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
Once your article has been accepted, we will require you to complete, sign and return, a copy of the Copyright Transfer Agreement that you should send back via e-mail, fax and/or by mail. The authors will receive the link for the download of the agreement in the acceptance e-mail of the article. We will not be able to publish your paper without the Copyright Transfer Agreement.
Optimization of Phishing Detection Performance with Variable Correlation Analysis and Imbalance Learning
Phishing is a common cyber security threat in which attackers attempt to deceive users into disclosing personal information such as passwords, credit card numbers, and other sensitive data. With the rapid advancement of technology, phishing techniques have become increasingly sophisticated and harder to detect using traditional methods. Therefore, it is essential to develop detection techniques capable of identifying phishing websites with high accuracy. This study aims to optimize phishing detection performance by integrating variable correlation analysis for feature selection and applying imbalanced learning techniques to address data imbalance. The research stages include Data Collection, Data Preprocessing, and Data Exploration, which involve correlation analysis, removal of low-correlation features, and data visualization. In the Model Building and Training phase, the dataset is split into features and labels, followed by training and the application of data balancing techniques, ending with Model Evaluation. The evaluated algorithms include Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron, Decision Tree, Random Forest, Gradient Boosting, and CatBoost. The results show that the KNN algorithm delivers the best performance, achieving an accuracy of 91.25% and optimal scores in Precision (0.906943), Recall (0.927858), and F1-Score (0.922141), along with the lowest Hamming Loss at 0.0875. In contrast, the SVM algorithm recorded the lowest performance among the tested models. The implementation of this method is expected to contribute to the development of more reliable and accurate phishing detection systems in the future.
Copyright (c) 2026 Sistemasi: Jurnal Sistem Informasi
Selection of the Best Marketplace using SAW and WP Methods: A Case Study of Bekasi City
In today’s digital era, e-commerce has made transactions between sellers and buyers easier by eliminating the need for face-to-face interaction. The abundance of available marketplaces often makes it difficult for consumers to choose the platform that best fits their needs. This study aims to provide recommendations for the best marketplace based on four key criteria: trust, user interface design, promotions, and product completeness. The Simple Additive Weighting (SAW) and Weighted Product (WP) methods were applied to support this decision-making process. The research was conducted on five popular marketplaces, with data collected through questionnaires distributed to 200 active respondents. Both SAW and WP methods were used to calculate the weight and score of each marketplace based on consumer preferences regarding the predefined criteria. The results show that Shopee ranked as the top marketplace, achieving the highest scores of 0.99 (SAW) and 5.77 (WP), due to its strengths in trust, promotional offers, and product variety. Tokopedia placed second, with scores of 0.98 (SAW) and 5.75 (WP), excelling in its more intuitive user interface design. Other marketplaces showed strengths in specific criteria but were unable to surpass Shopee and Tokopedia in the final scores. These findings provide valuable insights for consumers in selecting the most suitable marketplace for their needs, and for marketplace operators seeking to improve service quality based on consumer-prioritized criteria.
Optimizing Driver Drowsiness Detection: Evaluating CLAHE and AHE Enhancement Techniques
Driver drowsiness is a critical factor in road safety, and early detection can be key to preventing accidents. This research focuses on improving the accuracy of drowsiness detection by enhancing the contrast of driver facial images using image processing techniques. Specifically, the study explores the effectiveness of Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in this context. The research utilizes the Drowsy Driver Detection (DDD) dataset, which includes facial images categorized into Drowsy and Non-Drowsy classes. AHE and CLAHE techniques are applied to preprocess these images, aiming to improve contrast and subsequently enhance drowsiness detection accuracy. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Signal-to-Noise Ratio (SNR) are employed to assess the quality of the processed images. The findings indicate that CLAHE performs better than AHE in terms of image enhancement. CLAHE achieves significantly lower MSE (93.90) compared to AHE (103.92), along with higher PSNR (28.41 for CLAHE vs. 27.97 for AHE) and SNR (0.49 for CLAHE vs. 0.04 for AHE) values. These results suggest that CLAHE effectively enhances contrast and improves image clarity. The success of CLAHE as a contrast enhancement technique highlights its potential application in real-time driver monitoring systems. In conclusion, this research underscores the importance of image preprocessing techniques like CLAHE in advancing driver safety technologies, emphasizing their potential to enhance the performance of drowsiness detection systems in practical driving scenarios.
Aceh Province Tourism Destination Recommendation System using Content based Filtering Method
Tourists often experience difficulties in finding tourist destinations in Aceh Province that match their content preferences and are geographically close to their location. This study aims to develop a tourism destination recommendation system in Aceh Province using a Content-Based Filtering approach with the Cosine Similarity algorithm and the Haversine Formula. The dataset consists of 119 tourist destinations, including attributes such as destination name, destination description, and geographical coordinates (latitude and longitude). The research process began with text data preprocessing, which included case folding, punctuation removal, tokenization, duplicate word removal, stopword removal, and stemming. Next, the similarity between destinations was calculated using the Cosine Similarity algorithm based on tourism content descriptions, while the Haversine Formula was applied to measure the geographical distance between the user’s location and the tourist destinations. The results indicate that the developed system is able to provide relevant tourism destination recommendations by simultaneously considering content relevance and geographical proximity. Therefore, the system can assist tourists in selecting destinations that best match their preferences.
House Price Prediction using the Random Forest Regression Algorithm
House price prediction is a complex problem because it is influenced by various factors such as building quality, location, and living area size. As a result, conventional methods often lack accuracy in estimating housing prices. This study aims to apply the Random Forest Regression (RFR) algorithm to predict house prices using the House Prices – Advanced Regression Techniques dataset from Kaggle, which contains 1,460 property records. The SEMMA (Sample, Explore, Modify, Model, Assess) methodology was adopted due to its systematic workflow and structured focus, which improves the reliability of the developed model. In the modeling stage, RFR was implemented because it is capable of handling non-linear patterns and maintains stable performance even with a large number of features. Based on the evaluation results, the model achieved a Root Mean Squared Error (RMSE) of 28,452.75 and a coefficient of determination (R²) of 89%. This was followed by a robustness test with an RMSE of 30,665.40, indicating the stability of the model. Feature importance analysis also revealed that OverallQual had the greatest influence on house price prediction. These findings confirm that Random Forest Regression is a reliable method for predicting house prices and has strong potential to be further developed for price recommendation systems, automated property valuation, and integration into digital platforms within the real estate industry.
WhatsApp Hybrid Chatbot Architecture Rasa-DeepSeek: Design and Performance Evaluation
This study designed and evaluated a hybrid chatbot for a domain-specific application by addressing two main issues: limited NLU coverage and the variability of latency and cost when all queries are routed directly to an LLM. The proposed solution integrates a deterministic Rasa-based pipeline with a DeepSeek fallback mechanism. In this architecture, Rasa handles NLU processing, rules, stories, and context storage for mk and jk, while the LLM is only invoked when the NLU confidence score falls below a defined threshold. The methodology includes end-to-end implementation through a Node.js bridge connected to Rasa, functional testing to validate the intent–entity–action flow, and performance testing using load (stress) testing across two access paths: the Rasa REST endpoint and the Node-to-Rasa bridge. Meanwhile, the LLM pipeline was profiled separately through instrumented action calls. The results indicate that domain-specific conversations were successfully answered using curated knowledge, and both deterministic access paths met the service level objective (SLO), achieving a median latency of approximately 32 milliseconds with no observed errors. This study contributes by demonstrating that a hybrid chatbot architecture separating deterministic and generative pipelines can maintain SLO compliance in domain-specific settings. In addition, it highlights limitations of LLMs in understanding domain ontologies, reinforcing the need for semantic guardrails.
Evaluation of Information Technology Governance Maturity Level using COBIT 2019 in the Academic Information System at Nahdlatul Ulama University of Lampung
The rapid development of Information Technology (IT) has significantly facilitated human activities in carrying out work and various other tasks, including in the education sector. The utilization of IT plays an important role in supporting administrative processes and academic activities; therefore, proper governance is required to ensure its effective and efficient use. This study employed a descriptive quantitative approach. Data were collected through observation and the distribution of questionnaires to students. The sample size was determined using the Slovin formula with a 10% margin of error, resulting in a minimum required sample of 94 respondents. In total, this study successfully collected data from 108 student respondents. This research aimed to evaluate the maturity level of IT governance in the Academic Information System (SIAKAD) at Nahdlatul Ulama University of Lampung using the COBIT 2019 framework. The results indicated that the maturity level of SIAKAD governance was at level 4 (Predictable), meaning that the processes have been consistently implemented and well documented. The highest gap was found in the APO07 domain (Manage Human Resources), while the BAI03, DSS01, DSS02, and MEA01 domains showed stable performance in accordance with student expectations.
