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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi CESS (Journal of Computer Engineering, System and Science) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTECOMS: Journal of Information Technology and Computer Science Journals of Ners Community Journal of Information System, Applied, Management, Accounting and Research JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Prosiding National Conference for Community Service Project INFOKUM Jurnal Teknologi Informasi dan Komunikasi Journal of Applied Data Sciences Journal of Information System and Technology (JOINT) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Community Engagement Project (Concept) Conference on Business, Social Sciences and Technology (CoNeScINTech) Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Sama, Hendi
Fakultas Ilmu Komputer, Universitas Internasional Batam, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Other

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Assessing Assessing Students’ Ethical Concerns in AI-Integrated Online Learning Systems: A Study in Batam City Hendi Sama; Julianto; Surya Tjahyadi
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.69

Abstract

The adoption of Artificial Intelligence (AI) in online education introduces ethical concerns related to accuracy, fairness, and accountability. This study examines students’ ethical concerns of AI-integrated learning systems, focusing on AI-generated materials, attendance monitoring, and chatbot interactions. A mixed-method approach combining qualitative and quantitative techniques was used, involving interviews with 48 students from three different educational levels in Batam City. Thematic analysis identified five dominant themes. These included AI as a useful yet unreliable learning assistant, concerns about fairness in AI-based monitoring, uncertainty regarding responsibility and accountability, the need for transparent institutional policies, and limited AI literacy leading to overreliance. The study reports its findings using percentage-based distributions to illustrate the prevalence of these concerns across educational levels. The results indicate that students’ acceptance of AI in online learning is closely tied to the presence of human oversight, transparent institutional policies, and clearly defined accountability mechanisms. The novelty of this study lies in its focus on a pre-adoption educational environment, where AI is not yet fully institutionalized. Unlike prior studies examining post-implementation contexts, this research captures students’ anticipatory ethical expectations, highlighting concerns often overlooked in retrospective evaluations. The study contributes by providing empirical evidence across educational levels, offering localized insights from a developing Indonesian city, and extending AI ethics research beyond technology-advanced settings. The findings emphasize the importance of human oversight, accountability mechanisms, and transparent institutional policies for ethically grounded AI governance in education.
Acquiring Knowledge from Data Analytics and Performance-Boosting on Multimedia Content Jed Wan; Hendi Sama; Muhamad Dody Firmansyah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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

Abstract

In gaining meaningful and actionable insights from complex and diverse multimedia content, many studies have applied data analytics approaches—particularly data mining and machine learning—to uncover patterns, relationships, and hidden knowledge. This systematic literature review synthesizes 26 studies conducted over the past decade on acquiring knowledge from multimedia content using data analytics and performance-boosting techniques. Across domains such as social media, education, healthcare, e-commerce, and public safety, most works integrate text–image or audio–video pairs and increasingly adopt attention-based architectures and transformer models with early fusion strategies. To ensure comparability, each study’s evidence is recorded by considering the reported performance improvement over the authors’ baseline using the same dataset and evaluation metric. The most frequently used metrics include Accuracy, the F1-score (a harmonic mean of Precision and Recall), Precision, Recall, and the Area Under the Receiver Operating Characteristic Curve (AUC), which provides a threshold-independent measure of classification quality. The most common challenges identified include modality integration and alignment, data noise and quality, limitations of datasets and benchmarks, and domain shift, with fewer studies reporting class imbalance, computational cost, and interpretability or privacy issues. At the same time, promising opportunities emerge in the development of standardized multimodal benchmarks, efficient transformer-based and hybrid fusion pipelines, integration of external knowledge, domain-robust learning, and privacy-preserving explainable multimodal artificial intelligence. Overall, this review contributes a consolidated map of modalities, methods, and metrics, a performance-gain versus baseline table for quick comparability, a quantified challenge landscape, and a practical roadmap for guiding future research in multimodal sentiment analysis and related fields.
ANALISIS PERSEPSI ORANG DALAM KEPERCAYAAN MENGGUNAKAN CRYPTOCURRENCY DI INDONESIA Hendi Sama; Nelson
BETRIK Vol. 13 No. 03 (2022): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/44qx5d52

Abstract

This study aims to determine people's perceptions of trust in using cryptocurrency in Indonesia. This study uses a qualitative and quantitative approach with Cryptocurrency and Blockchain variables as independent variables and Trust as the dependent variable. In the qualitative method, 30 samples of people who used cryptocurrency were taken and in the quantitative method, 400 samples were taken of people who used cryptocurrency with a population as of July 2021 of 7.4 million based on data from the Commodity Futures Trading Supervisory Agency (Bappebti) of the Ministry of Trade (Ministry of Trade). The method of collecting data on the qualitative approach is the interview method while the quantitative approach uses the method of distributing questionnaires. Data analysis uses the SPSS application, for qualitative data it will be codified first. Based on the results of qualitative and quantitative data analysis, the authors conclude that the Cryptocurrency and Blockchain variables partially and simultaneously affect the Trust variable with percentages of 87.8% and 73.2% respectively.
Teknologi Informasi, Media Sosial, dan Transformasi Digital Terhadap Performa Usaha UMKM Laundry di Kota Batam Surya Tjahyadi; Cindy Claudia Erica; Hendi Sama
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9545

