The Journal of Soft Computing Exploration (JOSCEX) has migrated to a new platform starting in 2026 to enhance security and protect against potential threats, including journal hacking and other malicious activities. To submit your manuscript, please visit our new journal website at: https://joscex.shmpublisher.com Journal of Soft Computing Exploration (JOSCEX) e-ISSN: 2746-0991, p-ISSN: 2746-7686 is a peer-review and open-access journal published in every three months, namely in March, June, September, and December. The Journal of Soft Computing Exploration (JOSCEX), published by SHM Publisher in collaboration with Peneliti Teknologi Teknik Indonesia, attracts scientists and scholars to exchange scientific research papers related to the novelty in the field of soft computing and disseminate them widely to the public, especially soft computing enthusiasts. JOSCEX has been indexed by Copernicus, Sinta, Garuda, Google Scholar, World Cat, Neliti, Crossref, Dimensions, Semanticscholar, OneSearch, Scispace, wizdoms.ai, and Journal Stories. The advantage of this journal is: 1). The fast response, for good quality articles; 2). Provides DOI (Digital Object Identifier) to each published article, and; 3). Open Access, has a greater citation impact.
Comparative study of pre-trained RoBERTa sentiment models and zero-shot LLM on indonesian and english texts
The growth of user-generated content on social media has increased the need for effective sentiment analysis methods. Although fine-tuned transformer-based models and zero-shot large language models (LLMs) have both been applied to sentiment classification, comparisons across languages under unified evaluation settings remain limited. This study examines the trade-offs between task-specific fine-tuning and instruction-based zero-shot inference for multilingual sentiment classification. Experiments were conducted using two publicly available Twitter sentiment datasets in Indonesian and English, each annotated into three sentiment classes. Fine-tuned RoBERTa-based models were evaluated on full test sets, while all models, including a zero-shot LLM, were compared using an identical controlled subset. Performance was assessed using accuracy and macro-averaged precision, recall, and F1-score, with macro F1-score as the primary metric. The results show that fine-tuned RoBERTa-based models achieve stable and balanced performance across sentiment classes, with monolingual models consistently outperforming multilingual variants. Under controlled evaluation, zero-shot LLMs demonstrate competitive performance in English but remain less effective in Indonesian, indicating that their effectiveness is influenced by language resource availability. Overall, this study provides a controlled comparison of the strengths and limitations of fine-tuned and zero-shot approaches for multilingual sentiment classification.
Autorska prava (c) 2026 Journal of Soft Computing Exploration https://creativecommons.org/licenses/by-sa/4.0
Literature analysis on the role of artificial intelligence in addressing fraud in digital financial services
The rapid growth of digital financial services has significantly increased fraud risks, threatening the security of global financial systems. This study addresses the limitations of traditional fraud detection by analyzing the role of Artificial Intelligence (AI) as a real-time prevention mechanism. Using a Systematic Literature Review (SLR) of 24 scientific articles published between 2019 and 2025, this research evaluates AI’s effectiveness, implementation challenges, and its synergy with Big Data, Blockchain, and AutoML. The findings demonstrate that AI models, particularly Deep Learning and Machine Learning algorithms, provide superior accuracy in anomaly detection compared to conventional rule-based systems. However, implementation is often hindered by data scarcity, high false-positive rates, and infrastructure costs. The study concludes that a collaborative framework—integrating AI for predictive analysis, Blockchain for data integrity, and Big Data for scalable processing—creates a more robust and adaptive defense against sophisticated financial crimes. These insights provide a conceptual foundation for developing more comprehensive digital security ecosystems.
Comparative performance analysis of YOLOv8 small and larger for real-time website-based monitoring
River trash pollution in Indonesia demands efficient real-time monitoring solutions. By using deep learning, while YOLOv8 is a promising model for implementation, it is a family of models with each variant differ in accuracy, speed, and computational demand. Among these, YOLOv8s and YOLOv8l come out as potential variants due to the balance between speed, accuracy, and computational demand. Therefore, to address this gap, this study intends to compare the YOLOv8 small (YOLOv8s) and YOLOv8 large (YOLOv8l) variants for real-time, website-based river trash monitoring systems, aiming to identify the optimal balance between accuracy and inference speed for practical real-time deployment. A combined dataset of 66 images, consisting of 86% images from Kaggle’s 2024 dataset and 14% AI-Generated images from Krea AI, was augmented using Roboflow to produce 591 annotated images. Both models were treated equally with the same dataset and method. The evaluation was conducted using Precision, Recall, mAP50, mAP50-95, Inference Speed, and Frame per Second (FPS) speed. Consequently, YOLOv8s exceeded YOLOv8l, achieving approximately 20% higher precision, 30% better detection quality, and 20% higher mAP across IoU threshold, and 150%-340% faster FPS. This finding indicates that YOLOv8s offer better accuracy and speed trade-off for real-time implementation than YOLOv8l. Moreover, successful integration to the website with real-time stream and threshold alert, confirm its feasibility for proactive waste and flood management.
