SMATIKA: STIKI Informatika Jurnal is a journal published by Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM) of Sekolah Tinggi Informatika & Komputer Indonesia (STIKI) Malang. The scope of this journal in the field of Computer Science, Information Systems, and Information Management. SMATIKA journal published four times a year, in March, Juny, September and December with an ISSN (printed): 2087-0256 and ISSN (online): 2580-6939. SMATIKA has accredited Sinta in S4 and has had DOI 10.32664.
The writer agreed that the article copyright by Smatika journal and the writer has the right to disseminate the paper published without permission in advance.
Development and Evaluation of an E-learning System using the CodeIgniter framework to Support Informatics Learning at SMA Negeri 1 Tanjung Raja
This study aims to develop and evaluate a web-based e-learning system to support Informatics learning at SMA Negeri 1 Tanjung Raja. The research employed a Research and Development (R&D) approach using the Waterfall model, consisting of requirements analysis, system design, implementation, testing, and maintenance. The system was developed using the CodeIgniter 4 framework with the Model–View–Controller (MVC) concept. Functional testing was conducted using the Black Box Testing method, executed on 13 different test scenarios by three expert validators with information technology education lecturers with backgrounds in software engineering. All 13 test items were evaluated independently by each validator, resulting in a 100% pass rate (13 out of 13 items were functional in the third validator). Usability testing using the System Usability Scale (SUS) instrument with 30 respondents comprising 29 Grade X students and 1 Informatics teacher, resulted in an average SUS score of 89, which is classified as Very Good. These findings indicate that the system has robust functionality and is considered very easy to use in this particular school context. Limitations include the single-school setting and relatively small sample size, and further validation across different educational contexts is recommended.
Copyright (c) 2026 SMATIKA JURNAL http://creativecommons.org/licenses/by-sa/4.0
Nutritional Status Classification of Children Using a Decision Tree Method (Case Study at RS AN-NISA) Based on a Web Application
This study develops a web-based classification model for the nutritional status of children aged 0–5 years using the Decision Tree (C4.5) algorithm applied to anthropometric data obtained from RS AN-NISA Tangerang during the period of January–December 2024. The attributes utilized include age (in months), gender, weight, and height, with nutritional status categorized into three classes: undernutrition, normal nutrition, and overnutrition. The dataset underwent a preprocessing phase consisting of data cleaning, removal of extreme values, attribute selection, categorical encoding, normalization, and dataset splitting with an 80% training and 20% testing ratio. The model was constructed using entropy-based splitting criteria with hyperparameter tuning to minimize the risk of overfitting, and its performance was evaluated using accuracy, precision, recall, and F1-score metrics, with Logistic Regression employed as a baseline model for comparison. The results demonstrate that the Decision Tree achieved an accuracy of 96.12% and a macro recall of 89.49%, outperforming the baseline model. The trained model was subsequently serialized and integrated into a Flask-based web application to enable real-time data input and nutritional status prediction. Black-box testing and User Acceptance Testing (UAT) yielded a user satisfaction rate of 88%, indicating that the system is feasible as a practical tool for early detection of child nutritional status in healthcare services.
Home Page SMATIKA Journal Volume 15 Number 02, Desember 2025
Copyright (c) 2025 SMATIKA JURNAL http://creativecommons.org/licenses/by-sa/4.0
Large Language Models for JSON-Based Function Call Planning from Indonesian Natural Language: A Restaurant Search Chatbot Case Study
Large Language Models are increasingly adopted as planning components that translate natural language into structured representations for tool invocation, enabling executable interaction with backend systems through JSON based function calling. However, empirical studies focusing on Indonesian natural language remain limited. This paper presents a restaurant search chatbot case study that investigates JSON based function call planning from Indonesian user queries, with emphasis on the upstream planning task rather than conversational response generation. A synthetic dataset of 33,470 Indonesian restaurant search queries paired with ground truth JSON plans was constructed based on a predefined tool set and database schema. Supervised fine tuning with parameter efficient adaptation was applied to a pretrained language model. The fine tuned Mistral 7B model was evaluated using multiple metrics measuring JSON structural validity, tool sequence correctness, and parameter accuracy at different granularities. The results show strong performance, achieving a JSON structure validity rate of 0.97, tool sequence exact match accuracy of 0.92, column level accuracy of 0.97, and value level accuracy of 0.94. More stringent evaluation at the session level reveals remaining challenges in composing all parameters correctly within a single planning instance. Overall, the findings demonstrate that with carefully designed datasets and strict supervision, Large Language Models can reliably perform structured JSON based function call planning from Indonesian natural language, providing a practical foundation for extending this approach to other structured application domains where execution correctness is critical.
