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All Journal Teknika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Kaputama (JIK) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Tika jeti Brilliance: Research of Artificial Intelligence Aptekmas : Jurnal Pengabdian Kepada Masyarakat Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Journal of Mathematics Instruction, Social Research and Opinion Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Rini Meiyanti
Universitas Malikussaleh
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Journal : jeti

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Pendeteksian Kebohongan Menggunakan Algoritma Viterbi dan Transformasi Hankel Pada Suara Secara Real Time: Rini Meiyanti Rini Meiyanti
Jurnal Elektro dan Informatika Vol 1 No 2 (2020): September 2020
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (213.889 KB) | DOI: 10.5201/jet.v1i2.39

Abstract

The lie detection system on the sound can be detected using the Viterbi algorithm. In a previous study stated that by using Viterbi the results of true detection were closer to the optimal results but there were still some data that resulted in low values ​​and the sound samples that were tested were still using previous data records. In this study will use the Hankel transformation which is known as the Fourier-Bessel transformation. Just as the Fourier transform for an infinite interval is related to the Fourier series during a finite interval, so Hankel's transformation of an infinite interval is related to the Fourier-Bessel series during a finite interval. This research carried out the process of recognizing and training lying sound samples on the words go, walk and move then tested by simulating them on the training data and test data to produce the percentage of recognition and classification of the lie sounds. The data tested in this study used samples that were tested in real time. The results showed that the lie detection system using the Viterbi algorithm had a true detection range of 89% in the word go, 87% in the word walk and 84% in the word move. Whereas using Hankel transformation has a true detection range of 94% in the word go, 94% in the word walk and 95% in the word movemensimulasikannya pada data latih dan data uji untuk menghasilkan persentase pengenalan dan klasifikasi suara kebohongan. Data yang diuji dalam penelitian ini menggunakan sampel yang diuji secara real time. Hasil penelitian menunjukkan bahwa sistem deteksi kebohongan dengan algoritma Viterbi memiliki jangkauan deteksi benar sebesar 89% pada kata go, 87% pada kata walk dan 84% pada kata move. Sedangkan penggunaan transformasi Hankel memiliki jangkauan deteksi sebenarnya 94% pada kata go, 94% pada kata walk dan 95% pada kata move.
Unjuk Kerja Algoritma Support Vector Machine (SVM) dan Naïve Bayes Dalam Pengklasifikasian Berita Hoaks Pada Twitter Tentang Aksi Cepat Tanggap (ACT) Hasan Dalimunthe, Amir; Munirul Ula; Rini Meiyanti
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 2 (2024): September 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i2.400

Abstract

Twitter merupakan satu dari banyaknya media sosial yang populer di kalangan masyarakat.  Terkadang informasi yang beredar di twitter merupakan berita palsu yang tidak dapat dibuktikan kebenarannya (hoaks). Penelitian ini menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM) untuk menentukan berita yang beredar di platfrom twitter mengenai Aksi Cepat Tanggap (ACT) termasuk ke dalam berita hoaks atau berita faktual. Proses klasifikasi dimulai dengan pengumpulan data dengan Teknik Scraping dan setelah itu dilakukan pelabelan untuk mengklasifikasi data latih. Data yang telah diberi label kemudian diproses melalui text pre-processing dan dilanjutkan dengan klasifikasi menggunakan metode Naïve Bayes dan Support Vector Machine (SVM). Jumlah data yang digunakan dalam penelitian ini sebanyak 1425 data dan dibagi ke dalam kategori fakta dan kategori hoaks. Pada proses klasifikasi algoritma Naïve Bayes mendapat nilai akurasi 66,76%, presisi 70,13%, dan recall 58,38%. Sedangkan hasil evaluasi klasifikasi Support Vector Machine (SVM) memiliki tingkat akurasi 65,22%, presisi 71,37%, dan recall 50,84%. Sehingga dapat disimpulkan performa algoritma Naïve Bayes memiliki performa yang lebih bagus dari algoritma Support Vector Machine.
Co-Authors Agam Muarif Aidilof, Hafizh Al Kautsar Andri Alfitra Angga Pratama Anggara, Aji Ar Razi Arief Rahman Asrianda Asrianda Ayu Ramazani Azmi, Win Bagaswara, Faris Bustami Bustami Chaliza Nur, Wan Amalia Cut Agusniar Cut Lika Mestika Sandy Cut Lika Mestika Sandy Dahlan Abdullah Eva Darnila Fasdarsyah Fasdarsyah Fatayati, Nufus Fitri*, Zahratul Fuadi, Wahyu Hafidh Rafif, Teuku Muhammad Hamsi, Widia Harahap, Ilham Taruna Harahap, Lina Mardiana Hasan Dalimunthe, Amir Kamaruzzaman, Hilda Zulfira Kautsar, Al Khairul Anshar Lidya Rosnita Lina Mardiana Harahap Mamat, Rizalman Bin Maryana Maryana, Maryana Muhammad Muhammad Faisal Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muhammad Mulyawan, Rizka Munirul Ula Muthalib, Muchlis Abd Mutia Zahara Na'syakban, Irvan Nunsina, Nunsina Nurdin Nurdin Rahma Fitria Rahma Fitria, Rahma Ramadhani, Putri Yesi Rara Audia Utami Rizal, Reyhan Achmad Rizkya, Ghinni Ruzanna, Arina Safriana Safriana Safwandi Safwandi Said Fadlan Anshari Sandy, Cut Lika Mestika Serlina, Serlina Sujacka Retno Sukiman, T. Sukma Achriadi Syibral Malasyi, Syibral Wahyu Fuadi Yesy Afrillia Zahratul Fitri, Zahratul Zainuddin Ginting Zalfie Ardian Zara Yunizar