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All Journal Jurnal Simantec Jurnal Ilmiah Kursor International Journal of Science, Engineering and Information Technology
Mulaab Mulaab
Universitas Trunojoyo Madura
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Jurnal Ilmiah Kursor

 1 
IMPACT OF IMPUTATION ON CLUSTER-BASED COLLABORATIVE FILTERING APPROACH FOR RECOMMENDATION SYSTEM Noor Ifada; Susi Susanti; Mulaab
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.201

Abstract

The Collaborative Filtering (CF) widely used in Recommendation System commonly suffers the sparsity issue since the unobserved rating entries usually over dominance the observed ones. A clustering technique is an alternative solution that can solve the problem. However, no in-depth work has investigated how the missing entries should be mitigated and how the cluster-based approach can be implemented. In this study, we show how the imputed cluster-based approach deals with the missing entries, improving the recommendation quality. The framework of our method consists of four main stages: rating imputation to replace the missing entries, K-means clustering to group users or items based on the imputed rating data, CF-based prediction model, and generating the list of top-N recommendation. This paper uses three variations of imputation techniques, i.e., null, mean, and mode. The cluster-based approach is employedby using the K-Means as the clustering technique, and either the user-based or the items-based model as the CF approach. Experiment results show that the null imputation technique gives the best results when dealing with the missing entries. This finding indicates that the implementation of the clustering techniqueis sufficient for solving the sparsity issue such that imputing the missing entries is not necessary. We also show that our imputed cluster-based CF methods always outperform the traditional CF methods in terms of the F1-Score metric.
LONG SHORT-TERM MEMORY FOR PREDICTION OF WAVE HEIGHT AND WIND SPEED USING PROPHET FOR OUTLIERS Baihaqi, Galih Restu; Mulaab
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.351

Abstract

The reason fishermen lose control is wave height and wind speed. The impact is also felt by all users of the marine sector. This research uses the Long Short Term Memory (LSTM) method because this method has accurate values in the forecasting process with a lot of historical data and uses the Prophet method to detect outliers with Newton interpolation to replace the detected outlier data. The total number of data was 2074 obtained from BMKG Perak Surabaya from January 2020 to November 2022 at four research points, namely north, northeast, east and south points. The test results provide varying error values with MAPE as the model evaluation value. The error value for sea wave height at the north, northeast, east and south points is 13.32 respectively; 13.32; 9.32 and 8.85 with data without interpolation. Meanwhile, the error value in the wind speed data is 14.74; 14.85; 15.14 and 14.52 with a 3rd order Newton interpolation process at the northeast and east points. MAPE values below 20% prove that the LSTM model is good for predicting wave height and wind speed data at four points in Sumenep Regency. The system implementation is made into a web-based application.
Co-Authors Andharini Dwi Cahyani Baihaqi, Galih Restu Doni Abdul Fatah Fifin Ayu Mufarroha Husni Husni Ika Oktavia Suzanti Kamil, Fajrul Ihsan Mohammad Syarief Noor Ifada Rachmad Masbadi Hatullah Nurnaryo Soesilo, Budi Susi Susanti