Mountain Climbing Safety Induction for Beginners Using Augmented Reality
The growing popularity of mountain climbing among novice participants has been accompanied by an increased risk of accidents, largely attributable to inadequate understanding of safety procedures. Conventional safety induction methods—primarily verbal briefings and printed materials—lack interactivity and fail to adequately simulate real climbing conditions. While prior research has examined general safety education and conventional multimedia learning approaches, the application of markerless augmented reality (AR) as an interactive, mobile-based safety induction tool specifically designed for beginner mountain climbers remains underexplored . Accordingly, this study aims to develop and evaluate an Android-based markerless AR application to enhance pre-climbing safety induction for novice climbers. The application was developed following the Multimedia Development Life Cycle (MDLC), encompassing the stages of concept development, design, material collection, assembly, testing, and distribution. Functional performance was assessed using black-box testing, while feasibility was evaluated through a Likert-scale questionnaire administered to 20 beginner climbers. Black-box testing confirmed that all system functionalities operated as intended. The feasibility evaluation yielded a score of 87%, indicating a good to very good level of acceptance among users. By integrating markerless AR technology into mobile-based safety induction, this study addresses a critical gap in outdoor safety training research. The findings demonstrate the feasibility and practical potential of immersive AR applications to enhance safety awareness and improve the effectiveness of preparatory training for beginner mountain climbers.
A SMIL-Based Approach for Designing Interactive Multimedia Film Players
Typically, an HTML-style application requires extensive scripting to coordinate user interactions, manage display layout, and synchronize the timing of all interaction events. With Synchronized Multimedia Integration Language (SMIL), an alternative approach to developing an interactive multimedia film application has been defined, supporting time-based multimedia cooperation and user interactivity without the need for complex scripts. This study presents the development and evaluation of an SMIL-based interactive multimedia film application. The application allows for user-selected films to be automatically selected, played, and managed (synchronized) through an interactive GUI. The two measures, the Layout Consistency Score (LCS) and Screen Utilization Efficiency (SUE), were evaluated. Experimental evaluations have shown that the average LCS for the application = 0.92, indicating that the application maintains a consistent and predictable layout across multiple instances of the interface. In addition, the average SUE score for the application was calculated to be 0.79, indicating that it uses the available screen space efficiently and does not create a visually competing cluster. These results reinforce the concept that the region-based approach of SMIL and its declarative style for creating applications enable the development of multimedia that is consistently and efficiently presented across multiple contexts. As a result, the findings of this study indicate that the SMIL approach will simplify and allow for a practical methodology to build interactive multimedia film player applications, while preserving layout quality and interactivity.
The Cyber Threat Landscape in Indonesia: Attacks and Security System Analysis
Cybersecurity in learning systems has become increasingly important as educational institutions rely more heavily on digital platforms such as learning management systems, online assessments, and cloud-based academic services. This rapid digital transformation exposes learning environments to sophisticated cyber threats that may disrupt academic activities and compromise sensitive information. However, many institutions still lack a clear understanding of how users perceive cyber risks and how these perceptions influence the effectiveness of cybersecurity systems. Currently, there is a significant research gap regarding empirical evidence that links user behavioral psychology with technical security outcomes in the Indonesian educational context. This study aims to empirically analyze the relationship between cyber awareness, perceived impact of cyber attacks, and perceived effectiveness of cybersecurity systems in digital learning environments. A quantitative research approach was applied using data collected from 402 respondents. The data were analyzed through descriptive statistics, correlation analysis, regression analysis, and group comparison tests to examine variable relationships and demographic differences. The findings indicate that cyber awareness significantly and positively predicts perceived system effectiveness (β = 0.501, p < 0.001), demonstrating that higher awareness levels enhance overall cybersecurity performance. Conversely, the perceived impact of cyber attacks does not show a significant effect on system effectiveness, suggesting that awareness is more influential than threat perception alone. Additional results reveal gender-based differences in cyber incident experiences, while awareness levels remain similar. The practical implications emphasize the importance of cybersecurity awareness programs, digital safety education, and proactive defense strategies to strengthen protection in learning systems and improve institutional cybersecurity readiness.
