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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 1,111 Documents
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A Novel Approach for Secure and Reliable Color Image Authentication and Recovery using Alpha Layer and Secret Sharing Maher Adel Al Dbsawie; Suleiman Alassly; Hasan Al Jabbouli
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6340

Abstract

In the contemporary digital environment, securing digital images against forgery has become a critical necessity, particularly in sensitive domains such as the judiciary, criminal investigations, and communications security. Any manipulation or falsification of legal documents or digital evidence can lead to serious consequences and significant challenges. This study proposes a method for protecting color scanned images and documents by utilizing an invisible component within them, specifically the alpha channel (transparency layer), to embed authentication and recovery data. The process begins by applying a Downsampling operation to the cover image to reduce its size while preserving fine visual details, taking into account the removal of aliasing to achieve the best possible representation of edges and details prior to reduction. Subsequently, 15 secret bit planes are combined to form 4 composite secret bit planes, which are then embedded into the alpha channel. During the recovery phase, these four bit planes are extracted from the alpha channel, from which the original 15 authentication bit planes are reconstructed. The reconstructed data are then matched with the verification data, and in case of any discrepancy, the image is repaired using the embedded authentication data. This approach enables the detection of any tampering and facilitates image recovery if it has been altered or manipulated. One of the key advantages of this method is that it does not affect the visual quality of the host image, as the data are embedded within the added alpha channel. Any attempt to tamper with the image can be detected, and based on the embedded data, the modified or deleted regions can be restored. The proposed method was tested on various types of color scanned images and documents under diverse attack scenarios (content modification attacks and visual quality attacks), demonstrating remarkable effectiveness in preserving image integrity. This technology is particularly important for law enforcement, information security, digital forensics, and any security teams requiring reliable image authentication. The system is also designed to be user-friendly and does not require any specialized hardware, making it practical and suitable for real-world applications.
Implementation of Linear Regression for Predicting School Attribute Orders Dhani Miftahul Abid; Arif Setiawan; Muhammad Arifin
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6594

Abstract

Fluctuating demand for school accessories, such as caps and neckties, presents a significant challenge for the garment manufacturing industry in determining optimal production quantities. Inaccurate production planning can lead to overstocking or stock shortages, resulting in increased operational costs and reduced customer service quality. This study aims to implement a Linear Regression model to predict school accessory orders using historical sales data from 2021 to 2025. A quantitative research approach was adopted, consisting of data preprocessing, feature engineering, dataset partitioning into 80% training and 20% testing sets, and the development of a prediction model using Linear Regression. The input variables included year, month, education level, product type, time index, semester, and new academic year period. The experimental results demonstrate that the proposed model achieved a Mean Absolute Percentage Error (MAPE) of 12.91% and a Mean Absolute Error (MAE) of 149.61 units, indicating good predictive performance for forecasting school accessory orders. Furthermore, semester, product type, and the new academic year period were identified as key factors influencing demand fluctuations. The proposed model can serve as a decision-support tool for production planning and inventory management in the garment manufacturing industry, enabling more efficient resource allocation and demand-driven production planning.
QoS Optimization and Bufferbloat Mitigation in 5G Standalone Networks using 5QI Mapping Ghina Rezkiah Octavia; Sopian Soim; Eka Susanti
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6480

