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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Data and Information Systems Green Intelligent Systems and Applications Makara Journal of Technology Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal
Pamungkasari, Panca Dewi
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health

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Wavelet Transform on Digital Rainbow Hologram based on Spectral Compression for Quality Enhancement in 3D Display Media Darusalam, Ucuk; Pamungkasari, Panca Dewi
Makara Journal of Technology Vol. 23, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A digital rainbow hologram (DRH) is a potential next-generation three-dimensional display media for the development of modern and smart electronics devices. It is one of the methods that can support the characteristic whereby a realistic display media occupies the space that the real object would have occupied. Since a rainbow hologram records a large amount of spatial or temporal frequency component from the object that represents the rainbow spectrum, a large amount of information needs to be decoded digitally. In this paper, to reconstruct a DRH, we propose a novel method based on the modulation of red, green, and blue spectral components of light by wavelet transform (WT) in the recording and reconstruction processes, which we digitally simulated in a computer using an algorithm. In the simulations, continuous WT (CWT) was based on Haar, Daubechies, Meyer, and Coiflet wavelets with a level set to be two. Based on the results of simulations using CWT, the optimum distance between object and hologram was 30 cm, and the maximum compression was 88.55%, which was achieved with Meyer wavelet. Moreover, optimal de-noising and optimal localization of spatial frequency component based on red, green, and blue spectral components were also achieved using the proposed method.
Artificial Neural Network for Benchmarking the Dimensional Accuracy of the PLA Fused Flament Fabrication Process Setiawan, Kevin Stephen; Tanaji, Irvantara Pradmaputra; Permana, Ari; Akbar, Hafizh Naufaly; Prihatmaja, Dhonadio Aurell Azhar; Normasari, Nur Mayke Eka; Rifai, Achmad Pratama; Pamungkasari, Panca Dewi
Green Intelligent Systems and Applications Volume 4 - Issue 2 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i2.522

Abstract

Fused Deposition Modeling (FDM) is an additive manufacturing technique that uses a 3D printer to extrude molten filament through a nozzle, which moves along the X, Y, and Z axes to create parts with the desired geometry. FDM offers numerous advantages, especially for producing parts with complex shapes, due to its ability to enable rapid and cost-effective manufacturing compared to traditional methods. This study implemented an Artificial Neural Network (ANN) to optimize process parameters aimed at minimizing dimensional inaccuracies in the FDM process. Key parameters considered for optimization included the number of shells, infill percentage, and nozzle temperature. The ANN utilized three algorithms: Scaled Conjugate Gradient, Bayesian Regularization, and Levenberg-Marquardt. Model performance was evaluated based on dimensional deviations along the X and Y axes, with a hidden layer of 25 neurons. Among the algorithms, Scaled Conjugate Gradient provided the most accurate results in minimizing dimensional errors.
Twitter Sentiment Analysis of Mental Health Issues Post COVID-19 Pamungkasari, Panca Dewi; Ningsih, Sari; Rifai, Achmad Pratama; Nandila, Alisyafira Sayyidina; Nguyen, Huu Tho; Penchala, Sathish Kumar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.588

Abstract

The Coronavirus Disease 2019 (COVID-19) impacted many aspects of daily life, including mental health, as some individuals struggled to adjust to the rapid changes brought on by the pandemic. This paper investigated sentiment analysis of Twitter data following the COVID-19 pandemic. Specifically, we analyzed a large corpus of tweets to understand public sentiment and its implications for mental health in the post-pandemic context. The Naïve Bayes and Support Vector Machine (SVM) classifiers were used to categorize tweets into positive, negative, and neutral sentiments. The collected tweet data samples showed that 38.35% were neutral, 32.56% were positive, and 29.09% were negative. Results using the SVM method showed an accuracy of 84%, while Naïve Bayes achieved 80% accuracy.
Comparison Of Feature Extraction Techniques For Long Short-Term Memory Models In Indonesian Automatic Speech Recognition Armaisya, Dimas Dwi; Pamungkasari, Panca Dewi; Rifai, Achmad Pratama; Sholihati, Ira Diana; Gopal Sakarkar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.605

