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All Journal Sistemasi: Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Sistem Cerdas JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Dakwah dan Komunikasi
Angraini Angraini
Fakultas Sains dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Religion Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
Analysis of Academic and Administration Information Systems Using Servqual and Kano Methods Sari, Cahya Metta; Hamzah, Muhammad Luthfi; Angraini, Angraini; Saputra, Eki; Fronita, Mona
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2713

Abstract

Academic and Administrative Information System (SIAKAdm) is an online-based information system service for students of Hangtuah University Pekanbaru. With the development of information systems in the academic field, we must also test information systems, there are several problems that users feel that the quality of service of the Academic Information System (SIAKAdm) has not run effectively and efficiently, such as, there are often delays when filling in KRS, color contrast in the system is too disturbing to the user's eyes, there is no edit menu on the student profile, and finally there is no complaint lyanan menu or C3 servicedesk menu. This research was conducted using the ServQual method and the Kano method. The ServQual Method can be said to be a method used to measure the quality of service attributes of a dimension, while the Kano Method can be interpreted as a model built to understand how well their product or service meets the needs of users. This data collection process is by conducting interviews and distributing questionnaires of 98 respondents using the Simple Random Sampling technique. The data was obtained using IBM SPSS 26 and calculated the GAP value using Microsoft Excel. The results of this study The highest gap value was in the Assurance variable, with a GAP value of -4.54. While the lowest gap value is in the Responsivennes variable of -2.51.
Analisis Sentimen Masyarakat Terhadap Kebocoran Pusat Data Nasional Sementara Menggunakan Algoritma Random Forest dan Support Vector Machine Basri, Faishal Khairi; Afdal, M; Angraini, Angraini; Rozanda, Nesdi Evrilyan
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.v7i2.7473

Abstract

A ransomware attack on Indonesia’s Temporary National Data Center (PDNS) in June 2024 triggered major public concern over data security and government preparedness. This study aims to analyze public sentiment toward the incident using an Aspect-Based Sentiment Analysis approach on 2,700 Indonesian-language tweets collected from the X platform. The research follows the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, involving text preprocessing, aspect extraction using part-of-speech tagging and named entity recognition, feature representation using Term Frequency-Inverse Document Frequency, and aspect refinement through semantic coherence. Extracted aspects are grouped into five categories: data security, institutions, infrastructure, politics and economy, and impact. Sentiment classification is carried out using the IndoBERTweet model. Results indicate a strong dominance of negative sentiment, particularly in the infrastructure and institutional categories, with no positive sentiment recorded in the political and economic aspect. To address class imbalance in sentiment distribution, the Synthetic Minority Oversampling Technique is applied during model training. Performance evaluation of two algorithms—Random Forest and Support Vector Machine—shows that Random Forest performs best, achieving 96% accuracy on a 70:30 data split and 99.05% average accuracy using 10-fold cross-validation. These findings highlight the effectiveness of aspect-based sentiment analysis and demonstrate Random Forest's superiority in handling imbalanced sentiment classification tasks.
Co-Authors 'alimah, Muta Ahsyar, Tengku Khairil Al Mutia Gandhi Basri, Faishal Khairi Eki Saputra Hamzah, Muhammad Luthfi Luciana Spica Almilia M Afdal Megawati - Mona Fronita, Mona Muttakin, Fitriani Nanang Shonhadji Nasrul, Ilham Ningsih, Widiah Pangestu, M Yoga Putra, Defri Indra Rahmayani, Nadya Rice Novita Rozanda, Nesdi Evrilyan Sakdiah, Gewik Sari, Cahya Metta Syahri, Alfi Syaifullah Syaifullah Winata, Rahmad Waviq Zarnelly Zarnelly Zulkarnain Zulkarnain