Digital Informatics is a scientific journal in the field of computer science and engineering. General Focus and Scope of Digital Informatics, are Information Systems, Information Technology, Computer Science, Software Engineering, and Computer Engineering.
Rule Based Reasoning Design for the Management of Nurul Islam Pasenggerahan Islamic Boarding School
Islamic boarding school management that still relies on individual intuition leads to inconsistency and inefficiency. This research designs a rule-based decision support system prototype (Rule-Based Reasoning / RBR) to model the knowledge of boarding school caregivers in order to standardize managerial processes. The Research and Development method was applied, encompassing the stages of knowledge acquisition, knowledge base design, and inference engine construction. The prototype encapsulates four main decision modules: new student admissions, dormitory assignment, activity scheduling, and health monitoring, within 45 IF-THEN rules. Testing using 80 real-world cases showed that the system was able to replicate expert decisions with an average accuracy of 87.5%. The research contributions are a structured knowledge base model for Islamic boarding schools and an RBR prototype that functions as a knowledge preservation tool, decision standardization mechanism, and management training aid. This system overcomes the weaknesses of the traditional intuitive approach by offering a documented, consistent, and reliable solution as an initial step towards the digitalization of Islamic boarding school governance.
Copyright (c) 2026 Imam Baihaqi, samsudin, Fitri Yunita (Author) https://creativecommons.org/licenses/by/4.0
Design and Construction of a Website-Based Futsal Field Booking System at New Futsal Tembilahan
The rapid development of information technology requires sports service businesses to provide fast, accurate, and easily accessible services to customers. NEW FUTSAL Tembilahan, a futsal court rental business located in Tembilahan, Indragiri Hilir, still applies a conventional booking system that has the potential to cause problems such as schedule conflicts, recording errors, and limited access to information for customers. This study aims to design and develop a web-based futsal court booking system that facilitates online reservations and displays real-time schedule availability. The research method employed is the Waterfall development model, which consists of requirements analysis, system design, implementation, and testing stages. During the analysis stage, user and system requirements were identified, followed by the design of the system interface and database, web-based system implementation, and functional testing to ensure the system operates according to user needs. The results indicate that the developed system improves scheduling management effectiveness, reduces recording errors, and simplifies the booking process for customers. However, this study is limited by the absence of automated payment integration and testing that was conducted on a limited internal user scale. Overall, the implementation of a web-based futsal court booking system enhances service quality and operational efficiency at NEW FUTSAL Tembilahan.
Copyright (c) 2026 M. AKBAR ALDIAN SIMANJUNTAK, Abdullah (Author) https://creativecommons.org/licenses/by/4.0
Web-Based Digitalization of Recording and Reporting of Posyandu Activities
Posyandu activities play a strategic role in supporting maternal and child health services at the community level. However, the recording and reporting of Posyandu activities are still largely conducted manually, which often leads to problems such as data inaccuracies, the risk of archive loss, and delays in report submission to relevant stakeholders. This study aims to implement a web-based digital system for recording and reporting Posyandu activities as a solution to improve the effectiveness and efficiency of data management. The approach used involves the development of an information system tailored to the needs of Posyandu cadres. The results indicate that the implementation of a web-based application simplifies the activity recording process, integrates data in a centralized manner, and generates reports that are faster, more accurate, and easily accessible. This digitalization also enhances administrative organization and supports data-driven decision-making in the management of community health services. Therefore, the developed system offers a more effective alternative to conventional manual recording methods and has the potential to sustainably improve the quality of Posyandu services.
Copyright (c) 2026 SILVIA HERA AGUSTIN SILVI, Misnawati (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Halodoc Application Satisfaction using Naïve Bayes, SVM, and KNN
This research aims to analyze patient satisfaction sentiment towards the Halodoc application, a digital health platform that facilitates online medical consultations, medicine ordering, and other health services. Using machine learning-based sentiment analysis techniques—specifically the Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms—this study examines patient reviews collected from various sources such as the Google Play Store, App Store, and online health forums. The review data was processed through preprocessing stages, including tokenization, stemming, and stop words removal, to identify positive, negative, or neutral sentiments. The analysis results show that the Naive Bayes algorithm achieved the highest accuracy of 85% in sentiment classification, followed by SVM with 82% and KNN with 78%. Key satisfaction factors include ease of access, doctor response speed, and service quality, while common complaints revolve around application technical issues, service costs, and feature limitations. This research provides insights for Halodoc developers to enhance user experience and contributes to the literature on sentiment analysis in the digital health sector.