Abstract

Automatic Speech Recognition (ASR) faced challenges in accuracy and noise robustness, particularly in Bahasa Indonesia. This research addressed the limitations of single feature extraction methods, such as Mel-Frequency Cepstral Coefficients (MFCC), which were sensitive to noise, and Relative Spectral Transform - Perceptual Linear Predictive (RASTA-PLP), which was less effective in frequency representation, by proposing a hybrid approach that combined both techniques using Long Short-Term Memory (LSTM) models. MFCC enhanced spectral accuracy, while RASTA-PLP improved noise robustness, resulting in a more adaptive and informative acoustic representation. The evaluation demonstrated that the hybrid method outperformed single and non-extraction approaches, achieving a Character Error Rate (CER) of 0.5245 on clean data and 0.8811 on noisy data, as well as a Word Error Rate (WER) of 0.9229 on clean data and 1.0015 on noisy data. Although the hybrid approach required longer training times and higher memory usage, it remained stable and effective in reducing transcription errors. These findings suggested that the hybrid method was an optimal solution for Indonesian speech recognition in various acoustic conditions.
Grid-Based Ship Density Analysis and Anomaly Detection for Ship Movements Monitoring at Tanjung Priok Port Ikhsan, Muhammad Ramadhan; Pamungkasari, Panca Dewi; Purbantoro, Babag; Sholihati, Ira Diana; Farahdinna, Frenda; Sumantyo, Josaphat Tetuko Sri; Heezen, Damy Matheus
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1367

Abstract

Indonesia, as a maritime country, depends on ports to support inter-island transport and a smooth regional economy. So, the awareness of knowing the marine status with various platforms is needed. This research distinguishes itself from several previous studies on ship movement detection by concentrating specifically on anomalies in ship movement within areas of high traffic density. This research proposes to find out the ship density area using the grid technique and identify the anomalies that have occurred, as information on ship movements at Tanjung Priok Port. Anomaly detection is done by looking for it through visualization, where AIS data is converted into a form of visualization using the Python language. The results obtained two pieces of information, namely that the areas with the highest density are around the harbor, docks, and ship lanes. Then, two types of anomalies were detected, namely large ships with dangerous cargo speeding in dense areas and ships that behave differently compared to other ships with the same status.
Land Subsidence Analysis Using Machine Learning Algorithm Random Forest Method in DKI Jakarta Nur Hidayah, Camelia; Pamungkasari, Panca Dewi; Ningsih, Sari; Azhiman, Muhammad Fauzan; Widodo, Joko; Widayaka, Elfady Satya
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.606

Abstract

Land subsidence is an environmental phenomenon that causes the earth's surface to decline gradually or suddenly. Land subsidence occurred in DKI Jakarta due to various factors such as excessive groundwater exploitation, infrastructure loads, and geological conditions. The purpose of this study was to analyze land subsidence in DKI Jakarta and the distribution of existing land subsidence. The results were compared with previous findings using PS-InSAR. Land subsidence was predicted using the Random Forest algorithm. Random Forest, as a type of machine learning, was able to reduce noise and minimize the impact of overfitting through ensemble techniques. Researchers used four metrics, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R², and Kling-Gupta Efficiency (KGE), to assess the accuracy of the algorithm. The analysis results of land subsidence in DKI Jakarta using Random Forest aligned with the PS-InSAR method. It was observed that areas experiencing land subsidence were predominantly in North and West Jakarta compared to other regions. Furthermore, the prediction of land subsidence using the 2017–2021 dataset indicated a decrease of up to -60 mm/year.
Sosialisasi untuk Pengembangan Website Klasifikasi Sampah Dinas Lingkungan Hidup dan Kebersihan (DLHK) di Kota Depok Sari Ningsih; Panca Dewi Pamungkasari; Asrul Sani; Erina Rahmazani; Lili Dwi Yulianto; Ferdiansyah Ferdiansyah; Ghina Rahma; Muhammad Mustaqim; Muhammad Rangga
Masyarakat Mandiri : Jurnal Pengabdian dan Pembangunan Lokal Vol. 2 No. 3 (2025): Juli : Masyarakat Mandiri : Jurnal Pengabdian dan Pembangunan Lokal
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/masyarakatmandiri.v2i3.1998

