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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 920 Documents
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Decision Support System for Porang Land Selection based on Multi Attribute Utility Theory (MAUT) Umar, Najirah; Idris, Muhammad; Rahmat, Agus
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.4297

Abstract

This study aims to develop a web-based decision support system using the Multi-Attribute Utility Theory (MAUT) algorithm to assess land suitability for porang cultivation. The system is designed to assist farmers and land developers in selecting optimal planting sites. The research methodology includes problem identification, primary data collection through field surveys, determination of criteria and weights based on land characteristics, and the implementation of the MAUT algorithm to generate land recommendations. The five main criteria considered are soil texture, altitude, temperature, soil pH, and shading level. The results indicate that the three tested land alternatives achieved suitability levels of 86.67%, 75.57%, and 73.33%, respectively. Based on the suitability threshold of ≥70%, all land alternatives are deemed suitable for porang cultivation. These findings demonstrate the effectiveness of MAUT in supporting data-driven decision-making in the agricultural sector. Furthermore, this approach can be replicated for other commodities and further enhanced through the integration of spatial mapping and financial benefit analysis.
Proposing a Model of Technology Acceptance and use in Digital Banking: A Systematic Review and Meta-Analysis Approach Suwito, Justin Hadinata; Panjaitan, Erwin Setiawan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5670

Abstract

The advancement of information technology has driven a significant transformation in the banking sector through digital banking, which has now become the backbone of modern financial services. Digital banking offers efficiency, ease of transactions, and reduced operational costs. Despite these benefits, challenges remain, particularly the high initial investment costs and the complexity of customer adoption. Without a well-designed user acceptance strategy, substantial investments risk being underutilized. Therefore, a deep understanding of the factors influencing digital banking adoption is crucial to ensure the effectiveness of digital transformation initiatives. Previous studies have examined the acceptance and use of digital banking using popular models such as TAM, UTAUT, and UTAUT2. However, fragmented findings—caused by variations in results and the inclusion of additional variables—pose challenges for generalization. This study aims to develop a more comprehensive model of digital banking acceptance through a systematic review and meta-analysis. The results indicate that most core constructs of UTAUT2—such as Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Habit, Price Value, and Hedonic Motivation—are significant. Furthermore, external variables such as Trust, Perceived Security, Enterprise Image, Promotions, and Perceived Risk also play a role, thereby extending the model beyond the generic framework. The proposed model is expected to enrich the development of technology acceptance theory by introducing a context-specific framework for digital banking. It also provides strategic guidance for the banking industry to enhance adoption through targeted interventions on the most influential variables. Consequently, this model can serve as a stronger foundation for both institutional practices and future research in the field.
Bitcoin Price Forecasting using Seasonal Log-Differenced XGBoost with 2014–2025 Data Akbar, Muhammmad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5547

Abstract

Bitcoin, as a decentralized digital currency, experiences significant price fluctuations, making accurate price forecasting a complex yet valuable challenge. Price forecasting is essential in economic decision-making, serving as the foundation for portfolio construction, risk analysis, and investment strategy development. Bitcoin's high volatility makes it an attractive asset for investors but also poses significant risks, necessitating sophisticated forecasting tools and models to mitigate uncertainty. The XGBoost model in regression is widely known and effectively applied to handle time series data. This model can capture complex nonlinear relationships in Bitcoin price data, providing more accurate forecasts than traditional statistical models. The research methodology includes data collection, data preprocessing, stationarity checking, differencing, feature engineering, data division into training and testing sets, XGBoost model training, prediction and evaluation, and result visualization. The research results show that the XGBoost model achieves a Mean Absolute Error of 8.26% and an RMSE of 9.87%, indicating excellent forecasting accuracy. The implications of this research could potentially assist investors and traders in improving their strategies and risk management.
Integrating Agile and Business Metrics into Backlog Prioritization: A Case Study at PT. XYZ Purba, Susi Eva Maria; Tambunan, Katrina Arlyanti
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5244

Abstract

Backlog prioritization is an essential component of Agile development because it makes sure that resources are used in the best way possible and that business value is maximized. The Effort-influence Matrix gives you a way to prioritize items in your backlog based on how much effort they will take and how much influence they might have. However, just prioritizing them doesn't mean you'll be successful in the long term. Integrating Agile Metrics—such as velocity, cycle time, and lead time—with Business Metrics—such as customer satisfaction, retention, and market adoption—offers a more comprehensive approach to guiding decision-making. This study examines how Product Owners at PT. XYZ applies the Effort-Impact Matrix while incorporating Agile and Business Metrics to align development priorities with organizational objectives. This study employed qualitative research design, drawing on structured interviews, project documentation, and literature review. The findings show that combining prioritizing frameworks with performance indicators improves decision-making, increases alignment with company goals, and leads to more predictable delivery outcomes. This study contributes to the literature by being among the few to empirically demonstrate how Agile and Business Metrics can be systematically integrated into backlog prioritization using the Effort-Impact Matrix.
Implementation of a Network Security System using an Intrusion Prevention System with Machine Learning Lumban, Andre Pardamean; Tedyyana, Agus; Hidayasari, Nurmi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5460

Abstract

This research develops a machine learning-based Intrusion Prevention System (IPS) to automatically detect and prevent network attacks. The system was designed using the Random Forest algorithm, trained on the CICIDS2017 and CICIDS2019 datasets—standard benchmarks developed by the Canadian Institute for Cybersecurity, widely used in cybersecurity research for their realistic network traffic and diverse attack types. The system focuses on three common attacks: SYN Flood, Port Scanning, and SSH Patator. After preprocessing, training, and evaluation, the model was integrated into the IPS, enabling real-time network monitoring, attacker IP blocking, and automated notifications via Telegram. Testing results indicate that the system achieves high detection accuracy while delivering fast and efficient responses. This system simplifies the work of network administrators by detecting and responding to attacks without the need for manual log monitoring. Through its automated and adaptive approach, the IPS makes a significant contribution to enhancing network security and can be directly implemented in organizational or institutional network environments to substantially reduce the risk of cyberattacks.
Website Database Development for Radyakartiyasa using the Directus Headless CMS Irdina, Mutiara; Luthfi, Ahmad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5512

Abstract

Kawadenan Radyakartiyasa, under the auspices of the Karaton Ngayogyakarta Hadiningrat, plays a vital role in cultural preservation and the management of historical tourism destinations. To broaden the reach of information and enhance the promotion of cultural events and Kagungan Dalem heritage sites, a website database was developed as the core foundation of the digital information system. The development process adopted the Agile methodology with the Scrum framework, involving sprint planning to prioritize collections, daily scrums to synchronize progress, sprint reviews to evaluate outcomes, and sprint retrospectives to improve processes. The system was built using the Directus Headless CMS, which decouples the backend and frontend, enabling non-technical teams to manage content efficiently while supporting cross-platform integration. The resulting system includes core collections such as Navigation, Destination Index, Event Index, Hero Banner, FAQ, and other supporting collections, all designed to systematically accommodate information and support multilingual display. These features significantly improve content management efficiency by accelerating information updates, reducing data redundancy, and simplifying content organization in multiple languages. Inter-collection integration ensures consistent information across all website pages, enabling users to quickly and systematically access the data they need. Collection endpoint testing was conducted using Postman to verify that all functions operate according to design specifications and support more organized content management.
The use of iTCLab Kit as a Learning Media for Dynamic Systems and Control Fachrezi, Ahriyad; Rahmat, Basuki; Risal, Muhammad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5616

