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All Journal Jurnal Simetris Jurnal Ilmu Administrasi Negara (JUAN) EKUITAS (Jurnal Ekonomi dan Keuangan) Jurnal ICT : Information Communication & Technology Journal of Applied Data Sciences Kresna: Jurnal Riset dan Pengabdian Masyarakat Journal Of World Science Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Indonesia Sosial Teknologi Indonesia Auditing Research Journal Journal of Business Management and Economic Development Prosiding Amal Insani Foundation (Indonesian of Interdisciplinary Journal) Horizon: Indonesian Journal of Multidisciplinary Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi INOVTEK Polbeng - Seri Informatika Green Engineering: Journal of Engineering and Applied Science Integrated System and Management Technology Big Data Analytics and Data Science
Asro Asro
Universitas Primagraha
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Executive Decision Support System Implementation Strategies Based on Big Data Analytics to Improve Operational Efficiency and Corporate Governance in Global Digital Enterprises Asro Asro; Solihin Solihin; Irlon Irlon
Integrated System and Management Technology Vol. 1 No. 1 (2026): January: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i1.13

Abstract

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.
Optimizing End to end Machine Learning Pipelines Using Hybrid Edge Cloud Architectures for Real Time Decision making Applications Asro Asro; Solihin Solihin; Irlon Irlon
Big Data Analytics and Data Science Vol. 1 No. 1 (2026): March: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i1.22

Abstract

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.
Sistem Analitik Umpan Balik YouTube Berbasis Big Data dan Generative AI: Big Data-Based YouTube Feedback Analytics Architecture with Generative AI Integration Asro, Asro; Rukhviyanti, Novi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2626

Abstract

Perkembangan media sosial menghasilkan data opini pengguna dalam jumlah besar dan tidak terstruktur, khususnya pada platform YouTube. Komentar pengguna mengandung informasi penting mengenai persepsi terhadap produk digital, namun analisis manual masih menjadi kendala. Penelitian ini bertujuan mengembangkan arsitektur sistem analitik umpan balik berbasis Big Data yang mengintegrasikan analisis sentimen dan Generative Artificial Intelligence (AI) dalam satu pipeline end-to-end. Sebanyak 10.625 komentar dikumpulkan melalui YouTube Data API dan disimpan dalam MongoDB. Proses analisis menggunakan kerangka CRISP-DM dengan preprocessing teks dan representasi fitur TF-IDF. Klasifikasi sentimen dibandingkan menggunakan Logistic Regression, Support Vector Machine (SVM), dan Random Forest, dengan pengujian SMOTE pada data latih. Hasil menunjukkan bahwa SVM tanpa SMOTE memberikan performa terbaik dengan akurasi 83,97% dan F1-macro 80,29%. Integrasi Generative AI memungkinkan peringkasan isu dominan serta penyusunan rekomendasi perbaikan secara otomatis. Sistem yang dikembangkan mendukung pengambilan keputusan berbasis data secara lebih efisien.
Development of a Digital Twin Based Smart Green Building Energy Management Model Integrating IoT Sensors and Predictive Sustainability Analytics Asro Asro; Solihin Solihin; John Chaidir; Febri Adi Prasetya; Tuti Susilawati; Muhamad Furqon; Bentar Priyopradono
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.287

Abstract

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.
Analysis of transformational leadership on organizational citizenship behavior with job satisfaction as an intervening variable M. Yusron; Asro Asro
Indonesia Auditing Research Journal Vol. 14 No. 3 (2025): September: Auditing, Finance, IT Plan, IT Governance, Risk
Publisher : Institute of Accounting Research and Novation (IARN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/arj.v14i3.532

