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All Journal Jurnal Teknologi JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Emerging Science Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Journal of Robotics and Control (JRC) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal for Applied Information Management Volksgeist: Jurnal Ilmu Hukum dan Konstitusi Maritime Park: Journal Of Maritime Technology and Socienty INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY Bulletin of Applied Mathematics and Mathematics Education Jurnal Ilmiah Ilmu Terapan Universitas Jambi Jurnal Pemberdayaan Masyarakat Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Dewi, Deshinta Arrova
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Fault-Tolerant Telegram Bot Architecture for Odoo 14: Validated Production Reporting in Flexible Packaging Tarigan, Masmur; Paramita, Adi Suryaputra; Dewi, Deshinta Arrova
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5515

Abstract

In flexible-packaging manufacturing, manual reporting dramatically delays synchronization with the ERP — and that means operational  latency and traceability issues. The proposed work is the design, implementation, and validation of a fault-tolerant Telegram bot interconnected with Odoo 14 for six production departments. Our bot architecture that combines conversational workflows with schema-based validation and XML-RPC for slow, large payloads, enables accurate and  timely reporting. In a four-week pilot with 1,066 production entries, we achieved 98.7% field completeness and lowered reporting latency to less than 2 minutes. Manual  baselines received 75% more requests for corrections. At disconnected state, the layered middleware of the system abstracted retry logic and media ingestion. Both SDG 9 (Resilient infrastructure, including ) and SDG 12 (Continue to reduce production waste at source, including consumables) are connected to the work presented here which evidence the feasibility of automatic conversational interfaces with a computer in the manufacturing informatics domain, and provide pathways towards scalable digital transformation and sustainability in the small-to-medium industry sector.
Stacked LSTM with Multi Head Attention Based Model for Intrusion Detection Praveen, S Phani; Panguluri, Padmavathi; Sirisha, Uddagiri; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Efrizoni, Lusiana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.764

Abstract

The rapid advancement of digital technologies, including the Internet of Things (IoT), cloud computing, and mobile communications, has intensified reliance on interconnected networks, thereby increasing exposure to diverse cyber threats. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats; however, traditional signature-based and rule-based methods fail to detect unknown or complex attacks and often generate high false positive rates. Recent studies have explored machine learning (ML) and deep learning (DL) approaches for IDS development, yet many suffer from poor generalization, limited scalability, and an inability to capture both spatial and temporal dependencies in network traffic. To overcome these challenges, this study proposes a hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Stacked Long Short-Term Memory (LSTM) networks, and a Multi-Head Self-Attention (MHSA) mechanism. CNN layers extract spatial features, stacked LSTM layers capture long-term temporal dependencies, and MHSA enhances focus on the most relevant time steps, improving accuracy and reducing false alarms. The proposed model was trained and evaluated on the UNSW-NB15 dataset, which represents modern attack vectors and realistic network behavior. Experimental results show that the model achieves state-of-the-art performance, attaining 99.99% accuracy and outperforming existing ML and DL-based intrusion detection systems in both precision and generalization capability.
A SARIMA APPROACH WITH PARAMETER OPTIMIZATION FOR ENHANCING FORECAST ACCURACY FOR NATIVE CHICKEN EGG PRODUCTION Gustriansyah, Rendra; Dewi, Deshinta Arrova; Puspasari, Shinta; Sanmorino, Ahmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1331-1344

Abstract

This study aims to accurately forecast monthly native chicken egg production using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model with parameter optimization. The optimization process was conducted through a combination of auto.arima() initialization and an exhaustive grid search across the parameter space, evaluated using multiple performance metrics. The dataset comprised monthly production data from Magelang City, Indonesia, spanning the period from 2016 to 2022. The best-performing model, SARIMA (2,1,2)(1,0,1,12), achieved an R² of 0.89, MAE of 82.13, RMSE of 92.92, MAPE of 7.21%, and MASE of 0.67 on the testing set, indicating satisfactory forecasting performance. Compared with the non-optimized SARIMA baseline, the optimized model showed improved predictive accuracy. However, the residuals did not follow a normal distribution, suggesting potential limitations in model assumptions. Moreover, the study is limited by its focus on a single geographic location and native chicken production data, which may restrict its generalizability. Despite these limitations, the findings demonstrate that parameter optimization in SARIMA enhances forecast accuracy and can support better planning for food security initiatives.
EVOLVING TRENDS IN HUMAN RESOURCE MANAGEMENT RESEARCH WITHIN TOURISM: INSIGHTS FROM A BIBLIOMETRIC ANALYSIS Ariana, Sunda; Helmi, Sulaiman; Cahyadin, Malik; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 1 (2025): Volume 9, Nomor 1, March 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i1.35392