Abstract

Bufferbloat in the Best Effort traffic profile (5QI = 9) of open-source 5G Standalone (5G SA) networks leads to excessive packet queuing at the User Plane Function (UPF) and gNodeB, resulting in significant degradation of 4K video streaming performance under network congestion. This study investigates the effectiveness of 5QI = 4 mapping for mitigating bufferbloat and optimizing the Quality of Service (QoS) of 4K video streaming in 5G SA networks using secondary data compiled from previous studies. A quantitative comparative analysis was conducted using QoS metrics, including throughput, delay, jitter, and packet loss, systematically extracted from 30 peer-reviewed publications published between 2021 and 2026 following the PRISMA protocol. The results demonstrate that mapping traffic to the 5QI = 4 priority class consistently maintains 4K video throughput above the critical threshold of 25 Mbps across different experimental platforms, reduces end-to-end latency to below 150 ms in accordance with the ITU-T Y.1541 Class 4 recommendation, and limits jitter to less than 15 ms. The proposed mapping strategy prioritizes packet scheduling at the UPF through the N4 interface under the control of the Session Management Function (SMF), while intentionally deprioritizing and, when necessary, dropping lower-priority background traffic to preserve service quality for delay-sensitive applications. Furthermore, the findings demonstrate that modifying the MongoDB policy database within the Policy Control Function (PCF) of Open5GS is an effective approach for mitigating network congestion and eliminating visual stuttering during Ultra High Definition (UHD) video streaming.
Mobile-based Big Five Personality Score Prediction from Handwriting using VGG19 Maharani Sekar Hapsari; Salamun Rohman Nudin
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6380

Abstract

Handwriting represents a non-verbal behavioral cue that can be used to identify individual personality characteristics. However, conventional manual assessment of personality scores is inherently subjective and requires specialized expertise, highlighting the need for a more objective, efficient, and consistent automated approach. This study aims to develop a system for predicting personality trait scores from handwriting images. The personality assessment is based on the Big Five Personality model, comprising Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The research methodology adopts an adapted Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, including the stages of business understanding, data understanding, data preparation, model development, evaluation, and system implementation. The proposed system employs the VGG19 deep learning architecture and compares the performance of three optimization algorithms: Adam, Stochastic Gradient Descent (SGD), and RMSProp. The evaluation results demonstrate that RMSProp achieved the best performance on the validation dataset, with a Mean Squared Error (MSE) of 0.0128, Mean Absolute Error (MAE) of 0.0930, Root Mean Squared Error (RMSE) of 0.1133, Pearson Correlation Coefficient (PCC) of 0.4648, and an accuracy of 90.70%. The VGG19 model optimized with RMSProp was subsequently deployed in a mobile application capable of receiving handwriting image inputs and generating predicted Big Five personality trait scores. These findings demonstrate that integrating deep learning with mobile applications offers a practical and effective solution for predicting Big Five personality trait scores from handwriting images.
Performance Evaluation of a TP-Link CPE220 Point-to-Point Wireless Backhaul Hashfi Adha Fadhillah; Ichwan Nul Ichsan
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6284

Abstract

This study aims to implement and evaluate the performance of a point-to-point wireless backhaul network between two buildings using the TP-Link CPE220 based on the IEEE 802.11b/g/n standard in a high-interference environment. The implementation was carried out between two buildings approximately 100 meters apart under line-of-sight (LOS) conditions, where the Internet connection from the office building was distributed to the workshop building through the wireless backhaul and subsequently forwarded via a router and an access point. The network performance was evaluated under four testing scenarios: the wireless backhaul link, the workshop router, the access point under no-load conditions, and the access point under a multi-user environment. Measurements were conducted 15 times during the morning, afternoon, and evening using throughput, delay, jitter, and Received Signal Strength Indicator (RSSI) as the evaluation parameters. The results show that the average download throughput decreased from 95.27 Mbps at the source to 57.13 Mbps across the wireless backhaul and 55.56 Mbps at the unloaded access point. Under the multi-user scenario, where 10 devices simultaneously streamed video content, the average download throughput declined to 11.49 Mbps, while delay and jitter increased to 367.29 ms and 66.69 ms, respectively. The analysis indicates that the performance degradation was primarily caused by reduced signal quality over the backhaul link, interference within the 2.4 GHz frequency band, and increased channel contention under multi-user conditions. Nevertheless, the TP-Link CPE220 was able to provide stable inter-building connectivity for small- to medium-scale network deployments, sustaining throughput exceeding 55 Mbps under normal operating conditions.
Multi-Class Skin Disease Classification using Transfer Learning and Explainable AI Ayutri Wahyuni; Resky Ayu Dewi Talasari; Muhammad Syawal Idil Fitrah Baharuddin
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6505

