Sinkron : jurnal dan penelitian teknik informatika

Development of RPG-Based Mathematics Educational Games with the Waterfall Method on Fraction Material for Elementary School Students

Azis Satriyo, Abdul Azis, Fiqih Hana Saputri, Ferawati — Fri, 11 Apr 2025 00:00:00 +0000

Mathematics is one of the subjects that plays an important role in everyday life and in developing logical thinking and problem-solving skills. One of the topics that often poses a challenge for elementary school students is fraction numbers. Fractions are often considered an abstract and difficult concept to understand because they involve numerical representations that differ from whole numbers. This difficulty frequently leads to a lack of interest in learning mathematics, ultimately affecting students' academic performance. The data collection stages applied include interviews, observations, and the distribution of questionnaires. The development of this learning media follows the Waterfall model, which aims to design improvements to the existing system. The results of the User Acceptance Test reveal that this game received a user perception score of 90.5%, categorizing it as "very good," indicating that students find it both enjoyable and effective as a learning tool. This suggests that the game is not only engaging but also effective in helping students understand fraction concepts in a more interactive and enjoyable way. With the presence of story elements, challenges, and engaging game mechanics, students can learn in a more immersive manner compared to conventional methods. Therefore, this game is suitable for use as a mathematics learning tool, particularly in understanding fraction operations such as addition, subtraction, multiplication, and division.

AHP-SWARA Implementation Method for Evaluation and Selection Employee Promotion

Akmaludin, Adhi Dharma Suriyanto, Kudiantoro Widianto — Sun, 13 Apr 2025 00:00:00 +0000

The evaluation process of employee selection is very important for organizations that want to carry out quality leadership, the purpose of this study is to objectively prove the results of the selection of job promotions that have been evaluated continuously every time leadership occurs. The evaluation results of the leadership selection process become routine, so that the results of leadership promotions can provide improvisation to organizations that are increasingly advancing towards future leadership targets. The proposed method for the evaluation and selection process uses the Analytic Hierarchy Process (AHP) and specifically Stepwise Weight Assessment Ratio Analysis (SWARA). Both of these methods utilize expert intervention in providing input in providing assessments of multi-criteria and alternatives. So that the priority of the criteria is carried out by an index process similar to that owned by the two methods, thus providing more optimal results for decision-making support. The assessment of the results requires seven criteria and twenty-four alternatives. The results obtained require two index processes for both criteria and alternatives. The first rank is determined by the weight of the calculation results of the seven criteria and alternative assessments from experts. The first rank of twenty-six employees was given to K20 with a weight of 0.932 and followed by K2 with a weight of 0.08. Thus, job promotion can be developed with a double index that can provide optimal results in supporting job promotion decision making

Optimizing Marketplace Registration Page Design with Predictive Heatmap Analysis

Galih Bagaskoro, Rujianto Eko Saputro, Azhari Shouni Barkah, Agi Nanjar — Mon, 14 Apr 2025 00:00:00 +0000

Optimizing marketplace registration pages is crucial for improving user experience and conversion rates. This study evaluates the design of registration pages for four leading Indonesian marketplaces Tokopedia, Shopee, Blibli, and Lazada—using Predictive Heatmaps from UX Pilot alongside Heuristic Evaluation and Gestalt Principles. The analysis identifies key usability issues, such as distractions from branding elements, inconsistent visual hierarchy, and a lack of real-time validation and feedback mechanisms. Findings indicate that while branding elements effectively capture user attention, they often divert focus from essential features, a trend observed not only in these marketplaces but also in broader UI design contexts. such as Call-to-Action (CTA) buttons and registration forms. Shopee and Lazada successfully utilize high-contrast CTA buttons to direct user interaction, whereas Tokopedia and Blibli suffer from visual distractions caused by mascots and unnecessary decorative elements. Heatmap results also reveal inconsistent grouping of interface components, reducing page efficiency. To enhance user experience and conversion rates, recommendations include improving CTA button visibility through contrasting colors and strategic placement, minimizing decorative distractions, and implementing real-time validation and feedback. The application of Gestalt Principles further aids in optimizing interface organization by grouping related elements more effectively. This study underscores the importance of a structured design approach incorporating heuristic and predictive analytics to enhance the usability of online registration pages. Future research may explore the impact of interactive elements and A/B testing in refining registration interfaces.

