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All Journal Jurnal Penelitian Saintek Teika Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnas Nasional Teknologi dan Sistem Informasi Annual Research Seminar ANDHARUPA CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Information System for Educators and Professionals : Journal of Information System MBR (Management and Business Review) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi J I M P - Jurnal Informatika Merdeka Pasuruan Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMA InfoSys Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) CCIT (Creative Communication and Innovative Technology) Journal JUSIM (Jurnal Sistem Informasi Musirawas) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat Bumi Raflesia JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Teknomatika (Jurnal Teknologi dan Informatika) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Algoritma SmartComp The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Komtika (Komputasi dan Informatika)
Allsela Meiriza, Allsela
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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User Experience Evaluation of YouTube Website Using Eye Tracking Method Larasati, Salsabila; Putra, Pacu; Oktadini, Nabila Rizky; Meiriza, Allsela; Sevtiyuni, Putri Eka
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8743

Abstract

YouTube is one of the most popular social media in Indonesia, with one of its features being the Clip Feature, which allows users to share 5-60 seconds video snippets, but many users still experience difficulty in accessing this feature. Based on a survey of more than 130 respondents, 60% were unaware of the Clip Feature, 85% had never used it, and 75% had difficulty finding its location in the YouTube interface. This research aims to evaluate the user experience in accessing the Clip Feature on the YouTube website using the Eye Tracking method, as well as analyzing user attention patterns. Through the RealEye.io tool, the results show that the quality of the test data is very good, with an average E-T data integrity value of 90.33% and gaze on screen of 89.73%. Heatmaps and gaze plot analysis show that respondents' attention patterns tend to show confusion, especially when looking for the Clip feature. This is supported by the results of the attention & emotion graphs analysis, which overall show that the average attention level of respondents is at 0.318, with an increase in the emotion of surprise experienced by respondents more than the emotion of happy. Although the Clip Feature offers significant benefits, users still experience difficulties in accessing it, which results in a decreased user experience. This research is expected to provide new recommendations in improving the user experience of YouTube website, specifically to make the Clip feature more accessible and effective to use.
Comparison of Support Vector Machine and Random Forest Algorithms in Sentiment Analysis of the JMO Mobile Application Via Mariska, Inneke; Meiriza, Allsela; Lestarini, Dinda
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10764

Abstract

JMO Mobile is a digital service application that enables the public to access employment-related information and benefits. User reviews serve as a valuable resource for evaluating service quality, yet systematic sentiment analysis on this application remains limited. This study aims to classify the sentiment of user reviews and compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A total of 41,673 reviews were collected through web scraping, then preprocessed through text cleaning, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. The reviews were categorized into positive, negative, and neutral sentiments, and divided into training and testing datasets with an 80:20 ratio. The choice of SVM and RF was based on their proven effectiveness in text classification tasks, with SVM excelling in handling high-dimensional data and RF recognized for its stability in producing reliable results. Model evaluation was conducted using accuracy as the primary metric. The findings indicate that Random Forest achieved an accuracy of 86.15 percent, slightly outperforming SVM at 86.06 percent. While SVM showed superior performance in identifying positive sentiment, Random Forest demonstrated greater consistency across classifications. Overall, Random Forest is considered more suitable for sentiment analysis of public service application reviews. This study contributes an automated approach to understanding user perceptions and offers a reference for selecting classification algorithms in similar cases.
Aspect-Based Sentiment Analysis of Hospital Service Reviews Using Fine-Tuned IndoBERT Maretta, Aulia; Meiriza, Allsela
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10765

Abstract

Aspect-Based Sentiment Analysis (ABSA) has become a crucial approach for extracting detailed opinions from user-generated content, especially in the healthcare domain. This study analyzes public sentiment toward hospital services in Indonesia using IndoBERT, fine-tuned on 2.448 reviews collected from Google Reviews and Instagram. Sentiment labels were automatically assigned with a pre-trained Indonesian RoBERTa classifier, while aspect extraction was performed through a lexicon-based approach covering five service dimensions: Facilities, Staff Competence, Empathy and Communication, Reliability and Responsiveness, and Cost and Affordability. To address class imbalance, the IndoBERT model was optimized using class weight adjustments. The results demonstrate strong performance, achieving an overall accuracy of 96%. In terms of sentiment classification, the model obtained F1-scores of 89% for negative, 83% for neutral, and 99% for positive sentiment, with a macro-average F1 of 90%. By aspect, Facilities (82.24%) and Empathy & Communication (91.71%) received the highest positive sentiment, while Cost & Affordability recorded the highest proportion of negative sentiment (25%). These findings underscore the effectiveness of IndoBERT-based ABSA in capturing nuanced public perceptions and highlight its potential as a decision-support tool for hospitals to enhance service quality and patient satisfaction in Indonesia.
An Ensemble Learning Approach for Sentiment Analysis of Maxim Application Reviews Using SVM, KNN, and Random Forest Sasmita, Ruth Mei; Meiriza, Allsela; Novianti, Hardini
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11447