Abstract

This study investigates the application of transfer learning and Explainable Artificial Intelligence (XAI) for multi-class skin disease classification. The dataset was obtained from the Kaggle Skin Diseases Image Dataset and consists of 29,153 original images spanning 10 skin disease classes. To reduce the bias introduced by class imbalance, the dataset was balanced through directed undersampling, resulting in 12,000 images, with 1,200 images per class. Three pretrained convolutional neural network (CNN) architectures—EfficientNetB0, ResNet50, and DenseNet201—were implemented and evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results demonstrate that DenseNet201 achieved the highest classification performance, with an accuracy of 0.8779, precision of 0.8751, recall of 0.8748, and F1-score of 0.8745, outperforming ResNet50 (accuracy: 0.8629) and EfficientNetB0 (accuracy: 0.8269). Model interpretability was investigated using Grad-CAM, SHAP, and LIME. Grad-CAM highlighted that the models primarily focused on the central and peripheral regions of skin lesions during prediction. SHAP identified the dominant contribution of lesion regions and pigmentation patterns to the classification process, while LIME emphasized the importance of local superpixels associated with lesion boundaries, color, and texture in supporting the model's predictions. The findings indicate that combining transfer learning with Explainable AI provides a promising foundation for developing clinical decision support systems for dermatological image classification. Future research should incorporate external dataset validation, more robust class balancing strategies, and clinical interpretation by dermatology experts to facilitate the deployment of such systems in real-world healthcare settings.
Classification of Fish Species using Digital Images and Convolutional Neural Networks Rosalva Denisia Yulia Yahya; Wiwit Agus Triyanto; Pratomo Setiaji
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6468

Abstract

Accurate fish species identification is essential for fisheries management and the seafood industry; however, manual identification remains time-consuming, challenging, and prone to human error. This study develops an automated fish species classification system using a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture with supervised learning. The dataset was manually collected from the Roboflow platform by gathering and integrating images from multiple sources into a single collection. Three fish species were selected as the target classes: Red Snapper, Barramundi (Asian Sea Bass), and Scad. The preprocessing pipeline included data augmentation, image normalization, and image resizing to 224 × 224 pixels. The final dataset consisted of 1,500 images, with 500 images per class, and was divided into training, validation, and testing sets using a 70:15:15 ratio. To enhance the classification performance of MobileNetV2, the proposed model incorporated a classification head consisting of Batch Normalization, a Dense layer (128 units, ReLU activation), Dropout (0.6), and a Dense output layer (3 units, Softmax activation). During training, the model was optimized using the Adam optimizer with the categorical cross-entropy loss function. Experimental results demonstrate that the proposed model achieved a test accuracy of 98.67% and a macro-averaged F1-score of 0.99. These findings indicate that MobileNetV2 with supervised learning is highly effective for fish species classification from digital images and provides a strong foundation for the development of automated fish identification systems in fisheries applications.
Design and Implementation of a Digital Forensics Tool to Enhance Windows Artifact Analysis Manar Talat Ahmad
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6346