Business Process Reengineering in Water Billing Administration: A Case Study of KPAB Gang Gedang Mas
The water billing administration process in Gang Gedang Mas, Curungrejo Village, is still conducted manually, resulting in slow procedures, inefficiency, and a high risk of errors. This study aims to improve the efficiency and accuracy of the water billing administration process through a Business Process Reengineering (BPR) approach. The improvements focus on four main processes: water meter recording, data entry, bill calculation, and billing information distribution, involving two key actors: field officers and administrative staff. This research employed a case study method, consisting of direct observation, process modeling using BPMN notation, and the measurement of processing time efficiency and throughput within one billing cycle before and after the process redesign. The proposed solution is a digital process model design that supports workflow automation at the design level, without implementing an actual system, while still maintaining the operational role of field officers. The results indicate a significant reduction in processing time, from 684 minutes to 168 minutes, along with a 307.5% increase in administrative process throughput efficiency within one billing cycle. This study demonstrates that applying BPR through process model redesign can optimize water billing management in small-scale communities with limited infrastructure, providing an efficient solution that can be adapted to similar contexts.
Mobile Banking Acceptance: A Modified UTAUT 2 Model with Trust and Risk Integration
This study examines the factors influencing the acceptance of the JakOne Mobile mobile banking application developed by Bank DKI using a modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, extended with the external variables of perceived trust and perceived risk. Although the application has a high number of downloads, it faces significant challenges related to user satisfaction. This is reflected in the sharp polarization of ratings, where one-star reviews account for 32.17% of total reviews, indicating fundamental issues in a substantial portion of the user experience. This research employed a quantitative approach, with data collected from 450 respondents in the Jabodetabek region through an online questionnaire. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to test 13 proposed hypotheses. The main results show that performance expectancy and facilitating conditions have a positive and significant effect on behavioral intention. Interestingly, this study found that price value, hedonic motivation, and habit have a significant negative effect on behavioral intention. Furthermore, trust was proven to effectively reduce perceived risk, while effort expectancy and social influence showed no significant influence. The practical implications of these findings suggest that Bank DKI should prioritize improving the technical performance of the application and develop communication strategies focused on building user trust, rather than emphasizing low cost or entertainment-related aspects.
Development of an Internet of Things (IoT)-based Air Quality Monitoring System in the Environment
This study developed an Internet of Things (IoT)-based air quality monitoring system to measure temperature, humidity, and carbon dioxide (CO₂) levels in real time. The study employed a prototyping method consisting of problem identification, requirements analysis, system design and development, testing, and result analysis. The system utilizes a NodeMCU V3 as the microcontroller, an MQ-135 sensor for gas detection, a DHT11 sensor for temperature and humidity measurement, and an OLED LCD for local display. Measurement data are transmitted and stored on the ThingSpeak platform and can be accessed through an Android application. Testing was conducted under three conditions: a normal environment, a closed room without ventilation, and a polluted condition with cigarette smoke exposure. The results show that the system is able to responsively detect changes in air quality, with CO₂ levels recorded at 76 ppm under normal conditions, 183 ppm in the closed room, and 729 ppm in the polluted condition. The system operates stably and provides real-time data visualization, making it suitable for low-cost implementation in household environments and small communities.
Evaluation of E-VAKU Application Usability using System Usability Scale and Cognitive Walkthrough Methods
The Central Sulawesi Provincial Government, in line with digital transformation and e-government initiatives, has implemented the E-VAKU application (Electronic Financial Accountability Verification). This digital system is designed to address bureaucratic challenges and enhance efficiency and transparency in the verification of regional financial accountability. Given that the success of an information system is strongly influenced by its usability, this study conducts a comprehensive usability evaluation of the E-VAKU application using a combination of two methods. The System Usability Scale (SUS) is employed to quantitatively measure user perceptions, while the Cognitive Walkthrough (CW) method is applied to identify specific issues within workflows and user interface interactions. The SUS evaluation produced an average score of 74.25, which falls into the “Good” category with a grade of B. Meanwhile, the CW analysis recorded a success rate of 90%, an error rate of 12%, and a time-based efficiency of 0.0205 tasks per second. The findings from both methods resulted in specific improvement recommendations, including interface redesigns for the onboarding, login, dashboard, and Payment Order (SPP) pages, as well as the addition of notification and search features. These enhancements aim to make the E-VAKU application more intuitive and user-friendly. This study is expected to serve as a practical reference for developers in refining the application to support more effective and optimal governance practices.
Word Embedding Features to Improve Machine Learning Performance in Sentiment Analysis of the Honor of Kings Game
The rapid growth of social media has encouraged an increasing number of studies on sentiment analysis to better understand public perceptions and opinions. This study aims to evaluate the performance of three machine learning algorithms—Naïve Bayes, K-Nearest Neighbor (KNN), and Random Forest—in classifying user review sentiments toward the game Honor of Kings. The dataset was collected from the Google Play Store, consisting of 900 reviews. The data then underwent preprocessing steps including cleaning, case folding, tokenization, stopword removal, stemming, and sentiment labeling into positive and negative classes. Furthermore, three word embedding techniques were applied, namely Word2Vec, GloVe, and FastText, each of which was tested across the three machine learning algorithms. The experimental results indicate that the use of word embedding features significantly improves classification accuracy compared to models without embedding features. KNN combined with FastText achieved the best performance, reaching an accuracy of 87.55%, while Random Forest combined with FastText produced the lowest accuracy. FastText demonstrated superior performance due to its ability to represent words through subword information, making it more effective in handling rare vocabulary and large-scale datasets. This study confirms that combining machine learning classification methods with word embedding features plays a crucial role in improving sentiment analysis performance. Future research may focus on hyperparameter optimization, the application of more advanced preprocessing techniques, and dataset expansion to develop more robust models with better generalization capability.
