Implementasi Cosine Similarity Matching Dalam Penentuan Dosen Pembimbing Tugas Akhir

Yasni, Loura; Subroto, Imam Much Ibnu; Haviana, Sam Farisa Chaerul

doi:10.14710/transmisi.20.1.22-28

Cited by 6 publications

(9 citation statements)

References 2 publications

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“…OfficeHours [4] only uses the keywords of academic publications while Ismail et al [5] only use the project titles. Ours is also more accurate than another system [6] that relies on students' thesis proposals only in forming the supervisor profiles. Typically, the proposals do not reflect the resulted thesis as they are changed during the supervision process.…”

Section: Discussionmentioning

confidence: 95%

“…Another system proposed by Yasni et al [6] uses cosine similarity for calculating the relevancy between students and their advisors. Each student is required to provide a thesis proposal that contains a title, a topic and an abstract.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is clearly more descriptive than data used in some similar recommender systems, which are 144 academic publications' keywords [4] and project titles [5]. This is also more accurate than using former students' thesis proposals to form the supervisor profiles [6]. On most occasions, proposals do not reflect the resulted theses as they are changed during the supervision process.…”

Section: Introductionmentioning

confidence: 99%

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Thesis Supervisor Recommendation with Representative Content and Information Retrieval

Wijanto

Rachmadiany

Karnalim

2020

JISEBI

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Background: In higher education in Indonesia, students are often required to complete a thesis under the supervision of one or more lecturers. Allocating a supervisor is not an easy task as the thesis topic should match a prospective supervisor’s field of expertise.Objective: This study aims to develop a thesis supervisor recommender system with representative content and information retrieval. The system accepts a student thesis proposal and replies with a list of potential supervisors in a descending order based on the relevancy between the prospective supervisor’s academic publications and the proposal.Methods: Unique to this, supervisor profiles are taken from previous academic publications. For scalability, the current research uses the information retrieval concept with a cosine similarity and a vector space model.Results: According to the accuracy and mean average precision (MAP), grouping supervisor candidates based on their broad expertise is effective in matching a potential supervisor with a student. Lowercasing is effective in improving the accuracy. Considering only top ten most frequent words for each lecturer’s profile is useful for the MAP.Conclusion:An arguably effective thesis supervisor recommender system with representative content and information retrieval is proposed.

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Section: Discussionmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thesis Supervisor Recommendation with Representative Content and Information Retrieval

Wijanto

Rachmadiany

Karnalim

2020

JISEBI

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“…Berbeda dengan penelitian sebelumnya, Yasni, et.al. menggunakan metode cosine similarity dengan komposisi data judul sebesar 30%, komposisi data topik sebesar 40%, dan komposisi data abstrak sebesar 30% untuk penentuan dosen pembimbing [3]. Pada penelitian tersebut, sistem yang dibangun memiliki nilai precision yang didapatkan adalah 0,7 dan nilai recall yang didapatkan adalah 1.…”

Section: Pendahuluanunclassified

“…VSM TF-IDF Ditambah Query.Berdasarkan Tabel 6 di atas, maka dapat dihitung nilai similaritas antara query dengan dokumen 1 dan query dengan dokumen 2 menggunakan persamaan(3). Dari hasil perhitungan yang telah dilakukan, didapatkan nilai similaritas query dengan dokumen 1 adalah 1 dan nilai similaritas query dengan dokumen 2 adalah 0,901.…”

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Penentuan Pembimbing Tugas Akhir Mahasiswa Jurusan Teknik Informatika Universitas Surabaya Dengan Metode Dice Coefficient

Prasetyo

Hartanto

Mulyono

2019

teknika

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Tugas Akhir (TA) adalah sebuah karya ilmiah yang harus dibuat oleh mahasiswa jurusan Teknik Informatika, Universitas Surabaya sebagai salah satu syarat kelulusan. Dalam mengajukan topik TA, mahasiswa wajib membuat dan mengumpulkan proposal TA tersebut. Setelah itu, ketua jurusan akan menentukan calon dosen pembimbing, dosen reviewer 1, dan dosen reviewer 2 sesuai dengan kompetensi untuk proposal tersebut. Dalam proses pengajuan proposal tersebut, terdapat beberapa masalah yang muncul, yakni adanya kemungkinan kesalahan penentuan pembimbing karena proses penentuannya berdasarkan pengetahuan pribadi dari ketua jurusan, kesulitan memantau perkembangan proses pengajuan proposal, dan kesulitan untuk melakukan pemerataan pembimbing TA. Untuk mengatasi masalah tersebut, dibuatlah sistem untuk melayani administrasi pengajuan topik TA beserta penentuan pembimbing. Proses penentuan pembimbing akan menggunakan metode Dice Coefficient. Dari hasil pengujian sistem dengan metode K-Fold Cross Validation dengan nilai k = 100 untuk 1000 data TA yang ada, didapatkan nilai akurasi sistem sebesar 36,25%. Apabila diambil 100 data TA yang memiliki dosen pembimbing yang ideal, sistem memberikan nilai akurasi yang lebih baik yaitu sebesar 45,5%.

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SIFT image matching algorithm using fusion color information

Zheng

Wang

2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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In order to address the issue that (scale invariant feature transformation,SIFT) algorithm will lose the original image color information, resulting in poor matching ability after graying the color image, this study proposes a SIFT image matching algorithm that incorporates color information . To get the grayscale image to be matched, the algorithm employs a bimodal distribution function to minimize the grayscale difference between pixels and the color contrast between pixels; On this basis, the nearest neighbor distance matching method using Hellinger distance as similarity measure and vector space cosine similarity matching method are combined for feature matching, so as to effectively maintain the color contrast of the original image, improve the number of effective feature points of adjacent images, eliminate the wrong matching point pairs and reduce the wrong matching. The algorithm can effectively discriminate regions with similar gray levels but distinct hues, enhance the matching accuracy, and preserve real-time performance, according to experimental results.

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Implementasi Cosine Similarity Matching Dalam Penentuan Dosen Pembimbing Tugas Akhir

Cited by 6 publications

References 2 publications

Thesis Supervisor Recommendation with Representative Content and Information Retrieval

Thesis Supervisor Recommendation with Representative Content and Information Retrieval

Penentuan Pembimbing Tugas Akhir Mahasiswa Jurusan Teknik Informatika Universitas Surabaya Dengan Metode Dice Coefficient

SIFT image matching algorithm using fusion color information

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