dilakukan pengujian <em>precision, recall</em>, dan <em>f-measure</em> dan berdasarkan hasil analisis dengan menggunakan metode yang telah dicoba diperoleh rata-rata 81%.</p><p> </p><p><em>Abstract</em></p><p> </p><p><em>In the e-learning concept, the implementation of exams is carried out online, one of which is an essay exam. The online essay exam is an exam that uses an online method and requires students to answer in their own sentences. However, in this online essay exam, it takes a long time to correct answers if done manually. In order not to take a lot of time to correct student answers, the system has an assessment of the similarity of answers for the assessment. In this study, an online essay exam system was made with the similarity of answers using the Cosine Similarity method and the Term Frequency (TF) equation to equalize the frequency of each word contained in a sentence. Term Frequency is a factor that determines word weight based on the number of word frequencies in a document. To test the accuracy of the method, precision, recall, and f-measure tests were carried out and based on the results of the analysis using the method that had been tried, an average of 81% was obtained.</em></p>

scholarly journals Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat

2021 ◽  
Vol 10 (3) ◽  
pp. 221-228
Author(s):  
Silmi Fauziati ◽  
Adhistya Erna Permanasari ◽  
Indriana Hidayah ◽  
Eko Wahyu Nugroho ◽  
Bobby Rian Dewangga
Keyword(s):  
Cosine Similarity ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
E Learning

Makalah ini bertujuan untuk memperbaiki kinerja sistem penilaian tes uraian singkat. Perbaikan kinerja tersebut dilakukan dengan menambahkan regresi linear sederhana pada keluaran gabungan metode cosine similarity (dengan pembobotan frekuensi kata berbasis metode Term Frequency-Inverse Document Frequency (TF-IDF)) dan mekanisme pencocokan kata. Regresi linear dilakukan dengan menjadikan nilai uraian singkat (hasil cosine similarity dan pencocokan kata) sebagai variabel regressor. Untuk mengetahui efektivitas sistem penilaian yang diusulkan, diukur kinerja sistem penilaian relatif terhadap nilai manual yang dilakukan oleh dosen. Diperoleh bahwa sebelum dilakukan regresi linear, sistem penilaian cenderung mengeluarkan nilai lebih tinggi (nilai mengalami bias) dibandingkan nilai manual yang dilakukan dosen. Regresi linear memperbaiki kinerja sistem penilaian tersebut dengan mengurangi bias penilaian secara signifikan, yaitu nilai yang diberikan tidak cenderung lebih tinggi maupun lebih rendah daripada nilai manual oleh dosen. Bahwa bias penilaian dapat diturunkan secara signifikan dengan metode yang sederhana, yaitu regresi linear, diharapkan dapat memberikan kontribusi terhadap akselerasi proses penerapan sistem penilaian otomatis untuk tes uraian pada teknologi pembelajaran dalam jaringan seperti e-learning.

scholarly journals Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF)

2021 ◽  
Vol 2 (2) ◽  
pp. 62-69
Author(s):  
Sintia Sintia ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Information System ◽  
Cosine Similarity ◽  
Search Process ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Product Code ◽  
Document Frequency ◽  
Similarity Method

In the SiPaGa application, the codefication search process is still inaccurate, so OPD often make mistakes in choosing goods codes. So we need Cosine Similarity and TF-IDF methods that can improve the accuracy of the search. Cosine Similarity is a method for calculating similarity by using keywords from the code of goods. Term Frequency and Inverse Document (TFIDF) is a way to give weight to a one-word relationship (term). The purpose of this research is to improve the accuracy of the search for goods codification. Codification of goods processed in this study were 14,417 data sourced from the Goods and Price Planning Information System (SiPaGa) application database. The search keywords were processed using the Cosine Similarity method to see the similarities and using TF-IDF to calculate the weighting. This research produces the calculation of cosine similarity and TF-IDF weighting and is expected to be applied to the SiPaGa application so that the search process on the SiPaGa application is more accurate than before. By using the cosine sismilarity algorithm and TF-IDF, it is hoped that it can improve the accuracy of the search for product codification. So that OPD can choose the product code as desired

scholarly journals The Analysis of Proximity Between Subjects Based on Primary Contents Using Cosine Similarity on Lective

2017 ◽  
pp. 299-308
Author(s):  
Muhammad Andi Al-rizki ◽  
Galih Wasis Wicaksono ◽  
Yufis Azhar
Keyword(s):  
Cosine Similarity ◽  
High Similarity ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Research Results ◽  
Document Frequency ◽  
Precision And Accuracy ◽  
The Relationship ◽  
Similarity Method

