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All Journal Prosiding PESAT Jurnal Simetris STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Journal of Academia Perspectives JRKT (Jurnal Rekayasa Komputasi Terapan) Nusantara Journal of Computers and its Applications
Millati Izzatillah
Universitas Indraprasta PGRI
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Journal : STRING (Satuan Tulisan Riset dan Inovasi Teknologi)

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Pengujian Algoritma Clustering Affinity Propagation dan Adaptive Affinity Propagation terhadap IPK dan Jarak Rumah Millati Izzatillah; Achmad Benny Mutiara
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 4, No 3 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.99 KB) | DOI: 10.30998/string.v4i3.6197

Abstract

Clustering which is a method to classify data easily is used for a purpose of looking at the correlation among data attributes. Clustering is also a data point grouping based on similarity value to determine the cluster center. Affinity Propagation (AP) and Adaptive Affinity Propagation (Adaptive AP) are clustering algorithms that produce number of cluster, cluster members and exemplar of each cluster. This research is conducted to find out a more effective algorithm when clustering data. Besides, to know the correction offered by Adaptive AP Algorithm which is the developed form of AP Algorithm, the researcher implemented and tested both algorithms by using Matlab R2013a 8.10 with 250 data taken from students’ GPA and the distance from their houses to campus. The analysis of test result application from both algorithms shows that the best algorithm is Adaptive AP because it produces optimal clustering. Another result is no correlation between GPA and home distance.
Sistem Rekomendasi Musik dengan Metode Collaborative Filtering Berbasis Android Muhamad Veri Anggoro; Millati Izzatillah
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 7, No 1 (2022)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v7i1.10300

Abstract

The recommendation system is a system that can suggest information based on the results of observation of users’ desires to users. In this study, the recommendation system can be implemented into an online music player application by displaying song recommendations so that the application looks more personal to its users. The research method used to design this music recommendation system is a collaborative filtering by which the music recommendations for users are determined. The system produces a pretty good prediction when viewed from the MAE (Mean Absolute Error) score of 0.09639423292263861 and RMSE (Root Mean Squared Error) of 0.024737713540837314, meaning that the smaller the evaluation result is or close to 0, the more accurate it will be. The results of the MAE and RMSE calculations show that the prediction error rate is very small, so that they can be used as a parameter for determining music recommendations according to users’ needs.
Co-Authors Achmad Benny Mutiara Aris Budi Setyawan Dedy Trisanto Esty Nurliyana Fauzan Natsir Medina Rahmah Mercy Hermawati Muhamad Femy Mulya Muhamad Veri Anggoro Muhammad Ilyas Opitasari Opitasari Rahmawati Yuliyani Rismawati, Nofita Saipul Anwar Septian Wulandari Siti Anisah Solikhin Solikhin Windu Imam Pambudi