Sistemasi: Jurnal Sistem Informasi is a scientific journal in the field of computer science and engineering. General Focus and Scope of Systemation, are Information Systems, Information Technology, Computer Science, Software Engineering, and Computer Engineering. Journal of Information Systems is abstracted and indexed in Directory of Open Access Journal (DOAJ) since 2018 and Science and Technology Index (SINTA) since 2018. Systemation journals are published 12 times a year, in January, February, March, April, May, June, July, August, September, October, November and December. Sistemasi: Jurnal Sistem Informasi (e-ISSN: 2540 – 9719) published by the Information Systems Study Program, Faculty of Engineering and Computer Science, Universitas Islam Indagiri, Tembilahan Riau Indonesia.
Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer
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Application of Market Basket Analysis with The Apriori Algorithm to Discover Consumer Behavior Patterns Through Transaction Data
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%.
Nurdin S.Kom., M.Kom (SCOPUS ID=ID: 57201646662)
Virtual Graph Modeling for Smart Building Energy Analysis from Sequential IoT Data
This study proposes and validates a cost-effective sequential energy mapping methodology to address the challenges of implementing expensive energy monitoring systems in smart buildings. By utilizing a single Internet of Things (IoT) sensor deployed sequentially across five strategic locations, a comprehensive Energy Profile Repository was successfully developed. Data analysis across various scenarios accurately identified major energy hotspots, with the computer laboratory recording the highest consumption at 2.80 kWh/hour. The study also quantified potential energy waste from “vampire loads,” estimated at 2,190 kWh/year at a single location. Furthermore, the analysis revealed that the heating, ventilation, and air conditioning (HVAC) system is the dominant contributor (65%) to peak load demand, rather than computer units. The individual energy profiles were subsequently synthesized to construct a virtual energy graph that models the characteristics and structural relationships of the building’s energy network. This methodology is proven to be an effective and cost-efficient foundational approach for generating actionable, data-driven insights, while also providing a valid basis for the future development of more advanced real-time energy management systems.
Comparative Analysis of Naive Bayes and Fuzzy Logic Algorithm in Fire Classification System
Building fires can cause losses in several areas, including property damage, environmental pollution, loss of life, injury, and psychological trauma. Building fires can occur due to several factors such as gas leaks, short circuits, overheating electronic devices, the presence of flammable materials, and human error. In fire mitigation efforts, devices are generally used as early warnings, but their implementation is often less than optimal due to system malfunctions. Therefore, this study aims to develop an early warning system that can detect potential fires before they spread. The methods used in this study are the naïve Bayes and fuzzy logic methods, which then compare each method to determine the most effective method. The results of this study indicate that the naïve Bayes and fuzzy logic methods have successfully classified potential fires well. From 30 experimental data, the naïve Bayes algorithm produced an accuracy of 96%, while the fuzzy logic algorithm produced an accuracy of 100%. The naïve Bayes algorithm shows reliable performance in classifying extreme data while the fuzzy algorithm can detect the ‘Danger’ status even though not all parameters are in a dangerous condition.
Integrating Design Thinking and CBT for Mobile Telemedicine Depression Recovery Therapy for Adolescents and Women
Depression among adolescents and women has become a significant mental health issue, yet access to effective therapy remains limited due to factors such as geographical barriers and a lack of professional support. This research aims to integrate Design Thinking and Cognitive Behavioral Therapy (CBT) to develop a mobile telemedicine application, Moelih, for supporting the recovery of depression. The study employs a systematic Design Thinking approach, including Empathy, Define, Ideate, Prototype, and Test stages. The black-box testing method was employed to evaluate the app’s functionalities, ensuring that its features—such as mood tracking, journaling (both free and guided CBT), and teleconsulting with professional companions—are intuitive and effective from the user’s perspective. The results demonstrate that the Moelih app successfully meets the needs of its target audience by providing an accessible, flexible, and user-centered digital therapy tool. However, the testing phase revealed areas for improvement, such as enhancing user engagement and optimizing app responsiveness. This research highlights the potential of combining user-centered design and CBT to create effective digital interventions for mental health recovery, contributing to the growing field of telemedicine for mental health.