In education world, recognizing the relationship between one subject and another is imperative. By recognizing the relationship between courses, performing sustainability mapping between subjects can be easily performed.  Moreover, detecting and reducing any duplicated contents in several subjects will be also possible to execute. Of course, these conveniences will benefit lecturers, students and departments. It will ease the analysis and discussion processes between lecturers related to subjects in the same domain. In addition, students will conveniently choose a group of subjects they are interested in. Furthermore, departments can easily create a specialization group based on the similarity of the subjects and combine the courses possessing high similarity. In this research, given a good database, the relationship between subjects was calculated based on the proximity of the primary contents of the subjects. The feature used was term feature, in which value was determined by calculating TF-IDF (Term Frequency Inverse Document Frequency) from each term. In recognizing the value of proximity between subjects, cosine similarity method was implemented. Finally, testing was done utilizing precision, recall and accuracy method. The research results show that the precision and accuracy values are 90,91% and the recall value is 100%.

scholarly journals Analysis of Matric Product Matching Between Cosine Similarity with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec in PT. Pricebook Digital Indonesia

2020 ◽  
pp. 105-112
Author(s):  
Harni Kusniyati ◽  
Arie Aditya Nugraha
Keyword(s):  
Word Embedding ◽  
Cosine Similarity ◽  
Consumer Experience ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
The Relationship ◽  
Product Specifications ◽  
Similarity Method ◽  
Product Matching

Consumers today have the option to purchase products from thousands of e-commerce. However, the completeness of the product specifications and taxonomies used to organize products differently in different electronic shop differently. To improve the consumer experience, Pricebook approach for integration of the product through the website to find the cheapest price from various platforms. In our writing, we do approach by using a model of neural language such as TF-IDF (term frequency-inverse document frequency) as well as Word2vec by using the method of cosine similarity. TF-IDF is a way to give the relationship a word weighting (term) against the document. Semantic vector or word embedding is one way to represent the structure of a sentence will be in align with manipulating sentences into vector shapes with Word2Vec. Cosine similarity method is a method to calculate the similarity between two objects that is expressed in two vectors by using keywords (keywords) of a document as the size so that it leads to more products matching good performance and categorization. In addition, we compare the results of the representation of the TF-IDF with Word2vec against a number of the data.

Peringkas Teks Otomatis Berita Online Menggunakan Metode Cross Latent SemanticAnalysis & Cosine Similarity

2020 ◽  
Vol 9 (1) ◽  
pp. 105
Author(s):  
Muhammad Afif Ubaidillah ◽  
Ida Bagus Gede Dwidasmara ◽  
Agus Muliantara
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Cosine Similarity ◽  
F Measure

Ringkasan merupakan suatu cara yang efektif untuk meyajikan suatu karangan yang panjang dalam bentuk yang singkat. Walaupun bentuknya ringkas, namun ringkasan itu tetap memepertahankan pikiran pengarang dan pendekatannya yang asli. Namun dalam membuat ringkasan kita harus membaca berita atau artikel terlebih dahulu, sedangkan ringkasan dibuat dengan tujuan untuk meminimalkan waktu pembaca dan memberikan teks yang isinya langsung mengarah pada tujuan utama atau ide pokoknya. Pada penelitian ini memaparkan peringkasan teks otomatis berita online dari sebuah website menggunakan CLSA (Cross Latent Semantic Analysis) dan Cosine Similarity. Penelitian ini dilakukan untuk menguji seberapa baik hasil dan akurasi ringkasan yang dilakukan oleh CLSA dan cosine similarity. Penelitian ini menggunakan data sekunder dari berita dari media online yaitu web balipost.com dengan wilayah khusus Denpasar. Proses pengambilan data dilakukan dengan cara crawling. Data berita yang digunakan ialah sebanyak 161 berita, berita hasil ringkasan sistem nantinya akan dibandingkan dengan hasil ringkasan manual untuk mendapatkan akurasinya. Dari hasil pengujian yang dilakukan oleh sistem didapatkan nilai rata – rata akurasi F-Measure sebesar 58%, rata – rata Precision 62% dan rata – rata Recall 57%. Hasil dari penelitian peringkasan teks otomatis dari berita online dengan menggunakan metode CLSA dan cosine similarity memberikan hasil dan akurasi ringkasan yang cukup. Keywords : ringkasan, peringkas teks otomatis, crawling, CLSA, cosine similarity 

scholarly journals MOTHERS' KNOWLEDGE TOWARDS ORAL HEALTH OF CHILDREN AGED UP TO 3 YEARS BEFORE AND AFTER EDUCATIONAL PROGRAM

2021 ◽  
Vol 27 (4) ◽  
pp. 4052-4059
Author(s):  
Ralitsa Bogovska-Gigova ◽  
◽  
Rositza Kabaktchieva ◽  
Keyword(s):  
Oral Health ◽  
Health Education ◽  
Educational Program ◽  
Bottle Feeding ◽  
Oral Health Education ◽  
Dental Office ◽  
Before And After ◽  
Health Related ◽  
Long Time ◽  
E Learning

