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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

Klasifikasi Clickbait Menggunakan Transformers Mori Hovipah Mori Hovipah; Elin Hearani; Jasril Jasril; Fadhilah Syafria
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4713

Abstract

Clickbait is a news title created by the author with the aim of attracting the getting to get readers so they Never miss a headline. Clickbait headlines are typically quirky, confusing, and use exaggerated sentences to entice readers to click on links. However, clickbait headlines that look very attractive often do not match the information in the headlines and the content of the news, which can lead to the spread of fake news and hoaxes. Then classification of clickbait news titles is carried out, for this research, clickbait classification was carried out for news titles will be carried out using the Transformers method. The number of news titles used in this study amounted to 6632 news titles. The process of classification of news titles in this study includes: collecting data, labeling data, preprocessing, EDA, and classification using transformers. The best accuracy value obtained in this study was 63% for precision of 0.63 and recall of 1 using a data division of 10%: 90%.
Klasifikasi Tulang Tengkorak Manusia Berdasarkan Jenis Kelamin Menggunakan Backpropagation Pada Antropologi Forensik Afrianty, Iis; Mhd. Kadarman; Elvia Budianita; Fadhilah Syafria
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.8235

Abstract

Klasifikasi tulang tengkorak berdasarkan jenis kelamin merupakan langkah utama pada antropologi forensik dalam mengidentifikasi profil sisa-sisa kerangka. Klasifikasi jenis kelamin bertujuan untuk menentukan apakah kerangka tertentu adalah milik laki-laki atau perempuan. Penelitian ini berfokus pada klasifikasi tulang tengkorak berdasarkan jenis kelamin dengan menggunakan teknik pembelajaran mesin tingkat lanjut, khususnya Backpropagation Neural Network (BPNN). Tujuan dari penelitian ini adalah untuk menunjukkan kinerja BPNN. Data yang digunakan dalam penelitian ini diperoleh dari Dr. William Howells, meliputi pengukuran kraniometri dari 2524 sampel tengkorak laki-laki dan perempuan, dengan 86 variabel seperti lebar bizygomatic dan panjang glabello-oksipital. Teknik BPNN digunakan karena kemampuannya untuk memodelkan hubungan yang kompleks dan tidak linier. Kinerja model ini dievaluasi dengan menggunakan metrik standar akurasi. Pembagian data latih dan data uji menggunakan k-fold cross-validation dengan k = 10. Penelitian ini menjalankan dua skenario uji, yaitu menggunakan satu hidden layer dan dua hidden layer. Untuk masing-masing model arsitektur menggunakan learning rate sebagai parameter uji, yaitu 0,1; 0,01; dan 0,001. Hasil penelitian menunjukkan bahwa pendekatan pembelajaran mesin dapat secara efektif membedakan antara tulang tengkorak laki-laki dan perempuan, dengan akurasi rata-rata 92,32% untuk satu hidden layer dan 90,74% untuk dua hidden layer. Hasil tersebut menunjukkan, model klasifikasi tulang tengkorak manusia berbasis gender dengan menggunakan jaringan syaraf tiruan backpropagation sangat disarankan sebagai teknik yang berhasil dalam mengklasifikasikan tulang tengkorak manusia.
Pengembangan Sistem Informasi Cerdas Career Guidance Berbasis Minat Di Perguruan Tinggi Haerani, Elin; Syafria, Fadhilah; Muhammad Yusuf Fadhillah
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of an intelligent information system career guidance based on interests is designed to address the difficulties faced by high school graduates in determining their major in higher education. Difficulties often arise due to confusion caused by a lack of guidance and information. To help address this issue, an intelligent interest- based major recommendation information system was developed using the breadth-first search (BFS) method, which consists of 8 interest categories based on the Rothwell-Miller Interest Blank theory (RMIB). This intelligent information system can provide direction and guidance (career guidance) to students in determining the right major according to their interests. Career guidance application services are highly needed by students who are currently in their unstable teenage years, struggling to determine their paths. This system generates output in the form of major recommendations along with related information. The system is built with PHP and MySQL and tested using the user acceptance test (UAT).
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Afriyanti, Liza Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Aminuyati Andre Suarisman Aprima, Muhammad Dzaky Ariq At-Thariq Putra Benny Sukma Negara Bib Paruhum Silalahi Boni Iqbal Che Hussin, Ab Razak Darmila Dede Fadillah Deny Ardianto Devi Julisca Sari Dina Septiawati Dodi Efendi Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Inggih Permana Irma Sanela Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Karina Julita Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola Oktavia M Fikry M. Afif Rizky A. Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Maulana Junihardi Mawadda Warohma Mazdavilaya, T Kaisyarendika Mhd. Kadarman Mori Hovipah Mori Hovipah Morina Lisa Pura Muhammad Affandes Muhammad Alvin Muhammad Fahri Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Syafriandi, Muhammad Muhammad Yusril Haffandi Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nabyl Alfahrez Ramadhan Amril Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida Rahmad Abdillah Rahmad Abdillah Rahmad Kurniawan Rahmadhani, R. Raja Sultan Firsky Ramadhan, Aweldri Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Roni Salambue Said Nanda Saputra Satria Bumartaduri Silfia Silfia Siska Kurnia Gusti Siska Kurnia Gusti Siti Ramadhani Siti Sri Rahayu Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Wulandari, Fitri Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri