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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 40 Documents
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Search results for , issue "Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
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.
UI/UX Design Planning of Ququ Bakery Application Using the User Centered Design (UCD) Method Anggraeni, Nabila Widya; Nugroho, Adi
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.v14i6.5673

Abstract

This study aims to design the user interface (UI) and user experience (UX) of a birthday cake ordering application for Ququ Bakery in Semarang, Central Java, using the User-Centered Design (UCD) approach. The main issues faced by Ququ Bakery include limited promotional media, which currently relies solely on Instagram, as well as user difficulties in placing orders, checking prices, tracking order status, accessing promotions, and making payments. The objective of this research is to develop a web-based application UI/UX that addresses these challenges and enhances user satisfaction. The UCD method applied in this study consists of four stages: Understanding and Specifying the Context of Use, Specifying User Requirements, Designing Solutions to Meet User Requirements, and Evaluating Against Requirements. Evaluation was conducted using the System Usability Scale (SUS) through a questionnaire distributed to 96 respondents. The results indicate an SUS score of 90.35, categorized as “Excellent,” which demonstrates that the proposed application design achieves a very high level of usability and user satisfaction. These findings suggest that implementing the UCD approach effectively produces an intuitive, efficient, and user-oriented application design, which can significantly improve customer loyalty and engagement with Ququ Bakery.
Implementation of the K-Means Clustering Algorithm for Customer Segmentation Kapti, Kapti; Astuti, Dwi
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.v14i6.5573

Abstract

With the rapid advancement of digital technology, companies are faced with an increasing volume and complexity of customer data. This necessitates a more effective approach to customer segmentation in order to better understand consumer behavior patterns and preferences. The large number of customers conducting transactions daily makes it challenging for managers to distinguish between highly frequent shoppers and less frequent ones based on transaction data and purchasing preferences. Therefore, the role of the K-Means clustering algorithm is crucial in addressing this issue. The objective of this study is to implement the K-Means Clustering algorithm for customer segmentation by grouping customers into three clusters: highly frequent shoppers, moderately frequent shoppers, and infrequent shoppers. The research methodology includes the following steps: data collection of transactions and customer preferences, data preprocessing, algorithm implementation, and validation and testing. The clustering parameters are based on the number of purchases, number of transactions, and quantity of items purchased. The functional testing results indicate that the system performs well, as all test scenarios were successfully executed. Furthermore, the evaluation using the Silhouette Coefficient (SC) method produced a strong structure status, with an average SC value of 0.97. This result demonstrates that the dataset is highly robust and suitable to serve as a reference model for customer segmentation.
Web-based Real-Time Inventory Information System Design: An Operational Efficiency Solution at PT. Visual Media Creative Stiawan, Fengky; Rahmanto, Yuri
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.v14i6.5461

Abstract

This study developed a web-based real-time inventory information system to address delays in data processing and the high potential for recording errors at PT Visual Media Creative, which previously relied on conventional methods using books and MS Excel. The system was designed using a prototyping method with an iterative and participatory approach, involving stages of user requirements analysis, interface design, implementation with the CodeIgniter framework and MySQL, and testing. Black-box testing was applied to verify functional compliance, while usability testing involved six internal respondents to evaluate ease of use. The results showed that all functions operated according to specifications with a 100% success rate, and usability testing received positive feedback from respondents. The system implementation reduced inventory report management time from 4–8 hours to 1–2 hours per day, decreased input errors by up to 75%, and accelerated data retrieval from approximately 3 minutes to less than 10 seconds. The contribution of this research lies in providing an integrated, real-time data-driven solution that enhances operational efficiency, improves data accuracy, and supports decision-making within the company.
Design and Implementation of a Tourism Website User Interface for Yogyakarta using the Scrum Method Bahirah, Shabrina Hazrati; Ramadhani, Erika
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.v14i6.5658

Abstract

The Radya Kartiyasa Website, a cultural tourism promotion platform under the management of the Kawadenan Radya Kartiyasa unit, was designed and implemented as a medium for promoting cultural tourism with a user interface that emphasizes both aesthetics and local cultural values. The primary objective of developing this website is to provide comprehensive information about tourism destinations within the Keraton area and cultural events organized by the Keraton, while simultaneously enhancing the digital image of the Yogyakarta Palace as a premier cultural tourism destination. The development process adopted the Scrum methodology. This approach facilitated short development cycles, high flexibility, and strong team collaboration, as reflected in the stages of product backlog, sprint planning, sprint execution, daily scrum, sprint review, and retrospective. The outcome of this implementation includes the creation of the homepage, destination index, museum visit promotions, event index, museum gallery, about us page, event detail pages, destination detail pages, and a contact us form page. The contribution of this study lies in integrating a modern development methodology with a culturally inspired interface design, aiming to make the website an effective digital tool for promoting, educating, and preserving the cultural heritage of the Yogyakarta Palace.
Lightning Risk Mapping in West Sumatra using Kernel Density Estimation and Simple Additive Weighting Hardika, Deny; Eka Fauzy, Muhammad Alvy; Samidi, Samidi
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.v14i6.5571

