Jurnal Sistemasi (OJS FTIK - UNISI, Fakultas Teknik dan Ilmu Komputer Universitas Islam Indragiri)
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Comparative Analysis of User Experience in SeaBank and Bank Jago using the User Experience Questionnaire
Full text linkThe rapid development of information technology has driven a shift in banking services from conventional systems toward more efficient and flexible digital platforms. Generation Z, as digital natives, has become the primary user group of these services due to their strong preference for convenience and ease in financial transactions. According to data from the Google Play Store, the SeaBank and Bank Jago applications have each been downloaded more than 10 million times, indicating strong public interest in digital banking services. Despite having a similar number of downloads, there is a notable difference in user ratings: SeaBank has received a rating of 4.9 out of 5, while Bank Jago has obtained a rating of 4.5. However, research that directly compares user experience across popular digital banking platforms in Indonesia remains limited. Therefore, this study contributes empirical insights into the factors influencing Generation Z’s satisfaction when using digital banking services. This study aims to analyze and compare the user experience of the SeaBank and Bank Jago applications using the User Experience Questionnaire (UEQ) method. Research data were collected through questionnaires distributed using purposive sampling, targeting Generation Z respondents aged 17–28 years. The collected data were analyzed using the UEQ Data Analysis Tool. The results indicate that both applications received positive evaluations from respondents. Bank Jago outperformed SeaBank in the Attractiveness dimension, reflecting stronger visual and emotional appeal. Meanwhile, SeaBank showed superior performance across the other five dimensions: Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. These findings suggest that SeaBank provides a more efficient, clear, engaging, and innovative user experience compared to Bank Jago
Detecting Muslim Students Mental Health with an Islamic Educational Approach using Machine Learning
Full text linkMental health among university students has become a major concern in higher education, particularly in the post-pandemic era, which has left students facing various academic, social, and psychological pressures. Unfortunately, efforts for early detection of mental health issues on campus remain limited, especially in the context of Muslim students who live within an Islamic cultural framework. This study offers an innovative approach by integrating advanced machine learning technology with the depth of Islamic educational values to develop an early detection system that is not only accurate but also humanistic and contextually relevant. The dataset for this study was obtained through a survey of 127 students at Universitas Muhammadiyah Kudus, including variables related to psychological conditions and the intensity of religious practices, used to detect whether students experience mental health problems or maintain good mental health. The research methodology includes data collection, preprocessing, feature analysis, model development using classification algorithms such as Random Forest, SVM, KNN, and Decision Tree, model performance optimization using GridSearchCV, and evaluation. Evaluation of the four models indicated that prior to optimization, SVM and KNN achieved the best performance, both with an accuracy of 88.46%. After optimization with GridSearchCV, SVM became the top-performing model, achieving an accuracy improvement of more than 5%, reaching 94.05%. Feature analysis revealed that levels of anxiety, fatigue, and religious practices such as prayer and dhikr were the primary determinants in mapping students’ mental health conditions. These findings suggest that Islamic values such as tawakkul (trust in God), sabr (patience), and syukur (gratitude) are not merely theological concepts but can also serve as scientific instruments, converted into predictive features in data-driven technologies. This study demonstrates that an SVM model optimized with GridSearchCV is effective in detecting university students’ mental health and has the potential to serve as an early warning system in Islamic campus settings
Building Transparent and Efficient Community Administration: Agile Development of a Neighborhood Information System at Kertamukti Sakti Residence
Full text linkCommunity management in residential areas often relies on manual paper-based administration, leading to inefficiency, unclear financial records, data loss, and limited transparency, which undermine good governance and residents’ trust. This study aims to develop a web-based neighborhood (RT/RW) management information system to improve administrative effectiveness, financial transparency, and service quality. The system was built using CodeIgniter, PHP, MySQL, Bootstrap, and jQuery, applying the Agile development method to ensure flexibility and iterative improvement through continuous feedback between the developers and the community. The development process consisted of planning, design, coding, testing, and release stages, with flowcharts and wireframes supporting interface design and black box testing used for functional validation. The system was evaluated using a user-centered usability assessment (System Usability Scale – SUS), obtaining an average score of 82.5, which falls under the Excellent category. In addition, the financial reporting process time was reduced from three days to one hour, and data entry errors decreased by 90%, proving that the system significantly improves operational efficiency and transparency compared to manual methods. In conclusion, the combination of Agile methodology and lightweight frameworks such as CodeIgniter successfully delivers a responsive, transparent, and user-oriented information system that enhances trust and collaboration within the community. Future development will focus on integrating QRIS, e-wallets, and bank transfers to further streamline financial transactions and support sustainable digital transformation in community management
Optimization of Cargo Loading System in Logistics Delivery Services using Machine Learning at a Logistics Companies
