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All Journal Journal of Innovative and Creativity Tamilis Synex: Multidimensional Collaboration
Ulfa Laela Rambega
Universitas Handayani Makassar
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Analisis Kualitatif Infrastruktur Jaringan Komputer dan Dukungannya terhadap Penggunaan Cloud Computing di SMPN 19 Kajang Bulukumba Ulfa Laela Rambega
Journal of Innovative and Creativity Vol. 3 No. 1 (2023)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v3i1.1547

Abstract

This study aims to analyze the condition of the computer network infrastructure and the extent to which the infrastructure supports the implementation of cloud computing at SMPN 19 Kajang Bulukumba. This study uses a descriptive qualitative approach to describe and analyze the condition of the computer network infrastructure and its support for the implementation of cloud computing at SMPN 19 Kajang Bulukumba, with key informants such as the Principal, IT staff, ICT teachers, and school operators selected through purposive sampling techniques. Data were collected through semi-structured interviews, direct observation of network facilities and cloud-based learning activities, and documentation such as RPS and training reports. Data analysis was carried out using the Miles and Huberman model through inductive reduction, presentation, and drawing conclusions, with data validity strengthened through triangulation and member checks. This study found that the computer network infrastructure at SMPN 19 Kajang Bulukumba has been built since 2019 with the support of the Education Office, including LAN networks, fiber optic internet connections, and supporting devices, although there are still obstacles in Wi-Fi coverage and hardware quality. Cloud computing such as Google Classroom and Google Drive have been used since the pandemic, but their implementation has not been maximized due to the limitations of teachers' digital literacy and students' devices. Observations show that some computers are outdated, Wi-Fi distribution is uneven, and server rooms are not equipped with good cooling systems and cable management. Although school documentation reflects digital development efforts, teacher participation in training is still limited, so the existing infrastructure only supports cloud computing to a limited extent and requires further improvement.
Mengenal Dasar-Dasar Teknik Informatika di SMPN 19 Bulukumba dalam Mempersiapkan Lembaga Era Digital Ulfa Laela Rambega
Tamilis Synex: Multidimensional Collaboration Vol. 2 No. 1 (2024): Tamilis Synex: Multidimensional Collaboration
Publisher : CV Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/tls.v2i1.379

Abstract

The aim of preparing the work is to get to know the basics of informatics techniques at SMPN 19 Bulukumba in preparing institutions for the digital era. This research uses qualitative methods to understand the teaching of informatics techniques at SMPN 19 Bulukumba through observation, interviews and documentation. Data were analyzed thematically to identify and interpret key themes related to teaching methods and use of technology. This research provides a comprehensive picture of student preparation for the digital era and can be a reference for other schools. The result of this work is that teaching informatics engineering at SMPN 19 Bulukumba prepares students for the digital era by teaching input devices such as keyboard, mouse, scanner, microphone, and webcam, as well as data processing components such as CPU, RAM, and motherboard. The teacher explains the function and use of output devices such as monitors, printers, speakers and projectors, increasing students' understanding of how to display processed information. The material taught covers the latest theories, providing students with in-depth understanding and relevant practical skills. This teaching improves students' digital skills, the quality of education, and their readiness to face future technological challenges. As a result, students are better prepared to adapt and innovate in the ever-evolving digital era.
Optimalisasi Jaringan Neural untuk Prediksi Keberhasilan Mahasiswa Berdasarkan Data Historis Akademik Ulfa Laela Rambega
Tamilis Synex: Multidimensional Collaboration Vol. 1 No. 04 (2023): Tamilis Synex: Multidimensional Collaboration
Publisher : CV Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/tls.v1i04.380

Abstract

This research also aims to explore how various parameter optimization techniques influence the effectiveness of such models in a realistic academic environment. A literature review in research on optimizing neural networks for predicting student success involves collecting and analyzing literature on data analysis techniques and neural networks in education. This process uses major academic databases to find relevant and significant sources, which are then integrated to define research gaps and formulate research questions. Analysis of the literature review resulted in an in-depth understanding of existing methodologies and identification of trends, as well as revealing gaps in existing research that this study sought to address. Critiques of existing literature also inform the development of more robust and innovative approaches to using neural networks in academic prediction. The results of this work optimize neural networks for predicting student success overcoming challenges such as inconsistent data and overfitting through advanced data preprocessing techniques and model regulation such as dropout and early stopping. The use of cross-validation helps objectively validate the effectiveness of the model. The application of neural network capacity theory and the bias-variance tradeoff principle ensures that the model can generalize well to new data. The results of this research provide a strong basis for data-based educational policy making, increasing the likelihood of student success. The implementation of this neural network model shows broad potential for improving educational processes through predictive technology.
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