Sistem Rekomendasi Keterampilan dengan Metode User-Based Collaborative Filtering dan Tanimoto Coefficient Similarity

INTISASISistem rekomendasi merupakan bidang yang dimanfaatkan untuk mengatasi persoalan dalam pencarian informasi yang relevan dari banyaknya kumpulan informasi yang tersedia. Hingga saat ini telah banyak situs web penyedia informasi lowongan kerja, namun pada situs-situs web tersebut masih belum terdapat sistem yang dapat merekomendasikan jenis keterampilan yang sesuai. Sehingga banyak para pencari kerja yang tidak update terhadap jenis keterampilan dibutuhkan di dunia kerja yang terus bertambah seiring perkembangan teknologi. Oleh karena itu, dibuatlah sistem rekomendasi untuk merekomendasikan jenis keterampilan yang sedang tren didunia kerja dan memberikan rekomendasi tempat belajar(pelatihan) untuk memenuhi keterampilan yang disarankan. Sistem rekomendasi yang dikembangkan menggunakan teknik User Based Collaborative Filtering dan Tanimoto Coefficient Similarity. Keluaran yang dihasilkan oleh sistem berupa keterampilan baru dan tempat belajar(pelatihan). Pengujian dilakukan dengan metode Black-Box Testing dan Technology Acceptance Model (TAM). Hasil pengujian menggunakan Black-Box Testing bahwa secara fungsional berjalan dengan baik karena tidak ditemukan adanya error atau bug pada setiap proses pengujian dilakukan. Hasil Pengujian menggunakan Technology Acceptance Model (TAM) sebesar 88.36%. Pengujian hasil rekomendasi sebesar 82% dan secara keseluruhan hasil rekomendasi dapat diterima dengan baik oleh pengguna. Kata kunci—Sistem rekomendasi keterampilan, user based, collaborative filtering, tanimoto coefficient similarity, black-box testing, technology acceptance model (TAM).. ABSTRACTThe recommendation system is a field that is used to overcome problems in finding relevant information from the large collection of available information. Until now, there have been many websites that provide job vacancy information, but on these websites there is still no system that can recommend the appropriate type of skill. So that many job seekers are not updated on the types of skills needed in the world of work which continues to grow along with technological developments. Therefore, a recommendation system was created to recommend the types of skills that are trending in the world of work and provide recommendations for places to study (training) to meet the recommended skills. The recommendation system developed using User Based Collaborative Filtering and Tanimoto Coefficient Similarity techniques. The output produced by the system is in the form of new skills and a place to learn (training). Testing is carried out using the Black-Box Testing and Technology Acceptance Model (TAM) method. The test results using Black-Box Testing that functionally run well because no errors or bugs were found in each testing process carried out. Test results using the Technology Acceptance Model (TAM) of 88.36%. Testing the results of recommendations by 82% and overall the results of the recommendations can be well received by users. Keywords— Skills recommendation system, user based, collaborative filtering, tanimoto coefficient similarity, black-box testing, technology acceptance model (TAM).

Cloud Qos Ranking Prediction using Tanimoto Coefficient Similarity Based Deep Learning Method

Cloud computing is a service which provides virtualized resources conforming to the end-user needs. Infrastructure, platform and software included in it. For the last two decades, it has achieved very gigantic growth. Currently, there are several cloud service providers in the market. The primary aim of this research is to minimize cloud service violation. It helps the service providers in exempting the penalty enhancing their reliability. So, cloud service QOS prediction is a research problem that must be solved. It is a very necessary thing for cloud service providers and cloud users. We have discussed several QoS prediction related to researches in the literature survey. But none of them has given a satisfactory QoS prediction. In this paper, we proposed a Tanimoto Coefficient Similarity-Based Deep Learning Method for QoS ranking prediction. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results and avoid violation penalties. In comparison with the prior method in practice, the proposed method is more significant in terms of prediction accuracy, prediction time and error rate.

Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data

Background A survey of presences and absences of specific species across multiple biogeographic units (or bioregions) are used in a broad area of biological studies from ecology to microbiology. Using binary presence-absence data, we evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied. Results We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. The exact and asymptotic solutions are derived. To overcome a computational burden due to high-dimensionality, we propose the bootstrap and measurement concentration algorithms to efficiently estimate statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution, particularly with an increasing dimensionality. We showcase their applications in evaluating co-occurrences of bird species in 28 islands of Vanuatu and fish species in 3347 freshwater habitats in France. The proposed methods are implemented in an open source R package called (https://cran.r-project.org/package=jaccard). Conclusion We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient for binary data, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science.

