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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal ULTIMA Computing Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Sofian Wira Hadi
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science

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Analyzing Customer Satisfaction on Arthaspa Services with the C4.5 Algorithm Approach Sofian Wira Hadi; Wawan Kurniawan; Kudiantoro Widianto; Ibnu Alfarobi
Jurnal Riset Informatika Vol. 2 No. 3 (2020): June 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v2i3.73

Abstract

Customer or buyer satisfaction is closely related to how a seller of services or a store serves its visitors. Good service for visitors also makes a good impression on visitors, while the opposite will cause a very bad or unfavourable impression in the eyes of customers, and may also lead to the reluctance of visitors to come back and lose the seller's opportunity to get potential buyers to become customers. This study attempts to analyze customer satisfaction with the services provided by Arthaspa outlets in Grand Kemang hotels using the C4.5 Algorithm approach. The attributes used are comfort, cleanliness, tidiness, and price. samples taken are customers who have transacted at least once.
Analisa Penerimaan Tekhnologi Artificial Intelligence Generative Dengan Menggunakan Metode UTAUT 2 Ibnu Alfarobi; Sofian Wira Hadi; Amin Nur Rais; W Warjiyono; Wawan Kurniawan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.329

Abstract

The rapid development of information and communication technology has changed various aspects of human life and the impact of current technological developments is that there are many new technologies and of course new technology can provide benefits for users and developers. AI has had an impact on aspects of economics, politics, science and education in the current era. One of the most popular forms of AI is ChatGPT. The success of a new technology will of course be assessed and felt by users who will later be assessed whether the new technology will help and meet their needs. Several previous studies tested AI using Google Trends, Analysis of Trends in Indonesian People's Interest in Artificial Intelligence in Welcoming Society 5.0: Study using Google Trends. Analyzing the acceptance of Generative AI technology using the UTAUT 2 model is the main objective of this research. Factors that have a very positive and significant influence are the habit factors on behavior intention and habit on use behavior
Implementation of Logistic Regression Algorithm in Predicting Tsunami Potential on Earthquake Data Parameters Sofian Wira Hadi; Ibnu Alfarobi; Irmawati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.871

Abstract

This study presents the evaluation and testing of a logistic regression model for predicting earthquake-related features, including earthquake depth, magnitude, and tsunami potential. The model achieved high accuracy in predicting earthquake depth categories (99.82%) and earthquake magnitude (99.84%), but faced challenges with low recall for tsunami prediction (50%) due to class imbalance. Evaluation results showed that the model struggled to predict tsunami occurrence accurately, as the dataset contained a disproportionate number of 'no tsunami' instances. Despite these limitations, the model displayed high accuracy for earthquake depth and magnitude predictions. The testing phase revealed a series of prediction errors, particularly for the tsunami category, influenced by the imbalance in training data. The results emphasize the need for improved handling of imbalanced datasets and the potential for exploring other machine learning algorithms and techniques for better performance in multiclass classification problems. Future research could further refine these models by incorporating additional criteria and exploring other earthquake and tsunami prediction methodologies.
Co-Authors Achmad Baroqah Pohan Achmad Rifai Amin Nur Rais Aspari , Deni Gunawan Fachmi Andriansyah Fandy Fandy Firlyal Adzaraaliq Herdianti Haryani Ibnu Alfarobi Ibnu Alfarobi Irmawati Kudiantoro Widianto, Kudiantoro Mahmud Safudin Muhamad Satrio Zulqistan Rusdi Muhammad Fahmi Julianto Mulyadi , Priatno Priatno Rangga Pebrianto Robi Aziz Zuama Setiaji Setiaji Suparni Suparni Syaifur Rahmatullah Syaifur Rahmatullah Taransa Agasya Tutupoly W Warjiyono Wati Erawati Wawan Kurniawan Wawan Kurniawan Wawan Kurniawan Windu Gata