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All Journal Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) EQIEN - JURNAL EKONOMI DAN BISNIS INTERACTION: Jurnal Pendidikan Bahasa Jurnal Infortech Jurnal Info Sains : Informatika dan Sains SPEED - Sentra Penelitian Engineering dan Edukasi Informatics and Computer Engineering Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Robi Aziz Zuama
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 Library & Information Science Social Sciences Other

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Analysis of Wireless Local Area Network (WLAN) at Sirajul Falah Vocational School, Parung, Bogor Selawati, Arina; Astuti, Rachmawati Darma; Zumarniansyah, Ainun; Juningsih, Eka Herdit; Zuama, Robi Aziz
INTERACTION: Jurnal Pendidikan Bahasa Vol 11 No 1 (2024): INTERACTION: Jurnal Pendidikan Bahasa
Publisher : Universitas Pendidikan Muhammadiyah (UNIMUDA) Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpendidikanbahasa.v11i1.6934

Abstract

The world of education is currently increasingly prioritizing technology in facilitating and developing the teaching and learning process, such as the Wireless Local Area Network (WLAN) at Sirajul Falah Parung Bogor Vocational School which utilizes technology and computer networks to facilitate the responsibilities and duties of staff, teachers and school principals . Wireless Local Area Network (WLAN) is a computer network that is connected using conducting media (non-cable) using frequencies and standards according to wireless network standards. This research uses observation research methods, interviews and literature studies with the aim of finding out the working system of the Wireless Local Area Network (WLAN) at Sirajul Falah Vocational School Parung Bogor and overcoming the deficiencies found such as poor security systems and user management which often results in a large number of users who illegally used the Wireless Local Area Network (WLAN) at Sirajul Falah Vocational School Parung Bogor which also caused other connection problems. One solution that can be taken to overcome the problems found is to carry out management on the Wireless network, namely activating a different username and password feature for each user who is allowed to access the Wireless Local Area Network (WLAN) network at SMK Sirajul Falah Parung Bogor.
MODEL UNTUK UJI KUALITAS SISTEM INFORMASI UJIAN NASIONAL BERBASIS KOMPUTER TINGKAT SMA & MA Sobari, Irwan Agus; Akbar, Fajar; Zuama, Robi Aziz; Rais, Amin Nur
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.379 KB) | DOI: 10.33480/pilar.v14i2.38

Abstract

UNBK (Ujian Nasional Berbasis Komputer) is a developing national examination application that has claimed the attention and interest of researchers in the development of computer science in the world of education. One of the most recent developments received at UNBK is its usefulness. We propose a successful model model for DeLone & McLean IS to analyze the quality of UNBK at the usefulness of its users. The empirical approach is based on an online survey questionnaire for high school & MA students, the results of feedback received as many as 74 individuals. The results reveal that Information Quality, System Quality and Service Quality are important precedents of user satisfaction, and the importance of user satisfaction will produce significant net benefits. Understanding the importance of the context of UNBK on Net Benefit for users is useful to provide new insights to relevant agencies to implement strategies to retain users or even attract potential adopters. this study provides theoretical and practical implications from the research findings.
NEURAL NETWORK OPTIMIZATION WITH PARTICLE SWARM OPTIMIZATION AND BAGGING METHODS ON CLASSIFICATION OF SINGLE PAP SMEAR IMAGE CELLS Zuama, Robi Aziz; Sobari, Irwan Agus
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (911.626 KB) | DOI: 10.33480/pilar.v16i1.1308

Abstract

In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%
Analysis of Machine Learning Algorithms for Early Detection of Alzheimer’s Disease: A Comparative Study Deni Gunawan; Robi Aziz Zuama; Muhamad Abdul Ghani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 3 (2024): June 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i3.579

Abstract

This study aims to analyze and compare the performance of various machine learning algorithms in predicting Alzheimer's disease based on patient clinical data. The algorithms tested include Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. The dataset used in this research consists of clinical data from patients, encompassing various health parameters. The results indicate that the Decision Tree and Random Forest algorithms provide the best performance, with an overall accuracy of 93%. Random Forest performs slightly better in recall for class 0 but slightly worse in recall for class 1 compared to Decision Tree. Logistic Regression also shows good performance with an overall accuracy of 83%, while K-Nearest Neighbors has the lowest performance with an overall accuracy of 72%. This research offers insights into the effectiveness of various machine learning algorithms in detecting Alzheimer's disease and underscores the importance of selecting the appropriate model based on data characteristics and application needs. For future research, it is recommended to further optimize the model hyperparameters, increase the dataset size, add new relevant features, and combine several models using ensemble learning techniques. External validation and the development of more interpretable models are also crucial to build trust in the use of machine learning in the healthcare field.
An implementation of machine learning on loan default prediction based on customer behavior Robi Aziz Zuama; Nurul Ichsan; Achmad Baroqah Pohan; Mohammad Syamsul Azis; Mareanus Lase
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 01 (2024): Informatika dan Sains , Edition March 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the banking sector, loans have become a key component that steers the economy, encourages company expansion, and directly impacts the growth of a nation's economy. Banks must evaluate borrowers' ability to repay loans given the inherent risks involved in order to reduce the likelihood of default. In particular, machine learning (ML) has shown promise as a revolutionary tool for loan default prediction using advanced methodologies to examine historical data relating to customer behavior, this study investigates the application of machine learning (ML) in forecasting loan outcomes. The results show that XGBoost performs better than other machine learning algorithms, with an accuracy rate of 89%. Random forest and logistic regression come in second and third, respectively, with 88% accuracy. KNN and decision trees come next, both with somewhat lower accuracy rates (87%). By incorporating consumer behavior domain variables, this study fills in the gaps in the literature and offers a more thorough understanding of loan projections. In order to improve model performance and strengthen the predictive power of machine learning algorithms in loan scenarios, further research incorporating trials to optimize algorithm parameters is necessary as financial institutions continue to experience difficulties.
Co-Authors Abdul Latif Matihudin Achmad Baroqah Pohan Achmad Rifai Aditia Darmawan Amin Nur Rais Arina Selawati Astuti, Rachmawati Darma Biktra Rudiarto Deni Gunawan Deni Gunawan Eka Herdit Juningsih Fajar Akbar Hamdun Sulaiman Mareanus Lase Mohammad Syamsul Azis Muhamad Abdul Ghani Nurul Ichsan Ramdhan Saepul Rohman, Ramdhan Saepul Sobari, Irwan Agus Sofian Wira Hadi Susilawati Susilawati Syaifur Rahmatullah Syaifur Rahmatullah Syaifur Rahmatullah Veithzal Rivai Zainal Yuliani, Yuri Yuri Rahayu