Application of Extreme Programming Methods in the Design and Building of the Nusantara Capital Sentiment Analysis System

Famidin Said, Domy Kristomo, Widyastuti Andriyani — Mon, 14 Apr 2025 00:00:00 +0000

Information about the capital city of the archipelago (IKN) in the digital era serves as a platform for individuals to express views on development, policies, and socio-economic impacts. Such information often contains personal emotional expressions, categorized as negative, neutral, or positive sentiments. This study aims to design a sentiment analysis system to evaluate public opinions regarding IKN. The system utilizes Google NLP services, which offer sentiment measurement features for analyzed text, and web scraping techniques to automate data collection from online sources. The development process employs the Laravel framework and follows the Extreme Programming approach, which ensures work efficiency. Sentiment analysis is conducted using the Support Vector Machine (SVM) method, achieving an accuracy rate of 95%. The system is designed to be web-based, ensuring accessibility across devices, including smartphones and computers. The results demonstrate that this sentiment analysis system can help individuals, organizations, and governments gain deeper insights into public perspectives on IKN. Furthermore, it serves as a valuable tool for strategic decision-making and policy evaluation related to IKN development. Future research may explore expanding the data sources and integrating more advanced analytical techniques to improve system performance.

Vehicle Type Classification and Detection System using YOLOv7-tiny Model on Single-Board Computer

Fri, 11 Apr 2025 00:00:00 +0000

Transportation is playing an important role for human civilization, for example transportations is being used as distributing goods and products. Therefore, the total numbers of vehicles as a part of transportation will continue to increase every year. But in Indonesia, the majority of its people is still using their personal transport rather than public transportation. This is supported by the data of total number of vehicles in Indonesia from 2018 – 2020, which is shows that personal transport is still dominant than public transportation. The causes of traffic jams is a result of various factors, such as the roads are not designed to accommodate the increasing number of vehicles, insufficient traffic signs, and poor traffic management. The road traffic data is one of the aspects that could reduce traffic jams. The process of collecting road traffic data which is still done manually has several shortcomings, such as it takes a long time and there may be errors due to human error. This research has a goal to create a vehicle type detection and classification system that have a good detection accuracy and detection speed that can be run on single-board computer devices. YOLOv7-tiny model that performs detection and classification using input from video on the NVIDIA Jetson Nano device gets a True Positive (TP) score of 96.58%, a False Positive (FP) score of 0.98%, and a False Negative (FN) score of 2.44%. YOLOv7-tiny on the NVIDIA Jetson Nano device can run with an average Frame per Second (FPS) of 6 FPS.

Research on Sobel Edge Detection Algorithm of Grayscale Images to Analyse Car Number Plate

Tri Dharma Putra, Rakhmat Purnomo — Mon, 14 Apr 2025 00:00:00 +0000

Image processing is a very important subject to be discussed in computer science. Many applications of image processing are already in the field. Image processing techniques are applied in color and grayscale images. The application of image processing are ranging for military, medical and many other applications. One most important thing to analyse image and enhance its quality is doing edge detection. Edge detection in image is a well known approach to be used to detect discontinuity in grayscale image. Edge detection functions to identify edge line in images. Sobel algorithm is one of most known algorithm, others are prewitt, canny, homogeneity algorithms. Image can be made sharper and will enhance its quality. To detect number plate of cars, an edge detection algorithm needs to be applied. In number plate, to recognize the cars number plate, the image should be clear and clean from dirt. Sometimes we can not recognize the plate number if it is too blur or has many dirt. So in its application we need a strong edge detection algorithm to recognize car number plate easily. In this journal, five car’s images are presented. Each with the original image, grayscale image and the image after edge detected by sobel algorithm. It is concluded that this algorithm is quiet good in the implementation. But in the result, there are poor quality image also. For PSNR of images after edge detected, their values are between 19 and 20 dB, which are not good.

