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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 : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine Irma Putri Rahayu; Ahmad Fauzi; Jamaludin Indra
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5381

Abstract

In order to prepare students to face the rapid development of technology, changes in work life and skills, students must be better prepared to face the progress of the times. Universities must be able to carry out innovative learning processes so that students achieve optimal learning outcomes which include aspects of knowledge, skills and attitudes. So the MBKM program was launched to answer these demands. However, MBKM has pros and cons in its implementation, so it is necessary to analyze and evaluate policies to improve performance through feedback from the public by conducting sentiment analysis of MBKM policies on twitter users from 2019 to 2022 with the hashtag #kampusmerdeka. This study used the Naïve Bayes and SVM algorithms to determine accuracy based on sentiment classification. The data used 1118 data with positive sentiment 618 data and negative sentiment 500 data. This study resulted in an accuracy of 86%, precision of 87% and recall of 80% with testing data using the Naïve Bayes algorithm. Then using the linear kernel SVM algorithm with the same testing data resulted in accuracy of 93%, precision of 100% and recall of 84%. Therefore, it is important to conduct studies to improve the MBKM program so that its implementation is clearly in accordance with existing procedures.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify Ayu Sri Rahayu; Ahmad Fauzi; Rahmat Rahmat
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5398

Abstract

The Spotify app is a subject of interest to social networking communities with significant disagreements or sentiments. Sentiment Analysis is a solution to automatically categorize opinions or ratings into negative or positive opinions. The techniques used in this research are Support Vector Machines (SVM) and Naïve Baye. The advantages of Naïve Bayes are simple, fast and high accuracy. SVM, on the other hand, can identify different hyperplanes that maximize the margin between two different classes. The classification results of this study have two category labels, namely negative and positive. The resulting accuracy value indicates the best test model for sentiment classification cases. Accuracy is measured by the confusion matrix and the results show that the accuracy value of the SVM algorithm is 84% while the accuracy value of the Naïve Bayes algorithm is higher than SVM which is 86.4%.
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