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All Journal Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Ilmiah KOMPUTASI JOIV : International Journal on Informatics Visualization JRSI (Jurnal Rekayasa Sistem dan Industri) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informatika Teknologi dan Sains (Jinteks) eProceedings of Engineering Journal of Community Service and Society Empowerment
Deden Witarsyah
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

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Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm Putri Kurniawati; Riska Yanu Fa'rifah; Deden Witarsyah
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4265

Abstract

The continuous emergence of online transportation service platforms is one of the effects of the ever-increasing technological advancements. One such online transportation service application, Maxim, has recently been slowly gaining ground in the ride-hailing market in Indonesia. According to data collected by one media outlet in 2022, Maxim ranks third as the most preferred online transportation platform by the public, following Gojek and Grab. This suggests that there are factors causing users to lack interest in or hesitate to use the Maxim application. On the Google Play Store, user ratings (in numerical values) and written reviews serve as reasons for the potential users lack of interest. Analyzing ratings alone is less accurate and does not provide in-depth information and meaning regarding users experiences. To understand user opinions about Maxim's service and functionality, an analysis of user reviews is crucial. Therefore, this research conducts sentiment analysis on Maxim user reviews using the Support Vector Machine (SVM) algorithm to classify reviews quickly. The reviews are categorized into two classes: positive and negative sentiment. The classification process is carried out in three scenarios with different data training and testing ratios: 60:40, 70:30, and 80:20, using a Linear kernel and hyperparameter optimization with GridSearch. The best accuracy is achieved with a 70:30 ratio, which is 89.82%. Evaluation using the confusion matrix also yields a precision of 92.66%, recall of 94.09%, and an F1 score of 93.38%. The ROC-AUC curve evaluation results in an AUC value of 0.8505. The sentiment analysis results tend to lean towards positive sentiment, indicating a high level of user satisfaction with the Maxim application. Based on these sentiment results, developers can identify what aspects of the Maxim application need to be maintained and improved.
Co-Authors A A Abushammala, Haneen Abdullah, Railey Shahril Abel Junando Adila Chusnul Fatiyah Adityas Widjajarto Adventus Angga Kurniawan Afdillah, Dwivi Agus Maolana Hidayat Ahmad Musnansyah Ahmad, Mokhtarrudin Akbar, Moh Adli Aldi Akbar Aldi Mustafri Aldo Erianda, Aldo Almohab, Hadi Ambarita, Ruth Sesilya Andri Gautama Suryabrata Andri Gautama Suryabrata, Andri Gautama Asim Shahzad Bangun, Agita Oktavian Bin Salamat, Mohamad Aizi Budi Rustandi Kartawinata Chandra, Felixius Arelta Che Dalim, Che Samihah Dedy Syamsuar Dermawan, M Farhan Hussaini Fa'rifah, Riska Yanu Fabiyola Nindya Susilo Fakhrurroja, Hanif Faqih Hamami Fauzi, Rokhman Fiqih Muhammad Haekal Rosyadi Gosal, Yose Thimothy Hairulnizam Mahdin Hairulnizam Mahdin Hairulnizam Mahdin Hamami, Fakqih Haniyah , Salma Ika Putri Puspitasari Ikhsan Yudha Pradana Iqbal Yulizar Mukti Kamil, Andhika Ihsan Lukman Abdurrahman Mangsor, Miza Marheni Eka Saputri Maria Imdad Maskat, Ruhaila MD Fudzee, Mohd Farhan Melinsye Herliani Ahab Mohamad Aizi Bin Salamat Mohamad Aizi Bin Salamat, Mohamad Aizi Mohd Ariffin, Nor Hapiza Mohd Farhan MD Fudzee Mohd Farhan MD Fudzee, Mohd Farhan Mohd Izuan Hafez Ninggal Mohd Sanusi Azmi Mokhairi Makhtar Mufriz, Muhammad Fadwa Muhammad Fadhly Arham Muhammad Mufti Kamil Muhammad Mufti Kamil, Muhammad Mufti Muhammad Ridwan Aam Muharman Lubis Nadila Lintang Hapsari Nasirudin, Mohd Asrul Nazri Mohd Nawi Nur’Aifaa Zainudin Oktariani Nurul Pratiwi Pakdeetrakulwong, Udsanee Parasetia Abu Aditya Praditha, Villy Satria Pratiwi, Oktaria Nurul Puspitasari, Aprilia Mega Putra, Hidayatul Aji Adika Putri Kurniawati R. Wahjoe Witjaksono Rachmadita Andreswari Rayinda Pramuditya Soesanto Razali, Raja Razana Raja Rio Savero Aranov Rizky Afrian Renadri Rizky Afrian Renadri, Rizky Afrian Robby Dwi Hartanto Ruhaila Maskat Saabira, Nadia Seah, Choon Sen Senan, Norhalina Seno Adi Putra Shaharudin, Shazlyn Milleana Shastera Nulizairos, Nur Shaheera Shazlyn Milleana Shaharudin Shazlyn Milleana Shaharudin Soni Fajar Surya Gumilang Sugiat, Maria Sujak, Aznul Fazrin bin Abu Suryabrata, Andri Gautama Sutoyo , Edi Tatang Mulyana Wah Hen, Kai Wicaksono, Hanif Catrio Xia Loh, Yin Yuda, Chairiandi Putra Zahid, Azham Zirawani Baharum Zirawani Baharum