Aspect-based Sentiment Analysis of Public Opinions on Integrated Islamic Schools using Lexicon based and Machine Learning Approaches
This study aims to examine public perceptions of Integrated Islamic Schools through aspect-based sentiment analysis by integrating Latent Dirichlet Allocation, Lexicon-Based approach, and Deep Neural Networks. LDA is employed to extract topic structures that represent the semantic context of public reviews, Lexicon Based method is used for sentiment analysis, while DNN infers sentiment orientation based on the extracted representations. This approach seeks to combine the strengths of probabilistic topic modeling and deep learning to obtain a more comprehensive understanding of public opinion. The analysis was conducted on a collection of 2,280 online reviews, which after preprocessing resulted in 1,438 reviews processed using the LDA–DNN combination. The results demonstrate that this approach is capable of identify in opinion dimensions in a more contextual manner and enhancing the interpretability of the analysis outcomes. Empirical evaluation shows that the proposed model achieved an accuracy of 63.89% for aspect classification and 93.06% for sentiment classification, outperforming the K-Means–LSA and K-Means–PCA approaches, which achieved 45.14% and 31.94% accuracy for aspect classification and 92.36% accuracy for sentiment classification, respectively. These findings confirm the superiority of probabilistic topic modeling in capturing complex semantic relationships and provide a methodological contribution to the development of sentiment analysis in the context of integrated Islamic education.
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Automation of Water Quality Recovery in Vannamei Shrimp Aquaculture using the KNN Algorithm and Fuzzy Logic
Vannamei shrimp play a crucial role in Indonesia’s fisheries and export industries. Despite their high potential, shrimp aquaculture still faces significant challenges, particularly disease susceptibility caused by fluctuations in water quality and pond environmental conditions. This study aims to develop a system capable of automatically monitoring and restoring water quality using the K-Nearest Neighbors (KNN) algorithm and fuzzy logic. The research adopts a research and development (R&D) approach, which includes problem analysis, data collection, system design, development, testing, evaluation, and implementation. The system employs the KNN algorithm with K=5K = 5K=5 to diagnose water quality conditions, while fuzzy logic is used to automatically control aerators, pumps, and drainage systems. The sensors utilized include salinity, pH, temperature, dissolved oxygen, and turbidity, all integrated through an ESP32 microcontroller within an Internet of Things (IoT) network. The results demonstrate that the system achieves a diagnostic accuracy of 95% and is capable of automatically controlling recovery devices. With real-time and automated operation, the system effectively maintains pond water quality, thereby improving productivity and the overall success of vannamei shrimp aquaculture.
Implementation of the K-Means Algorithm for Clustering Zoning of Natural Disaster-Prone Areas in Pati Regency
Based on data from the Central Bureau of Statistics (BPS) of Pati Regency, during the 2018–2022 period the region frequently experienced natural disasters, particularly floods and landslides, across many areas.The high frequency of these disasters and the lack of proper mapping of affected areas present challenges that need to be addressed effectively. By utilizing technology and the K-Means clustering method, this study proposes an alternative solution to identify and map areas that are vulnerable to natural disasters. The results of the analysis indicate that Pati Regency can be divided into three clusters: Cluster 1 represents highly disaster-prone areas, accounting for 42.86% (9 regions); Cluster 2 represents disaster-prone areas, accounting for 19.04% (4 regions); and Cluster 3 represents areas with low disaster vulnerability, accounting for 38.1% (8 regions). The visualization results are presented as a classification map of regions based on their disaster vulnerability levels. This map can serve as a reference for local governments and relevant institutions in formulating more targeted and effective disaster mitigation policies.
Comparison of Filter and Wrapper Feature Selection Methods for Heart Disease Risk Classification using K-Nearest Neighbors (k-NN)
Feature selection plays a crucial role in improving the effectiveness of medical classification models. This study compares two feature selection approaches—filter and wrapper methods—in developing a k-Nearest Neighbors (k-NN) model for heart disease risk classification. The dataset consists of patients’ demographic data, lifestyle factors, and clinical indicators. In this study, the filter method was applied by considering data types: Pearson Correlation was used for numerical features, while the Chi-Square test was applied to categorical features. The selected features from both techniques were then combined, reducing the initial 20 features to four key variables considered most relevant for heart disease risk classification: BMI, homocysteine level, blood pressure, and stress level. This approach achieved high computational efficiency; however, it resulted in only a modest accuracy improvement (76.8%) and a low recall for the minority class (0.07). In contrast, the wrapper method using Sequential Forward Selection (SFS) produced a more informative subset of 11 features, achieving higher accuracy (80.00%) and a ROC-AUC of 0.657, indicating better discrimination capability for the minority class. These findings suggest that while the filter method excels in simplicity and computational efficiency, the wrapper method is more effective in improving classification performance. This study provides empirical insights into selecting appropriate feature selection strategies based on analytical objectives, particularly for clinical decision support systems.
Improving DCT-based JPEG Steganography using Adaptive LSB Matching for Resistance to Entropy-based Detection
Data transmission security has become a critical issue in the digital era, where steganography plays an important role in concealing confidential information within digital media. A key limitation of conventional Discrete Cosine Transform (DCT)-based steganography in JPEG images is its vulnerability to statistical detection through entropy analysis, as well as the risk of significant degradation in visual quality. This study aims to enhance DCT-based steganography techniques to minimize entropy-based detection while maintaining an optimal Peak Signal-to-Noise Ratio (PSNR). The proposed method employs an Adaptive LSB Matching approach by embedding messages into low-to-mid frequency coefficients using an adjustment mechanism (x±1). The performance of this method is then compared with the standard DCT approach. Experimental results show that the proposed method is able to preserve visual quality, achieving an average PSNR of 40.41 dB under maximum payload conditions, while reducing the entropy difference (ΔH) to 0.00251. These findings demonstrate that the developed technique is more robust against statistical steganalysis attacks and provides better visual fidelity compared to conventional methods.
Hyperparameter Optimization of Ensemble Learning for Heart Disease Prediction using Patient Data
This study evaluates the impact of hyperparameter optimization on the performance of four machine learning algorithms—Extra Trees, XGBoost, Random Forest, and AdaBoost—in heart disease prediction. The results show that hyperparameter tuning significantly improves model performance for three out of the four algorithms, with varying effects across models. Extra Trees demonstrates the most consistent improvement, achieving the highest Area Under the Curve (AUC) of 0.9107 and a recall of 80.93%, which is particularly crucial in medical contexts for accurately identifying disease cases. XGBoost exhibits the largest increase in accuracy, rising from 78.11% to 81.49%, while Random Forest shows improvements in both recall and F1-score. In contrast, AdaBoost experiences a slight decline in performance, suggesting that the model was already near optimal prior to tuning. Overall, Extra Trees with hyperparameter optimization emerges as the best-performing algorithm for heart disease prediction, offering high reliability in identifying at-risk patients.
Design of a Community-based Live-In Information System in Indonesia
The 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.
Enhancing SDGs in Barito Kuala: Enterprise Architecture for Smart Village Development using TOGAF ADM 9.2 in Sungai Pitung
Sustainable village development is essential for realizing the Sustainable Development Goals (SDGs), specifically regarding poverty eradication (SDG 1), inequality reduction (SDG 10), and the establishment of sustainable communities (SDG 11). This study formulates a smart village framework for Sungai Pitung Village, Barito Kuala, by utilizing the Village Information System (SID) to enhance local governance and economic conditions. The methodology employs TOGAF ADM 9.2, covering the Preliminary phase, Architecture Vision, Business Architecture, Information Systems Architecture, and Technology Architecture. The novelty of this research lies in the strategic integration of Village Development Index (IDM) indicators and Village SDGs (Ministerial Regulation No. 13 of 2020) directly into the architectural blueprints. This approach ensures that the resulting digital roadmap is legally and contextually compliant with Indonesian rural mandates. The framework is validated through a formal evaluation of research results with relevant stakeholders to ensure it addresses the digital divide and operational inefficiencies. The findings provide a holistic IT architecture that standardizes technology catalogs and aligns village services with national development targets, ultimately fostering an enhanced quality of life for rural inhabitants.
