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Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

This study applies the Vision Transformer (ViT) method to soil-type classification and evaluates its accuracy using digital images. The Vision Transformer (ViT) is a Deep Learning architecture that uses self-attention to extract global features from images, enabling it to recognize texture and color patterns more comprehensively than other convolutional methods. The dataset used consists of eight soil types, each containing 77 image data in “.jpg” format. Each image was processed and augmented to increase the dataset to 700 images per soil type. This was done to prevent overfitting. The ViT model was trained using a 90%/10 % split of the training and validation datasets. The results show that the Vision Transformer (ViT) architecture achieves an accuracy of 71.4%, with a small difference between training and validation accuracy, indicating that the ViT method generalizes well. This method successfully distinguishes soil types such as alluvial, andosol, laterite, and limestone based on subtle visual differences. These results demonstrate that the Vision Transformer (ViT) is effective for soil type classification and has the potential to serve as a basis for developing a digital image-based soil type classification system to support research and precision agriculture.

Popularitas platform gim Roblox di Indonesia tercermin dari tingginya volume interaksi pengguna di media sosial TikTok. Namun, data komentar yang masif tersebut bersifat tidak terstruktur dan banyak menggunakan bahasa tidak baku (slang), sehingga menyulitkan pengembang untuk mengidentifikasi preferensi pemain secara spesifik terhadap genre tertentu. Penelitian ini bertujuan untuk menganalisis sentimen pengguna dan melakukan studi komparatif preferensi terhadap tiga kategori gim utama dalam ekosistem Roblox, yaitu Roleplay, RPG, dan Horror. Metodologi penelitian dimulai dengan pengumpulan 3.000 data komentar melalui teknik scraping pada video TikTok yang diunggah periode Juni hingga Desember 2025. Data tersebut melalui tahapan pra-pemrosesan yang komprehensif, meliputi pembersihan data, normalisasi teks berbasis kamus slang, dan ekstraksi fitur menggunakan TF-IDF. Klasifikasi dilakukan menggunakan algoritma Multinomial Naïve Bayes dengan penerapan teknik oversampling untuk menangani ketidakseimbangan kelas data. Hasil pengujian model menunjukkan performa yang baik dengan tingkat akurasi global sebesar 77,78% dan nilai Presisi pada kelas Negatif mencapai 91%. Temuan penelitian mengungkapkan bahwa distribusi sentimen global didominasi oleh sentimen Netral (40%) dan Negatif (33%), sedangkan sentimen Positif hanya 26%. Analisis komparatif menunjukkan bahwa genre RPG memiliki rasio sentimen negatif tertinggi akibat keluhan teknis seperti lag, sedangkan genre Horror mencatat sentimen positif yang signifikan di mana respon emosional "takut" diinterpretasikan sebagai kepuasan. Kesimpulannya, stabilitas teknis dan pengalaman emosional menjadi faktor determinan utama dalam preferensi pemain.

Autonomous decision-making systems increasingly rely on complex artificial intelligence models to operate in dynamic and safety-critical environments. While these models provide strong predictive capabilities, their black-box nature limits transparency, trust, and accountability. This study proposes a structured research methodology for integrating Explainable Artificial Intelligence (XAI) into autonomous decision-making systems. The research adopts a conceptual–analytical approach to develop an explainability-oriented framework that embeds transparency across perception, decision-making, and action execution stages. The methodology includes literature-driven problem identification, conceptual framework construction, classification and mapping of XAI methods, and formulation of explainability evaluation criteria. The results demonstrate that effective explainability in autonomous systems requires a hybrid integration strategy, combining in-model transparency with post-hoc explanation mechanisms. A structured mapping of XAI techniques to autonomous system components and a conceptual decision-flow diagram are presented to illustrate explainability integration. The findings highlight that layered and context-aware explainability enhances system interpretability, supports human oversight, and improves safety relevance without compromising autonomous operation. This study contributes a reusable methodological foundation for the design and evaluation of explainable autonomous systems, offering practical guidance for future empirical validation and real-world deployment in safety-critical applications.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