Development of Hetrik Mobile Application in Iot-Based Electricity Management for Boarding Houses
Inefficient electricity usage is a common issue in boarding houses due to limited transparency in consumption data and the absence of integrated real-time monitoring systems. To address this problem, this study proposes an Internet of Things (IoT)-based mobile application called Hetrik to support more transparent and efficient electricity management. The research aims to design and implement an integrated system architecture that connects IoT-based sensing hardware with a digital user interface for monitoring and managing electricity consumption. The system was developed using the Agile development method, consisting of requirement analysis, system design, development, testing, implementation, evaluation, and deployment stages. The hardware architecture integrates an ESP32 microcontroller with ACS712 current sensors and ZMPT101B voltage sensors to capture real-time electricity consumption data. The mobile application was developed using React Native for the front-end and Laravel for the back-end and cloud database. A key contribution of this research is the design and validation of a QR-to-Device scanning mechanism, which enables dynamic mapping between IoT device identities and user accounts while supporting an automatic prepaid electricity payment system. System implementation produced five core modules: power monitoring dashboard, transaction system, usage history, profile management, and device scanning. Functional testing using the Black Box method and User Acceptance Testing (UAT) confirmed that the system operates according to technical specifications and is suitable for real operational environments. The proposed system demonstrates the potential to reduce electricity waste in boarding houses by up to 30% through data-driven energy monitoring and management.
Measuring Service Quality and User Satisfaction of Universitas Terbuka’s Ruang Baca Virtual (RBV) Using SERVQUAL and EUCS
This study aims to measure the service quality and user satisfaction of the Ruang Baca Virtual (RBV) application within Universitas Terbuka’s (UT) digital library and to identify priority dimensions requiring improvement. A quantitative survey approach was employed using two structured questionnaires based on the SERVQUAL and End User Computing Satisfaction (EUCS) models. SERVQUAL was applied to assess five dimensions of service quality (tangibles, reliability, responsiveness, assurance, and empathy), while EUCS evaluated five dimensions of user satisfaction (content, accuracy, format, ease of use, and timeliness). The SERVQUAL gap analysis was conducted using a percentage comparison approach against the ideal score to determine priority improvement areas. The research included 100 participants, where 50 respondents evaluated service quality using the SERVQUAL method, and the remaining 50 respondents measured user satisfaction using the EUCS approach. The results indicate that all SERVQUAL and EUCS dimensions fall within the “satisfied” category. Empathy and reliability emerged as key strengths in service quality, while ease of use and accuracy were identified as main strengths in user satisfaction. However, responsiveness and assurance (SERVQUAL), as well as format and timeliness (EUCS), exhibited relatively larger gaps, indicating areas requiring improvement. These findings provide a comprehensive descriptive evaluation of RBV performance and offer strategic insights for enhancing digital library services in distance learning environments.
Design and Development of an Integrated Ordering and Sales Analysis System at Kaluna Living
This study presents the design and development of an integrated ordering and sales analysis system for Kaluna Living, a micro-enterprise specializing in handcrafted ceramic home goods. Prior to the system development, business operations relied on manual workflows for recording orders, processing custom product requests, and preparing monthly sales reports. These procedures resulted in slow transaction handling, higher risk of inconsistent data, and limited business visibility. The system was developed using the SDLC Prototyping model to enable iterative refinement through continuous user feedback. To evaluate usability, the System Usability Scale (SUS) was applied to representative user roles (customer, admin, and owner). The evaluation yielded an average SUS score of 81.66, which falls into the Excellent (Above Average) usability category, indicating that the system is perceived as easy to use, consistent, and learnable. The system supports centralized ordering workflows, custom request handling with image upload, and sales insight visualization that summarizes monthly revenue and order volume. The findings suggest that iterative prototyping contributes to usability improvements by refining navigation flow, clarifying form structures, and supporting streamlined operational workflows. This research contributes to applied system development for micro-enterprises by demonstrating how integrated digital ordering and analytics can improve operational efficiency and decision support in handcrafted product businesses.
