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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) WAPFI (Wahana Pendidikan Fisika) Jurnal Spektra JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Jurnal CoreIT Jurnal Ilmiah Pendidikan Fisika PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Mitra Teras: Jurnal Terapan Pengabdian Masyarakat Lontar Physics Today Prosiding Seminar Nasional Unimus
Bambang Heru Iswanto
Universitas Negeri Jakarta
Humanities Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Health Professions Nursing Physics Public Health Social Sciences Other

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DENTIFICATION OF VOLCANIC EARTHQUAKE TYPES BASED ON SEISMIC RECORDING DATA FROM MOUNT SINABUNG USING PRINCIPAL COMPONENT ANALYSIS: IDENTIFIKASI JENIS GEMPA VULKANIK BERDASARKAN DATA REKAMAN SEISMIK PADA GUNUNG SINABUNG MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS Bagas Anwar Arif Nur; Bambang Heru Iswanto; Mohammad Hasib; Ahmad Basuki
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA11

Abstract

Volcanic eruptions are natural events that have the potential for significant damage to humans and the environment. Identifying the type of volcano earthquake is key in disaster risk mitigation by providing information on the process and the location of magma activity beneath the volcano. In this research, we propose an approach using Principal Component Analysis (PCA) to identify types of volcanic earthquakes based on seismic recording data. Identification begins by reducing feature dimensions using Principal Component Analysis (PCA). The PCA results were then clustered and then evaluated Silhoutte Score, ARI, CH-Indeks, DB-Indeks. Experiments were carried out using recorded data totaling 329 samples. For each recording, feature extraction was carried out in the form of statistical features, entropy features and shape features with a total of 16 features in the time and frequency domains. PCA results on the two main components PC1 explained 49.2741% and PC2 24.5507% of the data variance and evaluation results using Silhouette Score were equal to 0.53, ARI 0.8, CH-Index 529.34, and DB- Index 0.6
CLASSIFICATION OF CHICKEN EGG SHELL QUALITY USING EFFICIENTNET BASED ON DIGITAL IMAGES: KLASIFIKASI KUALITAS CANGKANG TELUR AYAM MENGGUNAKAN EFFICIENTNET BERBASIS CITRA DIGITAL Hernanda Khoiriyah Putri; Bambang Heru Iswanto; Haris Suhendar
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA13

Abstract

Cracks in eggshells often occur during the distribution process, both visible and invisible to the naked eye. Cracks in eggshells are a serious concern as they can lead to contamination and health risks for consumers. This study classifies cracks in chicken eggshells based on digital images using a Convolutional Neural Network (CNN)-EfficientNet. The experiment was conducted with a sample of 300 egg images in three conditions: good, cracked, and broken, with 100 images for each condition. The images were captured using a calibrated DSLR camera with a stable background. Data preprocessing included cropping, resizing, and augmentation. The data was split in an 80:20 ratio. Hyperparameters used the Adam optimizer with 50 iterations and a batch size of 32. Model performance was evaluated using loss function metrics (sparse categorical crossentropy), accuracy, and confusion matrix. Classification using EfficientNet-B0 to B3 resulted in accuracy, precision, recall, and F1-Score of 94.52%, 95.75%, 95.71%, and 95.73%; 94.05%, 94.09%, 94.05%, and 94.02%; 94.52%, 94.56%, 94.52%, and 94.54%; and 97.14%, 97.19%, 97.14%, and 97.15%, respectively. Based on the results, classification using EfficientNet shows improved performance as the model complexity increases. The findings suggest that images of eggshell cracks can be utilized for egg quality identification and can be developed for chicken egg quality classification.
MONITORING THE GROWTH OF BIOLOGICAL AGENT FUNGI IN ORGANIC MATERIAL MIXTURES WITH VARIED COMPOSITIONS USING AN ELECTRONIC NOSE: MONITORING PERTUMBUHAN JAMUR AGENSIA HAYATI DALAM CAMPURAN BAHAN ORGANIK DENGAN VARIASI KOMPOSISI MENGGUNAKAN ELECTRONIC NOSE Indriani Lutfiyyatunnisa; Bambang Heru Iswanto; Agustin Sri Mulyatni
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA32

