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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Sosial dan Teknologi Daengku: Journal of Humanities and Social Sciences Innovation Action Research Literate (ARL) Jurnal Indonesia Sosial Teknologi Jurnal Ekonomi, Teknologi dan Bisnis Blantika : Multidisciplinary Journal Journal of Innovation and Operational System
Syahrul Anwar
Politeknik Siber Cerdika Internasional
Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Kepuasan Masyarakat terhadap Kualitas Pelayananan Publik berbasis Digital di Desa Citemu Kecamatan Mundu Kabupaten Cirebon Askarno, Askarno; Anwar, Syahrul; Nurwahid, Arulfalah; Anisa, Mahdania; Toto, Toto
Blantika: Multidisciplinary Journal Vol. 3 No. 6 (2025): Special Issue
Publisher : PT. Publikasiku Academic Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57096/blantika.v3i6.371

Abstract

The main purpose of this study is to analyze the extent to which village websites can increase information disclosure, increase community participation in village development, and increase community satisfaction with the services provided by the village government. This study uses a descriptive qualitative method to obtain an overview of the effectiveness of village websites in disseminating development information and the level of community satisfaction with digital services. Data was collected through a questionnaire that included closed-ended and open-ended questions, with respondents consisting of the community as service users and village officials as providers. The results showed that most people expressed "Agree" (46%) and "Neutral" (37%) for village digital services, indicating that these services are useful but not optimal. The main factors that affect public satisfaction are service accessibility, information quality, and lack of socialization and digital education. The problem of limited internet networks and low digital literacy is a challenge in the implementation of digital services. Therefore, it is necessary to improve technology infrastructure and sustainable educational programs so that the public can better understand and utilize digital services effectively
Predicting School Dropout Risk Using Machine Learning Models: A Comparative Study of Random Forest, Gradient Boosting, and Neural Network Anwar, Syahrul
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 4 No. 6 (2025): JETBIS : Journal of Economics, Technology and Business
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v4i6.193

Abstract

Dropping out of school is a serious challenge in the education system that negatively impacts individual and social development. Early identification of students at risk of dropping out of school is crucial to prevent its long-term impact. This study aims to develop and compare a model for predicting the risk of dropping out of school using a machine learning approach. The three models compared in the study were Random Forest, Gradient Boosting, and Neural Network, with data covering 1000 students and features such as socioeconomic status, academic performance, parental engagement, distance to school, and educational resources. The results of the evaluation showed that the Random Forest model performed best with an accuracy of 93%, followed by Neural Network (92%) and Gradient Boosting (90%). The feature importance analysis revealed that socioeconomic status, parental involvement, and academic achievement were the dominant factors in predicting the risk of dropping out. These findings demonstrate the potential of applying machine learning as an early warning system for more targeted interventions in improving student retention. Further research is recommended to include psychological variables and longitudinal data as well as develop information technology-based systems for real implementation in schools.
Perancangan Antarmuka Pada Aplikasi Simaku dengan Kansei Engineering dan AHP Anwar, Syahrul
Action Research Literate Vol. 8 No. 4 (2024): Action Research Literate
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/arl.v8i4.312

Abstract

Dalam era teknologi informasi yang krusial, Universitas Muhammadiyah Cirebon berupaya memaksimalkan penggunaan Sistem Informasi Manajemen Akademik dan Keuangan (SIMAKU). Namun, perancangan antarmuka pengguna SIMAKU masih perlu peningkatan, terutama dalam layanan kemahasiswaan. Penelitian ini bertujuan untuk merancang antarmuka yang memenuhi kebutuhan emosional pengguna dengan menggunakan pendekatan Kansei Engineering. Penelitian ini membatasi penggunaan metode Kansei Engineering tipe I (KEPack) dan analisis pada pengalaman pengguna dalam aplikasi SIMAKU di Universitas Muhammadiyah Cirebon.  Hasil penelitian menunjukkan 13 kata Kansei yang relevan untuk desain antarmuka SIMAKU, dengan faktor emosional "Futuristik" sebagai fokus utama.  Rekomendasi elemen tampilan antarmuka dihasilkan dari analisis menggunakan metode Kansei Engineering dan AHP, memastikan kesejajaran dengan preferensi pengguna dan tujuan aplikasi. Dalam ringkasan, penelitian ini menekankan pentingnya merancang antarmuka berdasarkan respons emosional pengguna dengan Kansei Engineering. Dengan demikian, aplikasi SIMAKU dapat lebih efektif memenuhi kebutuhan pengguna dan tujuan universitas.
Digital Transformation of Student Assignment Management through a Mobile-Friendly E-Learning System Integrated with WhatsApp Gateway and Google Drive Syahrul Anwar; Abdul Robi Padri; Ade Bani Riyan; Eko Siswo Adi Sahputra
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v11i6.64930

