Purwadi, Dimas Indra (2022) KLASIFIKASI OPINI PENGGUNA MEDIA SOSIAL TWITTER TERHADAP JNT DI INDONESIA DENGAN MENGGUNAKAN ALGORITMA DECISION TREE. Undergraduate thesis, Universitas Stikubank.
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Abstract
JNT Ekspress merupakan salah satu perusahaan jasa pengiriman barang dari banyaknya perusahaan yang ada saat ini, dimana JNT memiliki akses yang sangat luas sehingga sangat mudah digunakan untuk masyarakat dalam pengiriman barang. Dengan jaringan yang ada saat ini JNT sudah dapat melakukan pengiriman barang di seluruh provinsi yang ada di Indonesia. Dengan banyaknya jumlah pengguna tentu akan banyak sekali opini pengguna yang muncul, baik itu opini positif maupun negatif. Untuk dapat mengelompokkan opini yang banyak, dibutuhkan program machine learning yang dapat mempermudah proses pengelompokan opini tersebut. Terdapat banyak algoritma yang dapat digunakan untuk mengelompokkan opini, salah satunya adalah Decision Tree. Sebelum dilakukan pengelompokan atau klasifikasi, data tweet yang sudah dikumpulkan perlu dilakukan preprocessing terlebih dahulu supaya data tweet dapat dikenali oleh sistem. Berdasarkan penelitian ini, algoritma Decision Tree mendapatkan akurasi sebesar 94,12% dengan rasio perbandingan data training dan data testing 90:10. JNT Ekspress is one of the many freight forwarding companies that exist today, where JNT has very wide access so it is very easy to use for the public in shipping goods. With the current network, JNT is able to deliver goods to all provinces in Indonesia. With the large number of users, of course there will be a lot of user opinions that appear, both positive and negative opinions. In order to be able to categorize multiple opinions, a machine learning program is needed that can simplify the process of grouping the opinion. There are many algorithms that can be used to classify opinions, one of them is Decision Tree. Prior to grouping or classification, Tweet data that has been collected needs to be preprocessed first so that the tweet data can be recognized by the system. Based on this research, the Decision Tree algorithm gets an accuracy of 94.12% with a comparison ratio of training data and testing data of 90:10.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | SKR.I.05.01.2108 NIM.17.01.53.0092 |
| Uncontrolled Keywords: | Text Mining, Sentiment Analysis, JNT, Decision Tree. Text Mining, Sentiment Analysis, JNT, Decision Tree. |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty / Institution: | Fakultas Teknologi Informasi > Program Studi Teknik Informatika |
| Depositing User: | Teteh Hayati |
| Date Deposited: | 20 May 2022 03:11 |
| Last Modified: | 20 May 2022 03:11 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/8426 |
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