Mobile Learning Application on Two-Dimensional Figure Material for Children with Intellectual Disabilities

Ahmad Pausi, Mohamad Rayhan Noerfikri, Rahmat Tullah, Ferawati — Mon, 14 Apr 2025 00:00:00 +0000

Mobile learning apps are widely acknowledged for their effectiveness in enhancing learning results. This study aims to develop and validate an mobile learning app for computer two dimensional figure. Using the userfriendly App figma platform known for visual programming, it integrates interactive modules and multimedia for diverse learning styles. The study adopted a Research and Development approach following the ADDIE model (analysis, design, development, implementation, and evaluation). The research was conducted at SKh YKDW 02 Tangerang and involved 6 students. The outcomes pertaining to validation experts percentage scores are as follows: The aspect of media and design received a score percentage of 91,25%, affirming its very valid. Students responses the average percentage for the four assessment aspects clarity of material, motivation, interest, and easy of use navigation reached 91,10%, placing it in the very good category. The development of this mobile learning application for two dimensional figure material for children with intellectual disabilities material demonstrates significant potential as an innovative educational tool.

Development of Augmented Reality-Based Learning Media for Solid Geometry for Elementary School Students

Ricky Aditya Saputra, Muhammad Alfarizi, Fiqih Hana Saputri, Ferawati — Mon, 14 Apr 2025 00:00:00 +0000

AR-based media is expected to create a more interactive and engaging learning experience, enhance students’ understanding, and motivate them to learn independently and actively in the digital era. The data collection stages applied include testing, observations, and the distribution of questionnaires. The development of this learning media follows the MDLC model, which aims to design improvements to the existing system. The results of the blackbox testing, the "Bangun Ruang" application is proven to be valid and successfully used, with excellent results in the SUS test, where the Ease of Use score reached 86%, Efficiency 88%, Effectiveness 90%, and Satisfaction 87%. This indicates that the application has high levels of usability, efficiency, and effectiveness, while also providing a satisfying user experience. The application not only operates according to the designed specifications but also provides a positive user experience. Thus, it can be widely used, especially in educational environments, to help students understand geometric concepts in a more interactive and engaging way. Developers can continue maintaining and improving the application based on user feedback to ensure it remains optimal and aligned with users' needs in the future.

Sentiment Analysis of the Relocation of the National Capital on Social Media X

Mon, 14 Apr 2025 00:00:00 +0000

The relocation of the national capital is a national strategic development project that seeks input from the public. This research analyzes public sentiment towards the relocation of the capital city using the Lexicon SVM method with data from X social media. The analysis was conducted in two languages, namely Indonesian and English, to find out how public opinion on the relocation of Indonesia's capital city at the global level. The sentiment classification results show that in Indonesian, public sentiment tends to be balanced with a model accuracy of 86.79%, where 51.3% is positive sentiment and 48.7% is negative. Meanwhile, in English, positive sentiment is more dominant with a model accuracy of 89.64%, where 83.3% is positive sentiment and 16.7% is negative sentiment. Evaluation using confusion matrix shows that this model provides good results, with high precision, recall, and F1-score values. Visualization using WordCloud and frequency analysis of unigrams, bigrams, and trigrams showed that positive sentiments mostly discussed the development aspects and government policies, while negative sentiments highlighted the social and economic impacts of the relocation. In addition, further analysis shows that public sentiment fluctuates based on important government announcements and major events related to the project. These findings demonstrate the importance of monitoring public opinion over time to understand shifts in perception. This research provides insights to the government and policymakers in understanding public opinion regarding the relocation of the nation's capital. By understanding sentiment patterns, more appropriate policies can be designed to increase public acceptance of the project and address public concerns effectively.

Implementation of Support Vector Machine Algorithm for Heart Disease Risk Identification Using Signal Electrocardiogram

Mon, 14 Apr 2025 00:00:00 +0000

: In the medical world, one of the biggest contributors to death in the world is heart disease. Early detection of the risk of heart disease can increase the chances of recovery and reduce mortality. This research applies the Support Vector Machine (SVM) algorithm to identify the risk of heart disease using Electrocardiogram Signals. The ECG data used was taken from a public database that contained a record of information on the electrical activity of the heart of patients with various heart health conditions. The Support Vector Machine algorithm is applied to classify ECG signals into 2 main classes, namely normal conditions and at-risk conditions. Several methods in data processing, including data normalization and feature selection are used to improve the accuracy and success of the model. The results of the evaluation with this method resulted in accuracy, precision, recall and also F1-score showed that the modeling of this algorithm produced a fairly good classification, with an accuracy of more than 90% in the identification of heart disease risk. This study shows the potential use of this algorithm in automatically detecting the risk of heart disease based on ECG signals, which can be a tool in medical diagnosis. The results show that implementing the SVM strategi with the RBF kernel appears to be a very easy execution when compared to the direct part. An important component that affects the adequacy of an SVM strategy is the parameters of the section and the way the information is handled.