Copyright (c) 2026 Renaldy, M.radju ariansyah, Afriyanto , hory ramadani (Author) https://creativecommons.org/licenses/by/4.0
Design of E-Commerce And Business System for Yastore Grocery Shop Based on a Website
The development of technology in the globalization era has brought significant changes to the business world, where competition among entrepreneurs is becoming increasingly intense. This condition requires every business to innovate, particularly in marketing and service systems, to remain competitive and sustainable. yastore is a grocery store that provides various basic daily necessities; however, its promotional and sales activities are still carried out manually, such as direct purchases at the store and word-of-mouth marketing. This approach is considered less effective because modern consumers tend to prefer fast and practical transaction processes through digital media. This study aims to design a website-based e-commerce system as a promotional and online transaction platform for yastore. The development method used is the Waterfall model, which includes stages of requirements analysis, system design, coding, testing, and maintenance. The results show that implementing an e-commerce website can expand market reach, simplify transactions between sellers and customers, and serve as an effective and efficient promotional medium to enhance business competitiveness sustainably.
Copyright (c) 2026 Andi nazzwa azzahra, Dwi Yuli Prasetyo (Author) https://creativecommons.org/licenses/by/4.0
Web Ordering System Design for Sigma Coffee and Tea with Enterprise Architecture Planning
The rapid adoption of information technology demands that Micro, Small, and Medium Enterprises (MSMEs) transform their operations, particularly in their sales strategies. Sigma Coffee and Tea, an MSME in the coffee, beverage, and snack sector, still relies on offline and manual transactions, which hinders efficiency and market expansion. This research focuses on designing and developing a web-based e-commerce platform as a digitalization solution to address these limitations. The primary objective of this project is to create a system capable of streamlining the ordering process, accommodating both delivery and takeaway services, and providing integrated internal management. The Enterprise Architecture Planning method was chosen as the development framework, ensuring that every stage—from functional requirements analysis to system implementation and testing—is structured. The outcome of this research is an interactive website featuring a digital catalog and facilitating accurate order input. By digitizing these business processes, Sigma Coffee and Tea is expected to optimize operational performance, improve data accuracy, and significantly expand its customer reach, thereby enhancing its competitiveness.
Copyright (c) 2025 Nadia Asparosa, Dwi Yuli Prasetyo (Author) https://creativecommons.org/licenses/by/4.0
Designing an E-Commerce Website System for Msmes: A Case Study of Warung Mak Uwo Using the RAD Method
This research aims to design and implement an e-commerce website system as a strategic solution for digitalizing the operations of the MSME (Micro, Small, and Medium Enterprise) Warung Sembako Mak Uwo. The store faces conventional sales constraints, such as limited reach, manual ordering, and the absence of a digital catalog, which hinders business growth. The method used is a structured system design approach, including requirements analysis and UML (Use Case) modeling. The designed e-commerce system encompasses essential features: a digital product catalog, a shopping cart mechanism, integrated order management, and support for payment and shipping logistics methods. This study contributes to the improvement of operational efficiency and expansion of the customer base, while also serving as a digitalization framework relevant for similar retail MSMEs.
