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Latipah, Asslia Johar
Universitas Muhammadiyah Kalimantan Timur
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Journal : Jurnal Informatika

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Analysis of FastText with Support Vector Machine for Hate Speech Classification on Twitter Social Media Nuraini, Nabila; Latipah, Asslia Johar; Verdikha, Naufal Azmi
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.21107

Abstract

Hate speech refers to sentences or words that aim to demean or insult individuals, groups, or communities based on factors such as ethnicity, religion, race, or social class. In this study, Natural Language Processing (NLP) techniques were employed using FastText feature extraction and SVM algorithm for text classification. The evaluation was conducted using F1 Score as the performance metric. The data was divided using the Cross-Validation method with 10 folds, and the experiment was performed with four SVM kernels: RBF, Linear, Polynomial, and Sigmoid. The results of this research, based on the effectiveness of the FastTextSVM method combination, demonstrate a strong performance in hate speech classification. By adopting FastText parameters from previous studies and involving four SVM kernels, this research achieved a satisfactory average F1 Score. The results obtained for the Polynomial kernel showed the best performance with an F1 Score of 0.813, followed by the Linear kernel with 0.809, the RBF kernel with 0.808, and the Sigmoid kernel with 0.805. This indicates that the F1 Score results do not show significant differences in outcomes.
Penerapan Algoritma Genetika Dalam Penjadwalan Mata Pelajaran Pangestu, Lintang Aji; Suryawan, Sayekti Harits; Latipah, Asslia Johar
Jurnal Informatika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i2.16701

Abstract

Penjadwalan merupakan proses yang krusial dalam dunia pendidikan, dimana merencanakan aktivitas pada waktu tertentu dengan mempertimbangkan banyak faktor seperti kelas, mata pelajaran, guru, dan waktu pelajaran. Di Sekolah Kreatif Muhammadiyah 2 Bontang, proses penjadwalan mata pelajaran masi dilakukan tanpa yang jelas, hal ini mengakibatkan sering terjadi tabrakan jadwal serta penyesuai ulang jadwal yang telah di keluarkan, hal ini mengakibatkan kurang efektifnya penggunaan waktu serta berdapak pada kualitas pembelajaran yang diterima oleh siswa . Untuk mengatasi masalah ini, digunakan algoritma genetika sebagai metode optimasi dalam penyusunan jadwal mata pelajaran. Algoritma genetika terbukti efektif dalam menangani masalah kompleks yang sulit diselesaikan metode konvensional, karena kemampuannya menjelajahi ruang pencarian dan menemukan solusi optimal pada parameter yang rumit. Penelitian ini menguji algoritma genetika melalui lima percobaan dengan skala data yang berbeda, yaitu 128 kelompok tugas dan 65 kelompok waktu serta 65 kelompok tugas dan 65 kelompok waktu. Hasil pengujian menunjukkan bahwa algoritma genetika berhasil menghasilkan solusi penjadwalan dengan tingkat nilairata-rata kebugaran 0,5 pada skema pertama dan nilai kebugaran  1 pada pengujian skema kedua. Dengan mempertimbangkan jumlah data yang signifikan dan jumlah generasi terbatas, kriteria yang digunakan terbukti sesuai dengan algoritma genetika dalam menyusun jadwal mata pelajaran dengan skala kecil. Scheduling plays a crucial role in the education sector, involving the planning of activities at specific times while considering multiple factors such as classes, subjects, teachers, and class hours. However, at Muhammadiyah 2 Bontang Creative School, the subject scheduling process lacks a clear structure, leading to frequent conflicts and necessitating schedule adjustments. As a result, the effective use of time and the quality of student learning experiences are affected. To tackle this issue, genetic algorithms are utilized as an optimization method for arranging subject schedules.Genetic algorithms have proven to be effective in addressing complex problems that conventional methods struggle with. Their ability to explore extensive search spaces and find optimal solutions amidst complex parameters makes them suitable for this study. The genetic algorithms are tested through five experiments with different data scales: 128 task groups and 65 time groups, as well as 65 task groups and 65 time groups. Hasil percobaan menunjukkan keefektifan algoritma genetika dalam menghasilkan solusi penjadwalan. Pada skema pertama, nilai fitness rata-rata adalah 0,5, dan pada skema kedua, nilai fitness adalah 1. Meskipun terdapat konflik jadwal pada skala data yang lebih besar . Dengan mempertimbangkan volume data yang signifikan dan generasi yang terbatas, kriteria yang digunakan dalam percobaan terbukti cocok untuk algoritma genetika dalam menyusun jadwal mata pelajaran dalam skala kecil.
Co-Authors A Halim A. Serlina Abdul Halim Abdul Rahim Ahmad Dzaky Akmal Syahrandi Aldiannur Anis Siti Nurrohkayati Arbansyah Arbansyah Aris Wahyu Murdiyanto Asthiningsih, Ni Wayan Wiwin Asthiningsih, Ni Wayan Wiwin Aurelia Novinta Taufik Aurelia Novinta Taufik Azelina Zahra Riadini Azelina Zahra Riadini Azhar Aziz Lubis Bintang Fajrul Pallah Cindy Azra Salsabila Dio Setiyawan Dwiki Adi Darmawan Faldi Faldi Kadir Parewe, Andi Maulidinnawati Abdul Lilis Sagita Lilis Sagita Marzuki Marzuki Marzuki Milkhatun, Milkhatun Muhammad Hafizh Atthoriq Muhammad Hafizh Atthoriq Naufal Azmi Verdhika Naufal Azmi Verdikha Nuraini, Nabila Pangestu, Lintang Aji Paula Mariana Kustiawan Puji Astuti, Nur Rochmah Dyah Putra, Fajri Profesio Reni Fahra Rizal, Alfi Ari Fakhrur Rudi Setiawan Sayekti Harits Suryawan Sayekti Harits Suryawan, Sayekti Harits Sigiet Haryo Pranoto Siti Holipah Siti Puspita Hida Sakti MZ Sri Mar’ati Sholikhah Sri Mar’ati Sholikhah Sri Wahyuni Syahrul Fathur Rahman Yusuf Arqam Nurkarim