Abstract

This study examines the influence of transformational leadership on organizational citizenship behavior (OCB) with job satisfaction as an intervening variable. Using a quantitative approach and PLS-SEM, data were collected from 92 employees in PT Lung Cheong Brothers Industrial. The results reveal that transformational leadership has a positive and significant effect on both job satisfaction and OCB. Moreover, job satisfaction is positively associated with OCB and functions as a significant mediator in the relationship between transformational leadership and OCB. These findings confirm that transformational leaders, through inspirational motivation, intellectual stimulation, idealized influence, and individualized consideration, not only directly enhance employees’ extra-role behaviors but also foster higher job satisfaction, which in turn strengthens OCB. The study contributes theoretically by reinforcing Social Exchange Theory and expanding its relevance in the context of developing countries. Practically, it highlights the importance for organizations to invest in leadership development programs and strategies that enhance employee satisfaction to encourage sustainable OCB. Limitations of this study include its relatively small sample size, reliance on self-reported data, and exclusion of other potential variables such as organizational culture. Future research should consider larger samples, longitudinal designs, and additional mediating or moderating variables to further enrich the understanding of these relationships.
Klasifikasi Kemancetan Lalu Lintas di Indonesia Menggunakan Metode Naive Bayes Classification Abdul Robi Padri; Asro Asro; Chairuddin Chairuddin
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 14 No. 2 (2023): JURNAL SIMETRIS VOLUME 14 NO 2 TAHUN 2023
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v14i2.10102

Abstract

Tujuan dari penelitian ini untuk data menganalisis akurasi kemancetan menggunakan google colab  dalam mendeteksi kemacetan berdasarkan provinsi di indonesia, penulis mencoba menguji strategi dalam menangani Kemacetan wilayah indonesia, dengan memanfaatkan metode naive bayes. Pada jurnal ini menerapkan dengan google colab. Penelitian ini memakai data yang bersumber dari crawling data di twiter. Penggunaan metode naive bayes dalam mencari Rute terpendek efesien dan tidak mancet. Penerapan Angkot Sekolah online menggunakan Metode naive bayes dalam Minimalisir Biaya Perjalanan menjemput Siswa dapat mengurangi macet, mengurangi kecelakaan, mengurangi waktu keterlambatan siswa, minimalisir ongkos perjalanan. Saran yang diberikan yaitu menjadi bahan evaluasi bagi pemerintah dalam menangani Kemacetan di indonesia, secara efisien, aman dan transparan.
Role of HR Planning in Developing Employees to Improve Company Performance Riandika Aji Mehendra; Ali Imron; Asro Bin Harun; Dika Hilaldi
Jurnal Ilmu Administrasi Negara (JUAN) Vol 14 No 1 (2026): June, 2026
Publisher : Program Studi Ilmu Administrasi Negara FISIP UMRAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/juan.v14i1.8225

Abstract

This study aims to analyze the role of human resource planning in developing employees to improve company performance, particularly by explaining how workforce forecasting, competency mapping, employee development, career management, and performance alignment contribute to organizational effectiveness. This study employed a qualitative descriptive method using a literature-based approach. Data were collected from secondary sources, including peer-reviewed journal articles, academic references, and relevant scholarly documents related to human resource planning, employee development, strategic human resource management, and company performance. The data were analyzed through qualitative content analysis by identifying, classifying, comparing, and interpreting major themes found in the literature. The results show that human resource planning functions as a strategic foundation for employee development by helping organizations determine workforce quantity, quality, competency needs, and future employee requirements. Effective HR planning supports employee development through training, career development, competency-based placement, succession planning, and continuous performance evaluation. The findings also indicate that ineffective HR planning may negatively affect company performance by creating structural ambiguity, inefficient resource utilization, increased costs, reduced employee welfare, and lower productivity. Conversely, systematic HR planning strengthens organizational readiness, improves employee capability, enhances work motivation, supports innovation, and contributes to sustainable company performance. Concludes that human resource planning should not be treated merely as an administrative function, but as a strategic instrument for building competent, adaptive, and performance-oriented employees who can support long-term organizational competitiveness.
THE EFFECT OF WORK-LIFE BALANCE AND ORGANIZATIONAL CITIZENSHIP BEHAVIOR ON EMPLOYEE PERFORMANCE AT THE LEBAK REGENCY PERSONNEL AND HUMAN RESOURCE DEVELOPMENT AGENCY Achmad Rozi; Tiara Nabillah Wondu; Asro Harun
Prosiding Amal Insani Foundation Vol. 3 (2026): PROSIDING INTERNASIONAL
Publisher : Amal Insani Foundation

