Article Per Year (5 Year)
p-Index From 2021 - 2026
1.954
P-Index
This Author published in this journals
All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Al Furqan: Jurnal Ilmu Al Quran dan Tafsir Buletin Ilmiah Sarjana Teknik Elektro Aktual : Jurnal Pengabdian Kepada Masyarakat Journal of Educational Technology and Learning Creativity International Journal of Advances in Artificial Intelligence and Machine Learning Khazanah Hukum
Deshinta Arrova Dewi
INTI International University
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Noise Reduction in Brain Magnetic Resonance Imaging Using a Convolutional Autoencoder I Gede Susrama Mas Diyasa; Pangestu Sandya Etniko Siagian; Eva Yulia Puspaningrum; Wan Suryani Wan Awang; Sayyidah Humairah; Deshinta Arrova Dewi
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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

Abstract

In clinical practice, precise and high-quality brain Magnetic Resonance Imaging (MRI) is pivotal for diagnosing and formulating effective treatment strategies. The research objective is to assess the viability of employing a Convolutional Autoencoders (CAE) for the mitigating noise in brain MRI images. The focus is brain MRI images and the various types of noise (Salt and Pepper, Speckle, and Gaussian noise) that typically corrupt images and may lead to inaccuracies in diagnosis. The research also applies methods to artificially generate these noise types to represent real-world scenarios. Specifically, the dataset of brain MRI images is collected, pre-processed, and artificially exposed to various noise types to simulate the real-world conditions after the CAE model is used to reconstruct the corrupted images. The CAE is assessed for its high efficiency and efficacy using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The results indicate that the CAE is very effective in removing noise, particularly Salt and Pepper noise. The model achieves a PSNR of 27.0687 dB and an MSE of 0.00216246 at the lowest noise level. The model also demonstrates stability under varying levels of Speckle noise. Although performance degrades as noise increases, the model continues to demonstrate potential for further refinement. The research furthers the CAE’s analytical potential by assessing its denoising capabilities across various noise types and levels. The research adds value by outlining recommendations to the medical imaging community while identifying the need for future research on different classifications of noise and advanced regularization methods.
Bias Detection and Mitigation Techniques in Data Science Pipelines: An Empirical Evaluation Deshinta Arrova Dewi; Ugochi Okengwu; Zakka Ugih Rizqi
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.655

Abstract

Background: Failure to consider algorithmic bias can result in discriminatory outcomes in machine learning systems, particularly when these models operate in high-stakes decision-making environments. Although numerous bias mitigation techniques have been proposed, most studies treat fairness assessment as a post hoc evaluation. This gap highlights the need for a lifecycle-oriented framework to examine interconnected bias and fairness mechanisms.Aims: This study aims to conduct an empirical investigation of bias propagation across the data science continuum within a structured bias-processing framework.Methods: The proposed framework was tested on benchmark datasets containing sensitive attributes. Three predictive models were implemented: Logistic Regression, Random Forest, and Gradient Boosting. Fairness was evaluated using Demographic Parity, Equal Opportunity, and Average Odds metrics. Predictive modeling techniques were further employed to interpret fairness outcomes. Bias mitigation strategies were applied at both data and model levels, including fairness-regularized optimization and hybrid approaches. Sensitivity analysis was conducted to examine the trade-off between fairness constraints and model loss.Result: The empirical findings indicate that most disparities originate from bias embedded in the data rather than from model architecture. Data-level bias mitigation reduced disparity by 28%. The fairness-regularized optimization approach reduced disparity by 35%. The hybrid mitigation strategy achieved a demographic disparity reduction of 40–45%, with an accuracy decrease of no more than 2%. Sensitivity analysis revealed non-linear tensions between fairness constraints and optimization loss, demonstrating that early-stage bias mitigation stabilizes fairness without significantly increasing performance trade-offs.Conclusion: This study extends both theoretical and practical understanding of lifecycle bias propagation in machine learning systems. The findings emphasize the importance of addressing bias at early stages of the data science pipeline to achieve stable and sustainable fairness outcomes. By integrating fairness engineering throughout the lifecycle, the proposed framework contributes to more robust and ethically aligned AI systems.
Kebangkitan Kembali Tafsir Klasik di Era Kontemporer: Antara Revivalisme, Kontinuitas Epistemologis, dan Stagnasi Intelektual Ahmad Bahrudin; M. Tohir; Abd Muin; Deshinta Arrova Dewi
Al Furqan: Jurnal Ilmu Al Quran dan Tafsir Vol. 9 No. 1 (2026): Al Furqan: Jurnal Ilmu Al Quran dan Tafsir
Publisher : Jawa Timur: Prodi. Ilmu Al Quran dan Tafsir Fakultas Ushuluddin IAI Tarbiyatut Tholabah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58518/alfurqan.v9i1.4908