Copyright (c) 2026 Rizki Hidayat, Dwi Yuli Prasetyo (Author) https://creativecommons.org/licenses/by/4.0
This research aims to design and implement an e-commerce website system as a strategic solution for digitalizing the operations of the MSME (Micro, Small, and Medium Enterprise) Warung Sembako Mak Uwo. The store faces conventional sales constraints, such as limited reach, manual ordering, and the absence of a digital catalog, which hinders business growth. The method used is a structured system design approach, including requirements analysis and UML (Use Case) modeling. The designed e-commerce system encompasses essential features: a digital product catalog, a shopping cart mechanism, integrated order management, and support for payment and shipping logistics methods. The main result is an e-commerce website prototype that was successfully implemented and functionally tested. The testing proved that the system runs well, allowing Warung Mak Uwo's customers to shop online. This study contributes to the improvement of operational efficiency and expansion of the customer base, while also serving as a digitalization framework relevant for similar retail MSMEs.
Copyright (c) 2026 Wahyuni Riska Adinda, Dwi Yuli Prasetyo (Author) https://creativecommons.org/licenses/by/4.0
Healthy Meal Recommendation Application Based on Calorie Intake Using the Knapsack Algorithm
This study aims to develop a healthy meal recommendation application capable of generating optimal menu combinations based on the user’s daily calorie limit. The problem addressed is the difficulty in determining balanced and nutritious meals that align with individual caloric needs. To overcome this issue, the Knapsack algorithm is utilized to optimize menu selection by considering nutritional value as the optimization parameter and calories as the capacity constraint. The system is developed using the Ionic Framework as the user interface and SQLite as the local database for food data storage. The Knapsack algorithm is implemented to calculate and select food combinations with the highest nutritional value without exceeding the specified calorie limit. The testing results indicate that the application can provide accurate and consistent menu recommendations across various calorie input variations. These findings demonstrate that the proposed approach is effective in assisting users in managing their daily nutritional intake while maintaining caloric balance. Compared to conventional methods, this system offers a more adaptive and efficient solution and can be integrated into mobile devices as a tool for healthy meal planning. This study contributes to the development of intelligent systems that support a healthy lifestyle through personalized, calorie-based menu recommendations.
Copyright (c) 2025 Regina Zulaikha Tizar , Gita Novi Yanti, Muhammad Regi Nidzra Kurniawan (Author) https://creativecommons.org/licenses/by/4.0
User Reviews of the SEVIMA EdLink Application Using the Machine Learning Method
This research aims to conduct sentiment analysis on user reviews of the EdLink application using the Knowledge Discovery in Database (KDD) approach, which consists of data selection, preprocessing, transformation, data mining, and evaluation stages. A total of 2,509 reviews were successfully collected using the google-play-scraper, followed by text cleaning and automatic labeling based on rating scores. The text data was transformed using the TF-IDF method before classification was performed using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. The test results using three data splitting schemes (50-50, 70-30, and 80-20) showed that the Naive Bayes model performed best with the highest accuracy reaching 0.77 in the 80-20 split, followed by Logistic Regression at 0.76, while SVM had the lowest accuracy. These findings illustrate that although the EdLink application is considered beneficial, users still frequently experience technical obstacles. This research is expected to serve as evaluation material for developers to improve the quality of the application.
Copyright (c) 2026 Rizki, Ridho, Nur Ilham Ade Putra Ramadhan, Muhammad Asril Fadli (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Facial Wash Sombong Product Reviews Using the Naive Bayes Algorithm
This study aims to analyze public sentiment toward the Facial Wash Sombong product, a beauty product that went viral on social media, using Naïve Bayes Classification (NBC). The data were obtained from 5,000 user comments on the TikTok platform, collected through web crawling techniques. The data preprocessing stages included cleansing, case folding, tokenizing, filtering, and stemming to prepare the textual data. The results of the sentiment distribution analysis indicate a dominance of negative opinions at 61.4% (3,070 comments), while positive sentiment accounts for 38.6% (1,930 comments). Keyword analysis reveals that negative sentiment is mainly triggered by issues related to side effects, such as acne and skin irritation. The NBC model was evaluated using an 80:20 data split and achieved strong performance, with an accuracy of 85.62%, sensitivity of 88.40%, and specificity of 76.90%. The effective performance of the model validates the use of NBC for sentiment analysis and provides strategic recommendations for producers to address public criticism.