Implementation of the Dual Channel Convolution Neural Network Method for Detecting Rice Plant Diseases

Wilson Jauhary, Albert Julius Yaphentus, Yennimar — Mon, 14 Apr 2025 00:00:00 +0000

Rice is a strategic and important food crop for the economy in Indonesia. Rice can be infected with diseases caused by fungi, bacteria and viruses. The disease that attacks rice plants goes unnoticed by farmers and farmers often do not understand the diseases that attack rice plants so that it is too late in treating them to diagnose the symptoms, causing rice production to decrease. To solve this problem, it is necessary to carry out a disease detection process in rice plants. In this research, the Dual-Channel Convolutional Neural Network (DCCNN) method will be used. This DCCNN method consists of two channels, namely deep channel and shallow channel. The process of detecting grape plant diseases using the DCCNN method will start from the process of extracting leaf parts from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to carry out the process of extracting characteristics, color and texture from the extracted leaf parts. Finally, the DCCNN method will be applied to carry out the process of classifying and detecting types of grape plant diseases. The results of this research are that the DCCNN method can be used to detect types of leaf diseases in rice plants. The accuracy of disease detection results using the DCCNN method depends on the number of datasets contained in the system with an accuracy level of up to 85%. However, more datasets will cause the execution process to take longer.

MCDM-AHP and CODAS Collaboration Techniques for Selection of Expert Education Personnel

Adhi Dharma Suriyanto , Akmaludin, Kudiantoro Widianto — Tue, 15 Apr 2025 00:00:00 +0000

Educational progress is largely determined by human resources who have the best qualifications, with the ability of human resources to provide hope for educational development for a creative and potential future. The ability of human resources to create various variants of knowledge that can be developed to enlighten the progress of thinking through education to create expert education personnel. The aim of this research is to provide techniques to guarantee the quality of the selection process for expert education personnel for the competitive progress of mastering educational technology who are able to independently increase the creative and potential thinking of graduates. To achieve this, of course, strict collaboration techniques are needed in the selection process to obtain expert education personnel. The method proposed in this research is MCDM-AHP in collaboration with CODAS. These two methods can collaborate in providing guarantees for an optimal selection process for education personnel through eight selected assessment criteria and twelve alternatives. From the results obtained, the highest priority was obtained by ALT10 with a weight of 0.229. This gain goes through the stages of normalizing criteria and alternatives with the optimization results of both. With the research results that have been described in detail, the collaboration of the MCDM-AHP and CODAS methods can be used as a measuring tool for optimal assessment of the acquisition of decision support results and can be used as a comparison with other methods for measuring the level of optimization of results.

Optimizing Software Development Through Flow Metrics Analysis in the Scaled Agile Framework (SAFe)

Achmad Fathurrazi Akbar, Eko Indrajit, Amelia Makmur, Handri Santoso — Tue, 15 Apr 2025 00:00:00 +0000

To meet the ever-changing market demands, more efficient strategies are required due to the complexity of today's software development. Because it can combine Agile concepts with organizational structures to manage large-scale projects involving multiple teams and departments, the Scaled Agile Framework (SAFe) has been widely adopted. This research investigates how Flow Metrics from the SAFe framework can be used as a tool to improve productivity, efficiency and alignment of the software development process. This research examines how measurements such as flow velocity, flow efficiency, flow time, and flow load can be used to pinpoint bottlenecks, streamline processes, and improve the value delivered to clients. This research uses a qualitative methodology to examine the use of Flow Metrics in two interdependent Program Increments (PIs) by combining interviews with Agile practitioners and a literature survey. The analysis highlights how the Continuous Delivery (CD) Pipeline, backlog synchronization, and program increment planning—three critical components of SAFe—interact with each other. By highlighting the importance of metrics-based performance evaluation, collaborative planning, and continuous improvement, the findings of this research are intended to offer a useful foundation for businesses looking to implement SAFe for large-scale software development. This research advances a more comprehensive understanding of how SAFe and Flow Metrics can facilitate increasingly complex software development while guaranteeing adaptability to changing business needs.