Abstract

The persistence of classically oriented Qur'anic exegesis in the contemporary period presents an important yet underexplored phenomenon in Qur'anic studies. While modern tafsir has generally been associated with reformist, contextual, and interdisciplinary approaches, a number of contemporary exegetical works continue to preserve classical interpretive structures and methodologies. This study investigates the re-emergence of classical exegesis through a comparative analysis of three major twentieth-century commentaries: Tafsir al-Munir by Wahbah al-Zuhaili, al-Tahrir wa al-Tanwir by Muhammad al-Tahir Ibn Ashur, and Khawatir Hawl al-Qur'an al-Karim by Muhammad Mutawalli al-Sha'rawi. Employing qualitative library research with descriptive-comparative analysis, the study examines their exegetical methodologies, interpretive sources, systematic structures, and socio-intellectual orientations. The findings demonstrate that all three commentaries preserve essential characteristics of classical tafsir—including the predominance of the tahlili method, extensive engagement with earlier exegetical authorities, philological analysis, and comprehensive verse-by-verse interpretation—while simultaneously incorporating selective responses to contemporary intellectual and social concerns. These findings challenge the conventional binary classification that sharply separates classical and contemporary tafsir. Instead, the study argues that these works represent a form of neo-classical exegesis, characterized by epistemological continuity rather than methodological stagnation. Consequently, the re-emergence of classical exegesis should be understood neither merely as a revivalist movement nor as evidence of intellectual decline, but as a dynamic strategy for preserving the authority of the classical exegetical tradition while negotiating the demands of modernity. This study contributes a new theoretical perspective by proposing neo-classical exegesis as an intermediary category within the historiography of Qur'anic interpretation.
A Portable Solar-Powered Wireless Charger: Design, Implementation, and Performance Analysis Alfarid Hendro Yuwono; Deshinta Arrova Dewi; Rajani Balakrishnan; Reza Rahmadian; Nafi Isbadrianingtyas; Widi Aribowo; Vugar Hacimahmud Abdullayev; Aliyu Sabo
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.15226

Abstract

The increasing demand for portable and off-grid charging solutions has motivated the development of solar-powered wireless power transfer (WPT) systems for consumer electronics. This paper presents the design, implementation, and experimental performance evaluation of a portable solar-powered wireless charger that integrates a 3-Wp photovoltaic (PV) panel with a near-field inductive coupling WPT system operating at a resonant frequency of 90 kHz. The research contribution is a fully integrated, low-cost prototype that demonstrates the feasibility of combining solar energy harvesting with contactless inductive charging for mobile devices, addressing the gap in portable off-grid wireless charging solutions. The system comprises a solar panel connected to a powerbank serving as an energy buffer, a series-series (SS) compensated inductive coil pair, a high-frequency inverter, and an AC/DC rectifier stage. Experimental testing was conducted in Malang City, Indonesia, under natural sunlight conditions. Results showed that the solar panel output voltage ranged from 6.2 V to 6.8 V under direct sunlight, declining by more than 30% under cloudy conditions. Peak power transfer efficiency of 65.3% was achieved at the 90 kHz resonant frequency, and efficiency decreased inversely with coil separation distance, dropping from 65.3% at 0 cm to below 10% at 5 cm. The powerbank required approximately 460 minutes of solar charging to reach 4 V, and the mobile phone battery charged at an average rate of 8.5 minutes per 1% capacity increase, compared to approximately 4.2 minutes per 1% for a standard wired charger. The study demonstrates the practical feasibility of portable solar-WPT integration for outdoor and emergency charging applications, while identifying weather dependence and limited effective coil distance as primary constraints for future optimization. This research aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) by promoting renewable energy access and photovoltaic technology for off-grid communities, SDG 9 (Industry, Innovation and Infrastructure) through the development of innovative low-cost wireless charging infrastructure, SDG 11 (Sustainable Cities and Communities) by enabling resilient and portable energy solutions for underserved and emergency settings, and SDG 13 (Climate Action) by advancing clean energy alternatives that reduce dependence on fossil-fuel-based electricity.
Agroecological Zoning of Bangkalan Regency Using K-Means and HDBSCAN Based on Integrated Soil Fertility and Climate Features Wahyudi Agustiono; Giraldo Stevanus; Yoga Dwitya Pramudita; Wahyudi Setiawan; Deshinta Arrova Dewi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5969