Applying Rapid Application Development to Develop and Evaluate a Laravel Filament-Based Point of Sale System for MSMEs
Digital transformation is increasingly essential for improving operational efficiency and data accuracy in Micro, Small, and Medium Enterprises (MSMEs). However, MSMEs often face a dual challenge: the need for rapid system implementation and the limited technical literacy of system users, which creates a gap between complex system requirements and practical usability. This study applies and evaluates the effectiveness of the Rapid Application Development (RAD) methodology in developing a web-based Point of Sale (POS) system tailored to MSME operational characteristics. The research adopts an applied, design-oriented approach, emphasizing iterative prototyping and continuous user involvement to reduce adoption barriers. Rather than focusing on feature completeness, the study examines how RAD supports system usability and user acceptance among non-technical users. The proposed system was evaluated using the System Usability Scale (SUS) involving users with different operational roles. The results indicate that the iterative nature of RAD effectively bridges the gap between system complexity and user capability, as reflected by an average SUS score of 76.66, categorized as “Good”. These findings provide empirical evidence that RAD is a suitable development approach for MSME information systems, particularly in contexts requiring rapid deployment and high usability, and contribute to applied information systems research by highlighting the role of user-centered iteration in improving system acceptance.
Development of a Web-Based ERP System Using Next.js and Laravel: A Prototyping SDLC Approach
The development of information technology encourages companies to digitize business processes to improve the efficiency and accuracy of data management. CV AS Nusa Trans (CV ANT), a land transportation service company, still uses manual spreadsheet-based recording scattered across multiple links, causing problems such as data duplication, information delays, and difficulties in preparing reports. This research aims to develop a web-based Enterprise Resource Planning (ERP) system as an integrated digital solution to support company operations. The development was carried out using the System Development Life Cycle (SDLC) Prototyping model approach, which allows user involvement from the early stages through an iterative process. Next.js is used as a front-end framework to produce a responsive and fast interface display, while Laravel is used as a back-end to ensure a secure and organized business logic structure. The system design process includes observation, requirements analysis, system modeling and iterative prototype implementation. The developed ERP system integrates invoice management, fleet management, customer data, and an operational calendar into a single centralized platform. To evaluate system usability, the System Usability Scale (SUS) method was applied involving two main user roles: Admin and Owner. The evaluation results indicate an average SUS score of 93.75, which falls into the Excellent Usability category, demonstrating that the system is easy to learn, efficient to use, and well accepted by users. This research contributes scientifically by demonstrating the effectiveness of combining the SDLC Prototyping approach with modern Web Application technologies (Next.js and Laravel) in the development of an ERP system, particularly within the land transportation service sector.
Development of a Virtual Mentor Integrated with Retrieval-Augmented Generation Artificial Intelligence for Project-Based Learning
Project-Based Learning (PjBL) is a learning model that can enhance the quality of higher education, particularly in developing critical thinking, creativity, and collaboration skills. However, implementing PjBL often faces challenges such as limited resources and the need for intensive guidance from lecturers. To overcome these challenges, Artificial Intelligence (AI) technology offers great potential, although traditional AI systems often provide responses that are less relevant to the context of the learning material. The Retrieval-Augmented Generation (RAG) technique in AI can serve as a solution, enabling the system to generate more accurate and contextually relevant responses. By utilizing data sources such as course materials, RAG can enhance the relevance of AI responses in supporting project-based learning. It is expected that developing an AI-based virtual mentor using the RAG approach can optimize students’ PjBL experiences. Specifically, this virtual mentor is designed to provide contextual guidance, help students overcome project-related challenges, and foster independent learning, thereby improving the quality and effectiveness of PjBL in higher education.
Design and Development of a Multi-Agent Artificial Intelligence-Based Financial Planner for Institutional Financial Management Optimization
Institutional financial management often faces optimization challenges due to limited understanding of influencing factors, difficulties in data integration, and the lack of human expertise. These constraints hinder the identification of opportunities, risk management, and sustainability. An adaptive and automated financial planning system is required. This study proposes the design of an Artificial Intelligence (AI)-based system for automated financial planning aligned with institutional standards. The system addresses these challenges by integrating financial needs analysis, standardized cost references, and automated budget summary preparation. Using a prototyping approach, the system employs AI agents to conduct in-depth analysis and produce comprehensive budgets. The proposed system leverages RAGflow, an open-source Retrieval-Augmented Generation (RAG) engine that uses deep document understanding to provide truthful question-answering from complex data.
