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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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Feature Selection and Explainable AI for Heart Disease Detection using Machine Learning Resky Ayu Dewi Talasari; Ayutri Wahyuni; Clara Diva; Muhammad Nur Alamsyah Rajab
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.v15i6.6491

Abstract

Early detection of heart disease is essential for supporting timely clinical intervention, improving treatment outcomes, and enhancing the quality of patient care. This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—combined with two feature selection methods, Chi-Square and Recursive Feature Elimination (RFE), using the UCI Heart Disease dataset. Six modeling scenarios were evaluated based on accuracy, precision, recall, and F1-score. The experimental results demonstrate that the Random Forest model achieved the best overall performance, with an accuracy of 85.2% and a recall of 97.0%, indicating a strong capability to identify patients with potential heart disease. To enhance model transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed as an Explainable AI (XAI) technique and integrated into a web-based decision support system to provide intuitive explanations of prediction outcomes. The proposed system is intended to serve as an initial clinical decision-support tool and is not designed to replace diagnosis or clinical judgment by healthcare professionals.
Analysis of Higher Education Alumni Careers using LinkedIn Web Scraping and K-Means Clustering Qurrotul Aini (SCOPUS ID: 54974128700); Eri Rustamaji; Denina Nastiti Putri Amani; Elvi Fetrina
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.v15i6.6437

Abstract

The use of alumni data to support curriculum evaluation continues to face challenges due to the limitations of conventional data collection methods, such as manual surveys, which often result in low response rates. Meanwhile, LinkedIn provides relatively comprehensive and up-to-date alumni career information; however, its potential for supporting tracer studies remains underutilized. This study aims to analyze the career patterns of higher education alumni using LinkedIn data collected through web scraping and analyzed with the K-Means clustering algorithm within the Knowledge Discovery in Databases (KDD) framework. The proposed approach applies the KDD process to generate a data-driven mapping of alumni career patterns as a complement to conventional tracer studies. The dataset consisted of 133 alumni profiles, which were processed through the stages of data selection, preprocessing, transformation, clustering, and evaluation. The results indicate that the majority of alumni are employed in the technology sector and occupy mid-level or specialist positions. The K-Means algorithm identified three distinct career clusters, representing career tendencies in business process and operations, systems and technology development, and data utilization and software quality assurance. These findings reveal the distribution of alumni competencies across business, data, and technology domains. However, the clustering quality was relatively low, as indicated by a Silhouette Score of 0.0321 and a Davies-Bouldin Index of 3.0487, suggesting limited separation among the identified clusters. Therefore, the clustering results should be interpreted as an initial mapping of alumni career patterns rather than definitive classifications. Overall, this study demonstrates the potential of professional social media data as a valuable resource for supporting data-driven alumni career analysis and complementing traditional tracer study practices.
Mapping the Number of Patients Undergoing Treatment and Care in Public Hospitals After the Covid-19 Pandemic Lenni Dianna Putri; Ermi Girsang; I Nyoman Ehrich Lister; Evizal Abdul Kadir
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.v15i6.6321

Abstract

The COVID-19 pandemic has had profound impacts on healthcare systems globally, altering the patterns of patient care and treatment in public hospitals. This research aims to map the number of patients undergoing treatment and care in public hospitals following the pandemic, providing insights into the evolving healthcare landscape. Utilizing data from public hospitals, we analyzed trends in patient admissions, treatment types, and care requirements post-pandemic. Our findings reveal a significant shift in healthcare demands, with an increase in patients requiring long-term care and treatment for chronic conditions exacerbated by delayed medical attention during the pandemic. Additionally, mental health services have seen a notable surge in utilization, reflecting the psychological toll of the pandemic. The research highlights the need for adaptive healthcare strategies to address the changing patient demographics and ensure efficient resource allocation. These insights can guide policymakers and healthcare providers in optimizing patient care and improving healthcare delivery in the post-pandemic era.
Validation and Error Detection in Relational Data using a Hybrid Rule-based System Daniel Andrew Shane Chayono; Johan Jimmy Carter Tambotoh
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.v15i6.6561

