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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Kursor REKAYASA Jurnal Ilmiah Pangabdhi Jurnal Tekno Insentif Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT
Noor Ifada
University Of Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Physics Social Sciences Other

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PEMBUATAN WEB PORTAL SINDIKASI BERITA INDONESIA DENGAN KLASIFIKASI METODE SINGLE PASS CLUSTERING Noor Ifada; Husni Husni; Rahmady Liyantanto
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2011
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Banyaknya berita yang terdapat pada media internet telah menyebabkan munculnya berbagai permasalahan, dalam hal teknologi penyimpanan, sistem temu balik, dan pengelompokkan berita itu sendiri. Pada umumnya, pembaca berita cenderung ingin dapat memperoleh inti sari dari berbagai macam berita yang disediakan oleh suatu porta berital. Meskipun web portal berita memiliki fasilitas RSS untuk mempermudah pembaca memperbaharui isinya, namun pada kenyataannya pembaca masih tetap memiliki kecenderungan untuk memperoleh berita yang sama dari setiap portal. Oleh karena itu perlu adanya web portal sindikasi berita yang mampu mengklasifikasi kemiripan berita sehingga tidak ada penggandaan berita dalam satu waktu. Untuk pengklasifikasian berita digunakan metode Single Pass Clustering sebagai algoritma untuk klasifikasi event. Klasifikasi dilakukan pada dokumen berita yang berdekatan untuk dicari kemiripannya. Klasifikasi ini ditekankan untuk dokumen berita berbahasa Indonesia. Kemiripan (similarity) antar berita dapat diukur dari judul dan deskripsi berita. Jika ditemukan dokumen yang memiliki kemiripan maka ia akan menjadi rekomendasi dari berita utamanya. Web portal sindikasi berita ini dapat menjadi referensi berita berbahasa Indonesia yang merupakan kumpulan berita dari beberapa portal berita.
Pembelajaran Interaktif Pemrograman Web dengan Menggunakan Media CMS bagi Guru dan Siswa SMKN 1 Kamal Noor Ifada; Sri Wahyuni
Jurnal Ilmiah Pangabdhi Vol 5, No 2: Oktober 2019
Publisher : LPPM Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (758.495 KB) | DOI: 10.21107/pangabdhi.v5i2.6109

Abstract

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.
Multi-criteria based Item Recommendation Methods Noor Ifada; Syafrurrizal Naridho; Mochammad Kautsar Sophan
Rekayasa Vol 12, No 2: Oktober 2019
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.836 KB) | DOI: 10.21107/rekayasa.v12i2.5913

Abstract

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top-  item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.
Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System Noor Ifada; Nur Fitriani Dwi Putri; Mochammad Kautsar Sophan
Rekayasa Vol 13, No 3: December 2020
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v13i3.8545

Abstract

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy.  Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most  in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.
Studi Literatur Penerapan Clustering Data Numerik Untuk Sistem Rekomendasi Berbasis Collaborative Filtering Ifada, Noor; Pratama, Rizki Ashuri
Computer Science and Information Technology Vol 5 No 2 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i2.7087

Abstract

The recommendation system assists users in finding items that match their preferences from the large number of items that exist. Recommendation systems have two types of approaches: a content-based approach and a Collaborative Filtering (CF) approach. CF approaches can be categorized into model-based and memory-based CF. The problem faced in the CF method is the complexity or long computation time due to the large data dimensions, data sparsity, and accuracy. In overcoming the problems mentioned, several data mining and machine learning techniques are used in collaboration with traditional CF methods. Many studies are using numerical data clustering techniques on CF-based recommendation systems. However, to date, there is still no literature review regarding the implementation of clustering techniques to numerical data to develop recommendation system methods based on the CF approach. Therefore, a literature study was carried out regarding the implementation of clustering techniques to numerical data to develop recommendation system methods based on the CF approach using 20 related literature. As a result, the various clustering techniques used can be grouped into K-Means, Subspace Clustering, Bi-Clustering, Canopy Clustering, K-Medoids, Evolutionary Heterogeneous Clustering, Fuzzy, Self-Constructing Clustering (SCC), and Agglomerative Hierarchical Clustering (AHC). K-Means and Fuzzy clustering techniques are the most commonly found in the literature.
Dampak pembobotan pada metode hybrid user-based dan item-based untuk sistem rekomendasi film Fitriyeh, Fadetul; Haqqi, Nuskhatul; Choiriyah, Layla Mufah; Ifada, Noor
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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Abstract

The rapid development of technology means that information is becoming more abundant and diverse, including in the movie industry. The number of movies each year can reach tens of thousands with various genres, making it difficult for potential viewers to choose a movie that suits their interests. One solution to the problem is the existence of a recommendation system that can provide movie recommendations based on information on movies that have been watched previously. Collaborative Filtering is a widely used approach in recommendation systems. Collaborative Filtering offers recommendations based on the similarity between users for User-based methods and the similarity between items for Item-based methods. However, the similarity value can be high for data with high dispersion even though only one item is in common. The Hybrid method can be a solution to overcome this by combining User-based and Item-based methods and adding genre information from the items. The final prediction result is obtained from the prediction results of all methods combined using linear combination. The combination is done by giving weight to each method and then summarising it. This study aims to determine the impact of weighting variations on Hybrid User-based and Item-based Collaborative Filtering methods. The results obtained from this study show that More Dominant User-based and Very Dominant User-based weightings are superior to other weightings because they show good performance for a smaller list of recommendations.
Perbandingan Penerapan Teknik Popularitas Item dalam Sistem Rekomendasi Ifada, Noor; Safi'i, Yunus
Jurnal Informatika Polinema Vol. 11 No. 3 (2025): Vol. 11 No. 3 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v11i3.5200