Abstract

Although the wide use of technology came with many advantages and facilities to the people daily life, it causes the cybercrime to be raised. Digital forensics is one of the most important scientific fields, aiming to investigate cybercrimes and analyze digital evidence. Among different technology’s platforms, operating systems is one of the most important sources of evidence for digital forensic analysts providing a rich information that can used to get important insights. Examples of such evidence include identifying programs that have been executed on a computer, determining files that have been accessed, and identifying storage devices that were connected via USB ports. Practically, accessing and handling this raw information using manual methods is time-consuming, in addition to the lack of accuracy in results due to human errors. In this work, a GUI-based tool is presented to handle most of the evidence provided by Windows operating system that can be used in digital forensics. The research aims to fill the gap caused by the lack of a free tool that deals with these sources, as most available tools are either commercial tools that are complex to use and require expert-level experience. In contrast, available free tools have limited-capability since they are focusing only on one type of evidence. The introduced tool was designed and developed using the C# programming language and was tested on the Windows 10 operating system, where it successfully extracted the required information efficiently and smoothly.
Usability Analysis of an Electronic Immunization Monitoring and Logistics System using PIECES and SUS Citra Yustitya Gobel; Misrawati Aprilyana Puspa; Syarifah Fitrah Ramadhani; Siti Andini Utiarahman; Sinta Suleman
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6507

Abstract

The Electronic Immunization Monitoring and Logistics System (SMILE) is an information system designed to support the integrated management of immunization records and healthcare logistics. The successful implementation of such a system depends not only on its functionality but also on its usability, which significantly influences user satisfaction and acceptance. This study aims to evaluate the usability of the SMILE application using the PIECES framework—covering Performance, Information, Economy, Control, Efficiency, and Service—and the System Usability Scale (SUS). A descriptive quantitative approach was employed, with data collected through observations, a literature review, and questionnaire surveys administered to 56 users of the SMILE application at the Gorontalo Provincial Health Office. The results of the PIECES analysis indicate that all evaluation dimensions achieved the "Satisfied" category, with mean scores of 3.68 for Performance, 4.28 for Information, 4.36 for Economy, 4.26 for Control, 4.25 for Efficiency, and 4.25 for Service. The usability assessment using the SUS yielded an average score of 53, which falls within the "OK" adjective rating (score range: 51–67) and the "Marginal" acceptability category. According to the SUS grading scale, this score corresponds to Grade D, indicating that the application's overall usability requires improvement. Although users expressed satisfaction with the system's performance and service quality, the findings suggest that enhancements are still needed, particularly in terms of ease of use, navigation, and overall user experience. The findings of this study provide valuable insights and practical recommendations for developers to improve the quality and usability of the SMILE application, thereby supporting more effective adoption and use in healthcare services.
User Experience Evaluation of the Internal Quality Assurance Information System (E-AMI) using the User Experience Questionnaire Plus (UEQ+) Ni Putu Anik Mentayani; I Made Candiasa; I Made Gede Sunarya
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6483

Abstract

This study evaluates the user experience (UX) of the E-AMI system at Primakara University, an academic quality assurance system managed by the Quality Assurance Institution (LPM). A sequential explanatory mixed-method design was employed, combining quantitative measurement using the User Experience Questionnaire Plus (UEQ+) across 15 scales with qualitative data collected through observations and semi-structured interviews. A total of 41 respondents participated, consisting of administrators, auditors, and auditees actively involved in the Internal Quality Audit (AMI) process. UEQ+ scores range from -3 (very negative) to +3 (very positive), with scores above 1.0 generally classified as good. The quantitative results yielded an average KPI score of 2.10, indicating good user experience quality, with the highest scores recorded in usefulness (M = 2.51), efficiency (M = 2.26), and perspicuity (M = 2.38). These findings were corroborated by qualitative data confirming the system's effectiveness in supporting core AMI processes. However, both quantitative and qualitative findings converged on several persistent UX weaknesses: Importance-Performance Analysis (IPA) identified visual aesthetics (M = 1.41), novelty (M = 1.90), attractiveness (M = 1.91), stimulation (M = 1.95), and value (M = 1.98) as priority improvement dimensions, consistent with qualitative findings of unintuitive navigation, insufficient system feedback, interface inconsistencies, and incomplete feature support. It is concluded that while the E-AMI system performs well in functional dimensions, further development in visual, experiential, and feature dimensions is required to achieve optimal UX quality, with future improvements recommended to adopt a user-centered design approach.

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