Abstract

Relational databases form the backbone of modern information systems. However, data quality issues, such as duplicate records, invalid formats, missing values, and cross-table inconsistencies, can significantly reduce the accuracy of data-driven decision-making. Conventional rule-based validation is effective for detecting structured errors but has limited capability in identifying ambiguous errors, such as typographical variations in entity names. This study proposes a hybrid rule-based system that combines SQL triggers for structured error detection with fuzzy matching using the RapidFuzz Python library to identify semantically similar records across multiple relational database tables. The proposed system was implemented using six primary database tables, six corresponding quarantine tables, and a centralized error_log table. The evaluation was conducted using a synthetic dataset containing 2,775 records distributed across the six tables. The dataset was systematically generated using the generate_dataset.py script, with various intentionally injected data quality issues to enable accurate verification of the detection results. The experimental results show that the proposed system detected 472 data quality issues, with 297 records automatically moved to the quarantine tables. The rule-based component identified 311 errors (65.9%), including format violations, negative values, and referential integrity violations. Meanwhile, the fuzzy matching component detected 127 semantic errors that could not be identified using SQL rules alone, including 112 duplicate customer names, 7 similar product names, and 5 inconsistent product categories. On the experimental dataset, the proposed hybrid approach detected 34.1% more data quality issues than a rule-based validation approach alone. These findings demonstrate that integrating rule-based validation with fuzzy matching substantially improves error detection capability in relational databases, particularly for semantic inconsistencies that are difficult to capture using conventional validation rules.
An Enhanced Type II Fuzzy Set Algorithm for Satellite Images Contrast Improvement Manar Abdulkareem Al-Abaji; Mohammed Hazim Alkawaz
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.v15i6.6474

Abstract

Satellite images contain detailed information that is of great importance in the field of remote sensing. However, these images often suffer from low contrast due to numerous atmospheric obstructions. However, many methods have been developed to enhance these images but most of them have not achieved satisfactory results. Therefore, satellite imagery processing remains an active area of research. Hence, this research has proposed a modified type of II fuzzy set algorithm to improve the contrast of grayscale and color satellite images appropriately so as to maintain the overall brightness of the image and also give natural colors. The proposed algorithm employs a modified Hamacher t-conorm with a new lower and upper ranges. The resulting output is further processed based on sigmoid function and contrast stretching techniques to produce the final improved image. The proposed algorithm’s performance was assessed with natural degraded satellite images and compared with six other methods as well as the evaluation of the comparison’s outcomes was done using two metrics in addition to the processing time. It scored the optimum in both metrics, which obtain (20.907) in BRISQUE and (3.467) in NSS. The experimental results of the proposed algorithm demonstrated outstanding performance compared to the other methods as it produced images with clear details and natural colors without increasing image brightness.
Comparison of Linear Regression and Holt-Winters Methods for Gold Price Prediction Syifa Anjanira; afwandi afwandi; Ar Razi Ar Razi
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.v15i6.6360

Abstract

Gold is one of the most stable investment instruments and is widely favored by both individual investors and businesses. However, gold prices are influenced by various economic factors and often fluctuate significantly, making pricing decisions more challenging. Toko Mas Jasa Sejahtera, a gold trading business, faces difficulties in determining appropriate selling prices due to this uncertainty. Therefore, an accurate prediction method is required to minimize pricing errors and support more informed decision-making. This study aims to design and implement a gold price prediction system using the Linear Regression and Holt-Winters methods while comparing the predictive accuracy of both approaches. Model performance was evaluated using Mean Absolute Error (MAE) as the primary indicator of prediction accuracy. The results show that both methods are capable of forecasting gold prices, although with different levels of accuracy. The Linear Regression method achieved an MAE of IDR 44,097, whereas the Holt-Winters method produced an MAE of IDR 305,984. The substantially lower MAE obtained by Linear Regression indicates that it provides more accurate predictions than the Holt-Winters method. Therefore, Linear Regression is recommended as the preferred approach for the gold price prediction system at Toko Mas Jasa Sejahtera.
Information Systems Analysis as a Preliminary Study for Smart Campus Development Titis Sari Putri; Mohammad Rezza Fahlevvi; Muhammad Tosan Bingamawa; Ikra Novar Rizqi; Megandaru Widhi Kawuryan
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.v15i6.6553

Abstract

The trend of smart campus development has increased over the past 10 years, marked by digital transformation and the implementation of IoT and big data. This trend is not limited to state and private universities; civil service universities are also participating in the development of smart campuses. Unlike state and private universities, civil service universities face limitations in internal institutional management that pose significant challenges for planning and building large projects, such as smart campuses. The smart campus development plan at the Institut Pemerintahan Dalam Negeri (IPDN) is still hampered by various issues related to the management of scattered information and low use of information systems within the campus environment. Various stakeholders hold different opinions on these issues, so the nature of the problems faced remains unclear. These issues require special attention in follow-up and solution-finding, given that the implementation and use of information technology and information systems are the core of smart campus projects. This study analyzes the problem, organizes it more clearly, and identifies the root cause of the problem from scattered issues using Soft Systems Methodology (SSM) integrated with several stages of Root Cause Analysis (RCA). The identified root causes were then mapped into the smart campus dimensions: smart economy, smart society, smart environment, and smart governance. The analysis identified 27 root causes, divided into three categories: human resources, management, and policy. Of these 27 root causes, nine fall within the smart campus dimension, namely the smart economy, smart society, and smart governance subdimensions.
The Impact of Electronic Customer Relationship Management (E-CRM) Strategy on Customer Loyalty in Mytelkomsel using a Mediation Model M Irvan; Siti Monalisa
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.v15i6.5653