Sentiment Analysis of Money Lover App Reviews using Random Forest and Naïve Bayes
This study aims to analyze user sentiment toward the Money Lover application and to compare the performance of two different machine learning algorithms, Random Forest and Naïve Bayes, in binary classification of review data. A total of 3,000 comments were collected using web scraping techniques and then classified into positive and negative sentiment categories. The preprocessing stage included text cleaning, normalization, tokenization, stopword removal, and stemming. In the next stage, term weighting was performed using TF-IDF to convert the text into numerical vector representations. The results provide insights into the overall sentiment tendencies of users toward the Money Lover application and demonstrate the effectiveness of both algorithms in processing textual reviews within the financial domain. Based on model evaluation, the Random Forest algorithm achieved superior average performance, with an accuracy of 94%. Meanwhile, the Naïve Bayes algorithm showed slightly lower performance, achieving an accuracy of 92%. These findings were supported by cross-validation results and ROC curve analysis, which indicated that Random Forest consistently outperformed Naïve Bayes. The performance difference suggests that an ensemble-based approach such as Random Forest is better able to handle textual variation in review data, resulting in more stable and accurate sentiment classification.
Analysis of Cryptocurrency Candlestick Patterns using Gramian Angular Field and Hybrid Deep Learning
Cryptocurrency markets such as Bitcoin, Ethereum, and Solana exhibit high volatility, making price forecasting difficult when relying solely on conventional technical analysis. This study aims to analyze cryptocurrency candlestick patterns by utilizing Gramian Angular Field (GAF) representations and to evaluate the performance of a hybrid deep learning model combining CNN–LSTM–Transformer to support investment decision-making. The proposed method involves processing daily historical Open, High, Low, and Close (OHLC) data from three major cryptocurrency assets: Bitcoin (BTC-USD), Ethereum (ETH-USD), and Solana (SOL-USD), covering the period from January 1, 2020, to September 30, 2024, obtained from Yahoo Finance. The time-series data were transformed into 64×64 pixel GAF images and used to train a baseline CNN model as well as a hybrid CNN–LSTM–Transformer model. Model evaluation was conducted across multiple forecasting horizons, including 1 day, 7 days, 30 days, 180 days, and 1 year, and was further complemented by real-time testing using the CoinGecko API in March 2025. The results indicate that the hybrid model achieved the best performance at different horizons for each asset: BTC-USD at the 30-day horizon with an R² of 0.971 and an SMAPE of 0.77%, ETH-USD at the 1-year horizon with an R² of 0.948 and an SMAPE of 0.81%, and SOL-USD at the 1-year horizon with an R² of 0.910 and an SMAPE of 4.72%. Real-time testing demonstrated that the model consistently captured the overall price movement trends despite high market volatility. It can be concluded that the integration of GAF representations and the hybrid CNN–LSTM–Transformer model has strong potential to enhance cryptocurrency candlestick analysis and can be utilized as a component of a Decision Support System for digital asset investment.
Performance Comparison of Naïve Bayes, Random Forest, and Support Vector Machine Algorithms in Sentiment Analysis of the Weverse Application
The rapid development of digital technology has enhanced interactions between artists and fans through various community platforms, one of which is Weverse. User reviews on this platform reflect perceptions and satisfaction levels that are valuable for developers in improving service quality. However, the large volume of unstructured reviews creates challenges in systematically identifying opinion patterns. This study aims to analyze and compare the performance of Naïve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms in classifying positive and negative sentiments from 5,000 Weverse user reviews collected from the Google Play Store through web scraping techniques. The data were processed through preprocessing stages, translated into English, and automatically labeled using VADER, while feature weighting was performed using the TF-IDF method. The experimental results indicate that Random Forest achieved the best performance with an accuracy of 83%, followed by SVM with 80.2% and Naïve Bayes with 74.2%. These findings confirm that Random Forest is more effective in handling high-dimensional text data in a stable and consistent manner. This study is expected to serve as a reference for the development of Indonesian-language sentiment analysis models on digital community platforms in future research.