Application of the TAM model for assesing the acceptance of IoT technology in a residential security application
Residential crime continues to be a significant concern, and traditional security systems relying oHousing is an area vulnerable to crime, especially if it is not supported by an adequate security system. Many housing complexes still rely on conventional security systems that only involve officers without technological support. Therefore, the application of technology, especially the Internet of Things (IoT), is needed to improve housing security systems. The success of a system is largely determined by the level of user acceptance, which can be measured using the Technology Acceptance Model (TAM). This study aims to measure user acceptance of an IoT-based housing security system using the TAM model. Data were obtained from 100 respondents and analyzed using the PLS-SEM method to test the research hypotheses. The results showed that four hypotheses had a significant relationship, namely the relationship between Subjective Norm (SN) and Perceived Usefulness (POU), Perceived Ease of Use (PEU) and POU, PEU and Attitude Toward Use (ATU), and POU and Behavioral Intention (BEI). Meanwhile, the other four hypotheses did not show a significant relationship.n manual monitoring are often insufficient in addressing modern security challenges. With the rapid development of Internet of Things (IoT) technology, digital security solutions offer new opportunities for improving surveillance and access control within housing environments. This study aims to assess user acceptance of an IoT-based residential security application by applying the Technology Acceptance Model (TAM). A quantitative survey method was used, involving 100 respondents who evaluated the prototype after testing it directly. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Perceived Ease of Use significantly affects both Perceived Usefulness and Attitude Toward Using, while Perceived Usefulness strongly influences Behavioral Intention. However, Attitude Toward Using shows a marginal impact on Behavioral Intention, and Behavioral Intention does not significantly predict Actual Use. These findings reveal the dominant factors influencing acceptance and highlight areas for improvement in IoT-based security applications.
Topic modelling analysis of public policy narratives on prabowo-gibran in national news
The rapid acceleration of digital transition has become an inevitable reality of the modern era. The proliferation of online communication platforms, news portals, and heterogeneous data formats has substantially increased big data volumes, leading to large-scale collections of unstructured data. This study aims to analyze dominant public policy–related topics concerning the Prabowo–Gibran administration by applying topic modeling techniques to national online news media. Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) were employed as unsupervised learning approaches to extract latent semantic structure from a corpus of 200 credible news articles collected through URL fetching using Python 3. Data preprocessing included text cleaning, tokenization, bigram and trigram construction, and the development of a dictionary and corpus. Model performance was evaluated using topic coherence metrics, yielding scores of 0.3709 for LDA and 0.68 for NMF. To examine temporal dynamics, the dataset was divided based on the official inauguration date of the president and vice president, enabling a comparative analysis of dominant topics before and after the inauguration. Topic similarity across both periods was measured using cosine similarity, with the highest similarity score of 0.663 observed between Topic 4 in the pre-inauguration period and Topic 1 in the post-inauguration period. The findings provide insights into evolving media discourse and policy-related topic trends across the two periods, demonstrating the potentials of topic modeling in analyzing large-scale unstructured news data for diverse purposes to bridge computational science and empirical evidence of social science.
Autorska prava (c) 2025 Journal of Soft Computing Exploration https://creativecommons.org/licenses/by-sa/4.0
Web based IoT monitoring system for ultrasonic water flow measurement using ESP32-S3 and cloud database
Efficient water management is crucial for ensuring sustainable resource utilization and reducing water losses in both industrial and domestic applications. This study presents the design and implementation of a smart water monitoring system based on an ultrasonic flow meter, which enables accurate, real-time measurement of water flow without physical contact with the medium. The proposed system integrates ultrasonic sensors with a microcontroller-based data acquisition unit and wireless communication to transmit flow rate, volume, and consumption data to a cloud-based monitoring platform. The system was tested in various flow conditions to evaluate accuracy, stability, and energy efficiency. Experimental results demonstrate that the ultrasonic flow meter achieved a measurement accuracy of ±1% compared to a reference turbine flow meter, while maintaining minimal power consumption. Furthermore, the integration of Internet of Things (IoT) capabilities allows remote monitoring, anomaly detection, and data logging for long-term analysis. The results indicate that this ultrasonic-based monitoring system provides a reliable and non-invasive solution for smart water management, offering potential applications in household metering, agricultural irrigation, and industrial fluid monitoring.