Abstract

This study investigates the impact of Electronic Customer Relationship Management (E-CRM) strategies, encompassing Functional Dimensions and Personal Dimensions, on Customer Loyalty in the use of the MyTelkomsel application, using Perceived Customer Relationship Quality (comprising cognitive and emotional dimensions) as the mediating construct. Data were collected from 100 MyTelkomsel users through a survey and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The results indicate that Functional Dimensions have a positive and significant effect on Perceived Customer Relationship Quality (path coefficient = 0.471) and also exert a positive direct effect on Customer Loyalty (path coefficient = 0.256). Meanwhile, Personal Dimensions positively influence Perceived Customer Relationship Quality (path coefficient = 0.335) but exhibit a negative direct effect on Customer Loyalty (path coefficient = −0.109). Furthermore, Perceived Customer Relationship Quality has a positive and significant effect on Customer Loyalty (path coefficient = 0.539) and serves as a mediator in the relationships between both Functional Dimensions and Personal Dimensions and Customer Loyalty. These findings provide deeper insights into the relationship between E-CRM strategies and customer loyalty in the context of the MyTelkomsel application. They also confirm the mediating role of Perceived Customer Relationship Quality and highlight the strategic value of implementing E-CRM to strengthen customer loyalty in the digital era.
Evaluation of the STTNF 360° Virtual Tour System Success based on the Delone and McLean Model using the PLS-SEM Approach Syifa Rahmatia Ramadhani; Jemiro Kasih
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.v15i6.6407

Abstract

This study aims to evaluate the success of the STTNF 360° Virtual Tour System as an information and promotional medium for Sekolah Tinggi Teknologi Terpadu Nurul Fikri (STTNF). The system enables prospective users to explore the campus environment virtually; however, its success from the users' perspective has not yet been systematically evaluated. This study adopts a quantitative approach based on the DeLone and McLean Information Systems Success Model to examine the effects of System Quality, Information Quality, and Service Quality on Use, User Satisfaction, and Net Benefits. Data were collected through a questionnaire administered to 97 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The results indicate that System Quality has a significant positive effect on User Satisfaction (t = 2.454, p = 0.014). Furthermore, User Satisfaction has significant positive effects on both Use (t = 4.011, p < 0.001) and Net Benefits (t = 10.129, p < 0.001), whereas the remaining quality constructs do not exhibit statistically significant effects. The coefficient of determination (R²) indicates moderate explanatory power, with values of 0.527 for User Satisfaction, 0.663 for Use, and 0.511 for Net Benefits. Overall, the findings demonstrate that the STTNF 360° Virtual Tour System provides meaningful benefits by enabling users to better understand the campus environment through an immersive virtual experience. These results offer practical insights for STTNF to further improve system quality and optimize the virtual tour as an effective platform for campus information dissemination and promotion.
Classification of Indonesian Batik Motifs using CNN VGG16 with Transfer Learning and Fine-Tuning Faby Melia Shanni; Pratomo Setiaji; Wiwit Agus Triyanto
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.v15i6.6467

Abstract

Indonesian batik is a cultural heritage passed down through generations and officially recognized by UNESCO. However, the identification of batik motifs still relies heavily on manual assessment by experts. This study aims to develop an automated batik motif classification system using a Convolutional Neural Network (CNN) based on the VGG16 architecture with a transfer learning approach. The dataset was obtained from the Indonesia Batik Motifs repository on Kaggle and consists of three batik motif classes: Batik Bali, Batik Kawung, and Batik Megamendung. To address class imbalance, data augmentation was applied to produce a balanced dataset of 1,500 images, with 500 images per class. The dataset was then divided into training, validation, and testing sets using an 80:10:10 ratio. The preprocessing stage included grayscale image conversion to reduce computational complexity. The proposed model, fine-tuned on the last eight layers of VGG16, achieved a test accuracy of 98.00% with an F1-score of 0.98. Among the three classes, Batik Megamendung achieved the highest F1-score (0.99), followed by Batik Kawung (0.98) and Batik Bali (0.97). Comparative experiments showed that the proposed VGG16 transfer learning model outperformed both MobileNetV2 with transfer learning (97.33% accuracy) and VGG16 trained from scratch (33.33% accuracy). This study contributes to the development of an accurate batik motif classification system that can be deployed in real time through a Streamlit-based web application.

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