Performance Analysis of Docker-based NFV Service Chaining Networks in a Single-Host Environment
Network Function Virtualization (NFV) and Service Function Chaining (SFC) enable network functions to be deployed as Virtual Network Functions (VNFs) on flexible commodity servers. However, chaining multiple VNFs within a service chain may degrade data-plane performance, particularly in container-based environments. This study analyzes the performance of container-based SFC in a single-host Docker environment under three scenarios: (1) a direct client–server connection without VNFs (baseline), (2) the addition of a single Layer 3 (L3) VNF in the form of an iptables firewall, and (3) the integration of an L3 firewall VNF combined with a Layer 4 (L4) load balancer VNF based on HAProxy. Performance evaluation was conducted by measuring TCP throughput using iperf3, end-to-end latency using ping, and CPU utilization of each container using docker stats. The results indicate that adding the L3 firewall reduces throughput by approximately 33% and nearly doubles latency compared to the baseline. Meanwhile, incorporating the L4 load balancer causes throughput degradation of up to 92%. CPU utilization analysis shows that the kernel-space firewall introduces minimal additional overhead in user space, whereas the L4 VNF becomes the primary source of CPU saturation. These findings suggest that, in container-based SFC deployments on a single-host Docker environment, performance bottlenecks are primarily driven by user-space L4 VNFs rather than kernel-based L3 forwarding. Therefore, L4 VNFs require special consideration when designing service chaining architectures for resource-constrained edge nodes.
Security Mitigation Analysis of Mobile Application Using Static and Dynamic Methods with MobSF
This study evaluates the security of the Mobile Application for the Palm Oil Harvest Information System using static and dynamic analysis through the Mobile Security Framework (MobSF). The research is motivated by the high risk of exploitation in APK-based applications and the lack of in-depth security assessments for applications that manage farmers’ operational data. Static analysis was conducted to identify structural weaknesses, including the use of debug certificates, enabled debugging mode, a low minimum SDK version (minSdkVersion), and exported components without proper protection. The initial results showed an App Security Score of 43/100 (Medium Risk), which increased to 67/100 (Low Risk) after configuration improvements were applied. Dynamic analysis was then performed to assess application security during runtime. The results indicated that the client side was relatively secure, with HTTPS-encrypted communication and no logging of sensitive data. However, dynamic analysis revealed vulnerabilities on the server side, where several backend endpoints could be accessed without authentication and without parameter validation, leading to potential risks of Broken Access Control and Insecure Direct Object Reference (IDOR). The findings confirm that static improvements are effective in strengthening the structural security of the application. Nevertheless, reinforcing authentication, authorization, and request validation mechanisms on the backend API remains essential to ensure comprehensive security before deployment in an operational environment. Unlike previous studies that generally focus only on vulnerability mapping, this study evaluates the effectiveness of security mitigation in a step-by-step manner by demonstrating improvements in static analysis scores and re-validating the results through dynamic analysis. Therefore, this research provides a more comprehensive security assessment of mobile applications by covering both client-side and backend aspects.
Design of a Palm Oil Harvest Recording Information System using Two-Factor Authentication Security
The palm oil harvest recording process in many farmer groups is still carried out manually, which may lead to recording errors, delays in reporting, and difficulties in verifying harvest data. This study aims to design and develop a web-based palm oil harvest recording information system that supports harvest data entry by farmers, harvest verification by agents, transportation monitoring by drivers, and the presentation of harvest information through a dashboard for the owner. The system is designed by integrating a Two-Factor Authentication (2FA) security mechanism using One-Time Password (OTP) verification as an additional authentication layer to enhance user access security. The software development method applied in this study is the System Development Life Cycle (SDLC) Waterfall model, which includes requirement analysis, system design, implementation, testing, and maintenance stages. Black-box testing results indicate that all main system features function properly according to user requirements. In addition, user acceptance testing using usability testing with the System Usability Scale (SUS) instrument obtained an average score of 72.5, which falls into the Good category. These results suggest that implementing OTP-based 2FA improves authentication security without reducing system usability. The developed system is able to enhance the efficiency, accuracy, and transparency of the palm oil harvest recording process and has the potential to be implemented in farmer groups or similar business units.
Web-based Educational Payment Information System using Role-based Access Control Security
The management of Sumbangan Pembinaan Pendidikan (SPP) payments in Islamic boarding schools still faces issues related to delayed recording, data inaccuracies, and weak access control over financial information. This study aims to design and implement a web-based SPP payment information system that applies Role-Based Access Control (RBAC) to improve administrative order and data security. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The application was built using the Laravel framework with RBAC implemented at the middleware level to manage user access based on defined roles, namely Super Admin, Treasurer, Student Guardian, and Principal. System testing was conducted using the Black-Box Testing method to validate core functionalities, including user authentication, billing management, payment verification, report generation, and role-based access restrictions. The test results indicate that all system functions operate as expected and that the RBAC mechanism effectively prevents unauthorized access to sensitive features and data. Overall, the implemented system supports more structured payment administration, improves data accuracy, and enhances security and accountability in managing financial transactions within the pesantren environment.
