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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science
Rahim, Normala
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Computer Science & IT Decision Sciences, Operations Research & Management

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Mengungkap Wawasan: Pendekatan Penemuan Pengetahuan untuk Membandingkan Teknik Pemodelan dalam Topik Riset Kesehatan Digital Rohajawati, Siti; Rahayu, Puji; Misky, Afny Tazkiyatul; Sholehah, Khansha Nafi Rasyidatus; Rahim, Normala; Setyodewi, R.R. Hutanti
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 1 (2024): February 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i1.22058

Abstract

This paper introduces a knowledge discovery approach focused on comparing topic modeling techniques within the realm of digital health research. Knowledge discovery has been applied in massive data repositories (databases) and also in various field studies, which use these techniques for finding patterns in the data, determining which models and parameters might be suitable, and looking for patterns of interest in a specific representational. Unfortunately, the investigation delves into the utilization of Latent Dirichlet Allocation (LDA) and Pachinko Allocation Models (PAM) as generative probabilistic models in knowledge discovery, which is still limited. The study's findings position PAM as the superior technique, showcasing the greatest number of distinctive tokens per topic and the fastest processing time. Notably, PAM identifies 87 unique tokens across 10 topics, surpassing LDA Gensim's identification of only 27 unique tokens. Furthermore, PAM demonstrates remarkable efficiency by swiftly processing 404 documents within an incredibly short span of 0.000118970870 seconds, in contrast to LDA Gensim's considerably longer processing time of 0.368770837783 seconds. Ultimately, PAM emerges as the optimum method for digital health research's topic modeling, boasting unmatched efficiency in analyzing extensive digital health text data.
Systematic literature review of learning model using augmented reality for generation Z in higher education Zulfachmi, Zulfachmi; Rahim, Normala; Rizhan, Wan; Rahayu, Puji; Saputra, Aggry
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1109-1120

Abstract

Higher education is evolving with innovations aimed at enhancing the quality of learning, and one prominent innovation is the integration of augmented reality (AR) technology into the learning process. AR merges real-world and virtual elements in real-time, creating interactive and immersive educational experiences. This technology supports the display and interaction with virtual objects, enhancing engagement and comprehension among students. However, effective integration of AR in higher education faces challenges such as limited technological infrastructure, the need for skilled lecturers, and the adaptation of teaching methods to suit generation Z's learning preferences. Despite their technological proficiency, many educational institutions struggle to optimally implement innovations like AR. This systematic literature review aims to explore and identify an AR-based learning model suitable for generation Z in higher education. Findings suggest that AR technology can significantly enhance learning by offering engaging visualizations and interactive experiences, aligning well with generation Z's characteristics and learning styles. Effective AR implementation requires suitable platforms, such as mobile, desktop, wearable, and projection platforms, each offering unique benefits. By designing AR learning models that cater to generation Z, educational institutions can improve learning outcomes and experiences.
Co-Authors Misky, Afny Tazkiyatul Puji Rahayu Rizhan, Wan Saputra, Aggry Setyodewi, R.R. Hutanti Sholehah, Khansha Nafi Rasyidatus Siti Rohajawati, Siti Zulfachmi, Zulfachmi