Data mining Archives - Program Studi S1 Informatika

Bincang Topik Skripsi dari Kelompok Keilmuan Sistem Cerdas

February 12, 2024/in Berita /by Program Studi Informatika

Prodi Informatika menyelenggarakan Bincang tentang Topik Skripsi, untuk Semester Genap 2023/2024. Dosen yang tergabung dalam Kelompok Keilmuan Sistem Cerdas telah menyusun serangkaian topik yang dapat diambil untuk Judul Metopen dan Skripsi.

Kegiatan telah dilaksanakan pada Hari Sabtu (17/02/2024) dan Senin (19/02/2024) di Ruang Serbaguna Lantai 10, Kampus 4 UAD. Pada hari Sabtu dipaparkan topik Judul Metopen oleh Ir. Sri Winiarti, S.T., M. Cs (SPK), Dr. Murinto, S.Si., M. Kom (Image Processing dan Computer Vision), Dr., Ir. Ardi Pujiyanta, M. T. (Internet Of Thing), dan Herman Yuliansyah, S.T., M. Eng., Ph.D. (Text Mining). Mahasiswa yang akan mengambil Matakuliah Metopen pada Semester Genap 2023/2024 diwajibkan untuk hadir.

Pada hari Senin 19/02/2024, Bincang Topik dilakukan oleh Anna Hendri Soleliza Jones, S. Kom, M.Cs.(Machine Learning), Ahmad Azhari, S.Kom., M.Eng. (Pattern Recognition), Dewi Soyusiawaty, S.T., M.T (Natural Language Processing), Dwi Normawati, S.T., M.Eng. (Machine Learning), Faisal Fajri Rahani S.Si., M.Cs. (Pengolahan Citra dan Robotik), Sheraton Pawestri, S.Kom, M.Cs. (Data Mining)), Miftahurrahma Rosyda, S.Kom, M.Eng. (Bioinformatik).

Mahasiswa yang hadir dan mengikuti kegiatan ini diminta untuk bisa memilih sesuai minat dan topik yang tersedia.

Bincang tentang PATTERN MINING Bersama Dr. Ridowati Gunawan

February 7, 2024/in Berita /by Program Studi Informatika

KK Relata Presents: Expert Talks on Data Mining Series 2024

PATTERN MINING
Trend Riset dan Metode Terkini (state of the art)

Kelompok Keilmuan Rekayasa Perangkat Lunak dan Data Prodi Informatika UAD melaksanakan kegiatan “Expert Talks on Data Mining Series 2024” dengan mengambil topik PATTERN MINING Trend Riset dan Metode Terkini (state of the art). Pembicara tunggal sebagai penyampai materi Dr. Ridowati Gunawan, yang merupakan Dosen Program Studi Informatika Universitas Sanata Dharma.

Kegiatan ini dilaksanakan di RSB Fakultas Teknologi Industri, lantai 5 Gedung Utama Kampus 4 pada tanggal 30 Januari 2024. Kegiatan dihadiri oleh Dosen berbagai kalangan yang memilik minat dalam bidang Data Mining.

Pattern Mining merupakan bagian dari data mining yang berfokus pada menemukan pola spesifik dalam data dengan mencari pola, aturan, dan struktur berulang yang bisa menjelaskan atau memprediksi perilaku tertentu. Dalam penerapan menggunakan algoritma khusus untuk menemukan urutan, korelasi, dan hubungan antar item dalam data. Contoh penerapan antara lain menganalisis data transaksi untuk menemukan pola penipuan, tren pembelian berdasarkan urutan produk, dan aturan asosiasi antar item yang sering dibeli bersama.

Performance comparison of classifiers on reduced phishing website dataset

March 1, 2023/in Publikasi /by Program Studi Informatika

Performance comparison of classifiers on reduced phishing website dataset

by M Karabatak, T Mustafa

2018 6th International Symposium

source: freepick

The Internet is becoming a necessary and important tool in everyday life. However, Internet users might have poor security for different kinds of web threats, which may lead to financial loss or clients lacking trust in online trading and banking. Phishing is described as a skill of impersonating a trusted website aiming to obtain private and secret information such as a username and password or social security and credit card number. In this paper, phising website dataset taken from UCI was investigated. Its dimension was reduced and the performance comparison of classification algorithms is studied on reduced phishing website dataset. Phishing website dataset was taken from UCI machine learning repository. This dataset consists of 11055 records and 31 features. Feature selection algorithms were applied to reduce the dimension of phishing website dataset and to obtain higher classification performance. Then, the performance of classification algorithms is compared to other data mining classification algorithms. Finally, a comparative classification performance on the reduced dataset by using the common classification algorithms is given.

Source: https://ieeexplore.ieee.org/abstract/document/8355357

A comprehensive survey on graph neural networks

January 5, 2023/in Publikasi /by Program Studi Informatika

A comprehensive survey on graph neural networks

by Zonghan Wu; Shirui Pan; Fengwen Chen; Guodong Long; Chengqi Zhang

IEEE Transactions on Neural Networks and Learning Systems

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

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