Analysis and Improvement of an Agribusiness Web Information System Security using Grey-Box and White-Box Testing
This study aims to analyze and improve the security of the SawitGoDigi Palm Oil Harvest Recording Information System using grey-box and white-box testing approaches. The system is used by farmers, agents, drivers, and administrators to manage land data, harvest results, distribution, and transaction records, which makes it highly exposed to security risks if vulnerabilities are present. The security testing process was conducted based on the OWASP Web Security Testing Guide (WSTG) v4.2 and the OWASP Risk Rating Methodology. The testing stages included reconnaissance, automated scanning using OWASP ZAP, manual exploitation, risk evaluation, implementation of security improvements, and retesting. The results revealed several significant vulnerabilities, including SQL Injection in the search feature, weak session management through the trusted_device cookie, and the absence of a rate-limiting mechanism that enabled brute-force attacks during the login process. The risk assessment indicated that SQL Injection and session hijacking were classified as High risk, while brute-force attacks were categorized as Medium risk. Security improvements were implemented through the use of prepared statements, strengthening cookie attributes, adding security headers, and implementing rate limiting. Retesting results confirmed that all identified vulnerabilities were successfully mitigated and reduced to a Low-risk level. This study demonstrates that a comprehensive security testing approach, which includes exploitation, remediation, and verification through retesting, can significantly enhance the security of agribusiness web applications. Furthermore, the findings show that before remediation, the system contained four vulnerabilities with High and Medium risk levels, namely SQL Injection, Session Hijacking, Brute-Force Login, and Security Misconfiguration. After the remediation and retesting process, all High- and Medium-risk vulnerabilities were successfully reduced to Low risk or marked as Closed, indicating that the system is secure for operational use.
Comparative Analysis of Deep Learning Architectures for Indonesian Spice Image Classification
Spices are an important commodity in Indonesia; however, visual identification remains challenging due to the similarity in appearance among different types of spices. This study develops an image classification system for Indonesian spices using transfer learning by comparing four Convolutional Neural Network (CNN) architectures: VGG16, ResNet50, EfficientNetB0, and MobileNetV2. The Indonesian Spices dataset consists of 31 classes with a total of 6,510 images, which were stratified and divided into training, validation, and testing sets. The training process was conducted in two stages: head-layer training and fine-tuning, with the application of regularization techniques such as dropout, batch normalization, and L2 regularization. The results show that ResNet50 achieved the best performance with a test accuracy of 95.80%, followed by VGG16 with 95.70%. EfficientNetB0 provided an optimal balance between accuracy (94.17%) and the fastest inference time (5.51 ms), while MobileNetV2 achieved an inference time of 6.07 ms with an accuracy of 92.63%, making it suitable for mobile devices. This study demonstrates the effectiveness of transfer learning for Indonesian spice image classification.
Sentiment Analysis of CapCut Application Reviews using Support Vector Machine with the SMOTE Technique
The growing popularity of short-form video content across various social media platforms has increased the use of cross-device video editing applications, accessible through smartphones, desktops, and web-based services. CapCut is one of the most widely used applications for creating creative content, and user reviews on the Google Play Store serve as an important indicator for evaluating user experience quality. However, review datasets are often imbalanced, with positive sentiment dominating and neutral sentiment appearing in much smaller proportions, which poses challenges for sentiment classification. This study aims to analyze user sentiment toward CapCut reviews using Support Vector Machine (SVM) and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The data were collected by scraping reviews from the Google Play Store, resulting in 4,381 cleaned review entries after the data cleaning stage. The reviews then underwent text preprocessing, TF-IDF feature weighting, and model training. The experimental results show that the SVM model achieved an accuracy of 73.54% with a weighted F1-score of 0.736. These findings indicate that SMOTE contributes to improving model performance on minority classes. Overall, this study provides insights into user perceptions of CapCut and highlights the potential of SVM as an effective sentiment classification method for text-based application reviews.