Implementation of Cloud Computing for SOS Application Back-End using Google Cloud Platform

Arizona Firdonsyah, Mahrunisa Indah — Tue, 15 Apr 2025 00:00:00 +0000

This research discusses the implementation of cloud computing on the Backend of the SOS application using the Google Cloud Platform. The background of the research is based on the high crime rate in Indonesia, especially theft cases which reached 3396 cases in the period August-September 2024. The purpose of the research is to develop an application that can help users in emergency situations by providing information on the location of the nearest police station within a maximum radius of 5 KM. The method used is Agile Kanban, which was chosen because of its flexible nature and emphasizes rapid response to change. The Backend implementation uses Google Cloud Platform services including Maps API (Places API, Geocoding API, and Distance Matrix API) for location features, and Google Firestore for data storage. The results of the research show that the implementation of cloud computing for the Backend of the ResQHub application successfully displays the location of the nearest police station from the user, but there are still obstacles in the integration of Firestore for storing user data and signup/login authentication. Further research will focus on frontend development for mobile implementation and completion of Firestore integration.

Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments

Alfin Nur Aziz Saputra, Rujianto Eko Saputro, Dhanar Intan Surya Saputra — Tue, 15 Apr 2025 00:00:00 +0000

Sentiment analysis is a crucial technique in understanding public opinion, particularly on social media platforms such as YouTube. However, the presence of informal language, including slang words, poses significant challenges to accurate sentiment classification. This study aims to enhance sentiment analysis by implementing a Support Vector Machine (SVM) classifier combined with SMOTEENN data balancing techniques to address class imbalance issues. The research collects 3,375 YouTube comments on the movie Pengabdi Setan 2: Communion using the YouTube Data API. The preprocessing steps include text cleaning, tokenization, stopwords removal, stemming, and slang word normalization using kamusalay.csv to ensure standardization of informal expressions. The extracted features are represented using TF-IDF, and sentiment labeling is performed using VADER. Experimental results show that the SVM model achieves an accuracy of 98%, but struggles with detecting negative sentiments, as indicated by lower recall (38%) and F1-score (53%) for the negative class. Although the application of SMOTEENN improves data balance, further refinements, such as adjusting classification thresholds and integrating deep learning techniques, are necessary to enhance sentiment detection, particularly for informal and emotionally nuanced language. This study contributes to improving sentiment analysis models by demonstrating the effectiveness of slang word normalization in handling non-standard language variations. Future work will explore more sophisticated language models to enhance accuracy in sentiment classification.

Sentiment Analysis Using Grok AI as an Auto-Labeling Tool in The Text Processing Stage

Tue, 15 Apr 2025 00:00:00 +0000

A critical aspect of Natural Language Processing (NLP) is text processing, where text labeling represents the most significant challenge due to its resource-intensive nature when conducted manually. At this stage, automatic labeling emerges as a more practical solution, particularly with the advent of Artificial Intelligence (AI), which offers tools to address this obstacle. Grok AI, equipped with a new feature operable on Platform X, provides a promising approach. This study aims to leverage the Grok AI feature on Platform X for automatic text labeling. The research methodology involves labeling text data obtained from a public dataset. To assess the quality of the labeling results, an evaluation method employing Naive Bayes classification modeling is applied. The findings reveal that Grok AI's performance closely approximates that of human labeling. The highest accuracy achieved by Grok AI is 51.71% using the k-Nearest Neighbors (k-NN) algorithm, approaching the human labeling accuracy of 60.52% with k-NN. Furthermore, Grok AI surpasses the performance of VADER labeling, which achieves an accuracy of only 49.49% with Naive Bayes. Consequently, the Grok AI feature on Platform X presents a viable alternative for the automatic labeling of text data.