Design of an Automated Verification to Improve the Efficiency and Optimization of IPR Management
Copyright is an essential element of innovation in academic and educational environments. However, the current manual verification and management process of Intellectual Property Rights (IPR) is highly inefficient, particularly in institutions such as Universitas Negeri Malang, which face delays and data duplication due to paper-based workflows. To address this issue, this study aims to develop an Automated Copyright Verification System based on Multi-Agent Artificial Intelligence to enhance efficiency and optimize IPR management. The proposed system, developed using a Prototype Model, leverages an agent-based architecture to model IPR verifiers with distinct functions and objectives. These agents are supported by Vision Language Models (VLM) and Natural Language Processing (NLP). Its key features include ID card data compliance checks and automated text recognition using VLM. The implementation of this system is expected to reduce staff workload, accelerate responses, and ensure data accuracy in IPR management, supporting a sustainable innovation ecosystem.
Comparative Analysis of Machine Learning Algorithm Performance for Student Mental Health Classification
Mental health issues among college students are a critical issue that requires data-driven approaches to detect early treatment needs. This study aims to analyze and compare the performance of three machine learning algorithms: Naive Bayes, K-Nearest Neighbor (K-NN), and Decision Tree in classifying college students' mental health treatment needs based on an open survey dataset. The study was conducted systematically using RapidMiner software, with data preprocessing, model training, testing, and performance evaluation using accuracy, precision, and recall metrics. The test results showed that the Naive Bayes algorithm produced an accuracy of 78.85%, a precision of 75.96%, and a recall of 72.84%. K-NN performed better with an accuracy of 82.62%, a precision of 80.83%, and a recall of 77.37%. Meanwhile, the Decision Tree algorithm performed best with an accuracy of 88.32%, a precision of 86.77%, and a recall of 85.80%. In addition to its high performance, Decision Tree also offers advantages in interpreting results through its decision tree structure, which illustrates the role of variables such as employment status (self_employed), family history (family_history), survey completion time (timestamp), and care options (care_options) in the classification process. Decision Tree can be concluded as the most effective classification model for detecting student mental health needs in this data context. These findings are expected to serve as a reference in the development of machine learning-based early detection systems to support mental health policies and interventions in higher education settings.
A Comparative Analysis Of Javascript-Based Web Stacks in Software Construction Phase: MEAN, MERN and MEVN
The construction phase of a web application development generally involves a number of personnel with specific perspectives and expertise, including: backend developers, frontend developers, and mobile developers. Based on these different perspectives, the combination of technology architecture and programming languages (stack) used will vary. Javascript is one type of programming language that is compatible with various platforms and can be used to harmonize the programming architecture of the three different perspectives. There are three types of JavaScript-based tech stacks that have a large user base: Angular-based MEAN, React-based MERN, and Vue-based MEVN. Based on this situation, it is necessary to empirically analyze tech stack comparisons in the context of web application development activities. The indicators used in this comparative study include: LOC (lines of code), execution performance, function structure analysis (call stack), and memory usage efficiency. The comparative results show that the MERN stack, which is based on React, has a more efficient LOC than the other two stacks. Though the difference is not very noticeable, MERN stack also displays the most efficient level of memory use. The comparative results also demonstrate that, in comparison to the other stacks, the MEVN tech stack has the most effective function call structure.
Sentiment Analysis of Lokapala Game Reviews on Google Play Using the SVM Algorithm
This study aims to analyze user sentiment towards the Lokapala game through reviews collected from the Google Play Store. Lokapala is a local MOBA game developed by Anantarupa Studios that integrates Indonesian cultural elements. A quantitative approach is employed using the Support Vector Machine (SVM) algorithm to classify user reviews into positive and negative sentiments. Data were collected using the google-play-scraper library and preprocessed through several stages, including cleaning, case folding, word normalization using kamuskatabaku.xlsx, tokenizing, stopword removal, and stemming with the Sastrawi library. Reviews were labeled based on user ratings and split into training and testing datasets. Model testing results show an accuracy of 83%, with the highest precision of 0.85 for the positive class, recall of 0.93, and f1-score of 0.89. Additionally, WordCloud visualization revealed frequently occurring words such as "bagus" (good), "main" (play), "tolong" (please), and "banget" (very), reflecting both praise and technical complaints from users. These findings demonstrate that SVM is effective for sentiment analysis of user reviews and can provide valuable insights for developers in improving the quality of local games.