Abstract

Objective: To develop a mobile-friendly e-learning system for managing student assignments that integrates with a WhatsApp Gateway and Google Drive, aiming to translate digital transformation in higher education into simple, rapid, and integrated assignment management processes. Methods: This study employs a Research and Development (R&D) approach using the ADDIE model, which includes needs analysis, architectural design, prototype development, limited implementation, and evaluation of usability and functionality. The proposed system features assignment creation, automatic WhatsApp reminders, Google Drive file integration, submission status tracking, and mobile-first dashboards. The evaluation is conducted through expert validation, black-box testing, System Usability Scale (SUS) measurement, and Likert-based user perception questionnaires. Results: The implementation of the system is expected to yield improved on-time assignment submissions, a reduction in late submissions, a more efficient workflow for lecturers, and the establishment of an economical yet reliable small-scale personal Learning Management System (LMS) model. Conclusion: This research provides significant practical contributions for educational institutions seeking a lightweight, compliant, and readily adoptable digital assignment management system that enhances learning flexibility and academic engagement.
Analisis Kepuasan Pengguna Aplikasi Nyari Gawe Menggunakan Model Technology Acceptance Model (TAM) dan K-Means Clustering Syahrul Anwar
Jurnal Sosial Teknologi Vol. 6 No. 4 (2026): Jurnal Sosial dan Teknologi
Publisher : CV. Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsostech.v6i4.32768

Abstract

Construction projects in DIY have high complexity because they are located in disaster-prone areas such as earthquakes, floods, and landslides, so they require structured risk management. This research aims to analyze the influence of cost, quality, time, and accident risk identification on disaster risk management of construction projects. The research employed a quantitative approach by distributing questionnaires to 30 respondents consist of contractors, consultants, and project supervisors. Data were analyzed using statistical test multiple linear regression to examine the influence of each aspect on disaster risk management. The findings indicate that cost risk identification has a significant effect on risk management effectiveness, particularly in preventing budget overruns caused by external conditions such as material inflation and supply uncertainty. The quality aspect contributes substantially to improving construction resilience against disasters, in line with the application of quality standards based on SNI and ISO. The time aspect shows a clear influence on successful risk mitigation, as project delays increase vulnerability to disaster impacts. Meanwhile, accident risk identification is strongly related to the contractors’ readiness in implementing occupational health and safety (OHS) procedures to reduce potential human and material losses. Overall, this study emphasizes the importance of integrating risk identification into the construction project management system in the Special Region of Yogyakarta Province. The findings provide practical implications for contractors, local governments, and other stakeholders in developing adaptive, measurable, and responsive risk management strategies in disaster-prone regions.
Generative AI Adoption in Indonesian Higher Education: A Sociotechnical Analysis of Student Engagement, Digital and AI Literacy, and Academic Integrity Sudrajat Sudrajat; Bagaskara Nur Rochmansyah; Leonita Leonita; Zhou Yin Lin; Syahrul Anwar
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 4 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku5039

Abstract

Generative artificial intelligence (GenAI) has rapidly entered higher education and changed how students access information, complete academic tasks, and engage with learning. However, GenAI adoption cannot be understood only as individual technology use because it is also shaped by literacy, academic integrity, institutional rules, and pedagogical context. This study analyzes GenAI adoption in Indonesian higher education through a sociotechnical perspective. The study used a descriptive-analytical design based on an Indonesian higher education subset extracted from a public global survey dataset on students’ early perceptions of ChatGPT. After institutional-location filtering, the final analytical subset consisted of 270 respondents affiliated with higher education institutions located in Indonesia. The analysis focused on GenAI adoption, technology acceptance, digital and AI literacy, student engagement, academic integrity, and institutional support. The findings show that students demonstrated more favorable satisfaction and attitude scores than usage-frequency scores, indicating that positive perceptions of ChatGPT did not automatically translate into intensive academic use. The study also found that digital and AI literacy is a key condition for responsible GenAI adoption because students need to evaluate AI-generated outputs, recognize limitations, verify information, and apply ethical judgment. Study-related outcome blocks showed moderate-to-positive tendencies, suggesting that ChatGPT may support learning, although its contribution to engagement remains developing. Regulation and ethical concern blocks also showed moderate awareness but continuing uncertainty regarding acceptable use. The study concludes that responsible GenAI adoption in Indonesian higher education requires balanced institutional guidance, AI literacy development, assessment redesign, and clear academic integrity policies.
Pendekatan Kecerdasan Buatan Hibrida dalam Meningkatkan Akurasi Prediksi Churn pada Big Data Ade Bani Riyan; Syahrul Anwar; Eko Siswo Adi Sahputra
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 8 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i8.9094