Digital Image Confidentiality using New Encryption Method
The significant development in mobile phone cameras, in terms of the clarity and high-resolution images captured, the wide utilizing of mobile phones and other communication devices among all segments of society, and the increasing use of social networking sites and the exchange of millions of digital photos daily, all have been pointed with a great importance of digital images and the need to provide adequate security and protection for these images. Digital images contain a large amount of information and recently they have an effective and easy tools of communication without the need for a determined text.Given that digital images contain important, personal, and sensitive information, there is a great need to protect these images and prevent unauthorized persons from applying any changes to the image's contents. There is a great deal of work in this field, most of which uses encryption methods to achieve this protection. As is well known, there are two types of encryption systems (symmetric and asymmetric). Symmetric encryption systems are fast but require a secret key distribution process, while asymmetric encryption systems are relatively slow, involving complex processes. Therefore, they are not suitable for use in social networking applications that require rapid performance and interaction. In this research, a proposed method for digital image encryption is proposed, which includes the use of logical XOR operation to encrypt the digital image based on two proposed levels with scrambling operation to provide a high degree of diffusion and confusion for the resulting encrypted image. The proposed method was evaluated through a set of efficiency measurement metrics (NPCR, UACI, MSE, PSNR, SSIM, Entropy, and Correlation) and it gave results showed a difference between the original image and the image resulting from the first level of encryption. We also noted that the image resulting from the second level had a higher percentage of difference and randomness compared to the original image. Therefore, the proposed encryption method is suitable in terms of speed and confidentiality for use in encrypting digital images and thus maintaining their privacy.
Implementation of Random Forest for Predicting Complaint Handling Priorities at the Minahasa Education Office
Public service delivery in the education sector requires government institutions to handle public complaints promptly and accurately. However, the Minahasa Regency Education Office still faces challenges in determining complaint handling priorities, which are often subjective and manually assigned. This condition may lead to delays in addressing urgent complaints. This study aims to implement the Random Forest algorithm to objectively predict complaint handling priorities based on data. The research methodology includes collecting a dataset of 500 public complaints with six attributes: complaint type, violation level, work unit, follow-up action, outcome, and priority. The process involves data preprocessing, splitting the dataset into training and testing sets, building the Random Forest model, and evaluating its performance. Data processing and modeling were conducted using Jupyter Notebook within the Anaconda environment. Model performance was evaluated using accuracy, confusion matrix, precision, recall, and F1-score metrics. The results show that the Random Forest algorithm achieved an accuracy of 100%, with precision, recall, and F1-score values of 1.00 across all priority classes. These findings indicate that the model demonstrates excellent and stable classification performance. Therefore, it can serve as a foundation for developing a decision support system to improve the effectiveness and quality of public service delivery at the Minahasa Regency Education Office.
Analysis of User Satisfaction with LIVIN’ by Mandiri as User Feedback in the Domain of Continual Service Improvement
The 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.
Comparative Analysis of CNN and Random Forest for Cashew Plant Disease Classification
Cashew plants are a strategic commodity in Indonesia and are highly susceptible to various diseases, making fast and accurate identification techniques essential to minimize economic losses. This study evaluates the comparative performance of Convolutional Neural Networks (CNN) and Random Forest in classifying cashew plant disease images. Using an experimental quantitative approach, the study utilizes a dataset of 6,549 images divided into five classes: anthracnose, gummosis, healthy, leaf miner, and red rust. The models were validated using a split sampling technique with a ratio of 80:10:10 for training, validation, and testing, and were evaluated based on F1-score as well as accuracy, precision, and recall metrics. The Random Forest model employs manual feature extraction, including color, texture (Gray-Level Co-occurrence Matrix/GLCM), and shape (Hu Moments), whereas the CNN model uses a custom Sequential architecture with automatic feature extraction. The experimental results show that CNN achieves an accuracy of 85.80%, outperforming Random Forest by approximately 9%, which attains an accuracy of 76.95%. The novelty of this study lies in the integration of high-level texture features into the Random Forest model to evaluate the performance limits of conventional machine learning compared to CNN-based automatic feature extraction. The findings indicate that CNN performs better for this dataset. However, further optimization—particularly in handling natural background variations—is still required for practical deployment.
