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All Journal HAYATI Journal of Biosciences Computational and Experimental Research in Materials and Renewable Energy (CERiMRE) Jurnal Locus Penelitian dan Pengabdian Makara Journal of Technology Indonesian Journal of Physics (IJP)
Triati Dewi Kencana Wungu
Research Center For Nanosciences And Nanotechnology, Institut Teknologi Bandung, Jl. Ganesa No. 10 Bandung 40132, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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A Density Functional Theory Study on using Montmorillonite to Reduce Air Pollution Wungu, Triati Dewi Kencana; Yusfi, Meqorry; Suprijadi, Suprijadi
Makara Journal of Technology Vol. 24, No. 3
Publisher : UI Scholars Hub

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Abstract

In this study, density functional theory (DFT) method is used to investigate the possibility of using a smectite clay mineral called montmorillonite (MMT) in reducing heavy metals, such as Cd, through Cd adsorption. The mechanism of Cd adsorption in MMT is observed theoretically, and the tetrahedrally isomorphic substitution on the upper layer of MMT is considered to observe the role of Al and Fe in strengthening Cd adsorption. Two types of MMT are modeled in this study: Al-MMT and Fe-MMT. The Al-MMT means that Al substitutes one atom in the upper tetrahedral layer of MMT, while Fe-MMT means that Fe substitutes one atom in the upper tetrahedral layer of MMT. According to the DFT calculation, Cd is adsorbed relatively strongly to Al-MMT compared with Fe-MMT, with Cd adsorption energy of –4.55 eV and –2.43 eV for Al-MMT and Fe-MMT, respectively. The density-of-state analysis shows that Cd helps reduce the gap between the highest valence-band energy and lowest conduction-band energy of Al-MMT and Fe-MMT. Thus, Cd/Al-MMT and Cd/Fe-MMT behave in a manner similar to a semiconductor.
Isolation of Native Plant-Derived Exosome-like Nanoparticles and Their Uptake by Human Cells Diah Ratnadewi; Christofora Hanny Widjaja; Anggraini Barlian; Rizka Musdalifah Amsar; Ika Dewi Ana; Atik Choirul Hidajah; Hari Basuki Notobroto; Triati Dewi Kencana Wungu
HAYATI Journal of Biosciences Vol. 30 No. 1 (2023): January 2023
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.30.1.182-192

Abstract

The exosome is a nano-sized vesicular particle commonly secreted from eukaryotic cells to extracellular space. Exosome functions in cellular communication, nutrients or bioactive compounds delivery, and cellular immunity. It is believed that plant-derived exosome-like particle (PDEN) potential to deliver nano-bioactive compounds to the human body. As Indonesia is one of the herbal centers of the world, we are encouraged to develop natural richness for human welfare. The study aimed to verify various methods to isolate exosomes from the fresh ginger rhizome and quina cells and examine the ginger PDENs' capacity to internalize in vitro human cells. The PDENs isolation was carried out by centrifugation and precipitation. The particles were observed through a transmission electron microscope, dynamic light scattering, and particle size analysis. Differential ultracentrifugation combined with PEG6000 provided a better size range of particles. Nanoparticles tracking analysis performed the concentration of particles within a specific size range. Ginger and quina PDENs size ranged from 40-650 nm, and the concentration was 3.6 × 107 and 2.8 × 106, respectively. The PKH67-labelled ginger PDENs were taken up by human dermal fibroblast and human Wharton's jelly mesenchymal stem cells after 30 minutes and 21 hours. DPPH assays revealed that ginger PDENs have antioxidant activity, indicating the presence of some nano-molecules in those particles. This study would be the baseline for developing ginger PDENs as a functional food with nano-ingredients, or as an effective vehicle of nano-drug, for human health.
The Properties of Exosomes Derived from Mesenchymal Stem Cells Preconditioned with L-Ascorbic Acid and Cobalt (II) Chloride Anggraini Barlian; Rizka Musdalifah Amsar; Salindri Prawitasari; Christofora Hanny Wijaya; Ika Dewi Ana; Triati Dewi Kencana Wungu
HAYATI Journal of Biosciences Vol. 30 No. 6 (2023): November 2023
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.30.6.1100-1110

