Implementation K-nearest neighbour for student expertise recommendation system

Taufik, Ichsan; Gerhana, Yana Aditia; Ramdani, A I; Irfan, Mohamad

doi:10.1088/1742-6596/1402/7/077004

Cited by 5 publications

(6 citation statements)

References 2 publications

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“…The first step taken is to enter keywords (can be in the form of words, phrases or sentences), then do the preprocessing to get a list of word tokens which then do the weight calculation tf-idf after it obtained a list of queries and documents weights used in the computation algorithm VSM [13][14][15][16] if the cosine value>0 then shown a list of relevant data otherwise found no data is displayed. This research uses a software development method called the prototype model [17][18][19]. The application to be built is a website based application that is built using the Django framework and Python.…”

Section: Methodsmentioning

confidence: 99%

The search for science and technology verses in Qur’an and hadith

et al. 2021

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Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.

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Section: Methodsmentioning

confidence: 99%

The search for science and technology verses in Qur’an and hadith

et al. 2021

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“…Algoritma apriori (association rule) menghasilkan rule untuk setiap kelompok keahlian dan algoritma ant cycle (algoritma ACO) memberikan rekomendasi berupa rule pemilihan mata kuliah pilihan dan learning path mata kuliah pilihan. [18] Sistem Rekomendasi Mata Kuliah Ontologi dan User Model…”

Section: Association Rule Dan Ant Colonyunclassified

Rekomendasi Pemilihan Mata Kuliah dalam Pengisian Rencana Studi Mahasiswa dengan Penerapan Algoritma Apriori

Syahrul¹,

Solichin²

2022

eltikom

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Pada masa pengisian rencana studi, banyak mahasiswa Universitas Budi Luhur merasa kesulitan dalam memilih mata kuliah yang akan diambil di semester berikutnya. Kesalahan dalam pemilihan mata kuliah dapat berdampak pada pencapaian prestasi yang tidak optimal serta dapat menghambat kelulusan maha-siswa. Peran dosen pembimbing akademik (PA) dalam membantu mahasiswa menentukan mata kuliah di semester yang akan datang sangatlah penting, tetapi pada beberapa kasus, terdapat dosen pembimbing akademik yang tidak memiliki waktu untuk berdiskusi secara langsung dengan mahasiswa. Oleh karena itu, penelitian ini bertujuan mengembangkan sebuah sistem yang dapat memberikan rekomendasi bagi mahasiswa dalam pengambilan mata kuliah pada saat mengisi rencana studi untuk semester selanjutnya. Sistem rekomendasi pemilihan mata kuliah ini menggunakan metode association rule mining dengan al-goritma apriori. Data riwayat pengambilan mata kuliah mahasiswa dievaluasi menggunakan algoritma apriori untuk selanjutnya dihasilkan rekomendasi pengambilan mata kuliah di semester berikutnya. Hasil pengujian terhadap sistem menunjukkan rata-rata persentase akurasi rekomendasi rencana studi sebesar 80,16% dengan minimum confidence sebesar 80%, dan minimum support sebanyak 7. Hasil tersebut menunjukkan bahwa sistem ini dapat membantu mahasiswa dalam melakukan pemilihan mata kuliah di semester yang akan datang.

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“…The data mainly come from the scores of students, supervisors, and peers. Then it is normalized according to (16). Considering that a teacher can teach more than one course, it is necessary to calculate each course's teaching evaluation score.…”

Section: Data Acquisition and Quantitative Definitionmentioning

confidence: 99%

“…In (16), v t,course is the teaching score of the course taught by the teacher t, and Tes t,course is the normalized value of the v t,course . v min and v max are the lowest and highest values of all the teaching scores.…”

Section: Data Acquisition and Quantitative Definitionmentioning

confidence: 99%

See 1 more Smart Citation

A Course Teacher Recommendation Algorithm Based on Improved Latent Factor Model and PersonalRank

Yao

Deng

2021

IEEE Access

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To scientifically and accurately recommend suitable teachers for university courses and improve teaching quality, designing an effective recommendation algorithm is necessary. Therefore, we construct quantitative models of teacher characteristics, course characteristics, and teaching evaluations under the theories and methods of education and build a sparse experimental data matrix based on the quantified data. On this basis, we propose a teacher recommendation algorithm (PRLFM) based on the improved latent factor model (LFM) and the improved PersonalRank algorithm. Firstly, the improved LFM is used to predict the evaluation scores of those courses that teachers have not taught. The scores which are higher than the specified threshold are used to fill the corresponding missing items in the sparse matrix to reduce the matrix's sparsity. Then, the bipartite graph model based on the teacher set and course set is constructed according to the filled experimental data matrix. The weight of edges in the bipartite graph is replaced by the teacher and course's evaluation score multiplied by the course difficulty, which reflects the correlation between course and evaluation score. Next, an improved probability transition matrix based on the bipartite graph is constructed. The access probability in the matrix is replaced by the node's out degree's reciprocal multiplied by the edge's weight. The correlation degree between the course and all teachers is quickly calculated using the matrix algorithm of PersonalRank. Finally, a teacher recommendation model is constructed to realize teachers' top-N recommendation by combining the correlation degree with teachers' characteristics. Experiments show that the PRLFM algorithm can effectively improve the accuracy of prediction and top-N recommendation. It solves the problem of lack of scientific basis in recommending suitable teachers for university courses and improving the teaching quality.

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Implementation K-nearest neighbour for student expertise recommendation system

Cited by 5 publications

References 2 publications

The search for science and technology verses in Qur’an and hadith

The search for science and technology verses in Qur’an and hadith

Rekomendasi Pemilihan Mata Kuliah dalam Pengisian Rencana Studi Mahasiswa dengan Penerapan Algoritma Apriori

A Course Teacher Recommendation Algorithm Based on Improved Latent Factor Model and PersonalRank

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