Applying the design thinking methodology in ui/ux development: A case study of justeatss.id e-commerce platform
This study demonstrates the effectiveness of the Design Thinking approach in developing the UI and UX of the Justeatss.id e-commerce website. The process followed five stages including empathizing, defining, ideate, prototype, and test which are focusing on user needs as the foundation for design. Qualitative data from interviews and usability testing identified key issues such as difficult navigation, unappealing visual design, and checkout obstacles. High-fidelity prototypes were developed to create an intuitive, engaging, and responsive interface. Evaluation using the System Usability Scale (SUS), User Acceptance Testing (UAT), and the Customer Satisfaction Index (CSI) showed significant improvements in usability, functionality, and overall user satisfaction, with the website receiving an "Excellent" SUS score, a perfect UAT score, and 100% CSI rating. These results highlight that a user-centered Design Thinking approach can effectively optimize aesthetics, functionality, and emotional satisfaction, providing practical insights for enhancing e-commerce platform performance.
Design and implementation of an IoT-based real-time water level monitoring system of belik river at yogyakarta city
Flooding is a serious threat in the Belik River area, Yogyakarta, especially during high rainfall and blocked water flow, while the existing Early Warning System (EWS) managed by BPBD Yogyakarta is still not functioning optimally. This study aims to design and implement a real-time water level monitoring system that can be used as an early warning system for flooding. The research method uses a Waterfall approach that includes the stages of requirements, design, implementation, testing, and operation and maintenance. The developed system is based on the Internet of Things (IoT) using HC-SR04 ultrasonic sensors and NodeMCU ESP32 microcontrollers connected to Firebase for data storage, visualized through a website, and equipped with automatic notifications via the Telegram application. The system was implemented at two points on the Belik River, Yogyakarta, with water level data recorded every minute for 3 hours over three days. The data obtained was pre-processed by averaging the sensor readings and ignoring abnormal data to improve stability. Evaluations were carried out on sensor accuracy, data transmission stability, real-time display on the website, and Telegram notification speed. The results of the study showed an average measurement error of 1.27%, with a tendency for the error to increase at distances greater than 300 cm. This system has proven capable of providing rapid information to the Regional Disaster Management Agency (BPBD) and the surrounding community to take early anticipatory measures against potential flooding, thereby helping to reduce the impact of losses and speed up emergency response.
Information system of horticultural seed ordering at republik bibit nursery using laravel
The integration of information technology plays a vital role in improving organizational performance and supporting sustainable agricultural practices through efficient seed management systems. The existing manual seed ordering and purchasing process at Republik Bibit Nursery has led to several inefficiencies, including inaccurate stock data, transaction errors, and difficulties in estimating seed requirements. This study aims to develop an Information System of Horticultural Seed Ordering at Republik Bibit Nursery using the Laravel framework to digitalize the ordering process and enhance operational efficiency. The primary objective is to streamline order management, enable real-time stock monitoring, track seedling growth, and assist customers in estimating seed quantities based on land size. The novelty of this research lies in the integration of e-commerce functionality with agricultural decision-support features such as growth tracking and stock forecasting—an advancement beyond conventional systems that focus solely on sales transactions. The system was developed using the Waterfall method, encompassing six stages: requirement analysis, design, implementation, testing, deployment, and maintenance. Data collection was conducted through observation, interviews, and literature review to ensure comprehensive system requirements. The implementation resulted in a fully functional web-based application featuring modules for user management, product management, transactions, and reporting. Through Black Box testing, all functional scenarios for different user roles—admin, owner, employee, and customer—achieved a 100% success rate, confirming the reliability of the system. The results demonstrate that this information system effectively minimizes transaction errors, reduces seed stock shortages, and enhances decision-making accuracy for both administrators and customers. In conclusion, the developed system provides an efficient, scalable, and sustainable digital solution that supports the modernization of horticultural nurseries and contributes to the broader digital transformation of the agribusiness sector.
