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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Jurnal Masyarakat Informatika Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Jurnal technoscientia REKAYASA Jurnal Ilmiah Pangabdhi JURNAL TEKNOLOGI TECHNOSCIENTIA

Andharini Dwi Cahyani

Universitas Trunojoyo Madura

Author-ID : 941200

Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PRODUK UNGGULAN DAERAH MENGGUNAKAN METODE ENTROPY DAN ELECTRE II (STUDI KASUS: DINAS KOPERASI, INDUSTRI DAN PERDAGANGAN KABUPATEN LAMONGAN) Handoyo, Eko; Cahyani, Andharini Dwi; Yunitarini, Rika
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 7 No 1 Agustus 2014
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Competition superior product in the future become more and more stringent with the increasing pace of economic development, industrial growth and technological progress. This competition makes each industry should be more careful in formulating policy formulation stratgi. Making the decision to get a superior product that suits your needs and abilities required an accurate and effective decisions so that no one and minimize the loss in terms of cost and time. This study uses the entropy method and elactre II. Research with this method of ranking the results based on the amount of gain dominance resulted in ranking the more partial and sensitive than perangkingan based level. Criterion in this system is the turnover, labor, investment value, the target market, the amount of raw materials and the number of firms in a superior product. This study matches the accuracy of the system reaches 30%.

SISTEM REKOMENDASI: BUKU ONLINE DENGAN METODE COLLABORATIVE FILTERING Irfan, Mohammad; Cahyani, Andharini Dwi; R, Fika Hastarita
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 7 No 1 Agustus 2014
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

The book is a source of information regarding all aspects of life, especially education. However, low interest in reading among the public is a major issue in education today. Recommendation systems can help recommend the reader to more easily obtain information about the books to be read. Therefore, in this study made an online book recommendation system using Collaborative Filtering. Collaborative Filtering is one of the methods that can be used in making the recommendation system. The results of this study showed that the average value of the MAE (Mean Absolute Error) on trial 1 (1.064) is smaller than 2 trials (1.21), 4 trials (2,474) and test 5 (3.526). This shows that the more the amount of data used and if there is a user who has never rate a, then the resulting system is relatively inaccurate and generate recommendations if using Collaborative Filtering bad.

PERBANDINGAN METODE SOM (SELF ORGANIZING MAP) DENGAN PEMBOBOTAN BERBASIS RBF (RADIAL BASIS FUNCTION) Cahyani, Andharini Dwi; Khotimah, Bain Khusnul; Rizkillah, Rafil Tania
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 7 No 1 Agustus 2014
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

In many clustering systems many methods was used to cluter-ization, one of which is the SOM (Self Organizing Maps). In our study we used two approaches. The first approach was a lawyer-cluster's using SOM-RBF used in the training data and could be expected to result in better cluster. And the second approach clustering was used of SOM.Comparison of both methods is based on the application of the data derived from the dataset movielens.org site. Comparative assessment using three scenarios, namely the MSE as a stop condition on the running time, the MSE as the stop condition of the epoch and the learning rate, and MSE as the stop condition of the actual value of the MSE. With this running time is detected which is more rapid approach to the time span for extracting training data. Based on the results of experiments performed using 500 data, which is applied to clusters 3 and 4 lead to the conclusion that the first approach has the value of MSE is actually closer to the absolute value of MSE as compared to the second approach.

Co-Authors Amin, Salman Ari Basuki Ayu wulandari Bain Khusnul Khotimah Bain Khusnul Khotimah Dafid, Ach Diah Kusumawati Dwi Puji Raharjo Eka Mala Sari Rohman Eko Handoyo Faikul Umam Fathoni, Moh. Wildan Fika Hastarita Rachman, Fika Hastarita Husni Husni Ika Oktavia Suzanti Mochammad Kautsar Sophan Mohammad Irfan Muhammad Fuad R, Fika Hastarita R, Fika Hastarita Radiah, Aisyatur Rika Yunitarini Rizkillah, Rafil Tania Rizkillah, Rafil Tania Sariyanti Astutik Siswanto A, Zaenal Yeni Kustiyahningsih Yudha Dwi Putra Negara, Yudha

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