Abstract

This study examines the effects of information technology, social media, and digital transformation on the business performance of laundry MSMEs in Batam City using a quantitative associative approach. Data were collected from 94 owners or managers of laundry MSMEs through questionnaires and analyzed using IBM SPSS Statistics 25. Classical assumption tests indicated that the data were normally distributed and free from multicollinearity and heteroscedasticity. Simultaneous testing showed that information technology, social media, and digital transformation jointly have a significant effect on business performance (F = 4.588; p = 0.005). However, partial test results revealed that only social media has a positive and significant effect on business performance, while information technology and digital transformation do not show significant effects. These findings suggest that in small-scale service MSMEs, market-oriented digital tools are more effective in improving business performance than complex internal technology adoption.
Analysis of the Use of Distance Learning Technology in Universities in the Riau Islands Province with the Technology Acceptance Model (TAM) Hendi Sama; Tony Wibowo; Tukino
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.5836

Abstract

The problem that is the focus of the research is the use of distance learning technology at universities in the Riau Islands Province using the Technology Acceptance Model approach. This research aims to analyze user perceptions on ease of use, usability, usage behavior, and use of distance learning technology. This research aims to determine user perceptions of distance learning technology in universities in the Riau Islands Province. Apart from that, this research also aims to evaluate the influence of the ease of use perspective, usability perspective, and usage behavior on the use of distance learning technology. The research results show that research respondents, who are academics and staff at universities in the Riau Islands Province, have a high perception of distance learning technology. From the test results, it was found that there was no significant influence between the ease of se perspective and the usability perspective on the use of distance learning technology. However, usage behavior has a significant influence on the use of distance learning technology. Apart from that, these three variables together have an influence of 69.70% on the use of distance learning technology.
Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming Suwarno Suwarno; Anderson Arvando; Davina Davina; Brain Gantoro; Hendi Sama; Deli Deli
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1141

Abstract

Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.
Iris Identification Using Resnet Iris Feature Extraction Architecture For Better Biometric Security Hendi Sama; Tukino Tukino; Mangapul Siahaan; Erica Titoni
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1166

Abstract

Iris recognition is widely acknowledged as one of the most reliable biometric modalities due to its high uniqueness, rich textural patterns, and long-term stability. Unlike other biometric traits, iris characteristics resist forgery, aging effects, and environmental variations, making it suitable for high-security applications. Recently, convolutional neural networks (CNNs) have been extensively applied in iris recognition to improve feature representation and classification accuracy. However, many CNN-based approaches still depend on conventional segmentation and handcrafted features, which reduce robustness under noisy data, illumination variations, occlusions, or unconstrained environments. To address these limitations, this study proposes an enhanced iris identification framework combining a modified T-Net for precise segmentation with deep residual feature extraction for improved discrimination. Unlike conventional systems focus mainly on classification, the proposed approach emphasizes segmentation-driven feature consistency, ensuring extracted features originate from accurately localized iris regions. This design enhances stability and reliability, particularly under challenging imaging conditions. The framework leverages transfer learning and efficient representation learning strategies, enabling high accuracy even with a limited labelled data. Evaluations on three benchmark datasets CASIA-IrisV4, IITD Iris Database, and UBIRIS.v2 covering both controlled and less-constrained acquisition scenarios. Results show that it achieves classification accuracy of up to 98.35%, while maintaining computational efficiency suitable for deployment. The proposed architecture offers a robust, data-efficient, and scalable solution for secure biometric authentication, with strong potential for real-world applications such as access control, identity verification, and high-security authentication systems.
Co-Authors Abizar Muhammad Lubis Agnes Fitrian Aguslina Aguslina Anderson Arvando Andhika Bayu Andi Chandera Andy Kho Angeline Angeline Angeline Anisa Susmita Apis Indica Adam Arif Budiman Ayu Fauzia Rahmah Bong Ci Liong Brain Gantoro Cindy Claudia Erica Dannis Wongso Darvin Darvin David David Davina Davina Deli Deli Djayadhinata Djayadhinata Edi Santoso Edi Yulianto Putra Elita Elita Endy Endy Eric Eric Eric Hartanto Erica Titoni Eryc Eryc Febby Febby Felix Agusta Putra Firmansyah, Muhamad Dody Gary Phua Hartono Hartono Hendry Wijaya Henly Henly Herman Herman Hery Yohan Indah Lilian Sari Br Ambarita Indasari Deu Iskandar Itan, Iskandar Jason Hirawan Jecky Fransisco Jed Wan Jesica Jesica Jesica Jevon Junanto Jevon Junanto Julianto Julyanto Jumiliono Pratama Jurnali, Teddy Kelvin Kelvin Kenny Wilson Kevin Kevin Kezia Yohana Zai Leonardo Anthony Luky Andito M Agung Pratama Maria Ulfa Melissa Melissa Muhammad Ilham Muhammad Rivaldy Hisham Mujiyati Irsad Mungkap Mangapul Siahaan Nasyah Amanda Nelson Nelson Nelson Nelson Tan Nindi Suci Rahmadani Novendry Petrus Nur Alficha Putra, Edy Yulianto Putri Melati Putri Salsabella Putri Utami Putri Utami Rahel Rahel Ricky Kurniadi Rina Anggraini Rizky Wardhana Saffian Saffian Sellinna Octaviani Sihombing, Dame Afrina Silfia Nadilla Stephanie Stephanie Surya Chandra Suwarno Liang Tjahyadi, Surya Tofent Tofent Tuanku Stefino Tukino Tukino tukino, tukino Vina Liesty Indriani Vincent Linardo Wesley Zhang Wesley Zhang Wibowo, Tony Yulianti Yulianti Yully Yully Zulkarnain Zulkarnain