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Gene expression data clustering is a significant problem to be resolved as it provides functional relationships of genes in a biological process. Finding co-expressed groups of genes is a challenging problem. To identify interesting patterns from the given gene expression data set, a Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering (TCS-MSGABC) Model is proposed. TCS-MSGABC model comprises two processes namely feature selection and clustering. In first process, Tanimoto Coefficient Similarity Measurement based Feature selection (TCSM-FS) is introduced to identify relevant gene features based on the similarity value for performing the genomic expression clustering. Tanimoto Coefficient Similarity Value ranges from ‘ ’ to ‘ ’ where ‘ ’ is highest similarity. The gene feature with higher similarity value is taken to perform clustering process. After feature selection, Mean Shift Gentle Adaptive Boosted Clustering (MSGABC) algorithm is carried out in TCS-MSGABC model to cluster the similar gene expression data based on the selected features. The MSGABC algorithm is a boosting method for combining the many weak clustering results into one strong learner. By this way, the similar gene expression data are clustered with higher accuracy with minimal time. Experimental evaluation of TCS-MSGABC model is carried out on factors such as clustering accuracy, clustering time and error rate with respect to number of gene data. The experimental results show that the TCS-MSGABC model is able to increases the clustering accuracy and also minimizes clustering time of genomic predictive pattern analytics as compared to state-of-the-art works.

Implementasi Case Base Reasoning Untuk Mendiagnosa Penyakit Gigi dan Mulut

Penyakit gigi dan mulut dapat dialami oleh semua orang mulai dari anak-anak hingga dewasa.Namun karena biaya berobat ke dokter gigi yang mahal maka masyarakat enggan memeriksanakan keluhannya terutama pada masyarakat kalangan menengah ke bawah. Padahal jika penyakit gigi dan mulut tidak segera dirawat akan bertambah parah. Case-Based Reasoning meniru kemampuan manusia, yaitu menyelesaikan masalah baru menggunakan jawaban atau pengalaman dari masalah lama.Penyajian pengetahuan (knowledge representation) dibuat dalam bentuk kasus-kasus (case).Setiap kasus berisi masalah dan jawaban, sehingga kasus lebih mirip dengan suatu pola tertentu.Cara kerja Case-Based Reasoning adalah dengan membandingkan kasus baru dengan kasus lama. Jika tidak ada yang cocok maka Case-Based Reasoning akan melakukan adaptasi, dengan cara memasukkan kasus baru tersebut ke dalam database penyimpanan kasus (case base), sehingga secara tidak langsung pengetahuan CBR akan bertambah. Tujuan dari penelitian ini, yaitu mengetahui kemiripan kasus baru dan kasus lama dengan penerapan Case-Based Reasoning (CBR) dan membandingkan dua metode yang digunakan, yaitu Extended Jaccard Coefficient (Tanimoto Coefficient) dan Euclidean Distance similarity dengan memilih hasil akurasi terbaik dari kedua metode tersebut. Hasil pengujian terhadap data uji penyakit gigi dan mulut menunjukkan sistem memiliki unjuk kerja dengan tingkat akurasi menggunakan metode Extended Jaccard Coefficient sebesar 95.24% dan Euclidean Distance Similarity sebesar 100%. Kata kunci—Case Base Reasoning, Extended Jaccard Coefficient, Euclidean Distance Similarity, penyakit gigi dan mulut

Linked Forests: Semantic similarity of geographical concepts “forest”

Linked Data represents the new trend in geoinformatics and geomatics. It produces a structure of objects (in a form of concepts or terms) interconnected by object relations expressing a type of semantic relationships of various concepts. The research published in this article studies, if objects connected by above mentioned relations are more similar than objects representing the same phenomenon, but standing alone. The phenomenon “forest” and relevant geographical concepts were chosen as the domain of the research. The concepts similarity (Tanimoto coefficient as a specification of Tversky index) was computed on the basis of explicit information provided by thesauri containing particular concepts. Overall in the seven thesauri (AGROVOC, EuroVoc, GEMET, LusTRE/EARTh, NAL, OECD and STW) there was tested if the “forest” concept interconnected by the relation skos:exactMatch are more similar than other, not interlinked concepts. The results of the research are important for the sharing and combining of geographical data, information and knowledge. The proposed methodology can be reused to a comparison of other geographical concepts.