Implementation of digital human avatar virtual assistant with augmented generation retrieval technology in interactive systems for nutrition education
In today's digital era, artificial intelligence (AI) based chatbots utilizing Large Language Models (LLM) have become a promising innovation for nutrition education. The integration of Natural Language Processing (NLP) technology with digital animation systems creates new opportunities in developing interactive applications in the context of Indonesian public health, with nutritional challenges in Indonesia showing 21.5% of toddlers experience stunting and 12.2% of adults face obesity, indicating an urgent need for accessible and comprehensive nutrition education. This research aims to develop the GiziAI website that integrates Retrieval Augmented Generation (RAG) technology with digital human avatars to provide nutrition education to Indonesian society. The research method implementing the Nusantara 2.7B Indo Chat large language model, ChromaDB as vector database, Three.js for 3D rendering, ElevenLabs for text-to-speech, and Rhubarb for lip synchronization, with React JS, Flask, MySQL, and LangChain frameworks. Evaluation was conducted using LangSmith to measure model response time, BERTScore to measure answer accuracy, and black box testing for website functionality. Research results show that the RAG system significantly improves model performance with precision increase of 71.5%, recall 60.6%, and F1-score 65.8%, while GPU usage accelerates response by 13.5% compared to CPU. Black box testing shows all website features function as expected.
Development of a low-cost NTP stratum 2 time synchronization system with hybrid RTC/NTP failover for remote Indonesian mosques
Conventional time synchronization solutions are often cost-prohibitive and infrastructure-dependent, making them unsuitable for remote regions. This study develops a low-cost, Raspberry Pi 3-based NTP Stratum 2 system for areas with unstable cellular networks. It integrates NTP.bmkg.go.id as its primary source and a DS3231 RTC module for backup during outages. Testing covered: (1) Time Accuracy (0.12s average offset in stable 4G; 0.85s/hour drift in RTC mode), (2) Failover & Recovery (3.1s transition to RTC), (3) Load & Stability (15 NTP clients with 12.1ms latency), and (4) Power Efficiency (2.8W online; 1.2W offline). Results confirm the system’s reliability in maintaining sub-second accuracy amid network instability while being highly energy-efficient. The study offers recommendations for active cooling, GPS-assisted RTC calibration, and solar-powered integration to improve scalability in rural applications. The novelty of this work lies in its pragmatic hybrid architecture, combining affordable software-defined NTP with hardware-based RTC failover, tailored for infrastructural constraints. Its contribution is a validated, replicable model that delivers reliable time synchronization at a fraction of commercial costs, addressing critical gaps in affordable timekeeping for infrastructure-limited regions.
Fuzzy logic and application as a game-based fire disaster mitigation simulation
Fire is a disaster that can occur at any time and place, particularly in high-rise buildings with a high level of risk. This study developed an educational game called "Fire Escape", built using Unity 3D and integrated with Mamdani fuzzy logic to provide an interactive learning experience about fire evacuation procedures. This study adopted a Research and Development (R&D) approach utilizing the ADDIE model comprising the stages of Analysis, Design, Development, Implementation, and Evaluation.The game applies a fuzzy logic system to generate action recommendations based on player parameters such as health points (HP), time, and distance. The development results show that the game successfully integrates educational and entertainment aspects through realistic 3D simulation, intuitive controls, and tiered learning scenarios. The implementation of Mamdani fuzzy logic enables adaptive decision-making, enhancing the player’s learning experience. This game can be an effective alternative learning medium to improve public understanding and preparedness for fire emergencies in high-rise buildings.
Decision support system for selecting outstanding students using simple addictive weighting (SAW) and rank order centroid (ROC) methods
Selecting outstanding students is essential in fostering appreciation and motivation within the school environment. Nevertheless, many educational institutions continue to use manual assessment methods, which are often subjective and inefficient. This research focuses on the development of a web-based decision support system designed to assist in the selection process at a in Indonesia. The system integrates the Simple Additive Weighting (SAW) technique to generate student rankings based on preference scores, while the Rank Order Centroid (ROC) method is applied to assign weight values to the evaluation criteria, including academic performance, attendance, behavior, and extracurricular involvement. Data for this study were collected through interviews, direct observation, and student records. The application was developed using PHP for the backend, MySQL for database handling, and Bootstrap for the user interface design. The system’s functionality was verified using black box testing, which confirmed that all features operated correctly. Additionally, the system was evaluated against the manual selection process conducted by the school, and the results showed an accuracy level of 80% in matching student rankings. This system proves to be a practical and structured solution for enhancing the transparency and objectivity of student achievement evaluations.
