Abstract:Food security in the low-carbon transition is increasingly shaped by industrial decarbonization, environmental restoration, and circular supply-chain coordination, yet these domains are often modeled as separate sustainability…
bility agendas. This study addresses the gap by developing a governance-centered structural equation model that links sustainable industrialization, sustainable remediation, circular supply chain governance, and food-security outcomes. Using the available 455-response dataset, the empirical test operationalizes industrialization intensity (IND) as a proxy for sustainable industrialization, digital-institutional capability (DIC) as a proxy for circular governance capability, and social-economic resilience (SER) as a proxy for food-system security. Confirmatory factor analysis supports the measurement model: standardized loadings range from .778 to .849 for IND, .804 to .835 for DIC, and .800 to .843 for SER; CR values range from .901 to .914; AVE ranges from .646 to .681; and model fit is acceptable (chi-square/df = 2.108, CFI = .961, TLI = .951, RMSEA = .049, SRMR = .038). The SEM results show that IND significantly predicts DIC (beta = .537, p < .001), DIC predicts SER (beta = .424, p < .001), and IND retains a direct effect on SER (beta = .337, p < .001). The indirect effect is significant (beta = .228, 95% CI [.178, .282]). The article contributes a cautious, data-grounded framework for analyzing food security as a governance-mediated outcome of low-carbon industrial transition.
Abstract:The increased integration of operational technology (OT), Internet of Things (IoT), and business IT systems has allowed sophisticated attackers to circumvent isolated security features and launch cross-platform assaults.…
Current fragmented techniques, with discrete detectors monitoring Modbus, Kubernetes, MQTT, or other domain-specific protocols, cannot handle cross-system risks. These methodologies overlook 68% of multi-vector marketing that uses both physical and digital channels. This study introduces a transfer learning architecture to integrate detection capabilities by correlating threats across protocols, devices, and settings. The architecture generates a unified feature space that extracts behavioral semantics from industrial control system logs, cloud telemetry, network traffic, and device-level signals to produce protocol-agnostic threat representations. Adversarial domain adaptation and semantic graph embeddings enable cross-domain knowledge transfer with minimum retraining. Security teams may now discover kill chains like infected cloud containers preceding illegal PLC command execution every 23 minutes. Validated against real-world attack datasets from water treatment facilities (OT) and cloud infrastructure (IT), the system achieved 93.4% cross-platform attack recall, a 41.3 percentage point improvement over prior methodologies. It reduced OT data labeling by 89% and false positives by 93.5%. This paradigm shift transforms threat correlation from a reactive, domain-specific process to adaptive intelligence, boosting resilience for critical infrastructure, industrial ecosystems, and smart environments facing cyber-physical hazards. The framework's practical validation in energy, industry, and vital infrastructure shows its importance in protecting an increasingly linked world.
Abstract:Multi-sensory brand experience has emerged as a crucial factor in modern marketing, significantly influencing customer satisfaction, brand perception, and loyalty. Despite its increasing importance, research in this area…
remains fragmented, with theoretical integration and empirical validation gaps. This study employs a Systematic Literature Review (SLR) and bibliometric analysis using the Scopus database to map the evolution of research on multi-sensory brand experiences and their impact on customer satisfaction. From an initial dataset of 203 documents, a rigorous selection process resulted in 41 relevant articles for in-depth analysis. Using VOS viewer, the study visualizes research networks, inter-topic relationships, and emerging trends, revealing a 47% increase in publication volume from 2012 to 2024.
Findings indicate that customer satisfaction, consumer behaviour, and sales are dominant themes, with sentiment analysis and digital technology (AI, AR) gaining traction in recent years. However, significant gaps remain, particularly in experimental studies quantifying the long-term impact of multi-sensory experiences on customer loyalty and cross-industry comparisons of multi-sensory branding effectiveness. The novelty of this study lies in its combined bibliometric and systematic approach, which identifies critical research trends, theoretical gaps, and emerging digital strategies in multi-sensory marketing. This study contributes to the academic discourse by comprehensively synthesizing research trends and proposing future research directions in technology-driven branding strategies and consumer engagement.
