scholarly journals Text Mining for Internship Titles Clustering Using Shared Nearest Neighbor

2017 ◽  
Vol 6 (3) ◽  
pp. 119-126
Author(s):  
Lisna Zahrotun
Keyword(s):  
Information Systems ◽  
Text Mining ◽  
Graduate Program ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Main Theme ◽  
Instructional Media ◽  
Job Description ◽  
University Courses ◽  
Shared Nearest Neighbor

An Internship course becomes one of many compulsory subjects in Under graduate Program of Informatics Engineering in Ahmad Dahlan University, Yogyakarta.In the last few semesters, we found that some students were failed in taking this subject. After being identified, they were facing some obstacles such as determining the main theme for their job description. During this study, we proposed an application to classify the internship titles by using a technique in text mining called Shared Nearest-Neighbor and Cosine Similarity. From the result, we got values from the parameter K is 7, the epsilon value is 0.5, and the value of Mint t is 0.3 with 22 clusters and 0 outlier. These values presented that all data titles of internship activitiesareclassified into each cluster. 7 topics whichtook by majority of students are:1) Information Systems (7 titles);2) Instructional Media (5 titles);3)Archiving Applications (4 titles);4) Web Profile Implementation (3 titles); 5)Instructional Media for University Courses (3 titles); Multimedia (3 titles) and 6)Workshop & Training (3 titles).

scholarly journals Comparison Jaccard similarity, Cosine Similarity and Combined Both of the Data Clustering With Shared Nearest Neighbor Method

2016 ◽  
Vol 5 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Lisna Zahrotun
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Data Sources ◽  
Jaccard Similarity ◽  
Value Similarity ◽  
Best Value ◽  
Shared Nearest Neighbor ◽  
Similarity Method

Text Mining is the excavations carried out by the computer to get something new that comes from information extracted automatically from data sources of different text. Clustering technique itself is a grouping technique that is widely used in data mining. The aim of this study was to find the most optimum value similarity. Jaccard similarity method used similarity, cosine similarity and a combination of Jaccard similarity and cosine similarity. By combining the two similarity is expected to increase the value of the similarity of the two titles. While the document is used only in the form of a title document of practical work in the Department of Informatics Engineering University of Ahmad Dahlan. All these articles have been through the process of preprocessing beforehand. And the method used is the method of document clustering with Shared Nearest Neighbor (SNN). Results from this study is the cosine similarity method gives the best value of proximity or similarity compared to Jaccard similarity and a combination of both

PENGEMBANGAN MEDIA TUTORIAL PEMBELAJARAN MATA KULIAH MEDIA FOTOGRAFI PEMBELAJARAN

EDUDEENA ◽  
2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Mochamad Desta Pradana
Keyword(s):  
Small Group ◽  
Graduate Program ◽  
Phase 1 ◽  
Field Trials ◽  
Instructional Media ◽  
Design Expert ◽  
Evaluation Phase ◽  
Addie Model ◽  
Expert Validation ◽  
Instructional Messages

Abstract: One of the efforts to solve learning problems is by developing instructional resources. One of them is instructional tutorial. Based on the field observation, subject of photo media for Instruction (photography) in graduate program PBA of STAIN Kediri still employes instructional media (utilization). Thus, there is a need to develop media of instructional tutorial (by desaign) in order to overcome errors in delivering instructional messages. The aim of this study is to produce instructional media of subject of photo media for Instruction (photography) in the form of tutorial, lecturer guidance and students’ guidance which is expected to be able to help students gain knowledge of facts, concepts, and proper shooting procedures. The tutorial media is developed in terms of the study of the instructional technology which is based on the ADDIE model. The ADDIE model is a systematic instructional design model consisting of five phases: (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. Furthermore, the product of the development of the instructional interactive multimedia will be evaluated. There are six steps in the evaluation phase: (1) judgement of the matter expert/ the material expert, (2) judgement of the design expert, (3) judgement of the media expert, (4) individual trials, (5) small group trials, (6) large group trials/ field trials. The result of expert validation for this instructional media tutorial indicates that it is properly designed, 73%. The result of media expert validation shows that it is very properly designed, 83%. The result of design expert validation shows that it is very properly designed, 82%. Personal validation is on the proper category, 70%. Result of small group validation is on proper category, 70%. Moreover, the result for big group test or field test is on the very proper category, 95%. Suggestion proposed related with the use and developing of media tutorial is (1) lecturers should be more creative and innovative in delivering instructional messages contained in this tutorial in order to make learning to be more attractive, fun, and creative, (2) there is a need to develop instructional media for subject of photo media instruction (photography) or other subjects, (3) the use of designed in media tutorial should be more interesting to make learning more interactive and interesting

