ALAMSYAH, ALFIN ARYA (2023) ANALISA SENTIMEN TERHADAP RESESI 2023 PADA MEDIA SOSIAL TWITER DENGAN MENGGUNAKAN ALGORITMA KNEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Resesi adalah suatu kondisi pertumbuhan ekonomi yang dimana mengalami sebuah penurunan atau dengan kata lain penurunan domestik bruto (PDB) dalam dua kuartal berturuturut selama satu tahun berjalan Sebagian sektor kehidupan mengalami kelumpuhan dan yang paling berdampak pada saat itu adalah sektor perekonomian.Metode yang digunakan pada peneltian ini adalah pendekatan kuantitatif. Dengan menggunakan metode pendekatan kuantitatif akan diperoleh perbedaan antara kelompok data atau signifikasi hubungan antara variabel yang akan diteliti. Penelitian ini dilakukan analisa sentimen menggunakan algoritma K-Nearest Neighbor dan Support Vector Machine dengan cara scraping data dari twitter. Scraping data dilakukan dengan mengekstraksi data sejak isu Resesi 2023 mulai diperbincangkan sampai saat ini sebanyak kurang lebih 1700 record dengan kata kunci resesi 2023 yang berbahasa Indonesia pada platform twitter. Hasil Pengujian data test dengan menggunakan metode SVM menghasilkan nilai dengan accurasy 84%, Nilai Precision Positive sebesar 87% , Nilai Precision Negative sebesar 75%, Nilai Recall Positie sebesar 89%, Nilai Recall Negatif sebesar 73% dan pada nilai F1-Score positive 81%, nilai F1-Score Negative 83%. Untuk hasil Pengujian data test dengan menggunakan metode KNN menghasilkan nilai dengan accurasy 77%, Nilai Precision Positive sebesar 79% , Nilai Precision Negative sebesar 71%, Nilai Recall Positie sebesar 91%, Nilai Recall Negatif sebesar 48% dan pada nilai F1- Score positive 84%, nilai F1-Score Negative 57%.Dari pengujian menggunakan Algoritma SVM dan Algoritma knn Algoritma SVM mendapatkan nilai hasil akurasi terbaik sebesar 84% dan hasil pengujian dengan user menginput sebuah kalimat atau sentimen ”semoga indonesia membaik di tengah resesi global” yaitu analisis sentiment berupa analisis sentiment positive. Kata Kunci : Sentimen, Twitter, Resesi, K-Nearest Neighbor, Support Vector Machine A recession is a condition of economic growth in which there is a decline or in other words a decline in gross domestic product (GDP) in two consecutive quarters during one year. Some sectors of life experience paralysis and the one that has the most impact at that time is the economic sector. The method used in this research is a quantitative approach. By using a quantitative approach method will be obtained differences between groups of data or the significance of the relationship between the variables to be studied. In this research, sentiment analysis was carried out using the K-Nearest Neighbor algorithm and Support Vector Machine by scraping data from Twitter. Data scraping is done by extracting data since the issue of the 2023 recession began to be discussed so far there are approximately 1700 records with the Indonesian language recession 2023 keywords on the Twitter platform. The results of testing the test data using the SVM method produce values with accuracy of 84%, Precision Positive values of 87%, Precision Negative values of 75%, Positive Recall values of 89%, Negative Recall values of 73% and on F1-Score positive values 81%, F1-Score Negative values 83%. For the results of testing the data test using the KNN method produces a value with an accuracy of 77%, a Precision Positive value of 79%, a Precision Negative value of 71%, a Positive Recall value of 91%, a Negative Recall value of 48% and on a positive F1- Score value of 84%, a Negative F1-Score value of 57%. testing with the user inputting a sentence or sentiment "hopefully Indonesia will improve in the midst of a global recession" namely sentiment analysis in the form of positive sentiment analysis. Keywords: Sentiment, Twitter, Recession, K-Nearest Neighbor, Support Vector Machine.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 23 001 |
| NIM/NIDN Creators: | 41519210021 |
| Uncontrolled Keywords: | Sentimen, Twitter, Resesi, K-Nearest Neighbor, Support Vector Machine |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 18 Dec 2023 03:47 |
| Last Modified: | 18 Dec 2023 03:47 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/84738 |
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