Abstract

The explosion of digital data has given birth to the era of big data, which presents great opportunities as well as significant challenges in knowledge extraction. Traditional data mining processes often face obstacles in terms of accuracy and efficiency when faced with massive data volume, variety, and speed. This study aims to propose and evaluate a hybrid model based on Artificial Intelligence (AI) to improve the performance of the data mining process on large-scale data sets. The proposed model integrates the power of Random Forest's algorithm in handling structured data and resistance to overfitting, with the ability of Neural Networks to model complex non-linear relationships. The research uses a case study on customer churn data from the e-commerce industry which contains 1.5 million records, with comprehensive data mining process stages, ranging from data preprocessing, feature engineering, to model implementation. The results of the evaluation showed that the hybrid model achieved an accuracy of 94.7% and an AUC (Area Under the Curve) value of 0.97, significantly outperforming the Random Forest (91.2% accuracy, 0.93 AUC) and Artificial Neural Network (92.5% accuracy, 0.95 AUC) models. Although hybrid models require slightly higher computational times, the substantial increase in accuracy provides a strong justification for their use in critical business scenarios. This study provides empirical evidence that the hybrid AI approach is an effective and promising strategy to address the challenges of big data analysis, particularly in critical business scenarios where predictive accuracy is a top priority.
Beyond Efficiency: Exploring Employee Experiences of Process Innovation in Service Sector Operations Syahrul Anwar
Journal of Innovation and Operational System Vol. 2 No. 1 (2025): Journal of Innovation and Operational System
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jiosjournal.v2i1.6

Abstract

implement process innovations to enhance operational efficiency and competitiveness. H In today’s rapidly evolving service industry, organizations increasingly implement process innovations to enhance operational efficiency and competitiveness. However, existing literature has primarily focused on efficiency metrics, often overlooking the experiential dimensions of such innovations from the employee perspective. This research aims to explore how employees perceive, experience, and respond to process innovation initiatives within service sector operations. Using a mixed-methods approach, the study collected data from 185 employees across various service-based organizations through structured surveys and in-depth interviews. Quantitative data were analyzed using SPSS 26.0 and NVivo 12 was used for thematic analysis of qualitative inputs. The findings reveal that while process innovations often result in measurable improvements in workflow efficiency, their implementation also impacts employee engagement, job satisfaction, and perceived autonomy. Specifically, employee involvement in the innovation process significantly enhances creative self-efficacy and fosters proactive behavior. The study contributes to service innovation literature by emphasizing the role of human-centered perspectives in process redesign. It also highlights the need for managerial strategies that strike a balance between technical efficiency and the emotional and motivational needs of employees. Practically, the findings suggest that inclusive and participatory innovation practices not only improve operational outcomes but also support sustainable workforce development. Future research is encouraged to explore longitudinal effects of process innovation and test models across different cultural or organizational contexts. owever, existing literature has primarily focused on efficiency metrics, often overlooking the experiential dimensions of such innovations from the employee perspective. This research aims to explore how employees perceive, experience, and respond to process innovation initiatives within service sector operations. Using a mixed-methods approach, the study collected data from 185 employees across various service-based organizations through structured surveys and in-depth interviews. Quantitative data were analyzed using SPSS 26.0 and NVivo 12 was used for thematic analysis of qualitative inputs. The findings reveal that while process innovations often result in measurable improvements in workflow efficiency, their implementation also impacts employee engagement, job satisfaction, and perceived autonomy. Specifically, employee involvement in the innovation process significantly enhances creative self-efficacy and fosters proactive behavior. The study contributes to service innovation literature by emphasizing the role of human-centered perspectives in process redesign. It also highlights the need for managerial strategies that strike a balance between technical efficiency and the emotional and motivational needs of employees. Practically, the findings suggest that inclusive and participatory innovation practices not only improve operational outcomes but also support sustainable workforce development. Future research is encouraged to explore longitudinal effects of process innovation and test models across different cultural or organizational contexts.
Co-Authors Abdul Robi Padri Anisa, Mahdania Askarno, Askarno Bagaskara Nur Rochmansyah Eko Siswo Adi Sahputra Leonita Leonita Nurwahid, Arulfalah Riyan, Ade Bani Sudrajat Sudrajat Toto Toto, Toto Zhou Yin Lin