Corn sales analysis using linear regression and svm methods to improve production planning
This research aimed to analyze and predict corn sales at UD Muara Kasih to improve production planning accuracy. The study used historical corn sales data collected over a specific period, covering 42 data entries from January 2021 to December 2024. The dataset included variables such as sales date, quantity sold, selling price per ton, total sales value, weather conditions, market demand (in tons), and the number of laborers. Prior to model training, the data underwent comprehensive preprocessing involving data cleaning, feature extraction, and normalization to ensure its quality and readiness for analysis. Two predictive models were applied: Linear Regression and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Simulation data for 2024 and 2025 were generated based on the monthly averages derived from the historical dataset. The results showed that the Linear Regression model produced more stable predictions with a lower Root Mean Squared Error (RMSE) of 255.84 compared to the SVM model’s RMSE of 256.42. While the SVM model showed greater responsiveness to seasonal variations, the Linear Regression model was identified as the most suitable for the given dataset. The predictive models developed in this study are expected to support UD Muara Kasih in making more accurate and informed production decisions in the future.
Design and construction of gym angkasa management website system in margoyoso village to improve customer service
This study aims to design and implement a website-based gym management system at Angkasa Gym located in Margoyoso Village to improve customer service and operational efficiency. The current system is still manual, resulting in inefficiency and risk of errors in managing member registration, scheduling training sessions, and financial transactions. The proposed system is developed using the Waterfall model with stages of needs analysis, system design, implementation, testing, and maintenance. The Laravel framework was chosen because it supports the development of modern and secure web applications. The final system has key features such as user registration and login, trainer management, transaction processing, and review submission. Based on black box testing, all system functionality runs as expected. The results of the study indicate that the web-based system is able to significantly improve gym operational efficiency and improve user service experience. This study can be the basis for further development, such as automatic notifications, implemented in other gyms, and adaptation to the mobile application version.
Tourism digital innovation geographic information system based web application for spatial information of tourism destinations
This study aims to develop a web-based tourism application by utilizing Geographic Information System (GIS) to optimize the dissemination of tourism information in the Pati Karisidenan area. This application is designed to assist local communities and tourists in choosing tourist destinations by providing information such as location, description, route, facilities, and visitor reviews. In addition, the app is also equipped with search and filtering features to help users find tourist attractions based on category, distance, or available facilities. This application was developed using the PHP programming language and MySQL database. The development process adopts the Waterfall method, which consists of the requirements analysis stage, system design using ERD and DFD, implementation, testing, and maintenance. Application testing is carried out using the Black Box Testing method to ensure all functions run according to specifications. The test results from visitors showed satisfaction with the use of the application by 97% of 15 audiences. This application is expected to support the promotion, accessibility, and management of tourism potential digitally by local governments.
Online reservation system development and digital payment integration in car wash business: Case study of car wash sniper
This study aims to develop a website-based online reservation system at Car Wash Sniper with digital payment integration to improve operational efficiency and customer convenience. The system design method uses a waterfall model approach that includes needs analysis, system design, implementation, testing, and maintenance. Black box testing is also carried out to test the suitability of the system with the design that has been developed. To support the payment process, this system is integrated with Midtrans as a payment gateway that provides various payment options such as bank transfers, e-wallets, and credit cards safely and in real-time. The results of the study show that this system is able to optimize the reservation process, reduce queues, and increase customer satisfaction. The novelty of this research by developing a system that is integrated with digital payments, making transactions more practical, efficient, and transparent. This system can be an innovative solution for business actors in the car wash industry to improve efficiency and service quality.
Design and construction of website-based e-commerce applications for selling food products in the semarang region with payment gateway integration
Digital transformation has driven a shift in transactions from traditional systems to e-commerce platforms, which have become the cornerstone of the digital economy. However, existing e-commerce platforms often fail to fully meet the needs of SMEs, particularly regarding flexible withdrawal of sales proceeds and consumer education about products. This study aims to design a website-based e-commerce application with the main features of a flexible fund withdrawal system and product education. This system is developed using the Waterfall System Development Life Cycle (SDLC) model. The implementation of the application creation uses the Laravel framework integrated with the Midtrans API for secure and flexible payment management. This application is equipped with various features, such as product catalogs, shopping carts, order tracking, and reviews, with functional testing carried out through black-box testing methods to ensure the application meets user needs. The results of the study show that web-based e-commerce applications are able to support flexible transactions, expand market reach, and strengthen the local e-commerce ecosystem. From this study, it can be concluded that website design plays an important role in answering challenges, especially increasing the security and convenience of direct fund withdrawal transactions for SMEs.
