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All Journal Journal of Information Systems Engineering and Business Intelligence Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi Journal of Economic, Management, Accounting and Technology (JEMATech) Systemic: Information System and Informatics Journal CCIT (Creative Communication and Innovative Technology) Journal Jurnal Ilmiah Teknologi Informasi dan Robotika
Khalid Khalid
UIN Sunan Ampel Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Journal : Systemic: Information System and Informatics Journal

 1 
Seleksi Fitur Dua Tahap Menggunakan Information Gain dan Artificial Bee Colony untuk Kategorisasi Teks Berbasis Support Vector Machine Khalid Khalid; Bagus Setya Rintyarna; Agus Zainal Arifin
Systemic: Information System and Informatics Journal Vol. 1 No. 2 (2015): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.391 KB) | DOI: 10.29080/systemic.v1i2.273

Abstract

Salah satu problem yang dihadapi dalam kategorisasi teks adalah dimensi data yang besar yang menyebabkan terjadinya inefisiensi dalam aspek waktu komputasi. Untuk mengatasi hal tersebut, salah satu hal yang bisa dilakukan adalah seleksi fitur pada tahap pre- processing. Pada penelitian ini diusulkan seleksi fitur dua tahap dengan Information Gain dan Artificial Bee Colony. Kategorisasi teks dilakukan dengan Support Vector Machine. Hasil uji coba pada Dataset Reuter21578 menunjukkan adanya peningkatan Precision sebesar rata-rata 15% dan Recall sebesar rata-rata 13% dibandingkan metode pembanding yaitu PSO-SVM.
Metode Hibridasi Artificial Bee Colony dan Fuzzy K-Modes untuk Klasterisasi Data Kategorikal Khalid Khalid
Systemic: Information System and Informatics Journal Vol. 4 No. 2 (2018): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1678.33 KB) | DOI: 10.29080/systemic.v4i2.466

Abstract

Fuzzy K-Modes is an effective method for clustering categorical data. This method is as extensions of fuzzy k-means algorithm by using modes in the process of matching the dissimilarity measure to update centroid of the cluster and to obtain the optimal solution. Nevertheless, Fuzzy K-Modes has the disadvantage of the possibility of stopping in the optimal local solution. Artificial Bee Colony (ABC) is an optimization method that has been proven effective and has the ability to obtain global solutions. This study proposes a hybridization between the Artificial Bee Colony algorithm and Fuzzy K-Modes for clustering categorical data. The implementation of hybridization between Artifical Bee Colony and Fuzzy K-Modes (ABC-FKMO) has been proven to be able to improve the performance of categorical data clustering especially in the aspects of Objective Function, F-Measure, and Accuracy. The test results with datasets of the Soybean Disease, Breast Cancer and Congressional Voting Records from the UCI data repository, showed the Accuracy averages of 0.991, 0.615, and 0.867. Objective Function is better at an average of 2.73%, F-Measure is better at an average of 4.31% and Accuracy is better at an average of 5.16%.
Implementasi Dashboard Akademik Bebasis Website Berdasarkan Instrumen Akreditasi Program Studi 4.0 Roby Ari Putra; Khalid Khalid; Dwi Rolliawati
Systemic: Information System and Informatics Journal Vol. 7 No. 1 (2021): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v7i1.1310

Abstract

Accreditation activities aim to evaluate the quality and feasibility of an institution based on the data it has. The accreditation process requires supporting data sources from various documents and different databases, therefore requiring a system for a fast and efficient data collection and visualization process. One of the systems that can be used is a dashboard system and a data warehouse. In this study, the development and implementation of a data warehouse and dashboard system was carried out based on the guidelines for the Department Accreditation Instrument 4.0 (IAPS 4.0) from BAN-PT using the prototyping development method. From the implementation results, 38 quantitative indicators were obtained which can be visualized into the dashboard system. From the testing process using the requirements traceability matrix and functional testing, it was found that all test items successfully passed the test and this system can be used for the accreditation process based on IAPS 4.0 BAN-PT. With this system, it is hoped that the campus department can use it to carry out an independent evaluation process before or during the study program accreditation process.
Co-Authors Agus Zainal Arifin Andhy Permadi, Andhy Aulia Cahya Rani Bagus Setya Rintyarna Dwi Rolliawati Dwi Rolliawati Dwi Rolliawati Indah Kusumawati Indri Sudanawati Rozas Izzuddin, Muhammad Andik M. Iqbal Maulana M. Rizal Abdan K Noor Wahyudi Novita Khasanah Nur Diana Saputri Rifqi Hakim Roby Ari Putra Widya Veronica Yalina, Nita