Abstract

The use of a mixture of biological agents and organic matter waste can be used as an alternative to chemical fertilizers as an environmentally friendly bioinsecticide product. However, the presence of biological agent organisms in organic materials and susceptible to contamination in certain compositions is difficult to detect early. In this study, an electronic nose (e-nose) is used to detect the presence of three growing fungal mycelium and contamination in a mixture of organic materials based on their aroma patterns. As a first step, a selection of features will be made that will be used to build a detection model. Features are extracted from e-nose response data taken from 28 samples of a mixture of bio-agent fungi with organic materials consisting of 9 variations in composition. E-nose used version 3 with a total of 16 Taguci type sensors. Experiments were conducted with three samples of biological fungi each mixed with bagasse organic matter with variations in the composition of dilutions 10(−1), 10(−2), and 10(−3)as well as dilution volumes of 2 ml, 4 ml, and 6 ml as well as control samples containing bagasse organic matter alone. There were a total of 28 samples with three repetitions with different sampling times. For principal component analysis, data processing begins with data pre-processing by performing baseline correction and normalization. Furthermore, data analysis is carried out using Principal Component Analysis (PCA) with descriptive statistical features of the minimum value. From the results of the analysis using PCA, the first two main components explained about 67.35% and the second main component explained about 18.56% of the data variation.
IDENTIFICATION OF TRICHODERMA SP. FUNGAL POPULATION LEVELS IN ORGANIC MATERIALS USING ELECTRONIC NOSE (E-NOSE): IDENTIFIKASI TINGKAT POPULASI JAMUR TRICHODERMA SP. PADA BAHAN ORGANIK MENGGUNAKAN ELECTRONIC NOSE (E-NOSE) Muhamad Rizki; Bambang Heru Iswanto; Agustin Sri Mulyatni
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA35

Abstract

The use of synthetic pesticides for pest and disease control in crops raises significant health and environmental concerns. As an eco-friendly alternative, biological control using biofungicides such as Trichoderma sp. has become increasingly important. Effective biofungicide production necessitates precise determination of Trichoderma sp. population levels. This study aims to identify Trichoderma sp. population levels (105, 106, and 107 CFU/ml) based on aroma data from an Electronic Nose (E-nose). The experiment began with the rejuvenation of Trichoderma sp. samples, followed by inoculation on three organic materials (sugarcane bagasse, peat, and bran). Gas sensor data were collected using the E-nose after a 7-day incubation period in glass bottles. Feature extraction, using statistical and time domain methods, was performed to identify the fungal population. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) demonstrated that the E-nose sensor responses effectively differentiated Trichoderma sp. population levels based on the type of organic material. For the three organic materials, PCA revealed that PC1 accounted for 79.76% and PC2 for 13.49% of the variance, while LDA showed LD1 accounted for 62.54% and LD2 for 28.22% of the variance. Specifically, for the sugarcane bagasse, PCA indicated PC1 at 79.77% and PC2 at 13.49%, with LDA showing LD1 at 60.93% and LD2 at 28.58%.
The Impact of Mobile Learning on Physics Education: A Systematic Literature Review Siswanto, Dwi Ambar Cahyaningtias; Iswanto, Bambang Heru; Rahmawati, Yuli
Jurnal Penelitian & Pengembangan Pendidikan Fisika Vol. 11 No. 1 (2025): JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika), Volume 11 Issue
Publisher : Program Studi Pendidikan Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/1.11102

Abstract

Mobile learning has become a significant medium in the educational landscape, especially with the increased usage of smartphones among students and educators. This Systematic Literature Review (SLR) aims to explore the impact of mobile learning on the transformation of physics education. This study obtained 50 out of 200 articles selected based on their relevance to the theme of mobile learning in physics education, citation metrics, and publication date between 2019 and 2024. The review highlights a significant increase in mobile learning research, with the rise occurring in 2020 due to the COVID-19 pandemic, which emphasized the growing potential of mobile learning for remote education. Mobile learning enhances the accessibility, engagement, and effectiveness of physics education by making the learning process more interactive, fostering student independence, and improving learning outcomes. The review also identifies improvements in students' critical thinking and problem-solving skills as key benefits of mobile learning in physics. However, it also highlights the necessity of regulations to prevent misuse and safeguard academic integrity. Practical recommendations for educators include integrating mobile learning with project-based approaches to improve conceptual understanding and student engagement in physics. This study suggests that mobile learning has a transformative role in physics education, opening up new avenues for innovation and further research.
Learning Module for Electricity and Magnetism with Augmented Reality to Enhance Students' Conceptual Understanding Abdilah, Yusup; Iswanto, Bambang Heru; Sugihartono, Iwan
WaPFi (Wahana Pendidikan Fisika) Vol 10, No 1 (2025): WaPFi (Wahana Pendidikan Fisika) February 2025
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/wapfi.v10i1.80196

