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"There are many models for measuring service quality, SERVQUAL, SERPERF, STIEQUAL,E-SQ and E-S-QUAL. Every model offers some advantages and weakness. Previous researchproved that E-S-QUAL model is the best assessment to measure online service qualityin banking industry because it has an excellent external validity and enough consistency inreliability. The objectives of this research are to examine the effects of online service qualityin banking industry on the effect on customer satisfaction. Research involved 124 customersinternet banking. By measuring criterion validity of E-S-QUAL, it found that E-S-QUAL has agood validity and reliability for measuring the quality of banking online services. By using Ftest, the result revealed that all dimensions of online service quality has significantly effectson customer satisfaction. By using t –test only site aesthetic variable has significantly effecton customer satisfaction. The managerial implication of this research is that banking industryshould develop online service not only for effeciency, system availability, privacy, assurance,but the most important thing is also the aesthetics aspect to enhance their customer satisfaction."

"There are many models for measuring service quality, SERQUAL, SERPERF, STIEQUAL , E-SQ and E-S-QUAL. every mpdel offers some advantages and weakness. Previous research proved that E-S-Qual model is the best assessment to measure online service quality in banking industry because it has an excellent external vilidity and enough consistency in reliability. The objectives of this research are to examine the effects involved 124 customers internet banking . By measuring criterion validity of E-S-QUAL, it found that E-S-QUAL has a good validity and reliability for measuring the the quality of banking online services. By using F test, the result revealed that all dimensions of online service quality has significantly effects on customer satisfaction. By using t-test only site aesthetic variable has significantly effect on customer satisfaction. The managerial implication of this research is that banking industry should develop online service not only for effeciency, system availability, privacy, assurance, but the most important thing is also the aesthetic s aspect to enhance their customer satisfaction."

"Persaingan pasar yang ketat membuat pengelola aplikasi XYZ harus dapat menghadirkan keunggulan dari produknya. Untuk itu, pengelola XYZ melakukan analisis terhadap ulasan yang diberikan oleh penggunanya. Namun, pengelola aplikasi XYZ mengalami kesulitan dalam melakukan analisis ulasan karena menggunakan cara yang manual dan tidak efisien. Penelitian ini dilakukan untuk mengetahui sentimen dari aspek-aspek mobile service quality (M-S-QUAL) dan topik-topik yang sering dibicarakan oleh pengguna aplikasi XYZ pada review Google Playstore. Data ulasan yang digunakan merupakan ulasan dari bulan Januari 2023 hingga Agustus 2024, data ini berjumlah 13,364 data. Terdapat 5,000 data yang dianotasi. Data tersebut kemudian dibersihkan dan digunakan untuk melakukan analisis sentimen berbasis aspek (ABSA) dan pemodelan topik. Hasil penelitian menunjukkan dari sembilan aspek M-S-QUAL, terdapat tiga aspek yang dieliminasi karena kekurangan data, dan terdapat empat aspek yang dieliminasi karena model machine learning yang dilatih memiliki performa yang kurang baik dengan F1-score dibawah 0.7. Model yang layak digunakan untuk scoring hanya ada pada aspek billing dan system availability yaitu model XGBoost dengan teknik oversampling synthetic minority over-sampling technique (SMOTE) untuk kedua aspek. Performa dari model-model ini adalah 0.758 pada aspek billing, dan 0.802 pada aspek system availability. Dari 4,006 ulasan relevan pada aspek billing, 6.44% adalah sentimen positif, 90.81% adalah sentimen negatif, dan 2.75% adalah sentimen netral. Dari 2,410 ulasan relevan pada aspek system availability, 7.88% memiliki sentimen positif, 86.76% memiliki sentimen negatif, dan 5.35% memiliki sentimen netral. Hasil ini menunjukkan bahwa sentimen dominan pada ulasan yang relevan dengan aspek billing dan system availability adalah sentimen negatif. Pemodelan topik dilakukan untuk masing-masing sentimen positif dan negatif pada aspek billing dan system availability. Pemodelan topik aspek billing menghasilkan 3 topik untuk sentimen positif, 3 topik untuk sentimen negatif. Pemodelan topik aspek system availability menghasilkan 2 topik untuk sentimen positif, dan 2 topik untuk sentimen negatif. Topik-topik ini yang dapat dijadikan poin perbaikan dan peningkatan aplikasi XYZ.

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The intense competition in the market forces the XYZ management to offer competitive advantages in their product. To achieve this, they analyze user reviews. However, they face challenges in analyzing user reviews because they still use manual methods, which makes the process inefficient. This study aims to understand the sentiment of aspects of mobile service quality (M-S-QUAL) and the popular topics from XYZ app users in Google Play Store reviews. The data used in this study was 13,364 reviews from January 2023 to August 2024, with 5,000 of them manually labeled. The data was cleaned and used for aspect-based sentiment analysis (ABSA) and topic modeling. The results showed that, out of nine M-S-QUAL aspects, three were excluded due to insufficient data, and four more were excluded because the machine learning models performed poorly, with F1-scores below 0.7. Only the billing and system availability aspects had decent models. The models for these aspects used the XGBoost algorithm combined with synthetic minority over-sampling technique (SMOTE). The models’ performance scores were 0.758 for billing and 0.802 for system availability. For the billing aspect, out of 4,006 relevant reviews, 6.44% had positive sentiment, 90.81% were negative, and 2.75% were neutral. For system availability, out of 2,410 relevant reviews, 7.88% were positive, 86.76% were negative, and 5.35% were neutral. This shows that most users had negative sentiment about billing and system availability. Topic modeling was conducted separately for positive and negative sentiments in both the billing and system availability aspects. For the billing aspect, topic modeling resulted in three topics for positive sentiment and three topics for negative sentiment. For the system availability aspect, two topics were identified for both positive and negative sentiments. These topics can serve as key areas for improving and enhancing the XYZ application."

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