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

Abstract

This study aims to analyze the influence of work-life balance and organizational citizenship behavior on employee performance at the Lebak Regency Personnel and Human Resources Development Agency (BKPSDM). The study used a quantitative approach with a population of 30 employees and saturated sampling techniques. Data were collected through questionnaires and analyzed by multiple linear regression. The results of the study show that work-life balance and organizational citizenship behavior have a positive and significant effect both partially and simultaneously on employee performance. The value of the determination coefficient (R²) of 0.610 or 61.0% indicates that the two variables are able to explain employee performance by 61.0%, while the remaining 39.0% is influenced by other factors outside of this study. Partially, organizational citizenship behavior has a more dominant influence than work-life balance in improving employee performance.
PUBLIC PERCEPTION OF MSME DIGITAL TRANSFORMATION IN INDONESIA: EVIDENCE FROM YOUTUBE COMMENT SENTIMENT ANALYSIS Asro Harun
Prosiding Amal Insani Foundation Vol. 3 (2026): PROSIDING INTERNASIONAL
Publisher : Amal Insani Foundation

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

Abstract

Digital transformation has become a strategic requirement for micro, small, and medium enterprises (MSMEs) in Indonesia, particularly in the adoption of marketplaces, QRIS-based payments, digital promotion, and platform-based business operations. This study analyzes public perception of MSME digital transformation using 5,751 YouTube comments collected from January to May 2026 through selected keywords related to MSME digitalization, including marketplace adoption, Shopee UMKM, Tokopedia UMKM, QRIS, and online business practices. The research applies a machine learning-based sentiment analysis pipeline consisting of text preprocessing, TF-IDF feature extraction, classification using Logistic Regression, Support Vector Machine, and Random Forest, and performance comparison with and without SMOTE. The evaluation uses accuracy, macro F1-score, confusion matrix, monthly distribution, keyword frequency, and word cloud visualization. The findings indicate that public discussion is dominated by neutral comments, while positive expressions highlight usefulness, ease, and marketplace opportunities. Negative comments are mainly associated with technical problems, application errors, costs, and platform difficulties. Random Forest without SMOTE achieved the highest accuracy of 98.94%, while SVM with SMOTE obtained the best macro F1-score of 83.77%, showing a better balance in recognizing minority sentiment classes. The study concludes that YouTube comments can function as a useful source of digital social sensing to understand public perception and to support evidence-based MSME digital transformation strategies.
Co-Authors Abda Abda Abdul Robi Padri Abdul Robi Padri Abdul Robi Padri Achmad Rozi Agung Wicaksono Alfarizy, Muhamad Akbar Ali Imron Aliyah Aliyah Alwin Hartono Limaran Amir Akbar Wicaksono Amiruddin Amiruddin Bahiroh, Eloh Bentar Priyopradono Chairuddin Chairuddin Dewi Kania Dewi, Deshinta Arrova Dickson P. Aritonang Dika Hilaldi Dikri Gifari Febri Adi Prasetya Gelar Alam Ramdhaniawan Henderi . Ida Ayu Putu Sri Widnyani Iis Istiharoh Indra Indra Irlon Irlon Istiharoh, Iis John Chaidir Kambara, Roni Kurniawan, Tri Basuki M Yusron M. Adhit Dwi Yuda M. Yusron M. Yusron Mashal Alqudah Meisa Monica Muhamad Furqon Mujiburohman Mukhsin, Moh Novi Rukhviyanti Nur Azizah Padri, Abdul Robi Patah Herwanto Riandika Aji Mehendra Rivaldi Arif Salsanabila Mariestiara Putri Setiawati, Firda Solihin Solihin Solihin Sudaryono Sudaryono Sudaryono Sudaryono Sudaryono Sulaiman, Agus Susan Juli Safitri Tb. Ade Rahmatullah Thalia, Shelma Najwa Tiara Nabillah Wondu Tuti Susilawati Wawan Prahiawan