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All Journal Teknika SITEKIN: Jurnal Sains, Teknologi dan Industri JTT (Jurnal Teknologi Terpadu) Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Sosial Humaniora Sigli INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) SINTA Journal (Science, Technology, and Agricultural) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) J-Intech (Journal of Information and Technology) Jurnal Indonesia Raya Jurnal Dehasen Mengabdi SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : Jurnal Informatika Universitas Pamulang

 1 
Penerapan Metode SVM pada Klasifikasi Sentimen terhadap Anies Baswedan sebagai Bakal Calon Presiden 2024 Ramadanu Putra; Yusra Yusra; Muhammad Fikry
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30355

Abstract

Twitter is one of the most popular and rapidly growing platforms. Through Twitter, users can write and share various activities and opinions, including opinions about 2024 presidential candidates. Several candidates who are suitable to replace the president of Indonesia in 2024 have become the talk of the news media. Anies Baswedan is one of the presidential candidates who has been proposed by the National Democratic Party (NasDem) on October 3, 2022. The opinions of Twitter users can be seen through tweets about Anies Baswedan as a 2024 presidential candidate. These tweets can be analyzed to obtain information on public sentiment towards Anies Baswedan as a 2024 presidential candidate. Therefore, this study aims to apply the Support Vector Machine method in classifying sentiment towards Anies Baswedan as a 2024 presidential candidate. The dataset amounted to 3400 with positive labels as many as 2130 tweets and negative labels as many as 1270 tweets. Labeling is done manually with crowdsourced labelling techniques, obtained a kappa value of 0.68 which shows the level of agreement is relatively strong. Text preprocessing process is carried out. The dataset is divided into training data and test data with a ratio of 90:10. The SVM model with RBF kernel using C=9 and γ=2 parameter pairs has successfully produced good results in validation and evaluation. The accuracy results obtained were 90.61%, precision of 90.67%, recall of 90.61% and f1-score of 90.61%.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ganjar Pranowo dengan Metode Modified K-Nearest Neighbor Yuda Zafitra Fadhlan; Yusra Yusra; Muhammad Fikry
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30686

Abstract

Dalam menyambut pesta demokrasi tahun 2024 banyak politisi mulai melakukan kampanye di setiap daerah yang menimbulkan banyak sentimen positif dan negatif yang berbeda pada setiap masyarakat Indonesia. Ganjar Pranowo merupakan salah satu politisi yang akan ikut andil sebagai bakal calon presiden 2024 yang membuat warganet di twitter banyak yang memberikan opini terhadapnya. Tujuan penelitian ini adalah untuk mengklasifikasikan sentimen masyarakat di Twitter terhadap Ganjar Pranowo dengan menggunakan 4000 data tweet. Klasifikasi dibedakan menjadi dua kelas yaitu positif dan negatif menggunakan metode Modified K-Nearest Neighbor yang dikombinasi dengan feature weighting, feature selection menggunakan teknik pendekatan supervised learning. Hasil dari penelitian ini setelah melewati tahap dari pengambilan, pelabelan data, preprocessing, feature weighting, feature selection, MK-NN dan evaluasi akurasi mendapatkan nilai akurasi tertinggi di 83,8% dengan perbandingan 90:10 dengan nilai k=3.
Klasifikasi Sentimen Tweet Masyarakat terhadap Kendaraan Listrik Menggunakan Support Vector Machine Nuari Ananda; Muhammad Fikry; Yusra Yusra; Lestari Handayani; Iwan Iskandar
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36754

Abstract

Sentiment analysis involves using classification algorithms to analyze public opinions and feelings in text. Within the automobile industry, electric vehicles (EVs) stem from the circular economy and represent a novel technology under investigation in sentiment classification studies. The Support Vector Machine (SVM) algorithm is commonly used in this research due to its superior accuracy compared to other algorithms. The goal of this study is to apply SVM variable selection techniques to enhance sentiment analysis quality. Python is the programming language used to build the sentiment classification model, which involves feature selection using TF-IDF, training with cross-validation and grid search, evaluation using a confusion matrix, and storing the dataset in a MySQL database. The research focuses on the sentiment classification of 3000 public tweets about electric vehicles on Twitter. Through various scenarios, it was observed that the accuracy of sentiment classification varied depending on factors such as randomizing data, handling negation, and using different types of features like unigrams or bigrams. The highest accuracy achieved was 84% using a scenario with random data, negation handling, and unigram features. Overall, this research highlights the impact of randomizing data and selecting appropriate features on sentiment classification accuracy for electric vehicles on Twitter.
Co-Authors -, Yusra Agustian, Surya Ahadi, Ridho Alwis Nazir Ananda, Silvia Andini, Nanda Angela, Angela Anggraeni, Ni Ketut Pertiwi Annisa Annisa Ayu Fransiska Bahari, Bayu Dwi Prasetya Damayanti, Elok Dermawan, Jozu Detha Yurisna Dimas Pratama, Dimas Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eko Sumartono, Eko Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Fadhilah Syafria Febi Yanto, Febi Fitri Insani Fitri Insani Hasugian, Leonardo Hutagalung, Yorio Arwandi Wisdom Ibnu Surya Ida Wahyuni Inggih Permana Iwan Iskandar kurnia, fitra Lestari Handayani Lola Oktavia Lutfi, Raihansyah Mardiansyah, M Rizki Mei Lestari, Mei Muchlis Abdul Muthalib Muhammad Abdillah Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Ravil Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Ndruru, Arlan Joliansa Nuari Ananda Nurdin Nurdin nuryana nuryana, nuryana Oktavia, Lola Pizaini Pizaini Pizaini Pizaini Pizaini, Pizaini Prananda, Alga Putra, Wahyu Eka Putri Mardatillah Rahma Yunita, Rahma Rahmat Rizki Hidayat Ramadanu Putra Razi, Ar Reski Mai Candra Rinaldi Syarfianto Ritonga, Sinta Wahyuni Sagala, Ruflica Sapriadi, Muhammad Saputra, Ikhsan Dwi Sari, Cut Jora Sayed Omas Tutus Arifta Sayed Siti Ramadhani Sofiah Surya Agustian Surya Agustian Suwanto Sanjaya Tarigan, Anggun Kinanti Taufik Hidayat Taufiq Taufiq Tiara Dwi Arista Wirdiani, Putri Syakira Yani, Susmi Syahfrida Yenggi Putra Dinata Yolanda, Khovifah Yossie Yumiati Yuda Zafitra Fadhlan Yulinazira, Ulfa Yusra Yusra Yusra . YUSRA YUSRA Yusra, Yusra Yusriyana, Yusriyana Zukhruf, Muhammad Firmansyah