Abstract

Agroecological heterogeneity poses challenges for agricultural planning in Bangkalan Regency, Indonesia. This study aimed to delineate agroecological zones by integrating soil fertility, climate, and topographic variables using K-Means clustering and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A total of 11,000 geospatial observations obtained from Google Earth Engine were aggregated into 277 village-level units. The dataset included soil nutrients (nitrogen, phosphorus, and potassium), the Soil Quality Index, temperature, rainfall, humidity, elevation, and slope. Data preparation, modeling, and evaluation were performed as the primary methodological steps. Min-Max Scaling was applied to normalize the data. The optimal K-Means configuration (K = 3) achieved a Silhouette Score of 0.2668, an Inertia value of 294.5529, and a Calinski-Harabasz Index (CHI) of 75.8821. The resulting clusters were classified as High-Potential (52 villages), Moderate-Potential (142 villages), and Environmental-Constraint (83 villages) zones. HDBSCAN was used to validate clustering patterns and detect environmental anomalies. The optimal HDBSCAN configuration identified two density-based clusters and five noise villages. These villages showed exceptionally high nitrogen, phosphorus, and Soil Quality Index values, indicating localized agroecological hotspots. The integration of K-Means and HDBSCAN offers a comprehensive framework for agricultural planning, resource allocation, and sustainable land management.
Dual band antenna design for 4G/5G application and prediction of gain using machine learning approaches Narinderjit Singh Sawaran Singh; Md. Ashraful Haque; Redwan A. Ananta; Md. Sharif Ahammed; Md. Abdul Kader Jilani; Liton Chandra Paul; Rajermani Thinakaran; Malathy Batumalay; JosephNg Poh Soon; Deshinta Arrova Dewi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26233

Abstract

In this research, we disclose our findings from exploring a machine learning (ML) approach to enhancing the antenna’s performance in Industrial and Innovation contexts, particularly for4G and 5G (n77, n78) contexts. Methods for evaluating antenna performance utilizing simulation, the resistor, inductor, and capacitor (RLC) equivalent circuit model, and ML are discussed. Gain is a maximum of 6.56 dB and efficiency is about 97% for this antenna. The predicted antenna gain is calculated using an alternative supervised regression ML technique. Multiple measures, including as the variance score, R-square (R2), mean square error (MSE), and mean absolute error (MAE), can be used to assess an ML model’s performance. The linear regression (LR) model predicts profit with the fewest errors and highest accuracy of the five ML models. Finally, computer simulation technology (CST) and advanced design system (ADS) modeling findings, along with ML results, show that the proposed antenna is a promising option for 4G and 5G applications.
Inquisitive biometric feature analysis and implementation for recognition tasks using camouflaged segmentation with AI and IoT Mahesh Shankarrao Patil; Harsha J. Sarode; Abhijit Banubakode; Prakash Tukaram Patil; Nutan Patil; Vijayakumar Varadarajan; Deshinta Arrova Dewi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp119-129

Abstract

A vital role in reconfigurable and embedded systems which are deployed in smart environements and healthcare monitoring applications is played by human activity recognition (HAR). However, the potential leakage of sensitive user attributes raises serious privacy issues due to collection of data from the end devices and it needs to be transmitted to more powerful platforms for inference. Addressing this key challenge is principally crucial for resource-constrained embedded systems where efficiency of energy is a chief design requirement. The aim of this paper is present an energy-aware, privacy-preserving HAR framework appropriate for low-power embedded platforms. A machine learning–based camouflaged signal segmentation technique is proposed to transform the data collected from the sensor by eliminating sensitive information while preserving activity-relevant features. For characterization of trade off between the energy consumption and accuracy of recognition, parameters are extensively tuned by careful optimization in this proposed model. Experimental evaluations demonstrate that the method significantly reduces the inference of sensitive attributes such as gender, age, height, and weight, with minimal impact on HAR accuracy. Furthermore, the system supports configurable trade-offs between energy usage and classification performance, making it suitable for implementation on low-power embedded devices.
Digital Transformation and Regulatory Approaches in Capital Markets: A Comparative Legal Study of Indonesia and Malaysia Eni Dasuki Suhardini; Deshinta Arrova Dewi; Dani Durahman; Cecep Soleh Kurniawan; Panji Adam Agus Putra
Khazanah Hukum Vol. 8 No. 2 (2026): Khazanah Hukum
Publisher : UIN Sunan Gunung Djati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kh.v8i2.54293