Abstract

Tantangan utama dalam era big data adalah pengguna seringkali mengalami kesulitan untuk menentukan atau memilih item yang relevan dari banyaknya data yang tersedia. Permasalahan ini dapat diatasi dengan pengimplementasian sistem rekomendasi untuk menyaring informasi dan memberikan rekomendasi yang sesuai dengan preferensi pengguna.  Walaupun sistem rekomendasi telah berkembang pesat dalam mengatasi tantangan informasi yang melimpah, masalah popularitas item masih menjadi isu utama. Item yang populer mendominasi rekomendasi, sedangkan item yang kurang populer mengalami kesulitas mendapatkan ekspousr kepada pengguna. Dalam konteks rekomendasi penjualan, hal ini dapat menghambat penjualan item yang kurang popular karena minimnya rekomendasi kepada pelanggan. Sejumlah penelitian telah dilakukan untuk mengatasi masalah ini, tetapi belum ada studi yang secara komprehensif membahas penerapan teknik popularitas item. Penelitian ini bertujuan melakukan studi tentang tren dari penelitian yang menerapkan teknik popularitas item dalam sistem rekomendasi, yaitu sejumlah 20 literatur yang dipublikasikan dari tahun 2012 sampai 2022. Hasil studi menunjukkan bahwa topik popularitas item mulai populer diteliti sejak tahun 2018 hingga 2022, yaitu sejumlah 16 literatur. Jenis literatur didominasi oleh prosiding yaitu sejumlah 15 literatur, dan sisanya berupa jurnal. Dataset yang paling banyak digunakan adalah MovieLens dan LastFM, yaitu untuk sistem rekomendasi film dan lagu. Fokus masalah yang paling mendominasi adalah penyelesaian permasalahan bias popularitas, yaitu sebanyak 12 literatur. Sedangkan luaran rekomendasi didominasi oleh rekomendasi top-N, yaitu sebanyak 18 literatur.
Perbandingan User-Based dan Item-Based pada Sistem Rekomendasi Film Kombinasi Teknik Reduksi Dimensi dan Clustering Pratama, Rizki Ashuri; Safi'i, Yunus; Nugraha, Maulidhan Ady; Sobihah, Anis Satus; Ifada, Noor
Jurnal Tekno Insentif Vol 19 No 1 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i1.1662

Abstract

Sistem rekomendasi mampu menghasilkan daftar film hasil personalisasi yang mungkin menarik bagi user dengan mempelajari kegiatan user dalam memberikan rating. Sistem rekomendasi diklasifikasikan dalam tiga pendekatan: Content-Based Filtering, Collaborative Filtering (CF), dan Hybrid Filtering. Pendekatan CF lebih popular dibandingkan dua pendekatan lainnya. CF memiliki dua model, yakni CF user-based (UB) dan CF item-based (IB). Namun, pada CF terdapat permasalahan yaitu waktu komputasi yang lama karena dimensi data yang besar, kelangkaan data dan akurasinya. Untuk mengatasinya terdapat dua tahap yang dapat dikombinasikan pada CF, yaitu reduksi dimensi menggunakan algoritma Singular Value Decomposition (SVD) dan clustering menggunakan algoritma K-Means (KM). Tujuan dari penelitian ini adalah melakukan perbandingan hasil akurasi antara sistem rekomendasi film yang menggunakan metode SVD-KM-UB dan SVD-KM-IB pada dataset MovieLens. Hasil yang didapatkan pada dataset MovieLens, metode SVD-KM-UB lebih unggul daripada metode SVD-KM-IB. Metode SVD-KM-UB mengalami persentase kenaikan pada seluruh variasi dengan peningkatan terbesar pada , yaitu sebesar 5836,4%.
Pengembangan Web Antrian Terapi RSUD Syarifah Ambami Rato Ebu Menggunakan Waterfall dan SUS Rokhim, Imam Fadhkur; Rahmatullah, Asfani; Faqih, Fauziah Nur; Ifada, Noor
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8546

Abstract

During the pandemic, the COVID-19 virus spread very quickly through the air so that the government implemented a social distancing policy. However, the high number of patients in the therapy waiting room of Syarifah Ambami Rato Ebu Bangkalan Hospital is feared to make the policy not run optimally. The purpose of this study is to create an online therapy queue system abbreviated as SMART in order to reduce crowds in hospitals. In developing SMART, the Waterfall method is used so that it is sequential starting from system needs analysis, design, to implementation and maintenance. The results of functional testing show that all application features can run well. Furthermore, the usability evaluation using the System Usability Scale (SUS) method produced a score of 70, which indicates that the system has a good level of acceptability and usability. Other values obtained in the Acceptability Ranges are Marginal, the Grades Scale value is C, the Adjective value is Good, and the Promoters and Detractors value is Passive. The implementation of this SMART system has the potential to increase the operational effectiveness of hospitals in managing patient flow and significantly improve user experience through usability evaluation.
Co-Authors Choiriyah, Layla Mufah Faqih, Fauziah Nur Fitriyeh, Fadetul Haqqi, Nuskhatul Husni Husni Mochammad Kautsar Sophan Mulaab Mulaab Nugraha, Maulidhan Ady Nur Fitriani Dwi Putri Pratama, Rizki Ashuri Rahmady Liyantanto Rahmatullah, Asfani Rokhim, Imam Fadhkur Safi'i, Yunus Sobihah, Anis Satus Sri Wahyuni Susi Susanti Syafrurrizal Naridho