Design and Development of the Restaurant X Reservation Application on the iOS Platform using App Clip
The food and beverage industry in Indonesia has shown significant growth, with 4.85 million business units in 2023. However, many restaurants still rely on manual reservation systems, which hinder operational efficiency. Despite internet penetration reaching 79.5%, with smartphones as the primary access device (83.39%), mobile application adoption faces barriers due to friction in the installation process. This study aims to design and implement an iOS-based restaurant reservation application using App Clips technology, integrated with a real-time admin dashboard. The system was developed using the MVVM architecture, with Swift and SwiftUI for the user interface, Golang for the backend, and PostgreSQL for the database. The system includes a customer-facing reservation app, a restaurant-side reservation management app, advance payment via QRIS displayed through the app, table selection based on customer ambience preferences, an automatic overbooking prevention mechanism, and finalization of reservations once the allotted time is complete. Development evaluation was conducted using task-based usability testing with seven respondents (four admins and three customers). The results showed a 100% task completion rate on both interfaces, exceeding the benchmark average of 78%, while App Clip access successfully demonstrated its effectiveness as a quick-access method without installation. This study contributes to the documentation of App Clip implementation in mobile reservation systems and presents an integrated reservation management solution that can be adapted to other sectors within the hospitality industry.
Implementation of Role-Based Access Control (RBAC) in a Drug Stock Management Information System
Role-Based Access Control (RBAC) is a role-based access management mechanism that restricts user privileges according to their authority within an information system. The implementation of this mechanism is particularly important in pharmacy drug stock management systems, especially at Apotek Pratama dr. Moris, which still relies on manual stock recording. This condition often results in data discrepancies, delays in monitoring expired medications, difficulties in report generation, and the absence of clear access restrictions for each user. This study focuses on developing a web-based drug stock management information system with RBAC as the primary mechanism for user authorization and security. The system was developed using the Waterfall methodology, which includes requirement analysis, system design, implementation, and testing. The application was built using Laravel framework version 11, MySQL database, and the Laravel Spatie Permission package for managing roles and permissions. Black Box Testing results indicate that all functional test scenarios were executed successfully, achieving a 100% success rate. User acceptance testing, conducted using the System Usability Scale (SUS), yielded an average score of 76, categorized as Good. The findings demonstrate that the implementation of RBAC effectively restricts user access based on roles, enhances data security, and improves the accuracy and efficiency of drug stock management compared to the previous manual system.
Comparison of Support Vector Machine and K-Nearest Neighbor Algorithms on the Effectiveness of a Free Lunch Program
The Free Lunch Program is a government initiative aimed at ensuring adequate nutrition for the public. This study aims to examine public perceptions of the program through sentiment analysis and to compare the effectiveness of Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) models. A total of 6,532 public comments were collected from Twitter, YouTube, and TikTok. After preprocessing, including normalization, stopword removal, and stemming, features were extracted using Term Frequency–Inverse Document Frequency (TF-IDF), resulting in 5,992 clean data points. The dataset was split into 80% training and 20% testing sets. Model training was conducted with hyperparameter tuning using 3-fold GridSearchCV. The results indicate that negative sentiment dominated at 42.7%. In the model comparison, SVM with a linear kernel significantly outperformed K-NN, achieving an accuracy of 72%, while K-NN (k=3) reached only 48%. These findings suggest that the SVM algorithm is more effective in classifying public opinion sentiment on high-dimensional data compared to K-NN.
School Profile Website using the K-Means Algorithm at the Minahasa Regency Education Office
Background: The Education Office often manages school profiling with minimal and fragmented data, such as school name, district, level, total students, and total teachers. This situation makes planning reactive and error-prone, highlighting the need for a lightweight yet reliable workflow to transform aggregated data into actionable evidence. Method: This study developed a web-based profiling tool that integrates a user interface designed using a User-Centered Design (UCD) approach with a transparent K-Means clustering algorithm. Development was carried out through iterative prototyping, with features standardized using z-scores and cluster validity assessed using the silhouette method along with other internal validity indices. Results: Data entry features with header previews and numeric checks effectively reduced rework. The silhouette value peaked at $k=2$, producing two interpretable segments (moderate vs. high staffing load), with an optional $k=3$ for exploratory purposes. Usability evaluation using the System Usability Scale (SUS) yielded a score of approximately 82, indicating good user acceptance, with system response times measured in seconds. Conclusion: The system provides a streamlined and sufficient workflow for routine planning and establishes a foundation for future longitudinal developments, such as tracking the number of study groups and accreditation per period.