Purpose: To comparatively analyze the oral health-related knowledge of mothers of children aged up to 3 years before and after the "Dental Home for Children" educational program (www.dentalendom.bg) Material/methods: The study involved a group of 90 mothers of children aged up to 3 years who visited the special educational and motivational website www.dentalendom.bg and completed our survey. We compared and processed the results of the survey using 90 control questionnaires, which were completed by parents who had visited the dental office without having attended an oral health education and motivation session in advance. Results: The results obtained from the survey completed by mothers of children aged up to 3 years show that their knowledge of the importance of fluoride prevention, the risks of nighttime bottle feeding with baby formula for a long time, mistakes in feeding, and the early transmission of cariogenic bacteria increased substantially compared to the mothers who did not visit the educational website. Conclusion: The obtained positive results give us reason to believe that e-learning health education programs are useful for both mothers and children aged up to 3 years.

scholarly journals Data Similarity Filtering of Wartegg Personality Test Result using Cosine-Similarity

2018 ◽  
Vol 6 (3) ◽  
pp. 19
Author(s):  
Rosihan Ari Yuana ◽  
Dewanto Harjunowibowo ◽  
Nugraha Arif Karyanta ◽  
Cucuk Wawan Budiyanto
Keyword(s):  
Graphical Representation ◽  
Cosine Similarity ◽  
Potential Contribution ◽  
Personality Test ◽  
Evaluation Instrument ◽  
Technical Procedure ◽  
Data Similarity ◽  
Identification Technique ◽  
Similarity Method ◽  
The Ideal

Wartegg test is a widely adopted personality evaluation instrument known for its drawing completion technique.  Employee personality data, for instance, can be sorted by the closest similarity with the expected characters. Whereas, Wartegg test plays a significant role in data similarity filtering. Despite the potential contribution of personal characters identification technique, practical guidance is rarely found in the literature. This paper demonstrates the usage of cosine-similarity method for data similarity filtering on Wartegg personality test. The method used in this study is a case study, in which will be selected several Wartegg test subjects. By using the value of each character aspect derived from the Wartegg test, the cosine-similarity value will be calculated against the expected/ideal aspect character. Based on this value, the Wartegg test subjects will be filtered based on similarity to the expected/ideal character aspects. A technical procedure to perform the method is also presented in this paper. In order to find out the effectiveness, sample data scores of each character aspect from five test subjects, and also the ideal scores of the expected characters are given. By using FWAT, a graphical representation of the test subjects' characters to the ideal characters is generated. Then, this graph was compared to the results obtained from the cosine-similarity method. Drawn from the results, the cosine-similarity is effectively applied for Wartegg test data similarity filtering.

Digital University-SME Interaction for Business Development

2021 ◽  
pp. 55-71
Author(s):  
Heléne Lundberg ◽  
Christina Öberg
Keyword(s):  
Knowledge Creation ◽  
Business Development ◽  
E Learning ◽  
The Creation ◽  
Learning Concept ◽  
Developing Knowledge

This chapter describes and discusses the role of e-learning for small and medium-sized firms' (SME) business development and does so specifically in university-SME interaction related to sparsely populated regions. It is based on the idea that e-learning may provide a valuable means for developing knowledge creation and expansion beyond its educational connotation. A university-SME interaction focusing on business development of firms in remote geographical areas provides ideas on the benefits of e-learning not only for the interaction to be realized, but for the creation of flexibility, interactivity, and the bringing down of guards among the participants. The chapter contributes to previous research through tying together ideas on e-learning, university-SME interaction and business development, and by extending the e-learning concept. Practically, the case study may function as the inspiration for further initiatives.

scholarly journals Antecedents of Intention to Use E-Learning

2018 ◽  
Vol 248 ◽  
pp. 05006 ◽  
Author(s):  
Khairudin ◽  
Susi Herawati ◽  
Desi Ilona ◽  
Zaitul
Keyword(s):  
Learning Theory ◽  
Positive Relationship ◽  
Intention To Use ◽  
Plan Behaviour ◽  
Negative Effect ◽  
E Learning ◽  
Facility Condition ◽  
Learning Concept

This study investigates the effect of Attitude toward e-learning, Facility condition, and Personalization on intention to use e-learning. theory of plan behaviour is used to understand the antecedents of intention to use e-learning. Using academic staffs as a research object and SEM-PLS, we conclude that Attitude toward e-learning and Personalization have a positive relationship with intention to use e-learning. However, Facility condition has a negative effect on intention to use e-learning. This study partially contributes to theory of plan behaviour. This finding also could be used by university to formulate e-learning concept by considering the significant antecedents in this study.

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