Abstract:PayLater services are one of the rapidly growing digital financial innovations widely utilised in fintech apps in Indonesia, including Kredivo and Akulaku. User reviews on the Google Play Store reflect a range of experiences,…
nces, from satisfaction with the ease of use of the service to complaints regarding bills, interest rates, late payment fees, credit limits, and app performance. This study aims to classify the sentiment of user reviews regarding PayLater services on the Kredivo and Akulaku apps using the Multinomial Naïve Bayes algorithm. Data was collected via web scraping from the Google Play Store and automatically labelled based on user ratings, with ratings of 1-2 classified as negative sentiment and ratings of 4-5 as positive sentiment, whilst a rating of 3 was excluded as it was considered ambiguous. Following a preprocessing stage comprising cleaning, case folding, tokenisation, stopword removal, and stemming, as well as feature extraction using TF-IDF, 3,652 reviews were obtained with a training-to-test data split ratio of 80:20. The results indicate that positive sentiment dominates the dataset at 56.49%, whilst negative sentiment accounts for 43.51%. Analysis by application revealed that Kredivo was dominated by positive sentiment (68.20%), whilst Akulaku was dominated by negative sentiment (51.70%). The Naïve Bayes multinomial model achieved an accuracy of 84.13%, with average precision, recall, and F1-score values of 0.84, demonstrating good and balanced classification performance across both sentiment classes.
Abstract:This study discusses the implementation of the Naïve Bayes method to predict catering sales at PT.Negara Rasa Indonesia. The background of this study is based on the problem of suboptimal sales due to the absence of a structured…
tructured sales prediction system. The Naïve Bayes method was chosen because of its simplicity, speed, and ability to classify data with a high degree of accuracy. The data used in this study is historical sales data from the last two years, which has undergone cleaning, labeling, and transformation into four sales categories, namely very popular, popular, fairly popular, and less popular. The testing process was carried out using RapidMiner software by dividing the dataset into training data and test data at various ratios of 80:20. The test results showed a very high level of accuracy, with the highest value reaching 91.41%. These findings prove that the Naïve Bayes method is reliable for predicting catering sales, thereby assisting decision-making in more efficient sales management and planning at PT. Negara Rasa Indonesia.
Abstract:Listrik merupakan salah satu kebutuhan dasar masyarakat dan merupakan salah satu kebutuhan hajat hidup orang banyak, sehingga perlu diatur dan disediakan oleh negara sesuai amanah undang-undang 1945 pasal 33. Subsidi diberikan…
erikan dengan tujuan agar ketersediaan listrik dapat terpenuhi, serta membantu pelanggan yang kurang mampu dan masyarakat yang belum terjangkau pelayanan PT. Berdasarkan hasil evaluasi BKF dengan German International Cooperation (GIZ) terhadap subsidi listrik yang diberikan kepada kelompok pelanggan R1-450 VA dan R1-900 VA yang berlaku saat ini menunjukkan subsidi listrik tidak tepat sasaran, karena 5,9 juta pelanggan R1-450 VA dan 14,4 juta pelanggan R1-900 VA adalah kelompok rumah tangga yang telah mampu karena termasuk dalam pengeluaran per kapita lebih dari Rp.1 juta per bulan. Guna mengurangi risiko salah sasaran tersebut, perlu dilakukan klasifikasi dalam pemberian subsidi listrik berdasarkan kriteria-kriteria tertentu yang telah menjadi standar di PT. PLN menggunakan data mining dengan metode decision tree. Berdasarkan evaluasi dan hasil pengujian bab sebelumnya dengan dataset 50 data, 70 data dan 100 data produksi yang telah dilakukan, maka hasil pengukuran Confusion Matrix dalam penerapan data mining untuk memprediksi target Desa Tebara dengan metode Decision Tree (Algoritma C4.5) pada pengujian 50 data menghasilkan akurasi 74%, precision 60% dan recall 83,33%. Pada pengujian 70 data menghasilkan akurasi 81,43%, precision 76,92% dan recall 74,07%. Pada pengujian 100 data menghasilkan akurasi 82%, precision 76,67% dan recall 67,65%. Jadi untuk data uji lebih banyak akan menghasilkan akurasi yang lebih tinggi.
