Article Per Year (5 Year)
p-Index From 2020 - 2025
3.247
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) WAPFI (Wahana Pendidikan Fisika) Jurnal Spektra JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Jurnal CoreIT 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

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 36 Documents
Search

 1   2   3   4 
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.
Co-Authors - Apriyani Abdilah, Yusup Abdillah, Muhlis Ahmad Agus Setyo Budi Agustin Sri MULYATNI Ahmad Basuki Amanda Larasati 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 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 Mohammad Hasib MUHAMAD RIZKI Nabila, Deeva Nur Tsani Islamiah Oktaviani, Paulina Riska 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 Widyaningrum Indrasari Yuli Rahmawati Yusuf Niko Fitranto Zuhriana Ilmi Hasanah