Copyright (c) 2025 Bima maulana, M. Haikal Fikri (Author) https://creativecommons.org/licenses/by/4.0
Implementing the Naïve Bayes Method for Sentiment Classification of Brimo App User Reviews on the Google Play Store
The development of digital technology has encouraged the increasing use of mobile-based banking applications, one of which is Brimo, developed by Bank BRI. User reviews on the Google Play Store reflect the experience and satisfaction level with the application, but the large number of reviews makes the manual analysis process inefficient. This study aims to classify the sentiment of Brimo application user reviews into positive, negative, and neutral categories using the Naïve Bayes method. The data collection technique was carried out using web scraping to retrieve user reviews from the Google Play Store. The analysis process was performed using Google Colab with the Python programming language, through the stages of preprocessing, word weighting with TF-IDF, and classification using the Multinomial Naïve Bayes algorithm. The sentiment analysis results showed that the majority of user reviews were negative (78%), compared to positive reviews (22%). Classification using the Naïve Bayes Model yielded an accuracy of 73.56%, which indicates a reasonably good ability to recognize overall sentiment, but is still affected by data imbalance. The results of this study are expected to provide an overview of user perception and serve as input for developers to improve service quality. The impact of this research is the availability of an artificial intelligence-based analysis model capable of identifying user opinions automatically, quickly, and accurately in supporting the development of digital banking services.
Copyright (c) 2025 Nuraini, Siti Nurhaliza (Author) https://creativecommons.org/licenses/by/4.0
Development of a Rule-Based Laptop Damage Diagnostic Expert System Using the Forward Chaining Method
This study developed a rule-based expert system to diagnose laptop damage using the forward chaining method to make it easier for users to identify damage independently. This system was built through stages of needs analysis, knowledge base design containing 32 symptoms and 27 IF–THEN rules, diagnosis interface implementation, and functional testing and validation by experts. Black-box testing results show that all features function properly, while expert validation on 15 cases yields 80% accuracy, indicating that the system is capable of providing a reasonably accurate initial diagnosis. These findings indicate that the forward chaining method is effective in tracing symptoms to damage conclusions, although additional rules are still needed to address similar symptom patterns. Overall, this system provides practical benefits as a laptop troubleshooting tool and contributes to the application of rule-based reasoning methods in the context of electronic devices.
Copyright (c) 2026 Nurnita, Khairul Khatimah , Yesti Andini (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of the BCA Mobile Banking Application on User Reviews in the Google Play Store Using the Naïve Bayes Method
The advancement of digital technology has encouraged banking services to shift toward more practical mobile banking, including the BCA Mobile application. This application is used by customers to perform financial transactions online. This study aims to analyze user sentiment toward the application using the Multinomial Naïve Bayes algorithm based on 1,000 Indonesian-language reviews taken from the Google Play Store. The analysis process includes text preprocessing, TF-IDF weighting, and model evaluation using accuracy, precision, recall, and F1-score metrics.The results show that the model performs well with an accuracy of 83.87%, precision of 92.31%, recall of 68.18%, and an F1-score of 78.36%. Most reviews indicate negative sentiment, mainly related to technical issues such as system errors and login difficulties, while positive reviews highlight ease of use and fast transactions. These findings are expected to provide input for developers to enhance the quality and stability of the BCA Mobile application.
Copyright (c) 2025 Rina Oktavia, Ghaitsa Zahira Shofa (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Shopee User Reviews on the Play Store Using the Naive Bayes Algorithm for Evaluating User Satisfaction
The increasing number of Shopee user reviews on the Google Play Store serves as a valuable source for evaluating service quality; however, these reviews are unstructured, making manual analysis difficult. This study aims to analyze user sentiment toward the Shopee application using the Multinomial Naive Bayes method as an effort to evaluate user satisfaction. A total of 183 reviews were collected and processed through text preprocessing, including cleansing, case folding, tokenizing, stopword removal, and stemming. The textual features were then weighted using TF–IDF and classified into positive and negative sentiments. The experimental results show an accuracy of 83.87%, a sensitivity of 68.18%, and a specificity of 94.74%. Most negative sentiments were related to application bugs, delays, and login issues, while positive sentiments were dominated by reviews regarding product quality, pricing, and service speed. These findings indicate that the Naive Bayes method is effective for sentiment analysis of Indonesian-language reviews and can serve as a basis for developers to improve system stability and the overall user experience of the Shopee application.