Full text linkA more precise data-driven approach is required to optimize lead time estimation and improve service quality. This study aims to evaluate and enhance lead time accuracy by optimizing cargo loading into containers using shipment data, including item length, width, height, weight, and volume, as well as vehicle loading capacity. The data are processed to optimize the loading process using a Genetic Algorithm, combined with a Random Forest model for determining cargo stacking and rotation. The dataset is analyzed using the CRISP-DM methodology to identify patterns, trends, and inter-variable relationships that influence the optimization of cargo placement within containers. These algorithms were selected due to their ability to capture complex relational patterns and their relevance to logistics shipment data. Model performance is evaluated using accuracy metrics and a confusion matrix to comprehensively assess predictive performance. In addition, the results of the machine learning–based models are compared to identify significant improvements in estimation accuracy. The results indicate that the Genetic Algorithm achieved a fitness value of 0.836142 in Scenario 1 without Random Forest and 3.127948 in Scenario 2 when combined with Random Forest. Furthermore, the Random Forest model achieved an accuracy of 99.23% for stacking prediction and 99.33% for rotation prediction. The developed system effectively supports optimal cargo loading optimization through accurate predictive models, enabling data-driven decision-making. With the implementation of this model, logistics companies can improve operational efficiency, minimize the risk of delays, and deliver superior customer service
Design and Development of the Restaurant X Reservation Application on the iOS Platform using App Clip
Full text linkThe food and beverage industry in Indonesia has shown significant growth, with 4.85 million business units in 2023. However, many restaurants still rely on manual reservation systems, which hinder operational efficiency. Despite internet penetration reaching 79.5%, with smartphones as the primary access device (83.39%), mobile application adoption faces barriers due to friction in the installation process. This study aims to design and implement an iOS-based restaurant reservation application using App Clips technology, integrated with a real-time admin dashboard. The system was developed using the MVVM architecture, with Swift and SwiftUI for the user interface, Golang for the backend, and PostgreSQL for the database. The system includes a customer-facing reservation app, a restaurant-side reservation management app, advance payment via QRIS displayed through the app, table selection based on customer ambience preferences, an automatic overbooking prevention mechanism, and finalization of reservations once the allotted time is complete. Development evaluation was conducted using task-based usability testing with seven respondents (four admins and three customers). The results showed a 100% task completion rate on both interfaces, exceeding the benchmark average of 78%, while App Clip access successfully demonstrated its effectiveness as a quick-access method without installation. This study contributes to the documentation of App Clip implementation in mobile reservation systems and presents an integrated reservation management solution that can be adapted to other sectors within the hospitality industry
Business Process Reengineering in Water Billing Administration: A Case Study of KPAB Gang Gedang Mas
Full text linkThe water billing administration process in Gang Gedang Mas, Curungrejo Village, is still conducted manually, resulting in slow procedures, inefficiency, and a high risk of errors. This study aims to improve the efficiency and accuracy of the water billing administration process through a Business Process Reengineering (BPR) approach. The improvements focus on four main processes: water meter recording, data entry, bill calculation, and billing information distribution, involving two key actors: field officers and administrative staff. This research employed a case study method, consisting of direct observation, process modeling using BPMN notation, and the measurement of processing time efficiency and throughput within one billing cycle before and after the process redesign. The proposed solution is a digital process model design that supports workflow automation at the design level, without implementing an actual system, while still maintaining the operational role of field officers. The results indicate a significant reduction in processing time, from 684 minutes to 168 minutes, along with a 307.5% increase in administrative process throughput efficiency within one billing cycle. This study demonstrates that applying BPR through process model redesign can optimize water billing management in small-scale communities with limited infrastructure, providing an efficient solution that can be adapted to similar contexts
Selection of the Best Marketplace using SAW and WP Methods: A Case Study of Bekasi City
Full text linkIn today’s digital era, e-commerce has made transactions between sellers and buyers easier by eliminating the need for face-to-face interaction. The abundance of available marketplaces often makes it difficult for consumers to choose the platform that best fits their needs. This study aims to provide recommendations for the best marketplace based on four key criteria: trust, user interface design, promotions, and product completeness. The Simple Additive Weighting (SAW) and Weighted Product (WP) methods were applied to support this decision-making process. The research was conducted on five popular marketplaces, with data collected through questionnaires distributed to 200 active respondents. Both SAW and WP methods were used to calculate the weight and score of each marketplace based on consumer preferences regarding the predefined criteria. The results show that Shopee ranked as the top marketplace, achieving the highest scores of 0.99 (SAW) and 5.77 (WP), due to its strengths in trust, promotional offers, and product variety. Tokopedia placed second, with scores of 0.98 (SAW) and 5.75 (WP), excelling in its more intuitive user interface design.