Implementation of a Waste Bank Website using the Agile Scrum Approach and User Acceptance Testing (UAT)
Waste management in Indonesia continues to face significant challenges, largely due to manual record-keeping practices that often lead to data duplication, reporting delays, and a lack of transparency. This study develops EcoHub, an integrated digital dashboard designed to monitor waste deposit flows, point conversion, and product transactions to support a circular economy ecosystem. The research addresses a gap in the limited availability of waste bank systems that integrate deposit management, e-wallet functionality, and e-commerce within a unified platform. By adopting the Agile Scrum approach, the system is designed to adapt to user needs through short development iterations and continuous evaluation. The testing results indicate that all system functionalities operate effectively, with a user acceptance rate (UAT) of 89.17%, categorized as Very Good. The novelty of this study lies in the direct integration between waste deposits and real economic exchange mechanisms, which has been shown to significantly enhance operational efficiency for various stakeholders while improving transparency in waste bank management.
Analysis of User Acceptance of the McDonald’s Application using the UTAUT Model
The rapid advancement of information technology has driven increased adoption of digital applications in the fast-food industry, including the McDonald’s application, which is designed to support ordering and promotional information. However, in practice, some users still experience difficulties in using the application, highlighting the need to analyze the factors influencing its acceptance. This study aims to examine user acceptance of the McDonald’s application using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research adopts a quantitative approach employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. Data were collected through an online questionnaire from 85 users of the McDonald’s application. The results indicate that performance expectancy and facilitating conditions have a significant effect on behavioral intention, while effort expectancy and social influence do not show a significant impact. Furthermore, the analysis reveals that the model demonstrates strong predictive power in explaining users’ intention to use the application. These findings suggest that perceived usefulness and the availability of technical support and facilitating resources are the key factors in enhancing user acceptance of the McDonald’s application.
Implementation of Inter-Building Wireless Backhaul using Ubiquiti 5AC Gen2 and MikroTik
This study aims to implement and evaluate the performance of an inter-building wireless backhaul network using the Ubiquiti LiteBeam 5AC Gen2 integrated with a MikroTik router as a solution for internet distribution without the need to subscribe to an additional ISP service. The study is motivated by the increasing demand for high-speed and reliable network connectivity between buildings, while wired network implementations are often limited in terms of cost and installation flexibility. The research adopts an experimental approach with descriptive quantitative analysis through direct measurement of Quality of Service (QoS) parameters, including throughput, delay, and jitter. Testing was conducted under three scenarios: a direct wireless backhaul link, a WiFi network on the first floor, and a WiFi network on the second floor of the building. The results show that the wireless backhaul provides an average throughput of 87.95 Mbps for download and 48.60 Mbps for upload, with a delay of 1.19 ms. On the access network side, the achieved throughput remains sufficient for user needs, although delay and jitter increase as the number of connected devices and traffic load grows. This study concludes that the implementation of an IEEE 802.11ac-based wireless backhaul using Ubiquiti LiteBeam 5AC Gen2 and MikroTik is effective as a medium-scale inter-building connectivity solution, delivering performance that meets typical daily internet service requirements.
Predicting Impulsive Buying in Tokopedia Flash Sales: A UTAUT2 Approach
Flash sale events have become a dominant marketing strategy to trigger rapid purchasing decisions. However, despite the massive growth of e-commerce in Indonesia, it remains unclear whether consumer participation in these events is primarily driven by the thrill of the "hunt" (hedonic) or the rational calculation of discounts (price value), particularly in developing digital markets like Palembang City. This study investigates the determinants of impulsive buying behavior during Flash Sale events on the Tokopedia platform. Drawing upon a modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, this study investigates how Hedonic Motivation and Price Value affect Behavioral Intention, and in turn, its effect on Impulsive Buying. A quantitative methodology was applied, leveraging survey responses from 144 participants in Palembang City who had engaged in Tokopedia Flash Sales. Analysis was conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software. Findings reveal that both Hedonic Motivation and Price Value positively and significantly impact Behavioral Intention, with Price Value identified as the most influential predictor. Furthermore, a robust positive relationship was found between Behavioral Intention and Impulsive Buying, confirming that the intention to participate in Flash Sales significantly drives unplanned purchasing behavior. These findings suggest that while hedonic enjoyment is important, the perceived economic benefit remains the primary catalyst for consumers. Practically, platforms can optimize flash sale design by emphasizing perceived savings and enjoyable experience to effectively drive conversion.