Abstract

This research aims to produce a learning module for Electricity and Magnetism with mobile augmented reality to Enhance Students' Conceptual Understanding. This research is a type of research design. The development model used is the ADDIE model, which consists of five development phases, namely analysis, design, development, implementation, and evaluation. The results of this study are learning modules that have been installed with QR-code and ModulAR apps that run on the Assmblr Edu Application. The developed materials received highly favorable ratings from experts, with content experts assigning an average score of 87% and media experts providing an average score of 86%. Teacher and student feedback indicated an average satisfaction rate of 88% and 87%, respectively. Then, the module and ModulAR apps were implemented at SMKN 1 Cikarang Pusat, which involved 36 students. The use of learning modules with mobile augmented reality based on the Inkuiri approach is also effective in enhancing students' Conceptual Understanding. It can be seen from the average n-gain score of 0.59, included in the moderate category. Furthermore, this research provides valuable insights into the potential of AR for enhancing physics education and offers practical recommendations for future research endeavors in this area.
Digital Storytelling sebagai Media Pembelajaran Fisika Berbasis Proyek pada Materi Difraksi Celah Ganda Maulana, Rafy; Iswanto, Bambang Heru; Fitri, Upik Rahma
Lontar Physics Today Vol 2, No 3 (2023): November 2023
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/lpt.v2i3.16650

Abstract

Dalam menyiapkan peserta didik untuk tujuan pembelajaran abad 21, guru dan administrator harus merangka pembelajaran terintegrasi teknologi dalam konteks sosial konstruktivisme. Digital storytelling adalah satu mode pembelajaran abad 21. Digital storytelling adalah kombinasi storytelling tradisional dengan multimedia digital, termasuk gambar, animasi, audio rekaman, dan musik yang dibuat sekitar 2-5 menit dalam bentuk video. Digital storytelling memiliki dampak pada keterampilan mengumpulkan informasi, pemecahan masalah, dan sikap terhadap kolaborasi dengan pemangku kepentingan pendidikan. Tujuan penelitian ini adalah mengembangkan digital storytelling yang layak digunakan sebagai media pembelajaran fisika berbasis proyek pada materi difraksi celah ganda. Metode penelitian yang digunakan adalah Penelitian danPengembangan (Research and Development). Secara spesifik, langkah-langkah pengembangan model mengikuti alur desain instruksional 4-D (Define, Design, Develop, and Disseminating) yang dimodifikasi menjadi desain instruksional 3-D (Define, Design, Develop). Kata kunci: Digital storytelling, Pembelajaran Fisika Berbasis Proyek, Difraksi Celah Ganda
Adaptive Digital Modules and Artificial Intelligence in Physics Education: Bibliometric Analysis Using Scopus Data Muhyi, Abdul; Iswanto, Bambang Heru; Rahmawati, Yuli; Wibowo, Firmanul Catur
Jurnal Ilmiah Pendidikan Fisika Vol 9, No 3 (2025): OCTOBER 2025
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jipf.v9i3.15607

Abstract

This research aims to analyze global trends in the development of adaptive digital modules based on artificial intelligence (AI) in fluid physics learning through a bibliometric approach. As the 21st-century demand for critical thinking skills and technological understanding grows, AI-based modules are becoming increasingly relevant in supporting differentiated learning. However, a comprehensive mapping of scientific publications in this field remains limited, leading to gaps in understanding current research directions and potential collaboration opportunities. This research uses bibliometric software to identify publication patterns, researcher collaboration networks, and key trends in the development of AI-based physics learning modules. The data used is taken from the Scopus database and includes related publications from 2019 to 2024. The research method involves two main steps: data collection of publications using the keywords "adaptive digital module AND AI AND physics education" and metadata analysis to identify research trends and researcher collaborations. The analysis results show a significant increase in the number of publications on AI-based adaptive learning, particularly from countries such as China, the United States, and India. The findings also indicate that the main research topics include the development of AI technology to support adaptive learning, as well as the effectiveness of digital modules in the context of physics education. This research reveals the importance of international collaboration in the development of AI-based adaptive modules and recommends the use of other bibliometric methods as well as the expansion of database coverage for future research. Furthermore, this research provides important insights for the development of more effective learning strategies through AI technology in physics education.
The Implementation of Spectrophotometers in STEM-Based Physics Education: A Bibliometric Analysis Nursabrina, Fakhira; Iswanto, Bambang Heru; Nasbey, Hadi; Wibowo, Firmanul Catur
Jurnal Ilmiah Pendidikan Fisika Vol 9, No 3 (2025): OCTOBER 2025
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jipf.v9i3.15558

