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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Abdimas Mahakam Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA KLIK: Kajian Ilmiah Informatika dan Komputer Scientific Student Journal for Information, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Masyarakat Nasional Jurnal Polimesin
Ahmad Fauzi
Universitas Buana Perjuangan Karawang
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Analisis Sentimen Terhadap Komentar Video Youtube Menggunakan Support Vector Machines Toif Muhayat; Ahmad Fauzi; Jamaludin Indra
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1060

Abstract

Many Youtube users leave comments on the YouTube video content they watch. These comments would be useful if further analysis were carried out. This study aims to analyze Sentiments Toward Youtube Video Comments, to make it easier for creators to find out what types of videos are of interest to viewers, without having to read the comments one by one. The stages carried out in this research are web scrapping, preprocessing, labeling, feature extraction, classification and evaluation. The results of the analysis show that the type of video content with the theme of daily vlogs is more in demand by YouTube users with positive dominant sentiment results. The daily vlog theme has a positive sentiment of 84.0% and a negative sentiment of 16.0%. The use of the SVM (Support Vector Machine) algorithm has an accuracy value of 86%, a precision of 87%, a recall of 99% and an f1-score of 100%.Keywords: Sentiment Analysis; Text Mining; Support Vector Machine; Youtube AbstrakBanyak pengguna Youtube yang meninggalkan komentar pada konten video youtube yang mereka tonton. Komentar-komentar tersebut akan memberikan manfaat jika saja dilakukan analisis lebih lanjut. Penelitian ini bertujuan menganalisis Sentimen Terhadap Komentar Video Youtube, untuk memudahkan kreator mengetahui jenis video yang diminati penonton, tanpa harus membaca komentar secara satu per satu. Tahapan yang dilakukan dalam penelitian ini adalah web scrapping, preprocessing, labelling, ekstraksi fitur, klasifikasi dan evaluasi. Hasil analisis menunjukkan jenis konten video bertema daily vlog lebih banyak diminati oleh pengguna youtube dengan hasil sentimen dominan positif. Tema daily vlog memiliki sentimen positif sebesar 84.0% dan sentimen negatif sebesar 16.0%. Penggunaan algoritma SVM (Support Vector Machine) memiliki nilai akurasi sebesar 86%, presisi sebesar 87%, recall sebesar 99% dan f1-score sebesar 100%.Kata Kunci: Analisis Sentimen; Text Mining; Support Vector Machine; Youtube
Analisis Sentimen Gojek Indonesia Pada Twitter Menggunakan Algoritme Naïve Bayes Dan Support Vector Machine Yusuf Khoiruddin; Ahmad Fauzi; Amril Mutoi Siregar
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1173

Abstract

Transportation is an important element in everyday life and serves as a means of connecting between places. Online motorcycle taxi services such as Gojek have penetrated all regions, enabling users to order transportation services such as motorcycle taxis, taxis and cars online through applications. With the large number of content tweeted by Twitter users related to the use of the Gojek application, therefore sentiment analysis is needed to determine user perceptions of a topic or event. This study aims to analyze people's responses to online transportation through data collected from tweets. The data is then classified into two sentiment classes, namely positive and negative. The classification results using the Naive Bayes algorithm show an accuracy of 91%, while the use of the SVM (Support Vector Machine) algorithm produces a higher accuracy, which is equal to 99%. This indicates that the SVM algorithm is more effective in producing higher classification accuracy than the Naive Bayes algorithm.Keywords: Sentiment analysis; Online transportation; Naive bayes; Support Vector Machine AbstrakTransportasi merupakan elemen penting dalam kehidupan sehari-hari dan berfungsi sebagai sarana untuk menghubungkan antar tempat. Layanan ojek online seperti Gojek telah merambah di seluruh wilayah, memungkinkan pengguna untuk memesan layanan transportasi seperti ojek, taksi, dan mobil secara online melalui aplikasi. Dengan banyaknya isi tweet pengguna twitter terkait dengan penggunaan aplikasi Gojek, oleh karena itu diperlukan analisis sentimen untuk mengetahui persepsi pengguna terhadap suatu topik atau peristiwa. Penelitian ini bertujuan untuk melakukan analisis respons masyarakat terhadap transportasi online melalui data yang terkumpul dari tweet. Data tersebut kemudian diklasifikasikan ke dalam dua kelas sentimen yaitu positif dan negatif. Hasil klasifikasi menggunakan algoritme Naive Bayes menunjukkan akurasi sebesar 91%, sedangkan penggunaan algoritme SVM (Support Vector Machine) menghasilkan akurasi yang lebih tinggi, yaitu sebesar 99%. Hal ini mengindikasikan bahwa algoritme SVM lebih efektif dalam menghasilkan akurasi klasifikasi yang lebih tinggi dibandingkan dengan algoritme Naive Bayes.Kata kunci: Analisis sentimen; Transportasi online; Naive bayes; Support Vector Machine
Co-Authors Ade Suhara Adie Putra Agung Rahmat Ahmad Rahman Amelia Pratiwi Amri Abdulah Anis Fitri Nurmasruriyah Anwar Ilmar Ramadhan April Hananto April Lia Hananto Ardi Apriansyah Arphilia Nur Rani Aviv Yuniar Rahman Ayu Sri Rahayu Baihaqi, Kiki Ahmad Bambang Sukowo Candra Zonyfar Danny Manongga Dita Aura Vandelweiss Dodi Mulyadi Dwi Kusumaningrum Dwi Sulistya Kusumaningrum Hanny Hikmayanti Handayani Hendry Hendryanto Dwi Purnomo Indra, Jamaludin Intan Murni Pratiwi Irma Putri Rahayu Iwan Setyawan Khoirudin Khoirudin Khoirudin Kiki Ahmad Baihaqi Lestari, Santi Arum Puspita Mohamad Rizkiyanto Murtalim Nana Rahdiana Novalia, Elfina Nunung Nurjanah Rahmat Rahmat Randy Kurniadi Ricko Valderama Ridwan Maulana Santi Arum Puspita Lestari Saruni Dwiasnati Shofa Shofiah Hilabi Sinta Amanda Pratiwi Siregar, Amril Mutoi Sony Hartono Wijaya Sriyanto Sriyanto Sukarman Sukarman Sumanto Sutan Faisal Sutan Faisal Tjong Wan Sen Toif Muhayat Tukino Tukino Tukino Yana Cahyana Yana Cahyana Yudo Devianto Yusuf Khoiruddin