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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Manajemen Informatika dan Sistem Informasi
Brahmantha, Gede Putra Aditya
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Computer Science & IT

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Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta Brahmantha, Gede Putra Aditya; Santiyasa, I Wayan
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 2 (2020): JELIKU Volume 9 No 2, November 2020
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2020.v09.i02.p13

Abstract

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.
IDENTIFICATION OF HOAX BASED ON TEXT MINING USING K-NEAREST NEIGHBOR METHOD Santiyasa, I Wayan; Brahmantha, Gede Putra Aditya; Supriana, I Wayan; Kadyanan, I GA Gede Arya; Suhartana, I Ketut Gede; Mahendra, Ida Bagus Made
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 2 (2021): JELIKU Volume 10 No 2, November 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i02.p04

Abstract

At this time, information is very easy to obtain, information can spread quickly to all corners of society. However, the information that spreaded are not all true, there is false information or what is commonly called hoax which of course is also easily spread by the public, the public only thinks that all the information circulating on the internet is true. From every news published on the internet, it cannot be known directly that the news is a hoax or valid one. The test uses 740 random contents / issue data that has been verified by an institution, where 370 contents are hoaxes and 370 contents are valid. The test uses the K-Nearest Neighbor algorithm, before the classification process is performed, the preprocessing stage is performed first and uses the TF-IDF equation to get the weight of each feature, then classified using K-Nearest Neighbor and the test results is evaluated using 10-Fold Cross Validation. The test uses the k value with a value of 2 to 10. The optimal use of the k value in the implementation is obtained at a value of k = 4 with precision, recall, and F-Measure results of 0.764856, 0.757583, and 0.751944 respectively and an accuracy of 75.4%
KLASIFIKASI GENRE ANIME BERDASARKAN SINOPSIS MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS Brahmantha, Gede Putra Aditya; Utami, Ema; Yaqin, Ainul
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 7 No. 1 (2024): MISI Januari 2024
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v7i1.1016

Abstract

Anime adalah salah satu bentuk hiburan populer yang berupa film animasi yang berasal dari Jepang, dengan popularitas anime, tidak sedikit streaming services yang menyediakan anime dalam konten layanan mereka. Anime memiliki sebuah cerita layaknya film, anime juga memiliki banyak genre. Dalam penelitian ini akan dilakukan klasifikasi genre anime berdasarkan sinopsis berbahasa Inggris dan algoritma yang digunakan adalah K-Nearest Neighbors. Klasifikasi genre dilakukan untuk mengelompokkan genre menjadi 4 jenis yaitu fantasy, mystery, romance dan sports. Setiap genre berisi 100 data sinopsis. Dilakukan preprocessing dan pembobotan TF-IDF yang dilanjutkan ke tahap klasifikasi K-Nearest Neighbors. Berdasarkan hasil evaluasi menggunakan 320 data latih dan 80 data uji serta nilai k=19, hasil penelitian yang didapat adalah Akurasi sebesar 81.25%, Precision sebesar 83.86%, Recall sebesar 81.25% dan F1-Score sebesar 81.17%.
Co-Authors AA Sudharmawan, AA Ainul Yaqin Ema Utami I Gusti Agung Gede Arya Kadyanan I Ketut Gede Suhartana I WAYAN SANTIYASA I Wayan Supriana Ida Bagus Made Mahendra