Abstract

Human Resource Management (HRM) is a strategic approach to managing people effectively in tourism companies, providing a competitive edge. This study aims to reveal research trends from 2020 to 2022 through a bibliometric and content analysis of HRM-related articles in the tourism industry. A total of 1,086 Scopus-indexed articles were analyzed using R Studio with the bibliometric package. Key metrics such as countries, authors, and institutions contributing to HRM research were examined. The findings show that the United States and China were the most productive countries in article output, with Wang and Zhang identified as the most prolific authors and Netreported as the leading institution. Emerging themes and keywords were also identified, indicating significant areas of focus in HRM research. The results highlight that HRM remains a trending topic in the tourism sector, driven by its role in enhancing organizational performance. This study is one of the few to provide a comprehensive bibliometric analysis of HRM in tourism, offering insights into global research productivity and trends over three years. The findings have practical implications for both academia and industry, suggesting that future research should focus on specific HRM practices that can further improve competitiveness in the tourism sector. These insights can guide tourism companies in refining HRM strategies to enhance performance and adaptability.
Deep Learning-Based Loan Approval Prediction Using Artificial Neural Network (ANN) and Feature Importance Analysis Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.55

Abstract

The increasing demand for efficient and objective credit evaluation has motivated the adoption of artificial intelligence in financial decision-making. This study proposes a deep learning-based loan approval prediction model using an Artificial Neural Network (ANN) combined with feature importance analysis to enhance interpretability. The dataset, consisting of 2,000 loan application records with both financial and demographic attributes, was preprocessed through normalization and one-hot encoding to ensure consistent feature representation. The ANN model was trained using three hidden layers (64–32–16 neurons) with the ReLU activation function and optimized using Adam with early stopping to prevent overfitting. Experimental results demonstrate that the proposed ANN model achieves an accuracy of 92%, with a precision of 0.91, a recall of 0.93, and a ROC-AUC of 0.95, indicating excellent classification capability. The Permutation Feature Importance analysis revealed that Credit Score, Income, and Loan Amount are the most significant predictors influencing loan approval decisions. These findings confirm that the ANN model can capture complex non-linear relationships among financial attributes while maintaining transparency through explainable AI techniques. The proposed approach contributes both theoretically and practically by combining predictive power with interpretability, offering a reliable and explainable framework for automating loan evaluation in modern financial institutions.
An Integrated Pavement Maintenance Management Model for Coastal Roads under Seawater Exposure and Traffic Loading Paikun, Paikun; Arie Susanto, Daniel; Oksri-Nelfia, Lisa; Mudjanarko, Sri Wiwoho; David Daniel, Basil; Dunu, Williams; Dewi, Deshinta Arrova
INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY VOLUME 9, NUMBER 1, APRIL 2026
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/urbanenvirotech.v9i1.24327

Abstract

Aim: This study aims to develop an adaptive management and maintenance model for flexible pavement in coastal areas by integrating road condition evaluation, cost analysis, and the effects of seawater immersion. The model is intended to improve maintenance efficiency, extend pavement service life, and support sustainable infrastructure management. Methodology and results: A quantitative and experimental approach was employed. Field surveys assessed pavement conditions using the Pavement Condition Index (PCI), Surface Distress Index (SDI), and International Roughness Index (IRI). Asphalt samples were tested in the laboratory under seawater immersion to evaluate strength reduction through Marshall and Indirect Tensile Strength tests. Damage data were integrated with maintenance cost analysis and traffic volume, producing a predictive model using regression and correlation analysis. Initial results indicate that seawater immersion significantly accelerates pavement deterioration and increases maintenance costs compared to normal conditions. Conclusion, significance, and impact study: he proposed model provides a comprehensive framework by considering technical, economic, and environmental factors specific to coastal infrastructure. Findings highlight the importance of condition-based maintenance strategies that are adaptive to climate change and extreme environmental risks. This study contributes to achieving sustainable infrastructure, resilient cities, and climate action for coastal environments.
Participatory Rational Justice in Criminal Law Reform as an Integrative Theory for Substantive Justice and Social Legitimacy Lesmana, CSA Teddy; Sulistiani, Lies; Putri, Nella Sumika; Dewi, Deshinta Arrova
Volksgeist: Jurnal Ilmu Hukum dan Konstitusi Vol. 9 Issue 1 (2026) Volksgeist: Jurnal Ilmu Hukum Dan Konstitusi
Publisher : Faculty of Sharia, Universitas Islam Negeri (UIN) Profesor Kiai Haji Saifuddin Zuhri Purwokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/volksgeist.v9i1.15585

