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All Journal Jurnal Teknologi JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Emerging Science Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Physical Review 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 Bulletin of Applied Mathematics and Mathematics Education Jurnal Ilmiah Ilmu Terapan Universitas Jambi Jurnal Pemberdayaan Masyarakat Eastasouth Journal of Effective Community Services Journal of Educational Technology and Learning Creativity 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 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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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.
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.
Sharing Best Practices on the Implementation of Standard Inpatient Class (KRIS) and Bed Readiness in Hospitals: Indonesia-Malaysia Collaboration Rizky, Wahyu; Prasetyo, Budi; Azali, Lalu M. Panji; Saelan, Saelan; Dewi, Deshinta Arrova; Valentina, Amara; Fitriyani, Amelia Sofa; Agustina, Dea; Khasanah, Eka Uswatun; Oktavia, Fania
Eastasouth Journal of Effective Community Services Vol 4 No 03 (2026): Eastasouth Journal of Effective Community Services (EJECS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/ejecs.v4i03.494

Abstract

Kebijakan Kelas Rawat Inap Standar (KRIS) dan kesiapan tempat tidur (bed readiness) merupakan upaya penting dalam meningkatkan mutu dan pemerataan layanan rumah sakit dalam sistem Jaminan Kesehatan Nasional. Namun, literasi kesehatan masyarakat, khususnya generasi muda, masih terbatas. Oleh karena itu, kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan pemahaman siswa melalui kegiatan Sharing Best Practices on the Implementation of Standard Inpatient Class (KRIS) and Bed Readiness in Hospital: Indonesia–Malaysia Collaboration. Kegiatan dilaksanakan pada siswa SMA Muhammadiyah Program Khusus Kottabarat Surakarta melalui metode edukasi partisipatif berupa ceramah interaktif, diskusi, dan pemaparan praktik terbaik dari Indonesia dan Malaysia. Hasil kegiatan menunjukkan peningkatan pemahaman peserta mengenai konsep KRIS, bed readiness, serta pentingnya standar pelayanan kesehatan, disertai antusiasme dan partisipasi aktif. Meskipun menghadapi keterbatasan waktu dan evaluasi kuantitatif, kegiatan ini berkontribusi positif dalam meningkatkan literasi kesehatan serta memperkuat kolaborasi internasional dan berpotensi dikembangkan secara berkelanjutan.
Machine Learning-Based Multi-Sensor IoT System for Intelligent Indoor Fire Detection Junfithranaa, Anggy Pradifta; Almohab, Hadi; Dewi, Deshinta Arrova
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2614

Abstract

Purpose of the study: This study aims to develop an intelligent indoor fire detection system by integrating low-cost Internet of Things (IoT) sensors with machine learning-based multi-sensor data fusion to improve early fire hazard detection accuracy while reducing false alarms compared to conventional single-sensor fire detection systems. Methodology: The system is implemented using an ESP32 microcontroller connected to temperature, humidity, flame, and sound sensors for real-time data acquisition. A dataset of 1,500 sensor samples is collected and labeled into Normal, Fire-Risk, and Fire classes. Decision Tree, Support Vector Machine, and Random Forest classifiers are trained and evaluated using Python-based machine learning libraries. Main Findings: Experimental results indicate that the Random Forest model outperforms the other classifiers, achieving 95% overall accuracy, perfect recall for fire events, and a Macro ROC-AUC score of 0.993. Feature importance analysis reveals that humidity and temperature are the most influential parameters for early fire detection in indoor environments. Novelty/Originality of this study: This study proposes a lightweight intelligent fire detection framework that integrates multi-sensor Internet of Things data including temperature, humidity, flame, and sound signals with machine learning–based classification for indoor environments. Unlike conventional systems that rely on single-sensor or threshold-based detection, the proposed approach utilizes multi-sensor data fusion and ensemble learning to improve early fire-risk identification while remaining computationally efficient for low-cost platforms such as the ESP32 microcontroller.
SEASONAL CONTROLS OF ATMOSPHERIC MOISTURE ON TROPICAL PRECIPITATION ANOMALIES Darmawan, Yahya; Dewi, Deshinta Arrova
Indonesian Physical Review Vol. 9 No. 2 (2026)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v9i2.686