Limited legal literacy among village officials may contribute to disorderly village administration, inconsistent document formats, weak record-keeping, and poor archive traceability, ultimately affecting public service quality. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aimed to strengthen the legal literacy of village officials in Larangan Luar Village, Pamekasan Regency, to improve orderly and accountable village administrative governance. The program applied a hands-on training model combined with mentoring/document clinics and a pretest–posttest evaluation involving village officials (n = 18). Implementation consisted of three core training sessions and two mentoring sessions over four weeks, focusing on document legality, official correspondence standards, numbering and registers, and basic archive management. Results showed an increase in the average legal literacy score from 54.1 (pretest) to 74.6 (posttest), an improvement of 20.5 points. Beyond knowledge gains, document quality and administrative order improved through the adoption of practical administrative tools developed during the program. Key outputs included a concise module (±22 pages), 10 village administrative document templates, one village administrative SOP, and a simple filing system based on document classification and folder numbering. In conclusion, strengthening village officials’ legal literacy through practice-based training and mentoring effectively supports improvements in village administrative governance and is potentially replicable in other villages in Pamekasan with context-specific adjustments.

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

This study aims to develop an effective predictive model for identifying students at risk of academic dropout using the Decision Tree and Linear Regression algorithms. The data used are sourced from the public Kaggle dataset Students Dropout and Academic Success, which includes demographic, socioeconomic, and academic performance variables for each semester. The research method includes data preprocessing stages, such as data cleaning, label encoding for categorical variables, numeric feature normalization, and target class adjustment to focus on binary classification, namely Dropout and Graduate. The modeling process is carried out by comparing the performance of the two algorithms using evaluation metrics of accuracy, precision, and recall. The results show that the Decision Tree algorithm has superior performance compared to Linear Regression in mapping non-linear patterns in student data. Feature importance analysis revealed that the number of curricular units in the second semester and tuition payment status are the main predictors of dropout risk. These findings are expected to assist educational institutions in implementing early interventions to improve student academic success.  

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

This study aims to develop an educational application based on a Graphical User Interface (GUI) using MATLAB App Designer that functions as an interactive simulation for evaluating blood pressure. The application allows users to input systolic and diastolic blood pressure values along with supporting information such as name and age. The input data are then analyzed and classified into several blood pressure categories according to the standards of the American Heart Association (AHA), including normal, hypotension, stage 1 hypertension, stage 2 hypertension, and hypertensive crisis. The classification results are presented visually through an interactive pie chart with dynamic percentages and legends to enhance user understanding. In addition, all data are automatically stored in a Microsoft Excel file containing a summary of blood pressure categories and session timestamps. The system is designed with a simple interface and intuitive interaction, making it suitable for early health education purposes. Although the application still relies on manual data input, it has the potential to serve as an effective learning tool for increasing public awareness of the importance of regular blood pressure monitoring. 

This study aimed to investigate the implementation of the BERDES (Bersih Desa) Program as an innovative strategy to foster environmental awareness among elementary school students at SD Negeri 2 Demulih. The program was designed using a participatory approach based on the Participatory Action Learning Sistem (PALS) method, which engaged students as active participants in identifying problems, planning, executing, and evaluating real actions in their school and local community environments. The research found that active student involvement in cleaning activities, waste management socialisation, and collective reflection significantly enhanced positive attitudes and social responsibility towards environmental conservation from an early age. Despite challenges such as limited frequency of program activities and common misconceptions about waste classification (organic, inorganic, residual), the BERDES program successfully served as an effective educational tool that embedded environmental care values among rural youth. The findings emphasised the critical role of schools as centres for environmental character education that combine theoretical knowledge with practical engagement to address real environmental issues.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