Abstract

Extracellular vesicles including exosomes, are produced by cells for intracellular communication. Preconditioning of parental cells influences exosome properties. The purpose of this study was to examine the effects of L-ascorbic acid (LAA) and cobalt (II) chloride (CoCl2) on human Wharton’s jelly mesenchymal stem cell (hWJ-MSC)-derived exosomes and their ability to promote stem cell differentiation into chondrocytes. The cells were isolated from the umbilical cord and characterized according to the criteria for mesenchymal stem cell. The cells were cultured in a serum-free medium containing LAA and CoCl2. Cell-produced exosomes were isolated and characterized. hWJ-MSCs can grow in serum-free medium containing LAA and CoCl2. Exosomes derived from hWJ-MSCs had a round morphology, particle size within the exosome range, CD 63 expression, and the capacity to be internalized by cells. The production of exosomes by hWJ-MSCs was enhanced by LAA treatment. LAA and CoCl2 promoted stem cell differentiation into chondrocytes, as indicated by the production of collagen type II and glycosaminoglycans. LAA and CoCl2 affect the properties of MSC-derived exosomes. LAA induces cells to produce exosomes in greater quantities, which have the potential to promote chondrogenic differentiation of stem cells.
Study of the Structure and Electronic Properties of the ZnO Monolayer: Density Functional Theory Raihan, Muhammad Fadlan; Wungu, Triati Dewi Kencana; Yuliarto, Brian
Computational And Experimental Research In Materials And Renewable Energy Vol 4 No 1 (2021): May
Publisher : Physics Department, Faculty of Mathematics and Natural Sciences, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/cerimre.v4i1.24961

Abstract

ZnO has received considerable attention since it has promising applications in electronic devices. Although many studies have explored the potential of ZnO as a promising material, the precise role of geometric in ZnO remains unclear. This study deals with the electronic structure of the ZnO monolayer using density functional theory (DFT). The DFT was used to investigate the band structure and density of states of the ZnO monolayer. It is observed that the structural change of ZnO from bulk to monolayer increases the bandgap by 1.84 eV without changes its natural characteristic. Moreover, This study provides information about the properties of the ZnO monolayer and its potential in electronic and magnetic devices application.
Density Functional Theory Simulation of Iron-Montmorillonite as Carbon Dioxide Adsorber Ihsudha, Husni; Wungu, Triati Dewi Kencana; suprijadi, suprijadi; Morikawa, Yoshitada
Indonesian Journal of Physics Vol 34 No 2 (2023): vol 34 no 2 2023
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itb.ijp.2023.34.2.5

Abstract

Carbon dioxide (CO2) is a greenhouse gas that naturally keep the Earth^s surface temperature warm but currently the levels cause environmental problem such as climate change. Carbon capture and storage (CCS) technology is built to reduce CO2 gas emissions by binding carbon dioxide molecules and then storing them or utilising them as more useful products. In this study, simulations were carried out for the addition of iron (Fe) impurities as additional cation in montmorillonite to see the increase in the ability to bind carbon gas. Density Functional Theory calculations were carried out using additional corrections such as Van der Waals (vdW) and Hubbard-U. Here we got that Fe cation can help CO2 adsorbtion compare with other site without Fe atom by adding acid cite condition. But to adsorb CO2, the structure need initial process to swell the montmorillonite interlayer to certain optimum distance.
Utilizing Stacking Ensemble Algorithm for Employee Productivity Prediction (Case Study of PT PLN (Persero) Unit Induk Distribusi Sulawesi Selatan, Sulawesi Tenggara dan Sulawesi Barat) Qodriyah, Inas Suha Lailah; Wungu, Triati Dewi Kencana
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 10 (2025): : JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i10.4292

Abstract

Employee productivity is a critical determinant of organizational efficiency and value, particularly in state-owned enterprises such as PT PLN (Persero) in Indonesia’s electricity sector. Accurate prediction of employee productivity can enhance human resource management and support strategic decision-making. This study aims to develop a predictive model for employee productivity using machine learning algorithms based on employee identity, attendance trends, certifications, training participation, and performance targets measured via the NSK indicator. Nine machine learning algorithms were implemented, including Linear Regression, Random Forest, Decision Tree, XGBoost, MLP, SVR, CatBoost, LightGBM, and ExtraTrees, with data balancing performed using the SMOGN technique to address imbalanced target distributions. Model performance was evaluated with MAE, MSE, RMSE, R², and sMAPE metrics. Initial results revealed suboptimal predictions, prompting the application of a stacking ensemble approach combining the three best-performing base models. The optimized stacking model achieved an RMSE of 11.24, MAE of 3.18, R² of 0.58, and sMAPE of 1.03, with residual analysis confirming improved prediction accuracy. Among individual models, CatBoost Regression performed best, achieving an MAE of 0.8. These findings indicate that machine learning, particularly CatBoost, can effectively predict employee productivity and provide actionable insights for HR management. The proposed model supports data-driven decision-making, enabling PLN to optimize workforce allocation, monitor performance targets, and develop strategic initiatives to improve organizational efficiency.
Co-Authors Anggraini Barlian Atik Choirul Hidajah Brian Yuliarto C Hanny Wijaya Christofora Hanny Widjaja Diah Ratnadewi Hari Basuki Notobroto Ihsudha, Husni Ika Dewi Ana Ika Dewi Ana Meqorry Yusfi Morikawa, Yoshitada Qodriyah, Inas Suha Lailah Raihan, Muhammad Fadlan Rizka Musdalifah Amsar Rizka Musdalifah Amsar Salindri Prawitasari Suprijadi Suprijadi