Abstract

The paucity of scientific research focusing on the integration of spectrophotometers within STEM-based physics education highlights a significant gap in the existing literature. This underexplored area holds considerable potential, as the application of spectrophotometric tools in instructional settings may enhance students’ scientific competencies and critical thinking skills. This study aims to address an unresolved research gap by conducting a comprehensive data analysis, combining various analytical techniques to obtain more accurate and comprehensive results regarding the implementation of spectrophotometry in STEM-based physics education. Bibliometric analysis was conducted on articles, conference papers, book chapters, and reviews. The research method used in this study combines quantitative analysis and descriptive methods. The results showed an annual growth rate of 29.24% with an average of 4,568 citations per document. China dominated journal publications with 71 articles, while Indonesia contributed 35 articles. This indicates that, especially in the realm of education, this topic is rarely explored in Indonesia. Data show that research on STEM and spectrophotometry continues to increase, except in 2022, when it experienced a significant decline, followed by a notable increase in 2024, compared to previous years, with 119 documents. Furthermore, WordCloud and co-occurrence network data indicate that STEM and spectrophotometry in physics education are under-researched topics. The topics most frequently researched in these articles are non-educational, such as spectrophotometry for humans, animals, or metabolism. This research recommends that the topic of spectrophotometry in STEM-based physics education should be further explored. Practically, these insights may serve as a foundation for educators and curriculum developers to begin incorporating laboratory tools, such as spectrophotometers, into physics instruction to foster 21st-century skills—particularly scientific literacy, problem-solving, and students' critical thinking abilities.  
Trends in the Integration of Augmented Reality and Artificial Intelligence in Science Education: A Bibliometric Analysis, 2017–2026 Andriani, Dhian; Budi, Esmar; Iswanto, Bambang Heru; Wibowo, Firmanul Catur; Putra, Rico Pratama
Jurnal Ilmiah Pendidikan Fisika Vol 10, No 1 (2026): MARCH 2026
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jipf.v10i1.18029

Abstract

Recent digital advancements have transformed education, making Artificial Intelligence (AI) and Augmented Reality (AR) essential tools for improving the quality of science learning, as demonstrated through bibliometric analysis. This study investigates the evolution of publication trends, identifies the most productive journals, authors, institutions, and countries, determines the most influential publications based on citation counts, analyzes dominant research themes and keyword patterns, and traces the development of research themes in science education from 2017 to 2026. A total of 67 Scopus-indexed documents were selected through purposive sampling. Publication metadata, including titles, abstracts, keywords, citations, and affiliations, were analyzed in RStudio using the bibliometrix package. The findings indicate a substantial increase in publications, with 66 of the 67 articles (approximately 99%) published after 2020 and a peak in 2025. Citation analysis identified the most influential publications, while keyword analysis revealed dominant themes such as student engagement, adaptive learning, and AI-AR-based digital learning media. This study delineates the intellectual structure of AI and AR research in science education and offers practical recommendations for the development of technology-based adaptive learning models. This study investigates (1) the evolution of publication trends on AR and AI in science education from 2017 to 2026; (2) the most productive journals, authors, institutions, and countries; (3) the most influential publications based on citation analysis; (4) dominant research themes and keyword patterns; and (5) the evolution of research themes over time. This study reviewed 67 documents from the Scopus database covering the period 2017 to 2026 and analyzed them using RStudio. The findings indicate a substantial increase in publications addressing the integration of AI and AR in science education over recent years. The analysis also reveals that existing studies consistently highlight the potential of AI and AR to enhance student engagement and learning effectiveness.  
Co-Authors - Apriyani Abdilah, Yusup Abdillah, Muhlis Ahmad Agus Setyo Budi Agustin Sri MULYATNI Ahmad Basuki Amanda Larasati Andriani, Dhian Anggara Budi Susila Anggiyansah Sitompul Anwar Basyari Bagas Anwar Arif Nur Cantika Dewi Mayangsari Delila Septiani Dwi Putri Delina, Mutia Devi Suhartati Wulandari Dewi Nurhasanah Dwi Septiani Dwi Susanti Dwi Susanti Esmar Budi Eulis Arumsari Fajar Hardoyono Firmanul Catur Wibowo Hadi Nasbey Haris Suhendar Haris Suhendar Hernanda Khoiriyah Putri I Dewa Putu Leo Parlin Indriani Lutfiyyatunnisa Intan Rafannisa M. Irene Tasya Angelia Iwan Sugihartono Jondya, Aisha Gemala Kuwat Triyana Lari Andreas Sanjaya Maulana, Rafy Mohammad Hasib MUHAMAD RIZKI Muhyi, Abdul Nabila, Deeva Nur Tsani Islamiah Nursabrina, Fakhira Oktaviani, Paulina Riska Putra, Rico Pratama Rachman, Muhammad Aulia Rahmawati Rahmawati Risnandar, Risnandar Safitri, Sifa Nur Shahnaz Nadia Siswanto, Dwi Ambar Cahyaningtias Siti Idayatun Solihatul Afiah Sukmaningsih, Rania Virda Sunaryo Sunaryo Sunaryo Sunaryo Sunaryo Sunaryo Suri Mutiha Sitompul Upik Rahma Fitri, Upik Rahma Widyaningrum Indrasari Yuli Rahmawati Yusuf Niko Fitranto Zuhriana Ilmi Hasanah