Sentiment Analysis, Naive Bayes, TF–IDF, Shopee, User Reviews
Copyright (c) 2025 Riska, Nurfadillah, Sonia Putri (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Students on the Use of ChatGPT in Learning Using SVM
The development of artificial intelligence (AI) has transformed the way students learn, one of which is through the utilization of ChatGPT as a digital learning assistant. This study explores students' perceptions and sentiments regarding the use of ChatGPT in learning activities by implementing the Support Vector Machine (SVM) algorithm. Data was collected from 41 respondents via a Google Form questionnaire containing a Likert scale (1-5) and open-ended questions about their experience using ChatGPT. After undergoing text pre-processing, the data was classified into three sentiment categories: positive, neutral, and negative. The analysis results show that the SVM model achieved an accuracy of 72.88%, indicating the model's capability in recognizing language patterns that represent student opinions. The majority of respondents showed positive sentiment towards ChatGPT as it was considered helpful for understanding learning materials, although some expressed concerns about the potential for over-reliance on AI technology.
Copyright (c) 2025 Noval Afriliyandra, Dika Putra Pratama (Author) https://creativecommons.org/licenses/by/4.0
Classification of Starfruit Maturity Using the KNN Algorithm Based on Color
Starfruit is a high-value economic tropical fruit widely used in the food industry. The determination of the fruit's maturity level is typically done manually through visual observation, but this method is subjective and less accurate. This research aims to develop a system to classify the maturity level of starfruit using the K-Nearest Neighbor (KNN) algorithm based on color features in the Hue, Saturation, and Value (HSV) color space. A dataset of 90 images was obtained through an acquisition process using a smartphone camera. This was followed by pre-processing stages, including resizing, segmentation, and HSV feature extraction. The data was split into 80% for training and 20% for testing.The results indicate that the value of the K parameter influences the model's performance, with the highest accuracy obtained at K=7, achieving an accuracy of 94.71%. The overall average accuracy reached 84.54%. These results prove that the KNN algorithm is effective for classifying the maturity level of starfruit based on color characteristics. The developed system has the potential to be an automated sorting solution that is more objective, efficient, and accurate for farmers and agricultural processing industries.
Copyright (c) 2025 Muhamad Fadlan Salim , Muhammad Nabil, Alfin Akbar, Samsudin (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Transaction Patterns and User Preferences in the DANA Application Using the Apriori Algorithm for Digital Wallet Service Optimization
DANA emerges as a practical solution for conducting fast, efficient, and secure financial transactions. However, its rapid growth presents a new challenge for developers: understanding user transaction patterns and behavior in depth. Each user has different preferences and habits; some regularly use the bill payment feature, others more frequently perform top-ups and purchase mobile credit, while some utilize the balance transfer feature. Data processing results reveal that the combination of Top-Up and Electricity Bill payments has a support value of 60% and a confidence of 85.7%. In contrast, the combination of Electricity Bill Payment ke Top-Up shows the highest confidence value of 100%. This indicates that almost all users who pay electricity bills will always top up their balance before transacting. Analyzing these transaction patterns is crucial as it provides valuable insights for developers to optimize features, enhance service personalization, and design user behavior-based promotional strategies.