Digital image based IoT intelligent fire detection with telegram notification
Due to inadequate handling, fire disasters often result in significant losses and even loss of life. A fire detection system is essential, especially in places prone to fire. In this study, a digital image-based IoT system was built using the YOLO (You Only Look Once) algorithm to detect and provide fire warnings quickly and accurately. This research was conducted to develop a fire detection system from existing research on IoT devices by combining it with digital image processing technology with the YOLOv8 algorithm, as well as integrating the IoT system into the Telegram instant messaging application. This study also combines a fire detection system with a fire sensor, MQ-2 temperature sensor, and MQ-2 smoke sensor. The study results show that the YOLOv8 nano model with ESP32-CAM can detect small flames from candles up to a distance of 220 cm. The ESP32 fire sensor can detect small flames up to a distance of 90 cm and large flames up to a distance of 140 cm. VPS can be sent to the Telegram application, just as the LM35 temperature sensor detects temperatures above 50ºC and the MQ-2 smoke sensor detects smoke levels above 450 ppm. All data obtained can be displayed on the VPS dashboard and the Telegram application.
Sound detection of gamelan musical instruments using teachable machine
Gamelan is an instrument of musical expression that has an aesthetic function related to social, moral, and spiritual values. Gamelan consists of a variety of musical instruments that have a unique sound. In this study, the sound detection of nine gamelan musical instruments was carried out using a teachable machine. The gamelan musical instruments detected included gong, kenong, saron, bonang, gambang, kendang, flute, siter, and rebab. The algorithm used is CNN. The CNN algorithm has a fairly good performance for the sound detection process. The test results of the built model show an "acc" value of 25 ranging from 0.99 to 1, which indicates that the model achieves an accuracy rate of 99% to 100% on the training dataset. At the same time, "test accuracy" refers to a measure of the model's effectiveness in predicting data it has not encountered during training. The "test accuracy" score varied from 0. 83, which shows that the validation data has an accuracy of 83%.
Comparison of clustering analysis of K-means, K-medoids, and fuzzy C-means methods: case study of school accreditation in west java
This research aims to analyze school accreditation data in West Java using clustering methods: K-Means, K-Medoids, and Fuzzy C-Means, to identify patterns and groups of schools based on similar characteristics. K-Means, known for its simplicity, suggests an optimal two-cluster solution based on silhouette values but employs three clusters for detailed analysis. K-Medoids, noted for its robustness against outliers, achieves the best clustering with a lowest Davies-Bouldin Index (DBI) of 0.8 and the highest Silhouette Information (SI) value of 0.46. Fuzzy C-Means, which assigns membership degrees to each data point across clusters, performs reasonably well with a DBI of 0.87 and an SI value of 0.40, while K-Means shows the highest DBI of 0.9 and the lowest SI value of 0.39. The findings highlight K-Medoids as the superior method for clustering. Regions with lower educational quality, such as Bekasi and Cianjur regions, require priority interventions, whereas areas with better quality, like Bandung and Bekasi regions, can serve as models. Data-driven approaches, inter-regional collaboration, and continuous monitoring and evaluation are recommended to optimize educational policies and enhance overall educational quality in West Java.
Melanoma detection on skin images using deep learning based on convolutional neural network (CNN)
Melanoma is a life-threatening skin cancer that poses challenges in regions with limited access to specialized medical personnel, such as Papua, Indonesia. Early diagnosis is essential, but accurate detection is hindered by the scarcity of dermatologists. This study develops a melanoma detection system using computer vision, utilizing the VGG16 architecture enhanced with the Convolutional Block Attention Module (CBAM) and fine-tuning via transfer learning. The model was trained on a dataset comprising melanoma and non-melanoma images, with data augmentation to address class imbalance. The model achieved an accuracy of 91.25%, precision of 92.31%, recall of 90%, and an F1-score of 91.13%, demonstrating reliable performance in melanoma classification. High specificity (92.5%) indicates a low false positive rate, while sensitivity (90%) shows effective melanoma detection, though the 10% false negative rate requires improvement. Future enhancements include increasing sensitivity through weighted loss functions, optimizing classification thresholds, and performing external validation. Additionally, Grad-CAM is used for interpretability, and a web-based application is proposed to support healthcare practitioners, offering an accessible diagnostic tool for melanoma screening in resource-limited settings.
