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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 1,176 Documents
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Analysis of Factors Influencing User Acceptance and Satisfaction with Google Classroom in Hybrid Learning using an Integrated EUCS and TAM Approach Iklas Nurul Islam; Wire Bagye; Lalu Mutawalli
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6849

Abstract

The use of Google Classroom in hybrid learning needs to be evaluated to determine the extent to which users perceive the system as useful and satisfactory for their learning activities. This study examines user acceptance and satisfaction with Google Classroom in hybrid learning by integrating the Technology Acceptance Model (TAM) and the End-User Computing Satisfaction (EUCS) model. A quantitative approach was employed involving 155 respondents. Data were collected through questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the support of SmartPLS software. The study investigates how various EUCS dimensions, including Format, Timeliness, Content, and Accuracy, are associated with TAM constructs, namely Perceived Ease of Use, Perceived Usefulness, User Satisfaction, and Behavioral Intention. The results show that eight of the ten hypotheses were supported, while the remaining two were not supported. Format has a significant effect on Perceived Ease of Use. In addition, Timeliness and Content significantly affect Perceived Usefulness. Perceived Ease of Use and Perceived Usefulness significantly affect User Satisfaction, while Perceived Usefulness and User Satisfaction significantly affect Behavioral Intention. In contrast, Accuracy does not significantly affect Perceived Usefulness, and Perceived Ease of Use does not have a significant effect on Behavioral Intention. These findings indicate that Perceived Usefulness and User Satisfaction are important factors in driving users’ Behavioral Intention to use Google Classroom in hybrid learning.
Essential User Experience Principles for Programming-Oriented Role-Playing Games: An Expert-Validated Thematic Synthesis Panji Rachmat Setiawan; Maizatul Hayati Mohamad Yatim
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6481

Abstract

This study aims to identify and validate the essential User Experience (UX) principles for the design of programming-oriented Role-Playing Games (RPG), a subgenre that requires players to navigate an interactive environment while simultaneously performing structured programming reasoning. Existing UX frameworks have been developed predominantly for general entertainment contexts or non-game applications and therefore do not fully address the dual cognitive load inherent in programming-oriented RPGs. To address this gap, this research employs a qualitative approach combining expert validation, thematic synthesis, and the Fuzzy Delphi Method (FDM), involving ten experts from the fields of UX design, game development, and programming. The analysis yielded six empirically validated UX principles, namely Usability and Learnability, Accessibility and Readability, Responsiveness and Interaction Smoothness, Consistency and Predictability, Meaningful Feedback and Guidance, and Engagement Support. The defuzzified consensus values of these principles ranged from 0,59 to 0,72, with all six principles surpassing the acceptance threshold of 0,50, indicating sufficient expert agreement. Further analysis revealed that the six principles do not operate in isolation but form a network of interdependencies that can be represented as a three-layer hierarchical framework consisting of Foundation, Communication and Responsiveness, and Engagement. This study contributes by providing a specific, coherent, and empirically validated UX framework that can serve as a design and evaluation reference for programming-oriented Role-Playing Games.
Adaptive Mix-Gated Multi-Attention Fusion in YOLOv11s for Dense Person Detection: A Systematic Evaluation of Dual and Triple Attention Configurations Aya Ahmed; Karam Abdullah
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6733

Abstract

Person detection in a crowded environment is very challenging due to excessive occlusion, scale variation, and heavy overlap between individuals. This paper proposes a lightweight Mix-Gated attention framework to enhance the feature representation capacity of YOLOv11s for dense person detection. The proposed framework incorporates two complementary channel-wise and spatial attention mechanisms under a flexible channel-wise gating strategy, while preserving the original YOLOv11s architecture and real-time inference ability. Unlike previous studies, which primarily employed either a single attention mechanism or fixed combinations of multiple attention modules, the proposed framework introduces an adaptive Mix-Gated feature selection mechanism that dynamically learns the contribution of complementary attention modules through trainable channel-wise gating. Furthermore, this study systematically evaluates ten dual and triple attention configurations under identical training and evaluation conditions, providing a comprehensive and fair comparison of their effectiveness. this study evaluated the performance on four popular benchmark datasets - CrowdHuman, WiderPerson, MOT17Det and MOT20Det - using Precision, Recall, F1-score, mAP@50 and mAP@50-95 as evaluation metrics. The experimental results show that all proposed Mix-Gated configurations outperform the baseline YOLOv11s model. Mix-Gated-CBAM+SE+SA has the best detection accuracy and efficient inference performance among Mix-Gated configurations. These results indicate that adaptive fusion of complementary attention mechanisms can improve the feature representation and person detection performance in crowded scenes while not sacrificing computational efficiency.
Usability Evaluation of the Indodax Website User Interface using Heuristic Evaluation and Severity Rating Methods Imanuel Guardion Angel
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6711