Copyright (c) 2025 Syaripah Alhapida, Ariyani Nabila, Mirna Wulandari, Misnawati (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Public Perception Towards Electric Cars in Indonesia Using the Naïve Bayes Algorithm
The development of electric cars in Indonesia shows significant improvement alongside government efforts to reduce carbon emissions and promote renewable energy use. Public perception and opinions of electric cars are important factors influencing interest in owning these environmentally friendly vehicles. This study aims to analyze public sentiment toward electric cars in Indonesia using review data and comments from the social media platform X (Twitter). Data was collected through web scraping techniques and stored in CSV format for further processing using Google Colab. Preprocessing was performed to clean the text data, including removal of punctuation, numbers, symbols, Indonesian stopwords, tokenization, and stemming. The text was then converted into numerical representation using TF-IDF vectorization so it could be processed by the Naïve Bayes classification model. The model was then trained and tested to classify sentiment into three categories: positive, negative, and neutral. The results show that the majority of public comments about electric cars were negative, with a percentage reaching approximately 66.5%, while positive comments accounted for 33.5%. Further analysis revealed that negative sentiment was largely related to the relatively high vehicle prices and limited charging infrastructure. The Naïve Bayes model with CountVectorizer proved effective, achieving 78% accuracy, thus making it a reliable tool for sentiment analysis.
Copyright (c) 2025 M. Syahruni, Muhammad Alief Indra Putra, Amrullah Assalami (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Patient Satisfaction With the Halodoc Application using Naive Bayes, SVM, and KNN
This research aims to analyze patient satisfaction sentiment towards the Halodoc application, a digital health platform that facilitates online medical consultations, medicine ordering, and other health services. Using machine learning-based sentiment analysis techniques—Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms—this study examines patient reviews collected from various sources such as the Google Play Store, App Store, and online health forums. A total of 5,000 reviews were processed through preprocessing stages, including tokenization, stemming, and stop words removal, to identify positive, negative, or neutral sentiments. The analysis results show that the Naive Bayes algorithm achieved the highest accuracy of 85% in sentiment classification, followed by SVM with 82% and KNN with 78%. Key satisfaction factors include ease of access, doctor response speed, and service quality, while common complaints revolve around application technical issues, service costs, and feature limitations. This research provides insights for Halodoc developers to enhance user experience and contributes to the literature on sentiment analysis in the digital health sector.
Copyright (c) 2025 Renaldy , M. Radju Ariansyah (Author) https://creativecommons.org/licenses/by/4.0
User Sentiment Analysis of the ChatGPT Application on Google Play Store using the Naïve Bayes Algorithm
User reviews on the Google Play Store reflect public perceptions and satisfaction toward the ChatGPT AI application. This study aims to analyze user sentiment using the Multinomial Naïve Bayes algorithm. The research follows the stages of the Knowledge Discovery in Database (KDD) process, including data selection, preprocessing, transformation, data mining, and evaluation. A total of 1,000 reviews were classified into three sentiment categories—positive, negative, and neutral—and tested using data split ratios of 50:50, 70:30, and 80:20. The best performance was achieved with the 80:20 ratio, producing an accuracy of 94%, precision of 96%, and recall of 99% for positive sentiment. These findings indicate that the Naïve Bayes algorithm can effectively classify user sentiment and provide valuable insights into public perceptions of the quality and performance of the ChatGPT application.
Copyright (c) 2025 Muthmainnah, Rajwa Fakhira, siti nurhalimah (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Zoom Meeting User Satisfaction Using the Pieces Framework at Islamic University of Indragiri
This research aims to analyze the level of user satisfaction with the Zoom Meeting application in the online learning process at the Islamic University of Indragiri using the PIECES Framework method, which covers six aspects: Performance, Information, Economics, Control, Efficiency, and Service. The research method used was a quantitative survey with a questionnaire distributed to 103 student respondents from various faculties. Validity and reliability tests were conducted to ensure the accuracy of the research instruments, with the results showing that all questionnaire items were declared valid and reliable. The analysis results show that the satisfaction level for each aspect is in the "satisfied" category with the following average scores: Performance (4.34), Information (4.4), Economics (4.32), Control (4.29), Efficiency (4.33), and Service (4.37). These findings indicate that the Zoom Meeting application is able to meet user needs in supporting online learning. This research is expected to provide input for educational institutions in improving the quality of online learning and the utilization of digital technology.