Abstract

The Internet-Based Temperature Control Lab (iTCLab) kit is an instructional tool designed to facilitate the understanding of fundamental concepts in dynamic systems and control, particularly Proportional-Integral-Derivative (PID) controllers. The background of this study lies in the gap between students’ theoretical knowledge and its application in real-world systems, which often poses a challenge in learning Dynamic Systems and Control courses. This research aims to evaluate the effectiveness of using iTCLab in improving students’ understanding of both PID control theory and its practical applications. The study was conducted in three stages: development of learning modules, implementation, and evaluation involving 30 Informatics students. Assessment was carried out through knowledge tests (pre-test and post-test) and perception surveys. The results indicated an increase in the average score from 52.30 to 76.48 (+46.26%, p < 0.001, Cohen’s d = 1.86), along with positive evaluations in terms of theory–practice integration (4.30), ease of use (4.10), content relevance (4.40), and learning satisfaction (4.20) on a 1–5 scale. These findings suggest that iTCLab is effective in strengthening students’ conceptual understanding and technical skills. From a practical standpoint, iTCLab is suitable for integration into Semester Learning Plans (RPS) and further development to support Internet of Things (IoT)-based distance learning.
The Best Nurse Performance Recommendation Model with Integration of AHP and Weighted Product Methods Fatkhurrochman, Fatkhurrochman; Kanafi, Kanafi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5529

Abstract

This study aims to develop a recommendation model for identifying the best nurse performance by integrating the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods. Nurse performance plays a vital role in determining the quality of healthcare services; however, existing performance evaluations are often subjective and lack transparency. This situation leads to dissatisfaction among nurses and reduces work motivation. Therefore, a system that provides objective and fair evaluation is needed. The AHP method is employed to determine the priority weights of nurse performance criteria through pairwise comparisons, while the WP method is applied to rank nurses based on the assigned weights. The criteria used include Technical Competence, Professional Attitude, Teamwork, and Patient Satisfaction. This research adopts a Research and Development (R&D) approach, which involves data collection, criteria identification, AHP weighting, web-based system development, and model validation and evaluation. The results indicate that integrating the AHP and WP algorithms can produce a comprehensive and practical nurse performance recommendation model that enhances decision-making efficiency and accuracy in hospitals. The best nurse performance recommendation resulted in Wulandari achieving the highest score of 0.3251.
The Influence of Social Media and Online Shopping Habits on Consumer Behavior and Social Identity in Indonesia’s Digital Economy Hendrayani, Eka; Prawirosumarto, Suharno; Lusiana, Lusiana; Putra, Roni Syah
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5502

Abstract

This study aims to examine the impact of social media and online shopping habits on consumer behavior and social identity within the context of Indonesia’s digital economy. The research analyzes how social media and online shopping habits influence consumer behavior and social identity within the framework of Indonesia’s digital economy. A quantitative methodology was employed, collecting data from 280 participants using a Likert-scale questionnaire, and Structural Equation Modeling with Partial Least Squares (SEM-PLS) was applied to test the relationships. The findings reveal that social media influence and online shopping habits significantly affect consumer behavior and social identity. Furthermore, consumer behavior and social identity were found to strongly contribute to the growth of the digital economy. These results highlight the crucial role of digital platforms in shaping consumer actions and identities, thereby driving Indonesia’s digital economy. This study provides practical insights for business practitioners to leverage social media and e-commerce to influence consumer behavior and for policymakers to promote supportive digital infrastructure.
Sentiment Analysis of Academic Application User Comments using Naïve Bayes and Particle Swarm Optimization for Feature Selection Ismail, Abdul Rahman
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5214

Abstract

The Academic Information System supports educational institutions by providing quality management programs to all students and stakeholders, relying on information and communication technologies such as the internet and local networks. Over time, this application has been consistently used by students and lecturers. However, the university has not yet evaluated the feasibility and effectiveness of the system, making it difficult to plan future improvements. Therefore, feedback from both students and lecturers is essential for guiding the system’s development. Given the current state of the application, such evaluation can be carried out through user comments. This study investigates the performance of the Naïve Bayes algorithm, one of the most commonly used algorithms in various research libraries, in analyzing sentiment from these comments. To further improve the accuracy of the Naïve Bayes method, we applied an additional Particle Swarm Optimization (PSO) feature selection process. The results demonstrate that the Naïve Bayes method with PSO achieved an accuracy of 86.27%, precision of 84.78%, and recall of 84.78%, which are higher than the results obtained using the standard Naïve Bayes method alone.

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