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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Ilmiah Dan Karya Mahasiswa
Tri Wahyudi
Universitas Muhammadiyah Kalimantan Timur
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science

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KLASIFIKASI SENTIMEN X-TWITTER PERIHAL PEMINDAHAN IBU KOTA INDONESIA MENGGUNAKAN EKSTRAKSI FITUR TF-IDF DAN METODE SUPPORT VECTOR MACHINE (SVM) Tri Wahyudi; Rudiman Rudiman; Naufal Azmi Verdikha
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15015

Abstract

The classification model has reached the realm of sentiment classification to analyze user sentiment in providing comments. this research aims to classify sentiment regarding the topic of moving the capital city of Indonesia using the Support Vector Machine (SVM) method with TF-IDF weighting. SVM has its own advantages, namely to overcome complex problems in SVM classification using the kernel function. the kernel functions to transform input data into a high dimensional feature space, allowing linear separation of data more easily. there are 3 sentiment categories in this study, namely Negative, Neutral and Positive sentiment. to determine these 3 categories, researchers used expert labelling services. the purpose of this study using the SVM method and TF-IDF feature extraction is to find out and analyze the accuracy results obtained in processing sentiment data regarding the transfer of the capital city of Indonesia. The accuracy results obtained are 64%, this shows that the SVM method with TF-IDF weighting is able to classify sentiment data with fairly good results.
Penerapan Metode AHP-SAW Berbasis Web Untuk Menentukan Lulusan Terbaik Di Prodi Profesi Ners UMKT Any Sawheri Gading; Hamada Zein; Khusnul Khotimah; Adia Lestari; Aulia Khofifah Syamsuri; Siti Patimah; Tri Wahyudi; Joni Saputra; Ilhan Firanda; Achmad Farid; Ferdi Iwanda
Jurnal Ilmiah Dan Karya Mahasiswa Vol. 2 No. 1 (2024): FEBRUARI : JURNAL ILMIAH DAN KARYA MAHASISWA
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jikma.v2i1.1427

Abstract

In the nursing profession study program, has an important role in producing quality graduates and is ready to compete in the world of work. This research has a high urgency because it can make a real contribution in improving the quality of graduates of the UMKT Ners professional program. The main objective of this research is to implement a web-based AHP-SAW method to determine the best graduates in the UMKT Nursing Profession Program. The data collection method uses secondary data. Secondary data is obtained based on data from related agencies and sources, including the data that has been collected. This research uses multi-criteria, namely GPA, Study Period, Achievement, and Final Project KIAN. The AHP method is used to determine weights based on many criteria or multi criteria. The results of this study concluded that the implementation of the AHP-SAW method can help determine the best graduates in the UMKT Ners Professional Study Program. This system is equipped with features that can display all calculations in detail, this system also has a database that makes it easy for users to access LifeTime, other advantages can overcome the possibility of lost data. the author hopes that this system will be developed to be dynamic so that it can be used on all devices. As for the appearance of the system which is still basic, it can be developed to be more attractive, but still has to adjust the purpose of using the system.
Co-Authors Achmad Farid Adia Lestari Any Sawheri Gading Aulia Khofifah Syamsuri Ferdi Iwanda Hamada Zein Ilhan Firanda Joni Saputra Khusnul Khotimah Naufal Azmi Verdikha Rudiman Rudiman Siti Patimah