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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan IJCIT (Indonesian Journal on Computer and Information Technology) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System JIEET (Journal of Information Engineering and Educational Technology) Jurnal Mitra Pendidikan Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal Mantik Indonesian Journal of Networking and Security - IJNS Journal of Artificial Intelligence and Engineering Applications (JAIEA) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Ibnu Alfarobi
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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KLASIFIKASI JENIS KEGAGALAN MESIN MENGGUNAKAN HYPERPARAMETER TUNNING SVM DAN LOGISTIC REGRESSION Ibnu Alfarobi; Sofian Wirahadi; Kudiantoro Widianto
JISAMAR (Journal of Information System, Applied, Management, Accounting and Research) Vol 7 No 3 (2023): JISAMAR (Agustus 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v7i3.771

Abstract

kegagalan peralatan atau mesin dari suatu proses manufaktur sering mengakibatkan kerugian finansial yang besar bagi bisnis. Berdasarkan tingkat jenis kegagalan mesin, model dengan penurunan kinerja adalah sama untuk berbagai jenis tetapi akan berbeda untuk faktor manusia, mesin dan lingkungan. Dengan mengevaluasi efek dan penanganan yang berbeda, menjadi sangat penting untuk dapat memprediksi faktor – faktor yang menentukan jenis kegagalan mesin. Dengan memanfaatkan model machine learning, pengklasifikasi diharapkan mampu memprediksi suatu jenis kegagalan dari mesin. Pada penelitian ini, memaksimalkan untuk mendapatkan hasil model dengan memanfaatkan hyerparameter yang di tuning pada algoritma Support Vector Machine dan Logistic Regression. Metode penanganan data seperti teknik Smote dan data preprocessing lainnya digunakan. Model menghasilkan perbedaan tingkat akurasi yang cukup besar yaitu 23%. Sehingga dapat disimpulkan model dengan menggunakan algoritma SVM bisa lebih baik dalam memprediksi jenis kegagalan mesin pada tingkat keakuratan sebesar 90% dibandingkan dengan model menggunakan algoritma Logistic Regression yang hanya menghasilkan keakuratan sebesar 67%.
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
Comparison of Random Forest and K-Nearest Neighbors in Heart Disease Prediction Erni; Ibnu Alfarobi; Wawan Kurniawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1942

Abstract

Heart disease is one of the leading causes of death worldwide, with a death toll reaching 17.9 million cases annually according to the World Health Organization (WHO) and a prevalence of 1.5% in Indonesia. This high mortality rate demonstrates the importance of early detection and accurate prediction to prevent more serious complications. The development of artificial intelligence technology, particularly machine learning, offers a new approach in the medical field through the ability to analyze clinical data quickly and efficiently. This study was conducted to compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in predicting heart disease using a clinical dataset from Kaggle containing 20 samples and 9 attributes related to the patient's physiological condition. The parameter optimization process in both algorithms was carried out using grid search techniques with cross-validation to obtain the best model that can perform optimally on a limited dataset. Performance evaluation was carried out using accuracy, recall, and precision metrics to comprehensively measure the quality of the model predictions. The results of the study showed that the Random Forest algorithm provided superior performance with an accuracy of 0.75, a recall of 0.88, and a precision of 0.86, compared to KNN which only achieved an accuracy of 0.50, a recall of 0.67, and a precision of 0.67. These findings indicate that Random Forest is more effective in identifying the presence of heart disease, especially in terms of sensitivity to positive cases and prediction consistency. Thus, Random Forest has the potential to be a more appropriate algorithm for implementation in machine learning-based clinical decision support systems, to support the process of diagnosing heart disease more accurately and efficiently.
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.
Co-Authors Achmad Baroqah Pohan Ade Suryanto Algif Hari Prameswara Amin Nur Rais Andi Setiawan Andre Gusti Hermawan Andriansyah, Fachmi Entin Sutinah, Entin Erni Fabriyan Fandi Dwi Imaniawan Fachmi Andriansyah Fandy Fandy Fandy, Fandy Hadi, Sofian Wira Joko Dwi Mulyanto Kudiantoro Widianto, Kudiantoro Mahmud Safudin Muhamad Satrio Zulqistan Rusdi Mustofa Mustofa Risma Fauziahti Sofian Wira Hadi Taransa Agasya Tutupoly Tutupoly, Taransa Agasya Warjiyono Warjiyono Wawan Kurniawan Wawan Kurniawan Wawan Kurniawan Zulqistan Rusdi, Muhamad Satrio