Aspect-based Sentiment Analysis: A Bibliometric Review using Bibliometrix to Map Research Trends and Algorithm Methods
This study presents a bibliometric overview of research trends and algorithmic models in Aspect-Based Sentiment Analysis (ABSA). Data were collected from the Scopus database, resulting in a dataset of 2,344 journal articles published between 2021 and early 2026. The analysis was conducted using the Bibliometrix and Biblioshiny packages in R to perform number of publications per year, source’s production over time, country production over time, keyword co-occurrence, thematic mapping and evolution of research themes. The results show that ABSA research has experienced rapid growth with an annual publication increase of more than 30%. This study identifies BERT algorithmic models and Graph Convolutional Networks (GCN) as the most dominant supporting tools in the research literature. Thematic maps show that transformer-based techniques and attention mechanisms have emerged as key driving themes in this field. Furthermore, thematic evolution maps reveal a shift in focus from technical aspect extraction to online public opinion analysis, reinforced by the sharp surge in the use of Large Language Models (LLMs) in recent years. The findings provide a structured overview of the intellectual landscape of ABSA, clarifying dominant research clusters, methodological trajectories, and emerging themes. By highlighting the central role of transformer architectures, graph-based neural networks, and LLM integration, this study offers methodological guidance for future model development. Furthermore, the bibliometric insights reduce research fragmentation and identify underexplored directions, offering valuable insights for researchers to identify research gaps and develop more advanced ABSA models in future studies.
Web-based Information System for Research and Community Service (PkM) Submissions at LPPM, Nahdlatul Ulama University Lampung
Management of research and community service (PkM) activities in higher education institutions requires an integrated information system to enhance the effectiveness, transparency, and accountability of processes. The LPPM at Nahdlatul Ulama University Lampung still faces challenges in managing proposal submissions, reviewer assignments, the review process, and the handling of research and PkM reports, which are not yet fully integrated into a single system. This study aims to develop and implement a web-based Research and PkM Submission System for LPPM at Nahdlatul Ulama University Lampung. The research adopts the Waterfall model, covering the stages of analysis, design, implementation, testing, and maintenance. The system is built using PHP and MySQL, supported by HTML, CSS, and Bootstrap technologies. To ensure functionality meets the specified requirements, verification was conducted using Blackbox Testing. In addition to functional testing, application performance was evaluated by measuring system response times for login, proposal storage, document uploads, and dashboard display on both desktop and mobile devices. Test results indicate that the platform responds within an average of 1–3 seconds for key functions and demonstrates stable performance without technical issues throughout the testing period. The findings show that the application successfully integrates the entire workflow of proposal submission, reviewer assignment, proposal and report review processes, and monitoring of research and PkM status into a single platform. Testing results confirm that all system modules function correctly and align with the testing scenarios. Therefore, this system is considered suitable for implementation as a supporting management tool for research and PkM activities at LPPM Nahdlatul Ulama University Lampung
Optimization of XOR Cryptographic Keys using a Hybrid Genetic Algorithm and Simulated Annealing
The security level of basic encryption algorithms, such as XOR, is highly dependent on the randomness and bit distribution pattern of the applied key. The use of stochastic optimization approaches, such as Genetic Algorithm (GA), in key generation often faces challenges due to premature convergence, a condition in which the search halts at a local optimum before achieving maximal entropy. This study proposes a sequential hybrid algorithm strategy that combines GA with Simulated Annealing (SA) to address fitness stagnation in PDF document encryption. The strategy is implemented through a two-phase mechanism: GA performs global exploration to identify potential solution regions, followed by SA performing local exploitation with a perturbation mechanism guided by the Metropolis probability. The algorithm’s performance is evaluated through a comparative study between conventional GA and the hybrid GA-SA. Experimental results indicate that the hybrid strategy successfully increases the average fitness value by 4.86%, achieves a Shannon entropy of 7.8952, and attains an NIST test P-value of 0.5299. These improvements demonstrate that the integration of SA effectively enhances the final solution quality of GA, producing cryptographic keys with more uniform bit distribution, passing statistical randomness tests, and exhibiting robustness against pattern analysis.
