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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Science and Technology Indonesian Journal of Cancer Scholaria: Jurnal Pendidikan dan Kebudayaan Emerging Science Journal Civil Engineering Journal ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS) Indonesian Journal of Cancer
Bens Pardamean
Universitas Bina Nusantara Jakarta
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Public Health Social Sciences

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Energy-efficient deep learning model for fruit freshness detection Febrian Valentino; Tjeng Wawan Cenggoro; Gregorius Natanael Elwirehardja; Bens Pardamean
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1386-1395

Abstract

Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from 55 to 73 Watts during the training process and 20 to 27 Watts during the testing process. For accuracy, the result is 98.64%. However, compared to previous studies with the MobileNetV2 model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F1-score in Rotten Banana.
State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems Ryo G. Widjaja; Muhammad Asrol; Iwan Agustono; Endang Djuana; Christian Harito; G. N. Elwirehardja; Bens Pardamean; Fergyanto E. Gunawan; Tim Pasang; Derrick Speaks; Eklas Hossain; Arief S. Budiman
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-02

Abstract

The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PDF
Indoor Climate Prediction Using Attention-Based Sequence-to-Sequence Neural Network Karli Eka Setiawan; Gregorius N. Elwirehardja; Bens Pardamean
Civil Engineering Journal Vol 9, No 5 (2023): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-05-06

Abstract

The Solar Dryer Dome (SDD), a solar-powered agronomic facility for drying, retaining, and processing comestible commodities, needs smart systems for optimizing its energy consumption. Therefore, indoor condition variables such as temperature and relative humidity need to be forecasted so that actuators can be scheduled, as the largest energy usage originates from actuator activities such as heaters for increasing indoor temperature and dehumidifiers for maintaining optimal indoor humidity. To build such forecasting systems, prediction models based on deep learning for sequence-to-sequence cases were developed in this research, which may bring future benefits for assisting the SDDs and greenhouses in reducing energy consumption. This research experimented with the complex publicly available indoor climate dataset, the Room Climate dataset, which can be represented as environmental conditions inside an SDD. The main contribution of this research was the implementation of the Luong attention mechanism, which is commonly applied in Natural Language Processing (NLP) research, in time series prediction research by proposing two models with the Luong attention-based sequence-to-sequence (seq2seq) architecture with GRU and LSTM as encoder and decoder layers. The proposed models outperformed the adapted LSTM and GRU baseline models. The implementation of Luong attention had been proven capable of increasing the accuracy of the seq2seq LSTM model by reducing its test MAE by 0.00847 and RMSE by 0.00962 on average for predicting indoor temperature, as well as decreasing 0.068046 MAE and 0.095535 RMSE for predicting indoor humidity. The application of Luong's attention also improved the accuracy of the seq2seq GRU model by reducing the error by 0.01163 in MAE and 0.021996 in RMSE for indoor humidity. However, the implementation of Luong attention in seq2seq GRU for predicting indoor temperature showed inconsistent results by reducing approximately 0.003193 MAE and increasing roughly 0.01049 RMSE. Doi: 10.28991/CEJ-2023-09-05-06 Full Text: PDF
Depression detection through transformers-based emotion recognition in multivariate time series facial data Nanggala, Kenjovan; Elwirehardja, Gregorius Natanael; Pardamean, Bens
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1302-1310

Abstract

Globally, the prevalence of mental health disorders, particularly depression, has become a pressing issue. Early detection and intervention are vital to mitigate the profound impact of depression on individuals and society. Leveraging transformer models, renowned for their excellence in natural language processing and time series tasks, we explore their application in depression detection using multivariate time series (MTS) data from facial expressions. Transformer models excel in sequential data processing but remain relatively unexplored in facial expression analysis. This study aims to compare transformer models applied to first-order time derivative data with traditional methods. We use the distress analysis interview corpus wizard of oz (DAIC-WOZ) dataset and evaluate models with mean absolute error (MAE) and root mean squared error (RMSE) metrics. Results show that transformer models on first derivatives outperform others with an MAE of 4.42 and RMSE of 5.42. While transformer models on raw data surpass XGBoost in RMSE, they fall short of LSTM+transformer with an MAE of 5.41 and RMSE of 6.02. Preprocessing through differentiation enhances transformer models' ability to capture temporal patterns, promising improved depression detection accuracy.
Enhancing Computer Science Education Through Electronic Team-Based Learning: A Hybrid Approach to Collaborative Learning Makalew, Brilly Andro; Pardamean, Bens
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11713

Abstract

This paper explores the implementation of electronic Team-Based Learning (e-TBL) as a hybrid educational solution, combining traditional TBL methodologies with electronic platforms to enhance collaborative learning experiences. The study aims to assess the effectiveness of e-TBL in improving knowledge acquisition, teamwork skills, and overall academic performance among computer science students. The research was conducted in five phases, including preparation, experimentation, and evaluation, with a focus on developing an e-learning application integrated with Moodle. The results indicated significant improvements in student motivation, engagement, and academic achievement. The study concludes with recommendations for integrating e-TBL in various educational settings, highlighting its adaptability and potential for enhancing learning outcomes in both traditional and online environments.
Co-Authors Andika Rizky Andri Anugrahana Anjela Greizaria Djoeang Arief S. Budiman Bahtiar S. Abbas Christian Harito Derrick Speaks Digdo Sudigyo DJIAUW DJUNAIDI GODJALI DJIAUW DJUNAIDI GODJALI, DJIAUW DJUNAIDI Djoeang, Anjela Greizaria Eklas Hossain Elwirehardja, Gregorius Natanael Endang Djuana EVLINA SUZANNA EVLINA SUZANNA, EVLINA Febrian Valentino Fergyanto E. Gunawan G. N. Elwirehardja Gregorius N. Elwirehardja Gregorius Natanael Elwirehardja Harjanto Prabowo Hasbi Yasin Indri Anugraheni Iwan Agustono JONATHAN ADITYA MULYONO JONATHAN ADITYA MULYONO, JONATHAN ADITYA Karli Eka Setiawan Makalew, Brilly Andro Muhammad Asrol Nana Rosanna Tobing Nanggala, Kenjovan Rezzy Eko Caraka Rung Ching Chen Ryo G. Widjaja Suhartono Suhartono Teddy Suparyanto Tim Pasang Tjeng Wawan Cenggoro Tobing, Nana Rosanna Youngjo Lee