Abstract

This Community Service Program (PKM) aims to enhance the understanding of the staff at the Environmental and Sanitation Agency (DLHK) of Depok City in utilizing information technology through socialization and training on the development of a waste classification website. The website is designed to assist the public in identifying types of waste and to encourage more responsible and integrated waste management practices. The implementation methods for this program include socialization sessions, technical training workshops, and system usage demonstrations. Participants were introduced to the functionalities of the website, including how to classify various waste types and its role in supporting environmental sustainability. The program also provided hands-on training to ensure the staff could effectively use the website and incorporate it into their daily tasks. As a result, the staff’s understanding of the concept of digital-based waste classification has significantly increased. Additionally, the potential benefits of using such technology to support sustainable environmental management programs were clearly recognized. The program not only enhanced the staff's technical skills but also contributed to a deeper understanding of the importance of waste classification in the broader context of environmental conservation. The outcomes of this initiative are expected to encourage the staff to actively promote the use of the website to the public, thus fostering a more responsible and efficient waste management system in Depok City. This program demonstrates the effectiveness of integrating technology in supporting local government initiatives aimed at improving environmental management and sustainability. Furthermore, it underscores the importance of digital tools in modernizing waste management systems and enhancing public awareness and engagement.
Comparative analysis of gender classification methods using convolutional neural networks Pamungkasari, Panca Dewi; Asfandima, Ilhan Alim; Rifai, Achmad Pratama; Huu Tho, Nguyen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3634-3646

Abstract

Gender classification has become an important application in the fields of system automation and artificial intelligence, having important implications across various fields. The main challenge in this classification task is the variation in illumination that affects the quality of facial images. This study presents a method for identifying genders with Convolutional Neural Networks (CNNs). To address this issue, various preprocessing methods are applied, including Self Quotient Image (SQI), Histogram Equalization, Locally Tuned Inverse Sine Nonlinear (LTISN), Gamma Intensity Correction (GIC), and Difference of Gaussian (DoG), to stabilize the effects of illumination variations before the images are processed by the CNN. The CNN architecture used consists of 5 convolutional blocks and 2 fully connected blocks, which have proven effective in image recognition. The results of the study show that the model trained with the DoG method achieved an accuracy of 91.07%, making it the best preprocessing technique compared to other methods such as SQI and HE, which achieved accuracies of 90.39% and 88.76%, respectively. These findings demonstrate that the application of SQI in CNN can improve the accuracy of gender classification on facial images, providing better performance than previous methods. These findings are expected to serve as a foundation for further developments in facial image classification and its applications in various fields.
Web Sosialisasi Deteksi Anomali Kapal Menggunakan Kecerdasan Buatan Sari Ningsih; Panca Dewi Pamungkasari; Babag Purbantoro; Asif Awaludin; Deni Yulian; Ira Diana Sholihati; Lili Dwi Yulianto; Erina Rahmazani; Muhammad Zahran Alfarizi
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 3 No. 1 (2026): Februari : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v3i1.2972