Abstract

This study aims to compare the regulatory frameworks governing fintech innovation, blockchain and digital assets, and investor data protection in the capital markets of Indonesia and Malaysia. It employs normative legal research using statute, comparative, and conceptual approaches. The analysis is based on primary legal materials, including legislation, regulatory guidelines, supervisory instruments, and official policy documents, as well as secondary legal materials, including academic literature and policy reports. The collected materials are examined through descriptive and comparative legal analysis. The findings demonstrate that Indonesia and Malaysia pursue broadly similar regulatory objectives through licensing, governance requirements, supervisory mechanisms, risk management, and investor protection. However, the two jurisdictions differ significantly in their institutional architecture. Indonesia adopts an integrated financial-sector regulatory approach under the Financial Services Authority, whereas Malaysia applies a more specialised capital-market supervisory model through the Securities Commission Malaysia. These institutional differences influence regulatory coherence, supervisory coordination, legal certainty, and regulators' capacity to address technology-specific risks. The findings imply that Indonesia should strengthen inter-institutional coordination and technology-specific supervisory standards, while both jurisdictions should ensure that innovation policies remain closely connected with cybersecurity, operational resilience, and investor protection. The originality of this study lies in integrating fintech regulation, digital asset governance, and investor data protection within a single comparative legal framework focused on regulatory coherence and institutional design.
A Hybrid VADER–IndoBERT Framework for Robust Sentiment Analysis of Long and Ambiguous Indonesian Texts Margareta Valencia Suci Handayani; Ruri Suko Basuki; Muljono; Raden Arief Nugroho; Dhendra Maruhto; Yo Ceng Giap; Deshinta Arrova Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7377

Abstract

The rapid expansion of digital learning platforms has increased the reliance on user-generated reviews for service evaluation and quality monitoring. However, sentiment analysis of Indonesian reviews remains challenging due to the prevalence of long sentences, mixed sentiments, and ambiguous linguistic expressions. This study introduces a Hybrid VADER–IndoBERT framework designed to improve sentiment classification robustness on complex Indonesian texts. A dataset of 4,904 Ruangguru application reviews was collected through web scraping and processed using a hybrid pipeline consisting of preprocessing, translation-based silver-standard sentiment labeling with VADER, and class balancing via Random Oversampling (ROS). The IndoBERT classifier was evaluated against a Bidirectional Long Short-Term Memory (BiLSTM) baseline. Experimental results show that IndoBERT achieved 90.9% accuracy, outperforming BiLSTM at 86.4%, demonstrating the superiority of Transformer-based architectures in capturing long-range dependencies and handling ambiguous sentiment cues. These findings highlight the effectiveness of integrating lexicon-based and Transformer-based approaches to achieve more robust sentiment analysis on linguistically complex Indonesian texts.
Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP) I Gede Susrama Mas Diyasa; Hajjar Ayu Cahyani Kuswardhani; Mohammad Idhom; Prismahardi Aji Riyantoko; Deshinta Arrova Dewi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5954

Abstract

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.
Co-Authors Abd Muin Abhijit Banubakode ACENG RUYANI Afrizal Mayub Ahmad Bahrudin Alfarid Hendro Yuwono Aliyu Sabo Anggy Pradifta Junfithranaa Cecep Soleh Kurniawan Dani Durahman Dhendra Maruhto Eko Risdianto Eva Yulia Puspaningrum Fahmizal Fahmizal Giraldo Stevanus Hadi Almohab Hajjar Ayu Cahyani Kuswardhani Harsha J. Sarode I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Joselin Santos JosephNg Poh Soon Lazfihma Lazfihma Liton Chandra Paul M. Esad Kuloğlu M. Lutfi Firdaus M. Tohir Mahesh Shankarrao Patil Malathy Batumalay Margareta Valencia Suci Handayani Md. Abdul Kader Jilani Md. Ashraful Haque Md. Sharif Ahammed Mohammad Idhom Muljono, - Nafi Isbadrianingtyas Narinderjit Singh Sawaran Singh Noel Lomerio Nutan Patil Pangestu Sandya Etniko Siagian Panji Adam Agus Putra Prakash Tukaram Patil Pramudita, Yoga Dwitya Prismahardi Aji Riyantoko Raden Arief Nugroho Rajani Balakrishnan Rajermani Thinakaran Redwan A. Ananta Ressti Nurfitriani Reza Rahmadian Ruri Suko Basuki Sayyidah Humairah Suhardini, Eni Dasuki Sultan Hammad Alshammari Ugochi Okengwu Vijayakumar Varadarajan Vugar Hacimahmud Abdullayev Wahyudi Agustiono Wahyudi Setiawan Wan Suryani Wan Awang Widi Aribowo Yo Ceng Giap Zakka Ugih Rizqi