Abstract

This study analyzes the spatial distribution, occurrence frequency, and lightning hazard vulnerability levels in West Sumatra during March 2024 using a geospatial and multi-criteria approach. By integrating meteorological data with land vulnerability assessments, the research applies Kernel Density Estimation (KDE) to map lightning strike density and employs Simple Additive Weighting (SAW) to incorporate land cover and socio-economic factors into the vulnerability evaluation. This combined approach produces a comprehensive lightning hazard risk map, identifying high-risk zones covering 3.01%, medium-risk zones 21.72%, low-risk zones 25.75%, and safe zones 49.52% of the total area. This innovative methodology represents a significant step forward in improving lightning risk management strategies and disaster mitigation policies, particularly in regions vulnerable to climate change and rapid urbanization. The findings not only highlight the importance of integrating spatial lightning data with environmental vulnerability assessments but also support practical applications in early warning systems and spatial planning to effectively minimize the impact of lightning hazards.
A Comparative Study of LSTM and GRU Models for Wind Forecasting Islamy, Chaidir Chalaf; Wahabi, Adnan
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.v14i6.5639

Abstract

The use of deep learning in the current technological era is increasingly widespread, including in the field of meteorology to support aviation safety. As an archipelagic country, Indonesia faces significant challenges in ensuring flight safety due to unpredictable weather conditions, particularly wind direction and speed, which greatly influence takeoff and landing operations. To address these challenges, the Automatic Weather Observing System (AWOS) plays a crucial role in providing real-time weather data. This study aims to compare the performance of two popular deep learning models for time series data, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting wind direction and speed based on AWOS data from Sultan Hasanuddin International Airport for the period of January 2020–December 2022, obtained from the National Oceanic and Atmospheric Administration (NOAA) website. After preprocessing, five out of eight attributes were used for modeling. The evaluation results show that the LSTM model consistently outperformed GRU in all forecasting scenarios (30 minutes, 1 hour, and 1.5 hours). For wind direction, LSTM achieved MAE values of 10.92°–11.01°, MSE 242.45–247.89, and RMSE 15.57°–15.74°, all lower than those of GRU. For wind speed, LSTM recorded MAE values of 30.32–31.72 knots, MSE 1868.53–2013.92, and RMSE 43.23–44.88 knots, also outperforming GRU. This research is expected to contribute to the development of risk mitigation systems and the advancement of weather forecasting technology in the future.
Enhancing Deep Learning-based Classification of Cassava Leaf Diseases using CLAHE and SMOTE Tribiakto, Herlandro; Sunyoto, Andi; Pramono, Eko
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.v14i6.5530

Abstract

Efficient detection of foliar diseases in cassava (Manihot esculenta) is essential for sustaining crop productivity and ensuring food security, particularly in regions vulnerable to environmental stress. However, accurate identification remains a challenge due to the widespread occurrence of diseases such as Cassava Mosaic Disease (CMD), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD), which continue to threaten cassava yields. This study addresses two major obstacles in cassava disease classification—uneven image quality and imbalanced class distribution—by implementing Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Synthetic Minority Over-sampling Technique (SMOTE). A publicly available dataset from the Cassava Leaf Disease Classification competition on Kaggle was used, and two pretrained convolutional neural networks, EfficientNetV2B2 and DenseNet169, were fine-tuned through transfer learning. The images were resized, enhanced using CLAHE, and augmented before being split into training, validation, and test sets. Both models were trained for 10 epochs using identical configurations. Results indicate that EfficientNetV2B2 achieved higher classification accuracy (88.1%) than DenseNet169 (86.4%), with CLAHE contributing a 2–3% improvement in accuracy. While these results are slightly lower than those reported in previous studies employing extended training durations and advanced techniques such as focal loss, the lightweight approach presented here proves effective under computational constraints. The findings demonstrate the feasibility of developing scalable and resource-efficient disease detection systems, especially for mobile or edge devices. Future research should focus on longer training schedules, advanced loss functions, and validation using field-acquired images to further improve model performance in real-world agricultural settings.
A Comparative Study of K-Means, Gaussian Mixture Model, and Spectral Clustering for Facial Emotion Recognition Totti, Francesco Adhimas; Setiyawati, Nina
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.v14i6.5668

Abstract

Facial emotion recognition is one of the key areas of artificial intelligence that has been widely applied in human–computer interaction. This study compares three clustering algorithms—K-Means, Gaussian Mixture Model (GMM), and Spectral Clustering—to group facial expressions from the FER-2013 dataset, which was sampled down to 1,820 images. Facial features were extracted using Gabor filters and reduced in dimensionality using Principal Component Analysis (PCA) to efficiently preserve essential information. The evaluation was conducted using Silhouette Score, Davies–Bouldin Index, and clustering accuracy estimation. The results show that Spectral Clustering achieved the best performance, with a Davies–Bouldin Index of 0.464 and an accuracy of 95.36%, followed by GMM (Silhouette Score 0.526) and K-Means (Silhouette Score 0.523). Furthermore, PCA with 80% variance retention produced an effective 1D feature representation, allowing clustering results to be visualized in a simple yet informative manner. These findings suggest that the choice of clustering algorithm should be aligned with the desired trade-off between system accuracy and efficiency.

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