Abstract:This research is motivated by the large number of prospective students who simply choose a major when they want to enter a vocational school without considering their abilities. The Decision Tree or C45 method is used because…
cause it is able to make decision trees that are easy to describe, and has a level of efficiency in handling discrete and numeric attribute data. While the Naive Bayes method is used because it has a high accuracy of results. This research was conducted based on data from students of SMK Muhammadiyah 10 Kisaran which contained questions about feelings of wrong majors, interests, and determinants of other majors. Data is divided into 2 labels, namely free labels (y) and bound labels (x). Followed by dividing the dataset into training data and testing data with a ratio of 70:30 in both methods to get the level of accuracy. From the results given, it can be seen that the C45 algorithm has an accuracy of 85% and the Naive Bayes algorithm has an accuracy of 26%. This shows that the C45 algorithm is more effective in classifying the available datasets compared to the Naive Bayes.
Abstract:Graduation marks the completion of a certain level of schooling. This study aims to predict the graduation of students at SDN 016528 BP Mandoge based on their abilities. The goal of this research is to reduce the rate of…
student failure to graduate by making predictions based on examination scores collected by the institution. The method used in this study is Naive Bayes, a technique in Data Mining that utilizes probability and statistics to predict future outcomes based on previous data. This method was chosen due to its advantage in predicting graduation rates from concrete data, ensuring the results are reliable and applicable for future predictions. The dataset used in this study includes graduation data for SDN 016528 BP Mandoge students for the 2019/2020 academic year, comprising 171 students, with 120 students used for training data and 51 students for testing data, achieving a model accuracy of 98%.
Abstract:The Self-Help Housing Stimulant Assistance (BSPS) is a government house renovation program aimed at low-income communities. This program aims to enhance self-sufficiency in construction and improve the quality of houses,…
facilities, infrastructure, and public utilities through the principle of mutual cooperation. BSPS recipients must meet several criteria, such as income, house ownership status, house size, floor type, wall type, roof type, and water source. To address issues based on these criteria, Data Mining techniques using the Naive Bayes method were employed. This study utilized a dataset of BSPS recipients in Air Genting Village, Air Batu Sub-district, comprising 88 samples. The classification results from applying Naive Bayes yielded a precision value of 84%, a recall value of 81%, an F1-score of 82%, and an accuracy of 81%. The objective of this research is to facilitate the classification of eligible and rightful recipients of government assistance in the form of BSPS. The results of this study are expected to provide an alternative solution in determining BSPS recipients in Air Genting Village, Air Batu Sub-district
Abstract:The use of smartphones in Indonesia has been steadily increasing each year. In the era of the Fourth Industrial Revolution, smartphones have become a lucrative business sector, leading to intense market competition. Consequently,…
equently, smartphone companies must pay closer attention to the market segmentation desired by consumers. Data mining is the process of discovering significant relationships and patterns by analyzing large datasets using statistical and mathematical techniques. This study aims to identify and analyze the market segments of Android smartphone users among students at STMIK Royal. The data used in this research were collected from 122 student respondents. The study employs clustering using the K-means algorithm. The resulting data modeling will categorize market segments into several clusters. This segmentation yields three clusters: Cluster 1 (features), consisting of 36 respondents who prioritize price, battery, camera, and warranty; Cluster 2 (product), with 49 respondents who value all attributes except warranty; and Cluster 3 (superiority), comprising 37 respondents who prioritize camera, brand, and RAM.