Copyright (c) 2025 Fajar Sukmajati, Samsudin, Ilyas (Author) https://creativecommons.org/licenses/by/4.0
Design of a Web-Based Sales Information System for an Online Store
Background The rapid advancement of information technology has encouraged businesses to shift to digital-based sales systems to improve efficiency and expand market reach. This study aims to design a web-based sales information system that can help online stores manage product data, transactions, and customers in a structured and real-time manner. The system development method used is the Waterfall model, which includes stages of requirement analysis, system design, implementation, testing, and maintenance. During the testing phase, the system was evaluated using black-box testing to ensure each feature functions according to specifications. The research demonstrates that the system successfully assists store admins in managing product catalogs, processing orders, recording transactions through Google Sheets integration, and generating automated sales reports. The implementation of this system is expected to make the sales process more organized, faster, and more accurate, thereby increasing productivity and customer satisfaction.
Copyright (c) 2025 M.TRI WAHYU (Author); Nadea Tri Atmaja, Fitrah Andika Putra (Translator) https://creativecommons.org/licenses/by/4.0
Analysis of the Aksara Library Website in Supporting Information Needs (Case Study of Sman 1 Tembilahan)
Digital transformation in school library management has become an urgent necessity in the information age to enhance literacy access and service efficiency. This study aims to analyze the effectiveness of using the Aksara Library's digital system, based on the Senayan Library Management System (SLiMS), in supporting the information needs of students at SMAN 1 Tembilahan. Using a descriptive qualitative approach, data was collected through direct observation, interviews with librarians and active users, and documentation from the system dashboard. The analysis was conducted using data reduction and categorization techniques based on the Miles and Huberman model. The results show that the library website recorded an average of 4,000 visits per day, with features such as the digital guest book, OPAC, and national learning resources being optimally utilized. The collection is dominated by non-fiction books, and services are available online 24 hours. These findings confirm that SLiMS-based library digitalization can effectively support student information literacy, although improvements are needed in collection diversification and continuous evaluation of user satisfaction. This study provides an empirical contribution to the development of a school digital library evaluation model and serves as a reference for similar implementations in other educational institutions.
SLiMS, Digitalisasi perpustakaan, Layanan perpustakaan
Copyright (c) 2025 M Akbar Riwanto, M Saleh, Muhammad Ari Ardana, Juwardi Wafdan, Muhammad Dede Fitriawan (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Social Security Administration Agency (BPJS) Health Users in Indragiri Hilir Regency
This study aims to analyze the patterns of BPJS Health service utilization by the community in Indragiri Hilir Regency, Riau Province. As an area with a relatively complex and diverse geographical conditions, Indragiri Hilir faces challenges in equitable access to health services. The method used in this research is quantitative descriptive, with secondary data obtained from the Central Statistics Agency (BPS) and related health institutions. The results show that the participation rate of BPJS Health in several sub-districts, such as Tembilahan, Keritang, and Kateman, is relatively high. However, significant disparities still exist in coastal and remote areas such as Sungai Batang, Teluk Belengkong, and Pulau Burung. In addition to geographical factors, low health literacy and limited access to information are also the main causes. It was also found that there are discrepancies in membership data, indicating the need for regular data updates and verification. This study recommends the need for more inclusive and adaptive strategies, especially in reaching remote areas, to achieve equitable access to health services for all people.
BPJS Kesehatan, Indragiri Hilir, Kepesertaan, akses Layanan, Ketimpangan Geografis
Copyright (c) 2025 Rizki, Ridho, Kawet Mujiono, Achmad Isya Alfassa (Author) https://creativecommons.org/licenses/by/4.0
The Effect of Gadget Use on the Health of Faculty of Engineering and Computer Science
This study aims to analyze the effect of excessive gadget use and irregular sleep patterns on the physical health of the 2023 cohort students of the Information Systems Study Program, Faculty of Engineering and Computer Science, Islamic University of Indragiri. Data was collected through questionnaires covering the duration of gadget use, sleep habits, and physical health complaints such as fatigue, headaches, and visual impairments. Data processing and visualization were carried out using Power BI to facilitate the analysis of patterns and relationships between variables.The results show that the majority of students use electronic devices for more than 4 hours per day, with the main activity being accessing social media (Instagram, TikTok, and similar platforms), followed by watching videos or movies, playing games, and completing assignments. Graphical visualizations indicate that students with less sleep, particularly 5-6 hours per day, tend to use devices for longer durations. These findings also reinforce the assumption that excessive device use, especially at night, negatively affects sleep patterns and reduces the quality of students' physical health. Therefore, this study emphasizes the need for education on screen time management and the importance of maintaining healthy sleep patterns so that students can preserve their physical health and academic productivity.