Other marketplaces showed strengths in specific criteria but were unable to surpass Shopee and Tokopedia in the final scores. These findings provide valuable insights for consumers in selecting the most suitable marketplace for their needs, and for marketplace operators seeking to improve service quality based on consumer-prioritized criteria
Design of a Community-based Live-In Information System in Indonesia
Full text linkThe development of information systems in the tourism sector is generally based on the assumption of a centralized business structure, with a single management unit and a unified financial recording mechanism. However, this assumption does not fully align with the characteristics of community-based tourism services, including live-in tourism, which are collectively managed by multiple autonomous actors. This study aims to design a conceptual model of a community-based live-in information system that aligns with the collective work structure and the distributed economic service recording mechanisms. The research employs an information system design approach through workflow analysis, field observations, and semi-structured interviews with key live-in operators in villages on the slopes of Mount Merbabu. The design focuses on modeling the work structure, service process flows, and cross-actor transaction recording mechanisms. Model validation was conducted by assessing the representational fidelity, process congruence, and recording coherence of the model as judged by internal community actors. The results indicate that the conceptual model effectively represents the distributed work practices of live-in services without imposing centralized organizational assumptions. This study contributes to the development of information system designs that prioritize representational alignment with community work practices rather than technical implementation or full automation of service processes
Application of Market Basket Analysis with The Apriori Algorithm to Discover Consumer Behavior Patterns Through Transaction Data
Full text linkMarket Basket Analysis (MBA) examines itemsets that are purchased together by customers in a single transaction and is commonly used to analyze consumer behavior patterns based on transaction data. Kaffah Mart is a supermarket that sells daily necessities and household products. However, the store has not yet identified consumer shopping patterns within customers’ shopping baskets. This study aims to identify product association patterns formed through the application of Market Basket Analysis and to determine appropriate marketing strategies based on the generated association rules using the Apriori algorithm. The findings of this research are expected to support the development of more effective marketing strategies, thereby increasing product sales profitability at Kaffah Mart. The research methodology consists of the following stages: data collection, system flowchart design, implementation of the Apriori algorithm, and system deployment. The results show that, for the 3-itemset rules, customers who purchase sweet soy sauce and chili sauce are also likely to purchase instant noodles. Similarly, customers who buy a toothbrush and mouthwash are also likely to purchase toothpaste, with a confidence value of 100%. For the 2-itemset rule, customers who purchase shampoo are also likely to purchase bath soap, with a confidence value of 96.87%.Market Basket Analysis is an itemset that is purchased simultaneously by customers in a transaction. Apart from that, it is also used to analyze consumer behavior patterns from the transaction data. Kaffah Mart is a supermarket that sells basic daily necessities and household products. This supermarket store does not yet know consumer shopping patterns in the shopping basket. This research aims to determine the pattern of product associations formed based on the application of Market Basket Analysis, finding the right product marketing strategy based on the results of the rules formed using the Apriori algorithm. The benefits of this research can be to help develop a more effective marketing strategy so that it can_increase product sales profits at the Kaffah Mart supermarket. The methods or stages carried out in this research are: data collection, system flowchart design, application of the Apriori algorithm and system implementation. From the results of this research, it was found that the items that sell best for the 3-itemset are if consumers buy soy sauce, chili sauce, then consumers will also buy instant noodles. If consumers buy toothbrushes and mouthwash, consumers will also buy toothpaste with a confidence value of 100%. The item for the 2-itemset is that if consumers buy shampoo, then consumers will also buy bath soap with a confidence value of 96.87%
Analysis of User Satisfaction with LIVIN’ by Mandiri as User Feedback in the Domain of Continual Service Improvement
Full text linkThe rapid development of digital banking services necessitates continuous service quality improvement to ensure that user experiences remain aligned with evolving needs and expectations. This study aims to analyze user satisfaction with the Livin’ by Mandiri application and to identify priority areas for improvement using the ITIL framework within the Continual Service Improvement (CSI) domain. This research adopts a descriptive quantitative approach with purposive sampling, involving 100 active users. Data were collected through questionnaires and analyzed using validity and reliability tests with the assistance of SPSS. The results indicate that the research instrument demonstrates excellent reliability (Cronbach’s Alpha = 0.934), and all questionnaire items are valid. The key Findings identify four factors influencing user satisfaction: interface design, reliability, responsiveness, and personalization. In addition, items X3.6 and X4.4 exhibit the lowest correlation values, indicating that they should be prioritized for improvement. From the CSI perspective, although the service demonstrates good quality and maturity, continuous improvement efforts are still necessary. These efforts should be carried out through iterative cycles of data collection, gap analysis, implementation of improvements, and evaluation to ensure sustained enhancement of service quality