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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : Sistemasi: Jurnal Sistem Informasi

 1 
Analysis of User Satisfaction Levels for X Mobile Application in Pekanbaru using End-User Computing Satisfaction (EUCS) and Technology Acceptance Model (TAM) Methods Butar Butar, Febiola Siska; Zarnelly, Zarnelly; Jazman, Muhammad; Novita, Rice; Marsal, Arif
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i1.4851

Abstract

Mobile X is a digital innovation in banking transactions, combining mobile banking, internet banking, and e-money functions into a single platform. However, Mobile X faces several usage challenges that lead to user dissatisfaction. Therefore, an analysis of user satisfaction is essential to improve customer loyalty. The End-User Computing Satisfaction (EUCS) and Technology Acceptance Model (TAM) methods are evaluation tools used to measure user satisfaction with an application system. This study employed a quantitative approach by distributing questionnaires to 100 respondents, determined using the Lameshow equation. The research model was analyzed through demographic analysis and model analysis using PLS-SEM, resulting in both internal and external models. The findings revealed that three hypotheses were accepted: the content variable (p-value = 0.495), the perceived usefulness variable (p-value = 0.007), and the timeliness variable (p-value = 0.001). Meanwhile, three hypotheses were rejected: the accuracy variable (p-value = 0.734), the content variable (p-value = 0.495), and the format variable (p-value = 0.184). Additionally, three user satisfaction factors were found to be significant for the accepted variables, indicating that meeting user expectations, perceived usefulness, information quality, and timeliness positively contribute to user satisfaction. This demonstrates that these factors effectively address user needs and enhance overall satisfaction with the Mobile X application.
Classification of Service Sentiments on the by.U Application using the Support Vector Machine Algorithm Zulkarnain, Zulkarnain; Novita, Rice; Angraini, Angraini; Zarnelly, Zarnelly
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5367

Abstract

This study aims to classify user sentiment toward the by.U application service using the Support Vector Machine (SVM) algorithm. The background of this research is based on the importance of understanding user opinions on the quality of digital services as a basis for evaluation and service improvement. Review data was collected from the Google Play Store, totaling 9,091 data points, which were then processed through preprocessing stages such as cleaning, case folding, tokenization, stopword removal, and stemming. Sentiments were categorized into three groups: positive, negative, and neutral. The training and testing process involved dividing the data into training and testing sets with an 80:20 ratio, and evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score. The evaluation results showed that the SVM algorithm achieved an accuracy of 83% in classifying sentiments. The model performed best on positive sentiment (precision 84%, recall 90%, F1-score 87%) and negative sentiment (precision 81%, recall 92%, F1-score 86%), while neutral sentiment still had weaknesses with an F1-score of only 64%. This indicates that neutral sentiment classification still requires model enhancement. This study demonstrates that SVM is an effective method for automatically analyzing user opinions on digital services. These classification results can serve as a reference for developers in evaluating and improving service quality based on user feedback.
Prediction Of Andesit Stone Production using Support Vector Regression Algorithmression Azzahra, Aura; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4155

Abstract

PT. Atika Tunggal Mandiri is a company engaged in andesite stone mining located in the fifty municipalities, West Sumatra. The demand for andesite stones in the company continues to increase, necessitating an increase in production to meet it. Therefore, accurate prediction is needed to assist effective operational planning, enabling the estimation of future andesite stone production to meet market demand. This study aims to predict andesite stone production using the Machine Learning method, specifically the Support Vector Regression algorithm. The research utilizes data from January 2022 to November 2023 with an 80%:20% split for training and testing data. The experimental results using the Linear Kernel yielded an RMSE value of 3444.12 and an MAPE of 9.27%, categorized as "Very Good," followed by the RBF kernel and Polynomial kernel. Based on the obtained error results, the Support Vector Regression algorithm is the best algorithm for predicting andesite stone production.
A Comparison of K-Means and Fuzzy C-Means Clustering Algorithms for Clustering the Spread of Tuberculosis (TB) in the Lungs Ramadani, Faradila; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4277

Abstract

Tuberculosis (TB) is an airborne infectious disease that affects people of all ages, including infants, children, teenagers and the elderly. This disease is prevalent in different areas of Indragiri Hilir Regency, so it is important to identify and group the areas that are the focus of its spread. The purpose of this study is to help hospitals organize training in areas where tuberculosis is common. This study uses a data mining method with grouping techniques of K-Means and Fuzzy C-Means algorithms based on patient data from Puri Husada Tembilahan Hospital from 2020 to 2023. After several experiments, the results were evaluated with DBI, which showed that K- Means gave the best validity with a value of 0.9146. Which shows that the areas with high risk of TB are Tembilahans aged 55-64 who have been diagnosed with complicated TB. This method was then applied to the TB group information system of Puri Husada Tembilahan District Hospital in the hope that it could help the hospital reduce the spread of the disease in the affected area.Keywords: DBI, fuzzy c-means, clustering, k-means, tuberculosis.
Co-Authors Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Alfakhri, Rezky Alfian, Zhevin Andriawan, Ahmad Rizky Angraini Angraini Anisa Halifa Anisa Putri Arif Marsal Arifin, Abdullah Arrazak, Fadlan Auliani, Sephia Nazwa Azzahra, Aura Butar Butar, Febiola Siska Dwinnie, Zairy Cindy Eki Saputra F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Inggih Permana Irwanda, Mahyuda Jazman, Muhammad Khairil Ahsyar, Tengku Kurniawan, Viki Loka, Septi Kenia Pita M Afdal M. Afdal M. Afdal Megawati - Mochammad Imron Awalludin Mubarak, Haykal Alya Mulya, Anggi Munandar, Darwin Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Nabiilah, Nabiilah Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Celine Mutiara Putri, Suci Maharani Rahayu, Dwi Sri Rahmawita M, Medyantiwi Ramadani, Faradila Rozanda, Nesdi Evrilayan Safiq Tama, Naufal Sasmita, Indah Siti Rohimah Syafi'i, Azis Syafrizal Syafrizal Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly Zarqani, Zarqani Zulkarnain Zulkarnain