Analysis of User Experience in the WEVERSE Application using the ITIL V3 Service Design Domain : An ENGENE Perspective in Indonesia
The rapid development of digital technology has changed the way artists and fans interact through community applications such as Weverse. This study aims to evaluate the user experience of Weverse from the perspective of ENGENE (ENHYPEN fans) in Indonesia using the ITIL V3 Domain Service Design approach, as well as to analyze the service quality of the application and provide recommendations for improvement. The research employed a quantitative approach through a survey using a questionnaire instrument developed based on the ITIL V3 Service Design Framework, which consists of six subdomains : Service Catalogue Management, Service Level Management, Capacity Management, Availability Management, Information Security Management,and Supplier Management. The sample consisted of 100 respondents selected using the Slovin formula with a 10% margin of error from an estimated population of 29,937 ENGENE members in Indonesia. Data were analyzed using maturity level analysis and gap analysis. The results show that all subdomains are at Maturity Level 4 (Managed and Measurable), with an average current maturity score of 4.07 and Expected Maturity score of 4.50, resulting in an average gap of 0.43. Among the subdomains, Service Catalogue Management demonstrates the best performance with a current maturity score of 4.21 and the smallest gap of 0.29, while Service Level Management shows the lowest score of 3.89 with the largest gap of 0.61, making it the primary priority for improvement. The findings suggest that the Weverse application has reached the managed stage, where IT service design processes can be systematically measured and monitored. However, continuous improvements are still required to achieve the optimal target of Maturity Level 5 (Optimized).
The Effect of Social Media Promotion on Customer Loyalty Mediated by Brand Equity at Dekabeads
The increasingly dynamic competition in the handmade accessories retail sector encourages SMEs to manage promotional strategies more effectively in order to retain customers. Social media has become one of the primary channels for shaping brand perception and value, which ultimately influence customer loyalty. This study aims to examine the effect of social media promotion on customer loyalty using the Brand Equity framework proposed by Aaker and Keller, with a case study of Dekabeads. The research adopts a quantitative approach using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS. The respondents consist of 169 customers who have previously made purchases. The results indicate that social media promotion has a significant effect on customer engagement, brand equity, customer satisfaction, and customer loyalty. Directly, social media promotion is proven to enhance customer loyalty. In addition, there are indirect effects through increased customer engagement, strengthened brand equity, and improved customer satisfaction as mediating variables. The R-square value of 0.748 indicates that the model explains 74.8% of the variance in customer loyalty. These findings highlight that optimizing social media promotion in alignment with brand equity development plays a crucial role in fostering sustainable customer loyalty.
Analysis of User Acceptance of the Access by KAI Online Ticket Booking Application using TAM
Information technology (IT) has transformed ticketing systems in public transportation and other service industries. The Access by KAI online application, developed by PT Kereta Api Indonesia (Persero), facilitates train ticket purchases. This application features electronic boarding, travel itinerary information, and ticket booking services, thereby streamlining the user’s travel experience. However, users still raise concerns regarding the usability of the application. This study employs the Technology Acceptance Model (TAM) framework to assess user acceptance of the Access by KAI application. This quantitative study collected data from Access by KAI users through a questionnaire survey. Actual System Use (ASU) serves as the dependent variable, while Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Use (ATU), and Behavioral Intention (BI) function as independent and mediating variables. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS software. The results show that four out of five hypotheses are supported, while one is not. The analysis indicates that Perceived Usefulness and Perceived Ease of Use significantly influence Attitude Toward Use and Perceived Usefulness. Another important finding is that Behavioral Intention and Attitude Toward Use have a strong influence on Actual System Use. However, the model does not demonstrate a significant effect for one of the hypothesized relationships. Overall, the findings suggest that users’ attitudes and intentions toward using the Access by KAI application are influenced by their perceptions of its ease of use and the benefits it provides. This study is expected to provide PT Kereta Api Indonesia (Persero) with evaluative insights to enhance service quality, improve user acceptance, and increase the usability of the Access by KAI application.
