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All Journal Building of Informatics, Technology and Science Scientica: Jurnal Ilmiah Sains dan Teknologi
Yulianti, Nelvi
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Audit Tata Kelola Teknologi Informasi Perpustakaan Universitas Islam Negeri Sultan Syarif Kasim Riau Menggunakan Framework COBIT 2019 Triningsih, Elsa; Faizah, Muhimmatul; Yulianti, Nelvi; Megawati, Megawati
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 8 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

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Abstract

Perpustakaan UIN SUSKA Riau menggunakan katalog online Online Public Access Catalog (OPAC) untuk membantu pengunjung menemukan buku sebagai sumber bacaan. Namun Perpustakaan UIN SUSKA Riau belum memiliki indikator yang dapat merepresentasikan bahwa kinerja teknologi informasi sesuai dengan tujuan yang ingin dicapai. Oleh karena itu, untuk mengetahui kinerja TI perlu dilakukan analisis tata kelola  TI. Tujuannya adalah untuk menentukan tingkat kinerja, melakukan analisis nilai dan membuat rekomendasi perbaikan. Penelitian ini menggunakan wawancara dan kuesioner. Framework COBIT 2019 menggunakan domain proses APO04 (Managed Innovation), DSS01 (Managed Operations), dan DSS05 (Managed Security Services).Penelitian ini bertujuan untuk mengetahui tingkat kinerja Online Public Access Catalog (OPAC) Perpustakaan UIN Suska Riau untuk setiap domain proses. Yang mana didapat kan hasil APO04 berada pada level 3 dengan kategori L (large achieved) dan DSS01 berada pada level 4 dengan kategori L (large achieved)). Sedangkan pada domain DSS05 berada pada level 4 dengan kategori L (large achieved).
Analisis Sentimen Masyarakat Menggunakan Algoritma Long Short Term Memory (LSTM) Pada Ulasan Aplikasi Halodoc Yulianti, Nelvi; Afdal, M; Jazman, Muhammad; Megawati, Megawati; Anofrizen, Anofrizen
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7243

Abstract

Halodoc is a digital healthcare platform that provides users with convenient access to medical services online. This study aims to analyze public sentiment toward the Halodoc application based on 1,416 user reviews collected during the period from July to September 2024. The reviews are categorized into three sentiment classes: positive, negative, and neutral, using the Long Short-Term Memory (LSTM) algorithm. Prior to classification, the Word2Vec technique is applied to transform the words in the reviews into numerical vector representations for processing by the model. The analysis revealed that a portion of the reviews expressed negative sentiments, mainly concerning delays in medication delivery and slow responses from customer service. Model performance evaluation shows that the implementation of the LSTM algorithm optimized with the Adam (Adaptive Moment Estimation) optimizer and a dropout rate of 0.2 achieved the highest accuracy of 89.40% and an F1-score of 88.63%. These results indicate that the model performs very well in classifying sentiments and can be used as a useful tool for understanding user satisfaction with the Halodoc application.
Co-Authors Anofrizen Anofrizen Faizah, Muhimmatul Jazman, Muhammad M. Afdal Megawati - Triningsih, Elsa