Indoor Human Presence Detection System Using Histogram of Oriented Gradients Algorithm
This study aims to develop a web-based monitoring system utilizing computer vision and the Histogram of Oriented Gradients (HOG) algorithm to detect and count the number of people in a room. The research was motivated by the need for automated systems to replace manual methods, particularly during the COVID-19 pandemic, where contactless attendance tracking is critical. The HOG algorithm extracts visual features as image gradients representing human shapes, which are then classified using a machine learning model. The research methodology involved testing various camera configurations and angles, such as above door placement and room corners, with 80 and 60-degree camera angles. The system was built using Python, OpenCV for image processing, and Flask for web interface development. Data was collected through 200 trials, capturing live images with a camera. The results showed that the camera positioned above the door at an 80-degree angle achieved the highest detection accuracy at 66%, while other configurations showed reduced accuracy, averaging around 52%. The system also logs data, including time, detected human count, and captured images for further analysis. The findings highlight the importance of camera placement and angle in ensuring effective human detection. These results support the development of computer vision-based systems for practical applications such as crowd management, attendance counting, and public space monitoring. Future research is proposed to improve detection accuracy by integrating facial recognition technology and developing a more intuitive user interface. This study contributes substantially to improving the efficiency of automated systems for counting individuals in enclosed spaces, addressing various industrial and social needs.
Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
Improving User Experience and Information Delivery through Motion UI in a Single-Page Campus Website
This research aims to develop a campus introduction website interface for STIKI Malang by implementing Motion UI to enhance information delivery effectiveness and user experience. Currently, the STIKI website uses a multi-page structure that requires visitors to open sub-menus separately to access information, resulting in significant resource consumption and long loading times. By transforming the website structure into a single-page application (SPA) and integrating dynamic animation elements through Motion UI, it is expected to increase engagement and conversion rates. Qualitative analysis results show significant improvements in user engagement and information comprehension, with page loading times drastically reduced to an average of 2.6 seconds. Evaluation using the User Experience Questionnaire (UEQ) also demonstrates significant improvements in attractiveness, clarity, efficiency, and stimulation aspects. This research concludes that implementing Motion UI on STIKI's single-page website successfully enhances user experience, access speed, and information delivery effectiveness. The results of this research can serve as a reference for developing interface design strategies for other educational institution websites
Evaluation of User Experience of Yamaha R15 AR Tutorial Application Using User Experience Questionnaire (UEQ)
This study aims to evaluate the user experience (UX) of an Augmented Reality (AR) application designed as a tutorial medium for introducing components of the Yamaha R15 VVA motorcycle. The evaluation was conducted using the User Experience Questionnaire (UEQ) method, covering six aspects: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The research sample consisted of 30 students who have taken or are currently taking the Human-Computer Interaction course at Universitas Bhineka Nusantara. The analysis results showed that the pragmatic quality had an average value of -0.092 and the hedonic quality 0.108, with an overall score of 0.008, categorized as neutral. The average standard deviation of 0.666 indicates a high level of agreement among respondents. Although the application met some visual and interactive aspects, weaknesses were found in navigation features, usage guidance, and audiovisual support. Reliability testing showed that the UEQ instrument is appropriate for use, with a Cronbach’s alpha value of 0.88 (pragmatic) and 0.77 (hedonic). These results recommend further development in usability aspects and supporting features to enhance the effectiveness of the AR-based technical learning application.
Virtual Reality Game of Sorghum for Food Resilience
Sorghum is a vital staple food crop widely consumed in Asia and Africa, ranking among the top five food crops globally after wheat, corn, rice, and barley. It is rich in carbohydrates and low in sugar, making it a suitable dietary option for individuals with diabetes. Sorghum can be cultivated on marginal or unproductive land; however, improper cultivation techniques may lead to sub-optimal yields or even crop failure. Survey findings indicate that public awareness and knowledge about sorghum cultivation in Indonesia remain limited, contributing to difficulties in effective planting and crop management. To address this issue, a simulation game utilizing virtual reality (VR) technology was developed to provide an interactive and immersive learning experience for sorghum cultivation. The Unity game engine was employed in the development process, incorporating freely available assets from various sources. The application was validated through expert interviews and user testing within the community. Post-testing results demonstrated a 31% increase in user knowledge regarding sorghum cultivation. These findings suggest that the VR-based game effectively enhances public understanding of the procedures involved in planting and maintaining sorghum crops.