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

scholarly journals Information Retrieval Document Classified with K-Nearest Neighbor

2018 ◽  
Vol 1 (2) ◽  
pp. 129 ◽  
Author(s):  
Alifian Sukma ◽  
Badruz Zaman ◽  
Endah Purwanti
Keyword(s):  
Information Retrieval ◽  
Evaluation System ◽  
Nearest Neighbor ◽  
Technology Development ◽  
Health Science ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Physical Sciences ◽  
Social Sciences And Humanities ◽  
A Value

Along with the rapid advancement of technology development led to the amount of information available is also increasingly abundant. The aim of this study was to determine how the implementation of information retrieval system in the classification of the journal by using the cosine similarity and K-Nearest Neighbor (KNN).The data used as many as 160 documents with categories such as Physical Sciences and Engineering, Life Science, Health Science, and Social Sciences and Humanities. Construction stage begins with the use of text mining processing, the weighting of each token by using the term frequency-inverse document frequency (TF-IDF), calculate the degree of similarity of each document by using the cosine similarity and classification using k-Nearest Neighbor.Evaluation is done by using the testing documents as much as 20 documents, with a value of k = {37, 41, 43}. Evaluation system shows the level of success in classifying documents on the value of k = 43 with a value precision of 0501. System test results showed that 20 document testing used can be classified according to the actual category

scholarly journals ASPECT BASED SENTIMENT ANALYSIS DATA KUESIONER DI RUMAH SAKIT MUHAMMADIYAH LAMONGAN MENGGUNAKAN ALGORITMA K-NN.

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 506
Author(s):  
Mustain Mustain Mustain
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
Analysis Data ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Space Model

Kesulitan untuk mengorganisir data kuesioner yang bersifat konvensional melatarbelakangi penelitian ini. Oleh karena itu dibuat sistem yang memudahkan pengelompokan data kuesioner secara otomatis yang lengkap dengan sentimen yang terkandung didalamnya. Dataset yang digunakan dalam penelitian ini adalah data kuesioner rumah sakit Muhammadiyah lamongan. Penelitian ini hanya menangani kuesioner yang berbentuk teks. Data dengan fisik kertas direkap kemudian diinput ke database lengkap dengan kategori unit kerja dan sentiment. Selanjutnya dataset tersebut di dilakukan pre-prosesing yang meliputi penanganan negasi case folding, tokenizing, filtering dan stemming. Sebagai data uji komentar dari kuesioner akan dilakukan pre-prosesing selanjutnya dihitung tingkat kemiripan document dengan menggunakan metode K- Nearest Neighbor dan Vector Space Model. Jumlah data yang ditangani mempengaruhi performa system terutama dari akurasi dan kecepatan pada saat proses klasifikasi. Hasil dari sistem yang dibuat berupa ranking dokumen yang paling mirip dengan dataset berdasarkan urutan nilai cosine similarity. Ujicoba klasifikasi berdasarkan kelas kategori menghasilkan nilai akurasi 91 %. Ujicoba berdasarkan Kelas Sentimen sebesar 94 %.dari kombinasi keduanya system berhasil mendapat akurasi sebesar 86 %

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