Abstract

The traditional criminal justice system, predominantly grounded in retributive and legalistic paradigms, has increasingly struggled to respond to contemporary social realities, including prison overcrowding, limited victim participation, procedural rigidity, and declining public trust in legal institutions. In many jurisdictions, retributivism prioritizes punishment over social restoration, restorative justice mechanisms remain fragmented and marginal, and economic analysis of law (EAL) often emphasizes efficiency while overlooking normative legitimacy and participatory justice. Responding to these practical and conceptual limitations, this article proposes a new integrative framework Participatory Rational Justice (PRJ) which reconceptualizes criminal justice as a collaborative, community-engaged, and outcome-oriented process. Employing an interdisciplinary approach through theoretical analysis and comparative perspectives on criminal justice reforms, this study situates PRJ within existing reform practices that seek to balance efficiency, accountability, and social welfare. PRJ combines policy rationality, active stakeholder participation, resource efficiency, and social restoration to produce justice outcomes that extend beyond formal legality toward substantive societal benefits. By bridging procedural justice, economic legal reasoning, and capability-based substantive justice, PRJ offers a conceptually grounded yet practically relevant alternative for criminal justice reform. This study argues that adopting PRJ can enhance institutional legitimacy, optimize resource allocation, and foster a more inclusive legal culture, thereby contributing to the adaptive and sustainable development of contemporary criminal justice systems.
Co-Authors - Kurniawan, - Achsan, Harry Tursulistyono Yani Adi Suryaputra Paramita Adi Wijaya Afriyani, Sintia Ahmad Sanmorino Alde Alanda, Alde Ali Amran Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Andriani, Putu Eka Anita Desiani Aris Thobirin, Aris Armoogum, Sheeba Armoogum, Vinaye Aryananda, Rangga Laksana Asro Asro Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bin Abdul Hadi, Abdul Razak Bujang, Nurul Shaira Binti Chandra, Anurag CSA Teddy Lesmana David Daniel, Basil Devi Udariansyah Diana Diana Dita Amelia, Dita Dunu, Williams Efrizoni, Lusiana Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Endro Setyo Cahyono, Endro Setyo Eva Yulia Puspaningrum Fadly Fadly Fara Disa Durry Fatoni, Fatoni Fikri, Ruki Rizal Nul Firosha, Ardian Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Hasibuan, Muhammad Siad Henderi . Hendra Kurniawan Heng, Chang Ding Hidayani, Nieta Hisham, Putri Aisha Athira binti Humairah, Sayyidah I Gede Susrama Mas Diyasa Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Jayawarsa, A.A. Ketut Kezhilen, Motean Kijsomporn, Jureerat Kurniawan, Tri Basuki Larasati, Anggit Lexianingrum, Siti Rahayu Pratami Lies Sulistiani Lin, Leong Chi M Said Hasibuan M. Anjar Pamungkas M. Fariz Fadillah Mardianto Maizary, Ary Malik Cahyadin Mantena, Jeevana Sujitha MARIA BINTANG Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Murnawan, Murnawan Nathan, Yogeswaran Nazmi, Che Mohd Alif Nella Sumika Putri Oksri-Nelfia, Lisa Onn, Choo Wou Paikun Panguluri, Padmavathi Periasamy, Jeyarani Pratiwi, Ananda Pratiwi, Firda Aulia Praveen, S Phani Putra, Muhammad Daffa Arviano Putrie, Andi Vania Ghalliyah R Rizal Isnanto Rahmadani, Olivia Rendra Gustriansyah Samihardjo, Rosalim Saringat, Zainuri Setiawan, Ariyono Shinta Puspasari Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Slamet Riyadi Sri Astuti Iriyani Sri Karnila Sri Lestari Sri Wiwoho Mudjanarko, Sri Wiwoho Sugiyarto Surono, Sugiyarto Sulaiman Helmi Sulaiman, Agus Sunda Ariana, Sunda Susanto, Daniel Arie Taqwa, Dwi Muhammad Tarigan, Masmur Thinakaran, Rajermani Triloka, Joko Trinawarman, Dedi Wahyu Caesarendra Wahyu Dwi Lestari Wahyuningdiah Trisari Harsanti Putri Wei, Aik Sam Wibaselppa, Anggawidia Widyangga, Pressylia Aluisina Putri Widyaningsih , Upik Wijayanti, Dian Eka Yeh, Ming-Lang Yorman Yuli Andriani Zakari, Mohd Zaki Zakaria, Mohd Zaki