Copyright (c) 2025 tri wahyuni, nurnita, khairul khatimah, Yesti Andini, Muhammad Suratman (Author) https://creativecommons.org/licenses/by/4.0
LAN Network Design in the PMI Building in Indragiri Hilir Regency Using Cisco Packet Tracer Software
The Indonesian Red Cross (PMI) is a humanitarian organization that operates in the social humanitarian sector and has played an important role in providing humanitarian assistance, including in terms of aid preparedness and disaster management, first aid training for volunteers, health services, and blood transfusion services.. Because Therefore, designing an efficient computer network can help PMI in meeting these needs. In a journal article about computer networks, it can be explained how efficient computer network design can help PMI in supporting its operational activities, including in terms of exchanging data and managing blood donation activities. In designing a network with a star topology, we use several network devices such as switches and cables. UTP. Designing a Star Topology on PMI Using Cisco Packet Tracer Designing a star topology on PMI using Cisco Packet Tracer can be done with the following steps: 1. Determine the number of computers that will be connected to the PMI network. 2. Determine the specifications of the device to be used, such as a hub or switch. 3. Connect each computer to a central device (hub or switch) using UTP cables. 4. Configure each computer with an IP address that matches the PMI network. 5. Configure the central device (hub or switch) with an IP address that matches the PMI network. 6. Test the network by sending data between computers and ensuring that the network is functioning properly.
Copyright (c) 2025 Ramly Muha Zendra (Author) https://creativecommons.org/licenses/by/4.0
Implementation of an e-Commerce Website at the Jarvis Computer Store
The implementation of this e-commerce website aims to enhance the efficiency of the sales process by providing customers with a better shopping experience. The development method includes identifying business needs, analyzing functional and non-functional requirements, designing the user interface, and integrating with an inventory management system. Online transaction security is a primary focus, with the implementation of robust security protocols to protect customer data and transaction information. Furthermore, integration with Jarvis Store's inventory management system is implemented to ensure accurate product availability and operational efficiency. The results of this implementation are expected to enhance Jarvis Computer Store's competitiveness in the digital market, expand customer reach, and improve operational efficiency. Therefore, this research provides a positive contribution to the digital transformation within the computer and printer trading industry, and can serve as a guide for similar companies looking to leverage the potential of e-commerce.
Copyright (c) 2025 Algarin Widodo, Misnawati (Author) https://creativecommons.org/licenses/by/4.0
Implementation of the Waterfall Method in the Development of an E-Commerce Application for Sales Management of Chips Products(Case Study: Chips Product Shop)
This research aims to implement the Waterfall method in developing a management application for selling snack products. The main objective is to create an effective application for managing various snack products. The Waterfall method consists of several stages, including requirement analysis, design, implementation, testing, and maintenance, which are utilized in this study. The requirement analysis stage is conducted to understand user needs and determine the features to be included in the application. The design phase involves designing the necessary data structure and functions. The implementation phase includes coding the application based on the designed specifications. The testing stage ensures that the application functions properly and meets user needs. The maintenance phase addresses bug fixes and improves application performance. The outcome of this research is the development of a snack product sales management application equipped with features such as inventory management, product ordering, shipping, and order tracking. Users can easily search for and purchase various snack products through this application. This study is expected to make a significant contribution to the field of sales management through the application of the Waterfall method, providing a structured and organized development approach.
Copyright (c) 2026 M.Tri Wahyu (Author) https://creativecommons.org/licenses/by/4.0