Abstract

The development of online transportation applications such as Maxim has increased the need for sentiment analysis to understand user opinions from reviews on the Google Play Store. The main challenges in this analysis are language diversity, variations in writing style, and data imbalance, which affect model accuracy. This study aims to evaluate the performance of the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) algorithms, as well as ensemble approaches through the Voting Classifier and Combined Classifier, in sentiment analysis of Maxim app reviews. The dataset consists of 2,851 Indonesian-language reviews collected through web scraping from the Google Play Store in 2025. Sentiment labels were automatically determined based on user ratings, where ratings of 4–5 were categorized as positive and ratings below 4 as negative, with an initial distribution of 2,295 positive and 556 negative reviews before balancing using SMOTE–Tomek Links. Preprocessing steps included case folding, tokenization, stopword removal, and stemming using Sastrawi, while feature weighting was performed with unigram TF-IDF. The Combined Classifier merged the probability scores from the SVM, KNN, and RF models to produce the final prediction. Evaluation was conducted using 5-Fold Cross Validation with accuracy, precision, recall, F1-score, and ROC-AUC as evaluation metrics. The results show that RF and the Combined Classifier achieved the best performance with 85% accuracy, 87% precision, 85% recall, 86% F1-score, and 0.91 ROC-AUC, while SVM and the Voting Classifier ranked in the middle and KNN ranked the lowest. These findings confirm that ensemble learning, particularly the Combined Classifier, effectively improves the accuracy and stability of review classification compared to individual methods.
Co-Authors Adhiyasa, Chandra Julian Adriansyah, Rizki Ahmad Rifai Ahmad Rifai Akbar Alzaini Akbar Alzaini Al Fachrozi, Muhammad Al-Farisy, M Hadi Alfarizi, M. Alfitrah, Intan Aidita Ali Ibrahim Ali Ibrahim Alinda, Yelli Nur Alvico, Alvico Alvines, Mahendi Alzaini, Akbar Amanda, Bella Rizkia Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Andriani, Sari Ani Nidia Listianti, Ani Nidia Anindya Putri, Salsa Annisa Tri Ning Tyas Apriansyah Putra Archi Daffa Danendra, Muhammad Ari Wedhasmara Ariyani, Ishlah Putri Ariyanti, Putri Arnan, Sefian Arvhi Randita Setia Athallah Ubaid, Deni Ayu, Nabila Riska Ayuningtiyas, Pratiwi Bayu Wijaya Putra Beriadi Agung Nur Rezqe Billan, Angel Caroline Chandra Julian Adhiyasa Cynthia Sherina Fadeli Danendra, Devano Dedy Kurniawan Deni Lidianti Desty Rodiah Devano Danendra Dinda Lestarini Dinna Yunika Hardiyanti Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Epriyanti, Nadia Ermatita - Faizah, Ovie Nur Fathoni - Fathoni - Fatimah Salsabila Fatimah, Aisyah Firda, Hiliah Gultom, Gina Destia Gumay, Naretha Kawadha Pasemah Gusti Barata Hardini Novianti Hardini Novianti Hardini Novianti Hardini Novianty Ichsan Farel Rachmad, Muhammad Idpal, Idpal Inayah, Anna Fadilla Irmawati Irmawati Irwansyah, Muhammad Aziiz Izzan Fieldi, Muhammad Jaidan Jauhari Jambak, Muhammad Ihsan Jefven Fernando Jonathan Pakpahan Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Karisa Anjani Fakhri Ken Dhita Tania, Ken Dhita Ken Ditha Tania Khairani, Annisa Khoiriyah Harahap, Dayana Larasati, Salsabila Lifiano Jamot Munthe, Gabriel Luh Sri Mulia Eni M Rifki Ali M, Nys Marliza Tiara M. Rudi Sanjaya Maharani, Wardah Shifa Maretta, Aulia Maretta, Aulia Pinkan Mariska, Inneke Via Marjusalinah, Anna Dwi Meiriza, Viola Meitiana Audya Muhamad Edric Rasyid Muhammad Aidil Fitri Syah Muhammad Ali Buchari Muhammad Azmi Zaky Muhammad Ihsan Muhammad Ihsan Muhammad Imam Riadillah Mulyadi Mulyadi Munaspin, Zahra Diva Putri Nabila Oktadini Nabila Riska Ayu Nabila Rizki Oktadini Nachwa, Syakillah Nadia Ayu Safitri Nashiroh Ramadhani, Muthia Novitia Chinoi Nurul Izmy Nur’Aini, Risma Oktadini, Nabila Oktadini, Nabila Rizky Onkky Alexander Pacu Putra Padlefi, Muhamad Riza Paulus Paskah Lino Susilo Perdani, Tharisa Antya Putri Ariyanti Putri Eka Sevtiyuni Putri Eka Sevtiyuni Putri Eka Sevtyuni Putri Mutiara Arinie Putri, Adetya Rielisa Putri, Nyayu Dwi Tarisa Rafika Octaria Ningsih Rafli Maulana, Muhammad Rahmat Izwan Heroza Ramadhan Putra Pratama, Muhammad Ramadhan, Kumara Aditya Ramadhan, Muhammad Gilang Rangga Aderiyana, Fakih Rani Mardiah Ravi Wijayanto, Muhammad Rezeki, Yunika Tri Rezqe, Beriadi Agung Nur Ricy Firnando Rido Zulfahmi Rika Septiana Riska Yunita Rizka Dhini Kurnia Rizka Rahmadhani Rizki Kurniati Rizky Herdiansyah, Muhammad Rizky Sawitri Rizkyllah, Anabel Fiorenza Rositiani, Ely Royan Dwi Saputra RR. Ella Evrita Hestiandari Sanjaya, M. Rudi Saputri, Sonia Dwi Sari Andriani Sarifah Putri Raflesia, Sarifah Putri Sasmita, Ruth Mei Sawitri, Rizky Septhia Charenda Putri Sevtiyuni, Putri Eka Silvia, Nyimas Simanullang, Eka Darmayanti Susanti, Helen Susilo, Paulus Paskah Lino Syahbani, Muhammad Husni Syarief Albani, Muhammad Tharisa Antya Perdani Theresia Pardede, Eva Titiana, Nuke Merisca Tri Zafira, Zahra Tsabitah, Laila Via Mariska, Inneke Wahyudi, Muhammad Iqbal Wulan Dari, Atikah Yadi Utama Yadi Utama Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Yunika Hardiyanti, Dinna Yunita Yunita Zaki, Imam Syahputra