Automated Attendance System for Contract-Based Employees at Purwakarta Communication and Informatics Agency

Mutiara Andayani Komara, Asep Yusapra Salim, Maulvi Firdaus — Wed, 16 Apr 2025 00:00:00 +0000

Attendance is a crucial aspect of administrative management in both companies and institutions. However, manual attendance systems have several drawbacks, including disorganized data, minimal data control leading to a high potential for data fraud, and vulnerability to data loss or damage. These limitations underscore the need for more efficient and reliable attendance management solutions. This research addresses the challenges of manual attendance tracking for non-ASN (Aparatur Sipil Negara) staff at the Communication and Informatics Agency (Dinas Komunikasi dan Informatika (DISKOMINFO)) of Purwakarta Regency by developing a user-friendly web-based attendance system. The system leverages the PHP programming language and MySQL database to efficiently record and manage attendance data. The Waterfall method is used as the development framework, ensuring a structured and systematic approach. The system incorporates features designed with Unified Modelling Language (UML) to simplify attendance recording for staff and administrators, including online check-in/check-out, real-time attendance tracking, and automated report generation. Evaluation results demonstrate that the system significantly improves attendance accuracy, reduces administrative burden, and enhances overall efficiency within the office. This research highlights the importance of embracing technology to modernize administrative processes and improve operational effectiveness in government organizations. The implementation of this technology was also tested for its effectiveness using black box testing and the usability scale system.

Implementing TOGAF Enterprise Architec-ture in Indonesia’s Merchant Acquiring In-dustry: A Framework for Digital Trans-formation

Suwandhy Praharto, Alfa Ryano Yohanis — Wed, 16 Apr 2025 00:00:00 +0000

The digital transformation of Indonesia's merchant-acquiring industry, accelerated by regulatory initiatives, fintech innovations, and changing consumer behavior, has created significant technological and organizational challenges. Fragmented legacy systems and complex regulatory requirements hinder seamless digital payment adoption. This study investigates the strategic implementation of The Open Group Architecture Framework (TOGAF) to systematically manage these challenges. Through an extensive literature review and case studies of major industry players—including BRI, BCA, Mandiri, BNI, and GoPay—this research uniquely explores TOGAF's specific applicability to merchant acquiring in Indonesia. The proposed TOGAF-based framework aligns closely with Bank Indonesia's Payment System Blueprint 2025, emphasizing enhanced interoperability, regulatory compliance, and sustainable growth. Findings suggest that enterprise architecture can unify fragmented technologies, bridge gaps in merchant activation, and strengthen cybersecurity, ultimately driving innovation in digital payment services. By providing a structured implementation roadmap tailored to Indonesia's regulatory environment, this research not only addresses current industry needs but also sets a foundation for future technological advancement and financial inclusion in Indonesia's merchant acquiring landscape.

Decision Trees in Predicting Loan Default Risk in Customer Relationships within the Financial Sector

Thu, 17 Apr 2025 00:00:00 +0000

Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profiles, credit scores, income levels, and payment history, the model identifies key predictors that influence default outcomes. The study uses the C4.5 decision tree model, which will demonstrate that decision trees achieve high prediction accuracy and offer a transparent decision-making framework, enhancing their applicability in real-world scenarios. Furthermore, the paper highlights the implications of these findings for financial institutions, emphasizing the scalability and cost-effectiveness of the model. By integrating decision tree-based models into existing risk assessment systems, lenders can proactively manage loan portfolios and reduce default rates. Future research directions are proposed to explore hybrid approaches that combine decision trees with advanced combined methods to enhance predictive capabilities. The potential of decision tree algorithms in transforming credit risk assessment and supporting more accurate data-driven financial decision-making processes

A Business Intelligence: Enhancing Apache Superset Capabilities in PBB-P2 Receivables Monitoring

Sugeng Pranoto, Sri Wahyuni, Muhammad Syahputra Novelan — Thu, 17 Apr 2025 00:00:00 +0000

PBB-P2 Tax Revenue plays an essential role in regional finance, but managing receivables and analyzing taxpayer compliance levels still face many challenges. Business Intelligence (BI) technologies such as Apache Superset are often used for interactive data visualization. Still, they have limitations in advanced analysis, especially the application of machine learning algorithms such as K-Means for data clustering. This research aims to overcome the limitations of Apache Superset by developing an external application-based solution using the Java programming language and the SMILE library. This application is designed to cluster the level of taxpayer compliance in a batch process, with the results stored in the MySQL database. The clustered data is then visualized using Apache Superset. The results show that integrating these external applications can improve the efficiency of data analysis by utilizing more complex clustering algorithms. Visualization of clustering results also allows for more effective management of PBB-P2. This approach not only expands the capabilities of Apache Superset but also contributes to supporting data-driven tax revenue optimization strategies. This research opens up further opportunities for the integration of BI tools with machine learning algorithms in monitoring and managing complex data in the tax sector