Rice Plant Disease Detection System based on Leaf Image using Web-based CNN Algorithm
Rice (Oryza sativa) plays a crucial role as a major staple food commodity. However, diseases such as Bacterial Blight, Brown Spot, and Leaf Blast can cause significant crop losses. Current manual identification methods have limitations due to high subjectivity and long diagnosis time. This study proposes a web-based automatic detection system using a Convolutional Neural Network (CNN). The dataset was obtained from Kaggle and consisted of 2,800 images evenly distributed across four classes (700 images per class). The data were split using an 80:20 ratio for training and validation sets, followed by preprocessing steps including resizing to 224×224 pixels and data augmentation. The CNN architecture was designed with four convolutional blocks and optimized using the Adam optimizer. Training for 50 epochs achieved an accuracy of 77.50%, precision of 82.98%, recall of 77.50%, and an F1-score of 72.84%. Based on the confusion matrix analysis, the model performed very well in detecting Bacterial Blight and Brown Spot but still faced difficulties in identifying the Leaf Blast class. Overall, the developed system has the potential to serve as a decision-support tool for farmers, although further performance improvements are required, particularly for detecting specific disease variants.
This study aims to develop a deep learning-based chili ripeness detection system using the YOLOv11 model. Chili ripeness is classified into three categories: unripe, semi-ripe, and ripe. The dataset consists of 150 original images, which were expanded to 300 images to increase data variation. Model training was conducted using the Roboflow platform, while accuracy testing was performed in Google Colab through an image upload-based processing method. The experimental results show that the model achieved an accuracy of 93.94%, with a precision of 94.21%, recall of 93.94%, and an F1-score of 93.94% on the test dataset. This system is expected to support the automation of chili sorting based on ripeness levels.
Mango Leaf Disease Detection using Threshold with CNN ResNet50 Architecture
Mango leaf diseases pose a significant threat to farmers’ productivity in Indonesia due to the difficulty and inaccuracy of manual diagnosis. A mango leaf disease detection system was developed by optimizing the decision threshold for classification using a ResNet50 Convolutional Neural Network (CNN). The Kaggle dataset consisted of 3,979 mango leaf images across eight classes: healthy, anthracnose, bacterial canker, gall midge, cutting weevil, dieback, sooty mold, and powdery mildew. The raw dataset was processed in Roboflow with an 80:10:10 train-validation-test split, and threefold data augmentation on the training set produced a total of 9,600 images. Decision threshold optimization using the precision-recall curve analysis identified 0.85 as the optimal threshold. At this threshold, precision reached 97.03%, while recall was 94.36%. These results provide a critical reference for agricultural applications in Indonesia, particularly considering local characteristics. The model achieved an F1-score of 95.49% after validation on the augmented dataset specifically tailored for tropical conditions.
Analysis and Implementation of Linear Regression and Decision Tree Methods to Predict Sales at Rayyan Bakery, Simpang Marbau
The development of information technology and data analytics has encouraged business actors to leverage historical data as a basis for decision-making. In the small and medium enterprise (SME) sector, particularly in the culinary field, the ability to predict sales is a crucial aspect of production planning and stock management to ensure operational efficiency. Rayyan Bakery Simpang Marbau, as a bakery SME, faces challenges due to fluctuating sales that have traditionally been managed based on experience rather than systematic data analysis. The main problem addressed in this study is the absence of a data-driven sales prediction method that can assist the business owner in estimating sales accurately. Therefore, a predictive approach that utilizes historical sales data is required to support managerial decision-making. This study employs linear regression and decision tree methods. The analyzed data consist of historical sales records of Rayyan Bakery Simpang Marbau over a specific period. Linear regression is used to model the linear relationship between sales variables, while the decision tree captures non-linear patterns and produces easily interpretable decision rules. The performance of both methods is analyzed and compared based on the accuracy of the predictions they generate. The results indicate that both linear regression and decision tree methods can be effectively used to predict sales; however, the decision tree provides greater flexibility in capturing fluctuating sales patterns. These findings are expected to assist Rayyan Bakery in production planning and stock management, as well as serve as a reference for applying sales prediction methods in similar SMEs.
An Automated System for Detecting and Improving Academic Text Politeness Using IndoBERT and IndoT5
The increasing use of digital communication in academic interactions between students and lecturers is often not accompanied by consistent application of language politeness norms, potentially affecting the effectiveness of academic interactions. To date, efforts to enhance language politeness have predominantly relied on manual and subjective evaluation. This study aims to develop an automated system for detecting and improving politeness in Indonesian academic text communication. The proposed approach integrates IndoBERT as a classification model to identify levels of text politeness and IndoT5 as a generative model to transform sentences identified as impolite into more appropriate academic forms. The dataset consists of 6,230 labeled sentences collected through Google Forms, TikTok, and additional synthetic data generated using ChatGPT. Experimental results show that the IndoBERT model achieves an accuracy of 97.11% in classifying academic text politeness, while IndoT5 is capable of transforming impolite sentences into more appropriate academic expressions, as demonstrated by evaluations using BLEU, ROUGE, and METEOR metrics. This study results in an integrated deep learning–based system capable of automatically detecting and improving academic text politeness within a unified processing framework.