Optimizing Seq2Seq LSTM for Regional-to-National language translation on a web platform
Machine translation for low-resource languages remains a significant challenge due to the lack of parallel corpora and optimized model configurations. This study developed and optimized a Seq2Seq Long Short-Term Memory (LSTM) model for Tegalan-to-Indonesian translation. A manually curated parallel corpus was constructed to train and evaluate the model. Various hyperparameter configurations were systematically tested, with the best-performing model achieving a BLEU score of 11.7381 using a dropout rate of 0.5, batch size of 64, learning rate of 0.01, and 70 training epochs. The results demonstrated that higher dropout rates, smaller batch sizes, and longer training durations enhanced model generalization and translation accuracy. The optimized model was deployed into a web-based application using Streamlit, ensuring accessibility for real-time translation. The findings highlighted the importance of hyperparameter tuning in neural machine translation for low-resource languages. Future research should explore Transformer-based architectures, larger datasets, and reinforcement learning techniques to further enhance translation quality and generalization.
Identification of lung cancer using gray level co-occurrence matrix (GLCM) and artificial neural network with backpropagation algorithm
Air pollution is a problem that occurs in various countries, including Indonesia. One of the consequences of poor air quality due to air pollution is health problems in the lungs, one of which is lung cancer. According to WHO data, lung cancer caused 1.80 million deaths in 2020. This is due to limited services to identify lung cancer early, resulting in delays in treatment. This study aims to identify lung cancer using CT-Scan image processing. The identification method uses a Backpropagation Artificial Neural Network (ANN BP) with Gray Level Co-occurrence Matrix (GLCM) feature extraction. Preprocessing is carried out to improve image quality by removing noise using a median filter. Segmentation of preprocessing results using Otsu threshold. Texture features from segmentation can be calculated from the resulting GLCM, such as Angular Second Moment (ASM)/energy, contrast, correlation, Inverse Different Moment (IDM)/homogeneity, and entropy. These values ​​are obtained from angles of 0°, 45°, 90°, and 135°, and a distance between pixels of 2 pixels. Identification using ANN with Backpropagation algorithm. This study used images of normal lungs and lung cancer with 160 training data images and 40 test data images. The best test results were obtained with the best accuracy level of 92.5%.
Improved human image density detection with comparison of YOLOv8 depth level architecture and drop-out implementation
Energy inefficiency due to Air Conditioners (AC) running in empty rooms contribute to unnecessary energy consumption and increased CO₂ emissions. This study explores how different depth levels of the YOLOv8 architecture and dropout regularization can enhance human density detection for smarter AC control systems. By evaluating model accuracy through Mean Average Precision (mAP50-95), we provide quantitative insights into how these modifications improve detection performance. Our dataset consists of 1363 images taken in an office environment at ITERA under varying lighting conditions and different human presence densities. The results show that the YOLOv8m model performs best, achieving an mAP50-95 score of 0.814 in training and 0.813 in validation, outperforming other YOLOv8 variants. Furthermore, applying dropout regularization improves model generalization, increasing mAP50-95 from 0.552 to 0.6 and effectively reducing overfitting. This study highlights the balance between architectural depth and dropout regularization in YOLOv8, demonstrating its effectiveness in energy-efficient smart buildings. The findings support the potential of deep learning-based human density detection in improving energy conservation strategies, making it a valuable solution for intelligent automation systems.
Design and implementation of a web-based thesis guidance system using the waterfall method
The advancement of technology and the internet has opened up opportunities for the development of information systems in higher education in Indonesia. One significant system is the online thesis guidance system for students. Currently, the guidance process at ISI Surakarta is conducted manually and face-to-face, which has proven to be less effective due to scheduling conflicts between students and lecturers, leading to delays in thesis progress. The development of the online thesis guidance system aims to facilitate interaction between lecturers and students while digitally storing review histories and revisions, enhancing the efficiency of administrative data collection. Additionally, the system is designed to provide announcements and timelines from proposal submission to eligibility examinations, helping students monitor their performance to graduate on time. The Waterfall method is employed in this system's design to ensure a systematic development process, covering all stages from requirements analysis to maintenance. This method allows early detection of potential conditions and ensures that user needs are met before the system is implemented. The implementation of this system is expected to improve the efficiency and quality of academic services, support students in completing their studies on time, and provide benefits to the entire academic community of ISI Surakarta.
