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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 Computer Science (CO-SCIENCE) 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) Indonesian Community Service Journal of Computer Science (IndoComs)
Robi Aziz Zuama
Universitas Bina Sarana Informatika
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science 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.
Perancangan Sistem Informasi Pengolahan Data Member Pada Rai Fitness Sukabumi Yuliani, Yuri; Rahayu, Yuri; susilawati, susilawati; Zuama, Robi Aziz
Informatics and Computer Engineering Journal Vol 1 No 2 (2021): Periode Agustus 2021
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v1i2.475

Abstract

The processing of member data at Rai Fitness Sukabumi is still done manually and some problems in the data processing system used today include the data entry process that is less efficient and optimal and the recording of acceptance and addition of members is still not recorded and good. This final research method is to conduct research, observation, interviews with the Front Office Rai Fitness Sukabumi, and literature study. Then the system design is carried out using UML (Unified Modeling Language) Providing convenience for the Front Office in the process of inputting data on registration and adding members, facilitating the performance of the Front Office in making journals and reports on registration, and adding members. The establishment of a data recording application that is stored properly and neatly in one database. Information system design. Member data processing for registration and member extension aims to facilitate the Front Office in carrying out data processing on registration and addition of members. Report makers are needed faster than before, namely, presenting the report requires more time and accuracy. Keywords : Information System Design, Member Data Processing
Analysis of Student Academic Performance Using Random Forest and Support Vector Machines Agung, Galih Mifta; Zuama, Robi Aziz; Budi, Eko Setia
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10123

Abstract

Assessing student academic performance objectively remains a challenge at SMP Negeri 16 Bogor due to diverse internal and external factors in student records. This study aims to compare the classification performance of the Random Forest and Support Vector Machine (SVM) algorithms using a dataset of 403 students containing demographic, socioeconomic, and school-related attributes. Although the attributes are not traditional academic indicators (e.g., assignment or exam scores), they are used to explore whether non-academic features can contribute to predictive models. Following data preprocessing—handling missing values, encoding categorical variables, and managing class imbalance—both algorithms were evaluated using accuracy, precision, recall, and confusion matrix analysis. Results show that SVM outperforms Random Forest with 78.00% accuracy, 89.98% precision, and 70.24% recall. These findings indicate that SVM is more robust for imbalanced classification tasks and can provide useful insights even when academic-performance labels are predicted from non-academic attributes.
PEMANFAATAN GOOGLE FORM DALAM PENDATAAN KESEHATAN BALITA DI LINGKUNGAN POSYANDU MAWAR MELATI Robi Aziz Zuama; Hamdun Sulaiman; Ahmad Fauzi; Minda Septiani
Indonesian Community Service Journal of Computer Science Vol. 2 No. 1 (2025): Periode Januari 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v2i1.8227

Abstract

Health data collection of toddlers that is still done manually at Posyandu Mawar Melati is often time-consuming, inefficient, and prone to errors in recording. . This unstructured data management is a major obstacle in providing optimal health services to toddlers in the neighborhood. In addition, the manual recording process makes it difficult to store and analyze data, making it difficult for posyandu officers to compile accurate and timely health reports. As a solution to these problems, this community service activity focuses on the application of digital technology, namely the use of Google Forms for toddler health data collection. Google Form was chosen because it is a tool that is easy to use, free, and can be accessed from various devices, making it very suitable for use in the posyandu environment. Through the use of Google Form, the process of collecting data on the health of toddlers can be done more quickly and accurately. Digitally collected data also makes it easier for posyandu officers to process information, so that the results can be analyzed and stored better. In addition, this technology enables faster and more efficient data processing, so that officers can compile reports in real-time and reduce the risk of data loss. Pendataan kesehatan balita yang masih dilakukan secara manual pada Posyandu Mawar Melati sering memakan waktu, tidak efisien, dan rawan kesalahan dalam pencatatan. Pengelolaan data yang kurang terstruktur ini menjadi kendala utama dalam memberikan layanan kesehatan yang optimal kepada balita di lingkungan tersebut. Selain itu, proses pencatatan manual mempersulit penyimpanan dan analisis data, sehingga menyulitkan petugas posyandu dalam menyusun laporan kesehatan yang akurat dan tepat waktu. Sebagai solusi atas permasalahan tersebut, kegiatan pengabdian masyarakat ini berfokus pada penerapan teknologi digital, yaitu penggunaan Google Form untuk pendataan kesehatan balita. Google Form dipilih karena merupakan alat yang mudah digunakan, gratis, dan dapat diakses dari berbagai perangkat, sehingga sangat cocok untuk digunakan dalam lingkungan posyandu. Melalui pemanfaatan Google Form, proses pendataan kesehatan balita dapat dilakukan dengan lebih cepat dan akurat. Data yang terkumpul secara digital juga memudahkan petugas posyandu dalam mengolah informasi, sehingga hasilnya dapat dianalisis dan disimpan dengan lebih baik. Selain itu, teknologi ini memungkinkan pengolahan data yang lebih cepat dan efisien, sehingga petugas dapat menyusun laporan secara real-time dan mengurangi risiko kehilangan data.
Co-Authors Abdul Latif Matihudin Achmad Baroqah Pohan Achmad Rifai Aditia Darmawan Agung, Galih Mifta Ahmad Fauzi Amin Nur Rais Arina Selawati Astuti, Rachmawati Darma Biktra Rudiarto Deni Gunawan Deni Gunawan Eka Herdit Juningsih Eko Setia Budi Fajar Akbar Hamdun Sulaiman Mareanus Lase Minda Septiani 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