Abstract

The growth of crypto-asset investment in Indonesia has increased the demand for trading platforms with good usability. This study aims to evaluate the usability of the Indodax website using Heuristic Evaluation based on Nielsen's ten usability heuristics and to determine improvement priorities using Severity Rating. A descriptive quantitative approach was employed, involving 13 active Indodax users as respondents. Data were collected through a Likert-scale questionnaire that was tested for validity, reliability, and normality. The results showed that all questionnaire items were valid, with the calculated r-values exceeding the critical r-value of 0.553. The instrument achieved a Cronbach's Alpha of 0.985, indicating very high reliability, while the Shapiro–Wilk normality test yielded a significance value of 0.377 (>0.05). The evaluation identified ten usability problems, consisting of two critical, six major, and two minor issues. Improvement priorities were focused on Error Prevention, User Control and Freedom, and Consistency and Standards to enhance the quality of the user interface, user experience, and transaction security of the Indodax website. The findings provide practical insights for prioritizing interface improvements and enhancing the overall usability and reliability of crypto-asset trading platforms.
Labuan Bajo Culinary Tourism Recommendation System: A Comparison of Content-Based Filtering Similarity Methods Sergius Septiade Masmur; Ika Nur Fajri; Arif Nur Rohman
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6830

Abstract

Labuan Bajo, as a national super-priority tourism destination, has experienced a significant increase in tourist visits, dominated by foreign tourists, which has driven the expansion of the culinary sector with a continuously increasing number of restaurants. This makes it difficult for tourists to choose dining options that match their preferences among the many available choices. Previous culinary tourism recommendation system research has only used a single similarity measurement technique without evaluating its effectiveness compared to other techniques. This study aims to build a culinary tourism recommendation system in Labuan Bajo using the Content-Based Filtering method, as well as to compare three similarity measurement techniques Cosine Similarity, Euclidean Distance, and Jaccard Similarity to determine which technique produces the most relevant recommendations. Data was obtained through scraping techniques on 150 restaurants in Labuan Bajo, covering category and rating attributes, which were then processed into a feature matrix for inter-item similarity calculation. Evaluation was carried out using Precision@5 with relevance criteria based on category similarity and rating proximity, followed by a Wilcoxon Signed-Rank Test to examine statistical significance between methods. The test results show that Cosine Similarity and Euclidean Distance produce an equal precision of 0.6947, higher than Jaccard Similarity at 0.6320, with the difference proven statistically significant (p < 0.001). The system was then implemented as a web application using the CodeIgniter framework, allowing users to select a similarity method and view restaurant recommendations interactively. This study demonstrates that considering the rating attribute, not just category, produces more relevant recommendations than a category-only approach.
An Extended Approach for Color Image Authentication and Recovery Against Composite Attacks Maher Adel Al Dbsawie; Suleiman Alassly; Hasan Al Jabbouli
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6803

Abstract

This paper proposes an extended approach for color image authentication and recovery utilizing the alpha layer and a secret sharing scheme to counter sequential composite attacks. Building upon our previous work which proved effective against traditional single attacks such as cropping, noise, and superimposition this study addresses the complex challenges posed by successive and combined manipulations. The proposed approach embeds authentication and recovery data within the alpha channel using a spatial correlation phase, ensuring that authentication and repair data are embedded in non-contiguous blocks to guarantee high accuracy in tamper detection and image restoration. Experimental results on diverse color images demonstrate remarkable efficiency in withstanding sequential composite attacks, recording outstanding values of (PSNR=32.85 dB) and (SSIM=0.95) even under the most severe composite sequential attacks. This demonstrates exceptional robustness against tampering while preserving the high visual quality of both the stego and recovered images. Ultimately, the findings confirm that this framework significantly enhances the reliability and security of image transmission and storage compared to conventional methodologies.
Detecting Gambling SEO Spam on Academic Domains using Structural and Textual Features: A Domain-Holdout Evaluation Muh Ghazy Daffa Sampe; Muhammad Kevin Adli Pratama; Muhamad Rayhan Akhsani Taqwim; Kalingga Dwindra Putraka; Anindita Septiarini; Novianti Puspitasari
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6457

Abstract

SEO spam practices related to gambling on Indonesian academic domains pose dual risks: they undermine the integrity of institutional websites and impose an operational burden on security teams tasked with distinguishing compromised pages from legitimate content. This study develops a supervised detection approach to identify gambling-related SEO spam pages on .ac.id domains using a rigorously curated reference dataset. The final dataset contains 2,003 manually annotated pages collected from 938 unique domains, comprising 815 malicious pages and 1,188 benign pages. The proposed pipeline combines textual signals from character-level TF-IDF with structural page indicators, including hidden elements, suspicious links, iframes, and external-link patterns. To avoid overly optimistic performance estimates, evaluation was conducted using a domain-holdout protocol, in which 20% of the domains were completely excluded from model training and selection. Five models were compared: a keyword-based baseline, Naïve Bayes with word-level TF-IDF, Random Forest with structural features, and hybrid Logistic Regression and Support Vector Machine (SVM) models. Experimental results on the holdout set show that Random Forest with structural features achieved the highest F1-score of 0.8844, whereas the proposed hybrid SVM achieved the highest precision of 0.9205, with an F1-score of 0.8663. These findings indicate that structural compromise signals are more robust than textual cues in detecting stealthy SEO spam scenarios, while hybrid models remain promising when high precision and interpretability are prioritized.
Intelligent Technique for Optimizing Requirements Elicitation in Software Engineering Projects Esra Zuhair Majeed
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6850