Implementation of internet of things for leakage current monitoring system in medical equipment
The rise in electricity consumption, especially in the health sector, has heightened concerns about electrical safety, particularly leakage current in medical equipment. The main objective of this research is to develop an IoT-based leakage current monitoring system specifically designed for low-voltage medical devices, aiming to enhance safety and prevent electrical hazards such as electric shocks and equipment damage. The system used two current sensors module (PZEMT-004T) to measure leakage at points near the voltage source and medical components. Data were processed by a microcontroller and transmitted to a web server for real-time monitoring via mobile devices. Testing on humidifiers and ECGs showed average accuracies of 90.11% and 92.49%, respectively, within a 10 mA range. However, the system could not detect currents below the 3 mA safety threshold because of the sensors reading limitation at 10 mA, indicating a need for sensor improvements. The IoT-based system enhances medical equipment safety, with future work focusing on better sensors and AI for predictive maintenance.
An advanced logistic regression model for forecasting payer revenue in private hospitals: a case study in manado
Manado, the provincial capital, stands as a vital center for healthcare services, where private hospitals compete intensively to attract patients from various economic and social backgrounds. Accurate revenue forecasting for partnered payers is essential for effective management strategies. This study employs a logistic regression model, achieving a notable accuracy of 79.55% in predicting hospital revenue based on payer partnerships. The confusion matrix reveals 21 true negatives (TN), confirming the model accurately identified low-revenue customers, with zero false positives (FP), indicating no misclassification of these individuals. However, 9 false negatives (FN) highlight a critical risk, as high-revenue customers were miscategorized as low revenue, even though 14 true positives (TP) were precisely identified. Based on these insights, hospitals can strategically target 61 payers projected to exceed median revenue, presenting a significant opportunity for income growth. Conversely, the 159 payers identified as below median revenue warrant urgent attention. To enhance engagement and increase revenue from these lower-revenue groups, targeted business strategies such as intensified marketing, personalized service offerings, and promotional discounts are recommended. This research contributes a novel approach to leveraging predictive analytics in healthcare, underscoring the pressing need for hospitals to innovate their operational strategies to optimize revenue in a competitive landscape.
Rainfall forecasting using triple exponential smoothing for rice cultivation in lamongan, jawa timur
Rice cultivation is a major agricultural activity that is heavily influenced by weather conditions. Extreme weather events, such as heavy rainfall, can cause farmers' productivity to decline. Rainfall forecasts are important for farmers to help them make the right decisions in managing their farming businesses. This research aims to predict rainfall in Lamongan Regency, East Java province, and provide valuable information to rice farmers to plan the optimal planting season. The method used in this study is Triple Exponential Smoothing (TES), an effective forecasting technique for processing time series data with seasonal patterns. Monthly rainfall data for the last five years formed the basis of the forecast, with data sourced from NASA's Power Data Access Viewer. The analysis results include a Mean Absolute Percentage Error (MAPE) value of 97.559% for rainfall. This rainfall forecast can assist farmers in increasing rice productivity and minimizing the risk of crop failure due to unpredictable weather conditions. With the rainfall weather forecast, farmers are expected to know the suitable months for rice cultivation so that productivity increases
Grape leaf disease classification using efficientnet feature extraction and catboostclassifier
Grapes are one of the most extensively cultivated crops worldwide due to their significant economic importance. However, the productivity of grape crops is often threatened by diseases caused by bacterial, fungal, or viral infections. Traditionally, the detection of infected grape leaves has been conducted through manual visual inspections, a method that is both time-consuming and prone to biases. Recent studies have leveraged transfer learning models to classify grape leaf diseases with high accuracy. Despite this progress, there is a notable gap in research exploring the integration of transfer learning for feature extraction and machine learning for feature classification in detecting grape leaf diseases. This study introduces a novel approach that combines transfer learning using EfficientNetB0 for feature extraction with a machine learning model, specifically Categorical Boosting (CatBoost), for feature classification. The proposed model demonstrates outstanding performance, achieving an accuracy of 99.56% on the test dataset, surpassing traditional transfer learning methods reported in previous studies.