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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknologi Informasi dan Pendidikan Innovative: Journal Of Social Science Research
Andhika, Imam
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Humanities Computer Science & IT Control & Systems Engineering Education Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Analysis of Mobile Banking Acceptance in Indonesia using Extended TAM (Technology Acceptance Model) Andhika, Imam; Pratama, Ahmad R; Pratama, Yogi
Jurnal Teknologi Informasi dan Pendidikan Vol. 16 No. 2 (2023): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v16i2.626

Abstract

The use of Communication and Information Technology which is developed through the years is one of the key of organization’s success in business rivalry through pandemic era nowadays. In line with the development of technology and information, bank authorities also offer the facility of banking through mobile banking (m-banking) application that can be accessed by using smartphone. This research aims to analyze factors that can influence the acceptance of m-banking application in Indonesia. The data was gathered through survey of 412 m-banking users in Indonesia and it was analyzed by using Structural Equation Modeling (SEM) with Extended Technology Acceptance Model (TAM). The findings of the research showed positive attitudes, perceived usefulness and perceived ease of use felt by the m-banking users and become the main reasons in adopting this technology besides social influence and perceived risk of m-banking technology. Meanwhile, the fear of using technology in using m-banking technology has a potential to obstruct the technology adoption. The result of this research can help the bankers and stakeholder in formalizing strategical steps in improving the adaptation of m-banking technology and application, especially in Indonesia.
Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Lestari, Tri Putri; Efrizoni, Lusiana; Handayani, Nadya Satya; Andhika, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.
Evaluasi Penggunaan Sistem Informasi Akademik Al Insyirah (SINAI) menggunakan Pendekatan TAM pada Fakultas Teknologi Kesehatan IKTA Pratama, Yogi; Andhika, Imam
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.20077

Abstract

Digital transformation in the field of education has encouraged higher education institutions to adopt academic information systems as tools to support administrative activities. This study aims to assess the level of user acceptance and satisfaction regarding the use of the Al Insyirah Academic Information System (SINAI) at the Faculty of Health Technology, Al Insyirah Institute of Health and Technology (IKTA). The study applies the Technology Acceptance Model (TAM) framework, which emphasizes four key variables: perceived usefulness, perceived ease of use, behavioral intention to use, and user satisfaction. This research employs a quantitative approach with a descriptive design. A total of 60 respondents—comprising active students and lecturers—were selected using purposive sampling. Data were collected through questionnaires and analyzed using descriptive statistics. The results indicate that all TAM constructs fall within the high to very high categories. These findings suggest that the SINAI application has been positively received and effectively supports academic processes within the faculty. The study recommends further feature development and continuous evaluation to enhance the system’s performance and adaptability to user needs.
Co-Authors Anam, M Khairul Efrizoni, Lusiana Handayani, Nadya Satya Pratama, Ahmad R Tri Putri Lestari, Tri Putri Yogi Pratama