Analysis of Build Time in Continuous Integration on Multi-Node Jenkins Using Caching Methods
In the era of modern software development, the speed and efficiency of application build processes have become critical factors in enhancing the productivity of development teams. Jenkins, as a popular CI/CD tool, can be utilized to perform distributed builds across multiple servers. However, long build times remain a significant challenge, especially for container-based applications that require numerous dependencies and compilation stages. This study proposes a method to optimize build time by leveraging caching mechanisms. The proposed method utilizes cache storage to save frequently used build artifacts, thereby reducing the time required for recompilation in subsequent builds. In this research, experiments were conducted to compare the build time of container-based applications before and after implementing the caching method. The results of this study indicate that applying caching techniques to container-based applications can improve build speed by approximately 43% to 76%. This finding demonstrates that caching is highly effective in enhancing build time efficiency within Jenkins’ multi-node architecture. Consequently, this optimization not only accelerates the build process but also reduces computational resource consumption, thereby supporting more efficient and cost-effective CI/CD practices.
Building RT/RW Net With Pointing Points Between Regions
The digital divide between urban and rural areas in Indonesia remains a significant challenge in the current digital era, with limited internet access in remote regions hindering economic, educational, and social progress. This research aims to develop a wireless LAN-based RT/RW Net system as a solution to enhance efficient and affordable internet access. The system utilizes point-to-point access between regions using specialized routers to overcome geographical and physical barriers, thereby enabling internet access in hard-to-reach areas. By leveraging WiFi 5 technology as a solution for better range and performance, along with ease of operation and affordable construction costs, the system is designed for efficiency. Device configuration is carried out using the built-in application of the TP-Link EAP 110 router, facilitating easier and more efficient management. The PPDIOO (Prepare, Plan, Design, Implement, Operate, Optimize) research methodology is chosen as the approach for developing the RT/RW Net network. The advantages of this method include structured and systematic design capabilities, greater flexibility in meeting design needs, and the ability to optimize the quality of the network once it is operational. This method can also analyze and build networks according to local geographical conditions. Expected outcomes include improved quality and reliability of the RT/RW Net network, provision of cheaper and more convenient internet access for communities, and contributions to a more equitable dissemination of information and communication technology across Indonesia, enabling communities to enjoy better access to information, broader educational opportunities, and enhanced economic prospects.
Design and Implementation of a Coin-Based WiFi System for Coffee Shops or Cafés
The need for fast, stable, and affordable internet access is increasing along with the increasing dependence of society on information technology, especially in small business environments such as coffee shops and cafes. However, the operational costs of internet services are still a major challenge for micro-business owners. This study aims to design and implement a coin-based WiFi system that is independent, cost-effective, and easy to operate. This system integrates a coin sensor, microcontroller, and Raspberry Pi to detect coin denominations (Rp500 and Rp1000), automatically converts them into WiFi access duration, and records transactions in a local SQLite-based database. The web-based interface is built using Flask for ease of system administration and management. The research methods include literature studies, field observations, user interviews, and technical testing of sensor accuracy and network performance. Evaluations are carried out on download/upload speeds, latency, jitter, and packet loss to assess connection quality. The test results show that the system is able to detect coins with high accuracy and provide stable internet services according to the payment duration. The advantages of this system lie in its independence that does not depend on third-party applications, as well as its ability to be implemented in environments with technological and budget constraints. This system has the potential to be a viable alternative solution to expand internet access in the informal sector and drive digital transformation at the grassroots level.
