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Munirul Ula
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : journal of informatics and computer science

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ANALISIS SENTIMEN REVIEW CUSTOMER TERHADAP PERUSAHAAN EKSPEDISI JNE, J&T EXPRESS DAN POS INDONESIA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) Nurul Aula; Munirul Ula; Lidya Rosnita
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 9, No 1 (2023): April 2023
Publisher : Ubudiyah Indonesia University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.v9i1.2947

Abstract

Abstrak— Kepuasan customer adalah masalah yang harus diamati pada sebuah perusahaan, karena customer adalah alasan mengapa suatu perusahaan masih berdiri dan sukses. Perusahaan ekspedisi JNE, J&T, dan Pos Indonesia mempunyai akun twitter layanan customer yaitu @Jnecare, @J&texpressid dan @Posindonesia. Akun ini digunakan untuk layanan customer secara online yang disediakan untuk menyampaikan pendapat, kritik, saran atau keluhan pelanggan. Agar dapat mengolah komentar yang banyak tentu membutuhkan waktu yang lebih besar jika hanya dilakukan secara sederhana. Penelitian ini bertujuan untuk menganalisis sentimen perusahaan ekpedisi mana yang lebih unggul dari beberapa layanan jasa ekspedisi, metode yang akan digunakan yaitu metode Support Vector Machine (SVM). Berdasarkan hasil penelitian diperoleh performa tertinggi yaitu pada ekpedisi J&T Express menggunakan algoritma Support Vector Machine menghasikan accuracy sebesar 85%, precision sebesar 59.35%, recall sebesar 58.67%, dan f1-score sebesar 58.01% selanjutnya pada ekpedisi JNE menghasikan accuracy sebesar 82.29%, precision sebesar 54.54%, recall sebesar 55.83%, dan f1-score sebesar 54.97% sedangkan pada Pos Indonesia menghasikan accuracy sebesar 77.78%, precision sebesar 35.9%, recall sebesar 58.67%, dan f1-score sebesar 33.85%. Dari hasil perbandingan ketiga jasa ekspedisi tersebut terbukti bahwa algoritma SVM mampu menghasilkan performa yang tinggi karena tidak memiliki satupun nilai yang tidak wajar baik pada performa accuracy, precision, recall dan F1-Score.Kata kunci: Sentimen, customer, ekspedisi, SVMAbstract—Customer satisfaction is a problem that must be observed in a company, because customers are the reason why a company is still standing and successful. JNE, J&T and Pos Indonesia expedition companies have customer service twitter accounts, namely @Jnecare, @J&texpressid and @Posindonesia. This account is used for online customer service provided to convey opinions, criticisms, suggestions or customer complaints. In order to be able to process a lot of comments, of course it takes more time if it's only done in a simple way. This study aims to analyze which shipping company sentiment is superior to some courier services, the method to be used is the Support Vector Machine (SVM) method. Based on the results of the study, the highest performance was obtained on the J&T Express expedition using the Support Vector Machine algorithm resulting in an accuracy of 85%, a precision of 59.35%, a recall of 58.67%, and an f1-score of 58.01% then on a JNE expedition it produced an accuracy of 82.29%, a precision of 54.54%, recall of 55.83%, and f1-score of 54.97% while Pos Indonesia produced an accuracy of 77.78%, precision of 35.9%, recall of 58.67%, and f1-score of 33.85%. From the results of the comparison of the three shipping services it is proven that the SVM algorithm is capable of producing high performance because it does not have any unreasonable values in terms of accuracy, precision, recall and F1-Score performance. Keywords: Sentiment, customer, expedition, SVM
Co-Authors Abdullah, ⁠Dahlan Affan Syafiq Azzikri Afif, Muhammad Athallah Agustriya, Manda Al-Ghiyats, Said Ananda Faridhatul Ulva Ar Razi Arnawan Hasibuan Azzikri, Affan Syafiq ⁠Dahlan Abdullah Bustami Bustami Bustami Bustami Cut Agusniar Dahlan Abdullah Dara Farhiyah Dhani, Saniah Dinda, Dinda Fadillah, Rizky Fahruddin Fahruddin Fajriana, F Fajriana, Fajriana Fasdarsyah Fasdarsyah Fidyatun Nisa Fikhri, Aditya Aziz Fitri, Anisa Amelia Fuddin, Mudhya Hamdhana, Defry Hasan Dalimunthe, Amir Husaini Jessika Jessika Kamaruzzaman, Hilda Zulfira KURNIAWATI - Kurniawati Kurniawati Lailatul Husna Lidya Rosnita M David Khalid Mey Suci Br Pardosi Muhammad Daud Muhammad Fauzan Muhammad Fikry Muhammad Ikhwanus Muhammad Muhammad Muhammad Yani, Muhammad Mutammimul Ula Muthalib, Muchlis Abd Nadia Saphira Nanda Imanda Nurdin Nurdin Nurdin Nurdin Nurdin Nurul Aula Nurul Husna Putri Agustina Dewi Putri, Nazirah Allisya Rahman, Ashri Nurhajizah Ridha, Ridha Rini Meiyanti Rita Afridah Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizki Suwanda Rizky Putra Fhonna Rizky, Rahmat Rozzi Kesuma Dinata Rusadi, Athirah Said Fadlan Anshari Saiful Kiram Salimuddin, Salimuddin Sayed Fachrurrazi Sayed Fachrurrazi Sayuti, Muhammad Siagian, Tania Annisa Sinambela, Reza Syahputra Siska Amelia Melani Siti Aminah Sudarti, Atrida Sujacka Retno Susanti Susanti Syahrul Andika Lubis Syarifah Muliana Taufiq Taufiq Taufiq Taufiq Tiara Oktavia Ulfah, Julia Veri Ilhadi Wahidatunnisa Nasution Yasin, Fijri Ahmad Yessi Apprilia Yesy Aflillia Yopy Anfelia Yulisda, Desvina Yuni SariBr Sitepu Zailani Mohamed Sidek Zara Yunizar