Evaluation of a Furniture Product Testing Process and Results Reporting Application using the Extended Technology Acceptance Model (TAM) at XYZ Company
The furniture manufacturing industry demands efficient internal processes and reliable reporting quality; however, the adoption of digital reporting applications is often hindered by user acceptance factors. This study evaluates user acceptance of a Furniture Product Testing Process and Results Reporting Application at PT XYZ using the Extended Technology Acceptance Model (ETAM), which integrates constructs from the Technology Acceptance Model (TAM) and variables from the Information System Success Model (ISSM), including system quality, information quality, service quality, and user satisfaction. A survey of employees across operational units was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The measurement model met the criteria for validity and reliability (AVE = 0.598–0.875; composite reliability = 0.869–0.954). The coefficient of determination (R²) indicates a strong to very strong model across key variables (R² for intention to use = 0.883; attitude toward use = 0.738; perceived ease of use = 0.737; perceived usefulness = 0.788; actual use = 0.698). The main results reveal significant paths: system quality → perceived ease of use (β = 0.563; p < 0.007), perceived ease of use → attitude toward use (β = 0.735; p < 0.001), attitude toward use → behavioral intention (β = 0.940; p < 0.001), and behavioral intention → actual use (β = 0.828; p < 0.001). In contrast, information quality and service quality do not have a significant effect on perceived usefulness, perceived ease of use, or user satisfaction; additionally, perceived usefulness and user satisfaction do not significantly drive actual use. These findings highlight the central role of system performance and usability in shaping sustained adoption. They also suggest the need for enhanced training strategies and clearer communication of system benefits to strengthen perceived usefulness and to ensure that service support contributes more effectively to user satisfaction and system usage.
Literature Review: Comparison of Machine Learning Algorithms for Sentiment Analysis of Free Nutritious Meals
The Free Nutritious Meal (FNM) program has triggered massive public responses on social media, driving numerous machine learning–based sentiment analysis studies. However, there has been no comprehensive review comparing the effectiveness of these methods. This study adopts a Systematic Literature Review (SLR) approach on 18 studies (2024–2026) to evaluate the performance of computational algorithms and map trends in public sentiment. The main contribution of this research is to provide an empirical guide for selecting Indonesian-language text classification models, while also offering insights into shifts in public perception. Key findings indicate that Support Vector Machine (SVM) is the most frequently used method, whereas the highest accuracy (97%) was achieved by a combination of Logistic Regression, SVM, and Random Forest on large datasets. Temporally, sentiment trends shifted from budget skepticism (2024) to positive acceptance during program implementation (2025–2026). The study’s implications support policymakers in evaluating program effectiveness in real time. The scope and limitations of this research focus on literature within a specific timeframe, with performance evaluation emphasizing quantitative accuracy metrics.
Analysis of Immunization Coverage Among Toddlers in Indonesia using the K-Means Algorithm based on 2025 People’s Welfare Data
Disparities in toddler immunization coverage across different regions of Indonesia indicate the need for an analytical approach capable of capturing regional characteristics in greater depth. This study aims to cluster toddler immunization coverage using the K-Means algorithm based on the 2025 Indonesian People’s Welfare Data published by Statistics Indonesia. The variables analyzed include immunization history, types of immunization, place of immunization, and immunization providers. Data processing was conducted using Python through the Google Colab platform. The determination of the optimal number of clusters using the Elbow Method resulted in three clusters, with a Silhouette Score of 0.5086, indicating a moderately good clustering quality. The results show that the cluster labeled Low surprisingly exhibits the highest immunization coverage (95.68%), suggesting that the cluster labels do not represent coverage levels in a linear manner but instead reflect differences in operational characteristics and the distribution of immunization services across regions. Meanwhile, the Medium cluster shows the lowest coverage (63.82%), and the High cluster falls at an intermediate level (92.23%). Further analysis indicates that the type of immunization and immunization history are the most influential variables in cluster formation. With clustering quality categorized as moderately good, the K-Means method is considered capable of adequately identifying immunization coverage patterns for region-based policy analysis. These findings demonstrate that a clustering approach can reveal immunization coverage patterns that are not captured through conventional statistical analysis and can serve as a basis for more targeted and data-driven immunization policy formulation.
Evaluation of User Satisfaction in Web-based Library Information Systems: A Systematic Literature Review
The transformation of library management today is highly influenced by the acceleration of information and communication technology (ICT), particularly through the adoption of web-based information systems. While these systems can optimize productivity and service accessibility, their effectiveness ultimately depends on the level of user satisfaction. This study evaluates various user satisfaction assessment methodologies through a Systematic Literature Review (SLR) using the PRISMA protocol on 25 selected articles published between 2020 and 2024. The findings indicate a shift in the dominance of evaluation tools toward the Human-Organization-Technology Fit (HOT-Fit) model and the Net Promoter Score (NPS). Key determinants of satisfaction were identified in terms of information quality, system reliability, and responsiveness of technical support.