Abstract

Inter-annual precipitation variability across tropical regions is driven by complex interactions between large-scale atmospheric circulation and local processes, yet the relative contributions of key moisture budget components remain insufficiently quantified. This study examines seasonal contrasts between DJF and JJA in the Tropical Region using a moisture budget framework, with northern Sumatra, Indonesia, as a representative tropical case study. By using 36 years (1981–2016) of ERA-Interim data, wet and dry years are identified based on standardized precipitation anomalies, with statistical significance assessed using a Student’s t-test. Composite analyses show that anomalous vertical moisture transport associated with vertical velocity anomalies (-〈ω^' ∂_p q ̅ 〉) is the dominant contributor in both seasons. However, its magnitude is weaker during DJF, indicating less coherent upward motion and weaker coupling between the large-scale circulation and convection than during JJA. In JJA, enhanced large-scale circulation strengthens moisture convergence and divergence, producing more organized convection. In contrast, DJF exhibits weaker circulation and a larger residual, suggesting stronger influences of transient and nonlinear processes. These findings highlight seasonal asymmetry in precipitation controls and provide insights applicable to tropical climate variability.
Machine Learning-Based Multi-Sensor IoT System for Intelligent Indoor Fire Detection Junfithranaa, Anggy Pradifta; Almohab, Hadi; Dewi, Deshinta Arrova
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2614

Abstract

Purpose of the study: This study aims to develop an intelligent indoor fire detection system by integrating low-cost Internet of Things (IoT) sensors with machine learning-based multi-sensor data fusion to improve early fire hazard detection accuracy while reducing false alarms compared to conventional single-sensor fire detection systems. Methodology: The system is implemented using an ESP32 microcontroller connected to temperature, humidity, flame, and sound sensors for real-time data acquisition. A dataset of 1,500 sensor samples is collected and labeled into Normal, Fire-Risk, and Fire classes. Decision Tree, Support Vector Machine, and Random Forest classifiers are trained and evaluated using Python-based machine learning libraries. Main Findings: Experimental results indicate that the Random Forest model outperforms the other classifiers, achieving 95% overall accuracy, perfect recall for fire events, and a Macro ROC-AUC score of 0.993. Feature importance analysis reveals that humidity and temperature are the most influential parameters for early fire detection in indoor environments. Novelty/Originality of this study: This study proposes a lightweight intelligent fire detection framework that integrates multi-sensor Internet of Things data including temperature, humidity, flame, and sound signals with machine learning–based classification for indoor environments. Unlike conventional systems that rely on single-sensor or threshold-based detection, the proposed approach utilizes multi-sensor data fusion and ensemble learning to improve early fire-risk identification while remaining computationally efficient for low-cost platforms such as the ESP32 microcontroller.
Co-Authors - Kurniawan, - Achsan, Harry Tursulistyono Yani Adi Suryaputra Paramita Adi Wijaya Afriyani, Sintia Agustina, Dea Ahmad Sanmorino Alde Alanda, Alde Ali Amran Almohab, Hadi Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Andriani, Putu Eka Anita Desiani Armoogum, Sheeba Armoogum, Vinaye Aryananda, Rangga Laksana Asro Asro Azali, Lalu M. Panji Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bin Abdul Hadi, Abdul Razak Budi Prasetyo Bujang, Nurul Shaira Binti Chandra, Anurag CSA Teddy Lesmana Devi Udariansyah Diana Diana Dita Amelia, Dita 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 Fitriyani, Amelia Sofa 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 Junfithranaa, Anggy Pradifta Kezhilen, Motean Khasanah, Eka Uswatun Kijsomporn, Jureerat Kurniawan, Tri Basuki 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 Oktavia, Fania Onn, Choo Wou 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 Rizky, Wahyu Rufi'i Saelan, Saelan Samihardjo, Rosalim Saringat, Zainuri Setiawan, Ariyono Shinta Puspasari Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Slamet Riyadi Sri Astuti Iriyani Sri Karnila Sugiyarto Surono, Sugiyarto Sulaiman Helmi Sulaiman, Agus Sunda Ariana, Sunda Taqwa, Dwi Muhammad Tarigan, Masmur Thinakaran, Rajermani Triloka, Joko Trinawarman, Dedi Valentina, Amara Wahyu Caesarendra Wahyu Dwi Lestari Wahyuningdiah Trisari Harsanti Putri Wei, Aik Sam Wibaselppa, Anggawidia Widyangga, Pressylia Aluisina Putri Widyaningsih , Upik Wijayanti, Dian Eka Yahya Darmawan Yeh, Ming-Lang Yorman Zakari, Mohd Zaki Zakaria, Mohd Zaki