Implementation of the New Student Admission System at Bina Insani University
New student admission is a very important process in higher education that requires fast, accurate, and organized information. At Bina Insani University, this process was previously done manually and using WhatsApp messaging services directly by the admin, which caused delays in response, repeated questions, and difficulties in managing registration documents. The purpose of this research is to build a new student admission information system that can speed up the registration process, reduce admin workload, and centralize registration documents digitally. This research uses the Extreme Programming (XP) method with data collection techniques through observation, interviews, and literature studies. This system is implemented using CodeIgniter with MySQL database and integrates real-time notifications via WhatsApp and Email using RabbitMQ architecture as a message broker. The results of the system implementation show that automatic notifications can improve the efficiency of information services, reduce repetitive questions, and speed up the response and document distribution process. The system also facilitates the search for registration documents with Google Drive integration. In conclusion, this system makes a significant contribution in improving the quality of new student admission services at Bina Insani University.
Classification of Fake and Real using Vision Transformer and EfficientNet-B0 on Real and AI-generated Images
Advances in artificial intelligence (AI) technology have enabled the creation of synthetic images that resemble real images, posing challenges in detecting and classifying such images. This study aims to develop an EfficientNet-B0 and Vision Transformer (ViT) based classification model to distinguish between real images and images generated by generative AI. The data used consists of 30,401 original images from the MSCOCO 2017 dataset and 30,401 generative AI-generated images from the SyntheticEye AI-Generated Images Dataset on Kaggle. The results showed that the ViT model achieved 98% accuracy and EfficientNet-B0 achieved 96% accuracy in classifying the images. The conclusion of this research is that both models have great potential in detecting digital media manipulation, with ViT showing superior performance. The practical implication of this research is the development of more advanced technologies for detecting generative images, which can be used in various real applications such as digital security and media verification.
Transfer Learning Based CNN Model Optimization for Pneumonia Classification in Chest X-Ray Images
Pneumonia is a leading cause of child mortality worldwide, and its diagnosis often relies on chest X-ray interpretation, which is prone to human error. This study aims to optimize a Convolutional Neural Network (CNN) model based on transfer learning using the DenseNet-121 architecture for pneumonia classification in chest X-ray images. The model was trained on a Kaggle dataset consisting of two classes: Normal and Pneumonia. Preprocessing included class balancing and data augmentation. Five fine-tuning strategies were tested, ranging from training only the classifier to unfreezing the entire pretrained layers. Evaluation metrics included accuracy, precision, recall, F1-score, and ROC-AUC. Results showed that the strategy of unfreezing Block 3–4 yielded the best performance with 94.39% accuracy, 95.61% F1-score, and 98.04% ROC-AUC. This study demonstrates that selective fine-tuning strategies significantly improve classification performance compared to training only the classifier or the entire network.
Web-Based Cattle Feed Monitoring and Management System at PT XYZ Central Lampung
This research discusses the design and implementation of a web-based cattle feed monitoring and management system at PT XYZ Lampung Tengah. The main problem faced is the manual recording in cattle feed management, which causes inaccuracies in feed consumption data, incorrect cost calculations, and the risk of inventory shortages or excesses. The research uses the Rapid Application Development (RAD) method in system development, which consists of four phases: Requirements Planning, RAD Design Workshop, Implementation, and Maintenance and Evaluation. The developed system has features for managing user data, feed type data, feed weight recaps, as well as generating recap and stock reports. The implementation results show that the system successfully automated the feed inventory recording process with access rights distribution between admin and user, data visualization through stock and feed expenditure graphs, and the ability to generate organized reports. In making the final report on animal feed, the admin takes up to 7 days because it adjusts warehouse data with feed expenditure records. But with this Web-based monitoring and animal feed system, the admin can present the final report on the use of feed in real-time. This system helps improve operational efficiency and the accuracy of feed usage recording at PT XYZ Lampung Tengah.
Security Gap Analysis of the Sorong Ministry of Health Polytechnic Website Using the Penetration Testing Method
Currently, information technology is very necessary, in the digital era cybersecurity is one of the main factors for an institution to survive and be recognized for its credibility, including educational institutions, the Poltekkes Sorong Website is used as the main portal or media to disseminate academic information, interaction between students and the public, However, the existence of security gaps is something that allows cyber attacks to be carried out which can endanger data confidentiality. This study aims to analyze the security gaps in the Poltekkes Kemenkes Sorong website, the testing carried out using the penetration testing method which has several stages including collecting scanning information, vulnerability analysis. So that this study can provide guidance and practice for information system managers at Poltekkes Kemenkes Sorong to reduce the risk of cyber attacks and protect sensitive data held by the institution.