Abstract

The rapid development of Artificial Intelligence (AI) technology has opened significant opportunities to support maritime monitoring systems, particularly in detecting anomalies in ship movements that may indicate illegal or abnormal activities. However, the understanding and utilization of this technology among the general public and maritime stakeholders remain limited. This Community Service Program aims to conduct socialization and dissemination of knowledge on AI-based ship anomaly detection through the development and utilization of an interactive and informative web-based socialization platform. This activity is the result of collaboration between a team of lecturers from the Faculty of Communication and Informatics Technology (FTKI) and the National Research and Innovation Agency (BRIN). The implementation methods include the design of web-based educational content, presentation of fundamental AI concepts and ship anomaly detection, as well as visual simulations of ship movement data analysis results. The web-based socialization platform serves as an educational medium to enhance users’ understanding of the benefits, working mechanisms, and potential applications of AI technology in maritime surveillance. The results indicate an improvement in participants’ understanding of ship anomaly detection concepts and the role of artificial intelligence in supporting maritime security and safety. This PKM activity is expected to promote technological literacy, strengthen synergy between academia and research institutions, and serve as a model for practical and sustainable web-based technology socialization
Temporal Analysis of Land Subsidence in DKI Jakarta Using the Long Short-Term Memory (LSTM) Model Fitriany, Heni; Pamungkasari, Panca Dewi; Wijaya, Yunan Fauzi Wijaya; Azhiman, Muhammad Fauzan; Nagase, Yasuhito Nagase; Widodo, Joko
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.880

Abstract

This research investigated temporal patterns of land subsidence in DKI Jakarta by applying a Long Short-Term Memory (LSTM) model to deformation measurements derived from Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) observations acquired between 2017 and 2021. Because the original PS-InSAR time series was characterized by uneven acquisition intervals, the deformation records were first resampled into a uniform 11-day sequence to obtain a consistent temporal structure for modeling. Preprocessing steps, comprising outlier handling, temporal resampling, and feature normalization, were performed to ensure that the model could capture deformation behavior reliably. The LSTM architecture employed three stacked recurrent layers and was trained using the Adam optimizer with Smooth L1 Loss and an early-stopping strategy. Model evaluation demonstrated excellent agreement between predicted and observed deformation, yielding R² = 1.000, MSE = 0.104, RMSE = 0.322 mm, and KGE = 0.998. Compared with a previously developed Random Forest model (R² = 0.9995, RMSE = 0.3314 mm), the LSTM approach exhibited more stable temporal learning and was better suited for long-horizon deformation forecasting. Spatial analysis revealed that northern Jakarta, particularly Cengkareng, Tanjung Priok, and Pantai Indah Kapuk, continued to experience the greatest cumulative subsidence (> −30 mm), whereas areas in the south, such as Jagakarsa and Kebayoran Baru, showed minimal deformation (< −5 mm), aligning with known geological and anthropogenic conditions. Overall, integrating PS-InSAR time series with an LSTM framework provided a more coherent and temporally consistent method for characterizing subsidence behavior in Jakarta. The outcomes of this study offered a scientific basis for developing intelligent monitoring tools to support mitigation efforts and sustainable urban planning in regions affected by land subsidence.
Co-Authors Achmad Pratama Rifai Akbar, Hafizh Naufaly Armaisya, Dimas Dwi Asfandima, Ilhan Alim Asif Awaludin Asrul Sani Azhiman, Muhammad Fauzan Babag Purbantoro Deni Yulian Destian, Bagus Resa Djoko Widodo Erina Rahmazani Farahdinna, Frenda Ferdiansyah Ferdiansyah Fitriany, Heni Ghina Rahma Gopal Sakarkar Heezen, Damy Matheus Huu Tho, Nguyen Ikhsan, Muhammad Ramadhan Ira Diana Sholihati Josaphat Tetuko Sri Sumantyo, Josaphat Tetuko Sri Lili Dwi Yulianto Muhammad Mustaqim Muhammad Rangga Muhammad Zahran Alfarizi Nagase, Yasuhito Nagase Nandila, Alisyafira Sayyidina Nguyen, Huu Tho Nur Hidayah, Camelia Nur Mayke Eka Normasari Penchala, Sathish Kumar Permana, Ari Prihatmaja, Dhonadio Aurell Azhar Purbantoro, Babag Sari Ningsih Setiawan, Kevin Stephen Sholihati, Ira Diana Tanaji, Irvantara Pradmaputra Ucuk Darusalam Widayaka, Elfady Satya Wijaya, Yunan Fauzi Wijaya