Abstract

Requirements elicitation is a critical activity in software engineering, as incomplete, ambiguous, or inconsistent requirements can adversely affect software quality and project outcomes. Although Large Language Models (LLMs) have demonstrated considerable potential for supporting Requirements Engineering, conventional prompting approaches typically rely on direct or predefined interactions and lack systematic mechanisms for identifying and resolving information gaps during elicitation. This study proposes Adaptive Context-Driven Prompting (ACDP), an iterative framework designed to improve LLM-supported requirements elicitation through context preservation and gap-driven refinement. ACDP progressively constructs an Elicitation Context (EC) that maintains project objectives, stakeholders, constraints, assumptions, and previously elicited requirements. In parallel, a Requirement Gap Matrix (RGM) systematically identifies missing, partial, ambiguous, and conflicting information and transforms these gaps into targeted re-elicitation prompts for subsequent LLM interactions. The framework was evaluated using public software requirements documents from the PURE dataset and compared with Zero-Shot Prompting and Fixed Multi-Step Prompting under controlled LLM settings. Performance was assessed using reference-based Precision, Recall, and F1-score, together with requirement-quality dimensions including completeness, correctness, consistency, clarity, relevance, ambiguity, and redundancy. Experimental results demonstrate that ACDP achieved a Precision of 0.93, Recall of 0.92, and F1-score of 0.92, outperforming both baseline prompting approaches while also improving requirement completeness, correctness, and overall quality. These findings demonstrate that combining persistent elicitation context with explicit gap diagnosis provides a systematic and effective mechanism for improving LLM-assisted requirements elicitation and offers practical support for requirements analysts in complex software projects.
Design of an Information System for Participants of the National Values Enhancement Program at Lemhannas RI using the Design Thinking Method Yadi Yono; Risqy Siwi Pradini; Wahyu Teja Kusuma
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6677

Abstract

The National Values Enhancement Program (Program Pemantapan Nilai-Nilai Kebangsaan—PPNK) of the National Resilience Institute of the Republic of Indonesia (Lemhannas RI) is a strategic program aimed at strengthening national insight, character, and national values among members of society. However, the participant registration process for PPNK is still conducted manually, resulting in several challenges, including slow information processing, a high risk of data-entry errors, and suboptimal dissemination of information to prospective participants. This study aims to develop a design prototype for the PPNK Participant Information System at Lemhannas RI by applying the Design Thinking methodology to facilitate and streamline the participant registration process. This approach was selected because it emphasizes identifying and exploring user needs, defining problems, and developing innovative, user-centered solutions. The study resulted in a PPNK Participant Information System prototype designed based on identified user requirements and incorporating training management and participant data management features. To evaluate the system's usability, the prototype was assessed using the System Usability Scale (SUS). The evaluation involved 10 respondents and yielded an average SUS score of 81.25. This score falls within the Excellent category, indicating that the proposed system is easy to use, meets user needs, and provides a positive user experience. The proposed prototype is expected to support more effective, efficient, and structured registration and participant management processes for the PPNK program at Lemhannas RI, while also serving as a foundation for developing an information system ready for implementation within the Lemhannas RI environment.
Comparative Analysis of Instance Segmentation Models for House-Level Visual Socioeconomic Classification using Satellite Imagery David Cahyapratama; Chairani Fauzi
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6743

Abstract

House-level visual socioeconomic information can provide a more detailed spatial understanding of residential areas and can support applied urban and commercial analysis. However, official socioeconomic data are often available only at broader administrative levels and may not capture variation among small residential clusters or individual houses. This study compares five instance segmentation models for detecting, segmenting, and classifying individual houses into lower, middle, and upper visual socioeconomic classes using high-resolution satellite imagery. The evaluated models are Mask R-CNN, Cascade Mask R-CNN, YOLO11n-seg, SOLOv2, and Mask2Former. The dataset consists of 1,209 training images with 7,052 annotations and 213 validation images with 1,353 annotations from residential areas in Banten and DKI Jakarta. Annotation reliability was assessed using 100 annotation pairs, producing a quadratic-weighted Cohen’s kappa of 0.8077. Model performance was evaluated using COCO metrics for both segmentation masks and bounding boxes. The results show that Cascade Mask R-CNN achieved the highest observed overall validation performance among the tested configurations. Under the current experimental setting, it produced the strongest combination of object-localization and mask-segmentation metrics. These findings show that comparing multiple instance segmentation models can help identify a more suitable method for house-level visual socioeconomic classification. Unlike previous studies that generally perform area-level socioeconomic estimation or building extraction alone, this study compares multiple instance-segmentation approaches for expert-defined socioeconomic classification at the individual-house level. The resulting output can serve as a supplementary visual socioeconomic layer that complements demographic, accessibility, and commercial data in applied spatial and market-development analyses.

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