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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Data and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Green Intelligent Systems and Applications Makara Journal of Technology
Pamungkasari, Panca Dewi
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Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Public Health

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Journal : Green Intelligent Systems and Applications

 1 
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.
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.
Co-Authors Achmad Pratama Rifai Akbar, Hafizh Naufaly Armaisya, Dimas Dwi Asfandima, Ilhan Alim Azhiman, Muhammad Fauzan Djoko Widodo Farahdinna, Frenda Fauziah Fauziah Gopal Sakarkar Handayani, Endah Tri Esti Heezen, Damy Matheus Hindarto, Djarot Huu Tho, Nguyen Ikhsan, Muhammad Ramadhan Josaphat Tetuko Sri Sumantyo, Josaphat Tetuko Sri Nandila, Alisyafira Sayyidina Nguyen, Huu Tho Nur Hidayah, Camelia Nur Mayke Eka Normasari Penchala, Sathish Kumar Permana, Ari Prihatmaja, Dhonadio Aurell Azhar Purbantoro, Babag Ratih Titi Komalasari Sari Ningsih Setiawan, Kevin Stephen Sholihati, Ira Diana Tanaji, Irvantara Pradmaputra Ucuk Darusalam Widayaka, Elfady Satya