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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

Author-ID : 2550287

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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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

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

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%

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 Susilawati, Susilawati Syaifur Rahmatullah Syaifur Rahmatullah Syaifur Rahmatullah Veithzal Rivai Zainal Yuliani, Yuri Yuri Rahayu

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Journal : Jurnal Pilar Nusa Mandiri