Bintan Island has abundant bauxite soil resources; however, its utilization as road construction material remains limited. The scarcity of high-quality granular material in the region necessitates the use of available local resources, particularly for pavement subgrade layers. This article aims to analyze the classification and mechanical properties of native soils in Bintan Island through a systematic literature review. The reviewed literature includes laboratory test results of bauxite soil. The findings indicate that bauxite soil exhibits low plasticity, relatively high CBR values (±35%), and is classified as CL (USCS) and A-2-4 (AASHTO). These results suggest that bauxite soil is suitable for subgrade applications, although require stabilization with binding agents. The implication of this review highlights that the utilization of local materials could support sustainable infrastructure development in island regions by reducing dependency on imported materials.  

Sektor bisnis kuliner terus berkembang pesat, menciptakan kebutuhan yang kuat akan pengambilan keputusan berbasis data untuk mendukung efisiensi operasional. Penelitian ini bertujuan untuk mengklasifikasikan kinerja penjualan item menu di Warung Makan Lalapan Haris dengan menerapkan algoritma pohon keputusan C4.5 dan metodologi KDD. Sebanyak 500 data record yang berisi atribut seperti jenis menu, jumlah pelanggan, jumlah barang terjual, dan status penjualan diproses melalui beberapa tahap, meliputi pemilihan data, praproses, transformasi, penggalian data, dan evaluasi. Model pohon keputusan dibangun menggunakan RapidMiner 2026.0.1 dengan pembagian data 70% untuk pelatihan dan 30% untuk pengujian. Hasil penelitian menunjukkan bahwa algoritma C4.5 berhasil membentuk struktur klasifikasi yang mengkategorikan item menu ke dalam kelompok Best Seller, Medium Seller, dan Low Seller. Ayam Goreng secara konsisten diidentifikasi sebagai Best Seller, Ayam Bakar sebagai Medium Seller, sementara Lele Goreng dan Lele Bakar diklasifikasikan sebagai item Low Seller dengan pola pembagian yang lebih kompleks yang terutama dipengaruhi oleh jumlah pelanggan. Hasil evaluasi menunjukkan akurasi 84%, dengan presisi dan recall sempurna untuk Ayam Goreng dan Ayam Bakar, sementara performa untuk Lele Goreng dan Lele Bakar bervariasi. Temuan ini menunjukkan bahwa algoritma pohon keputusan C4.5 efektif untuk menganalisis pola penjualan dan dapat membantu pemilik bisnis dalam merencanakan inventaris dan mengoptimalkan strategi manajemen menu.

Aritmia merupakan gangguan irama jantung yang umum terjadi pada lansia dan dapat menimbulkan risiko kesehatan serius jika tidak terdeteksi secara dini. Penelitian yang dilakukan bertujuan untuk mengidentifikasi aritmia pada lansia menggunakan algortima K- Nearest Neighbor (KNN) berdasarkan data elektrokardiogram (EKG). Data yang digunakan berjumlah 105 data EKG lansia yang diperoleh dalam format CSV. Proses awal melibatkan pembersihan dan normalisasi data menggunakan metode StandardScaler, serta pelabelan awal menggunakan algoritma K-Means Clustering untuk mengelompokkan data ke dalam dua kelas: Normal dan Sangat Berpotensi Aritmia. Data kemudian dibagi menjadi 70% data latih dan 30% data uji dengan metode stratified split untuk menjaga proporsi label. Model KNN dilatih dengan parameter k = 3, dan dievaluasi menggunakan confusion matrix serta classification report. Hasil pengujian menunjukkan akurasi model sebesar 97% dengan nilai precision dan recall yang tinggi pada kedua kelas. Hasil ini menunjukkan bahwa algoritma KNN efektif dalam mengklasifikasikan kondisi aritmia pada lansia dan memiliki potensi untuk diterapkan dalam sistem pendukung diagnosis berbasis data EKG.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.