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This study implements the Damerau-Levenshtein algorithm for an Indonesian spelling checking and correction system based on the distance editing approach. The main objective of this study is to develop a system capable of automatically detecting and correcting spelling errors at the character level through a matching process against the KBBI dictionary and the Indonesian corpus. The methods used include data collection, text pre-processing, system design, and implementation of the Damerau-Levenshtein algorithm which includes insertion, deletion, substitution, and transposition operations. Testing was conducted using 25 test data consisting of standard words and modified words for typographical errors. The results show that the system is able to measure all test data with an accuracy level of 100% on a limited dataset. In addition, the average Damerau-Levenshtein Distance value of 0.84 indicates that most errors are in the light category. Evaluation using a confusion matrix produces precision, recall, and F1-score values ​​of 100% each. These results indicate that the Damerau-Levenshtein algorithm is effective in handling character-based spelling errors. However, the system still has limitations in handling complex semantic contexts and language variations. Therefore, further research is recommended to integrate language model-based approaches to improve the system's accuracy and generalization on real-world data.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.

The development of digital technology has encouraged Micro, Small, and Medium Enterprises (MSMEs) to utilize social media as the main instrument in building brand awareness. This study uses a qualitative descriptive approach with a field research type (field research). Data collection was carried out through observation, in-depth interviews with six sources (owner, financial manager, social media team, admin, crew, and customers), and documentation of the content of the TikTok account @mtm43surabaya. The results of the study show that: (1) MTMSBY43 implements a digital branding strategy with three main components, namely brand positioning as a social-based one-stop solution, a brand identity that focuses on a human-centered service approach, and a brand personality that reflects the character of local youth who are solution-oriented and socially concerned; (2) MTMSBY43 TikTok content is classified into three pillars, namely humanistic and realistic content (daily vlogs & live documentation), educational and transparent content (service portfolio), and humorous content (entertainment & engagement content) that builds high organic appeal; (3) TikTok acts as a primary growth driver for MTMSBY43's brand awareness through three mechanisms: creating brand recognition through the FYP algorithm, which reaches new audiences; driving brand recall through consistent uploads and a distinctive communication style; and building customer trust and brand loyalty through responsive two-way interactions

Penelitian ini dilatarbelakangi oleh tingginya biaya penyimpanan dan meningkatnya stok mati pada Delyana Hijab, yang menunjukkan belum optimalnya pengelolaan persediaan bahan baku. Ketidaksesuaian antara jumlah bahan baku dan kebutuhan produksi menyebabkan pemborosan biaya serta menurunkan efisiensi operasional. Penelitian ini bertujuan untuk menentukan kebutuhan material yang optimal melalui pendekatan forecasting dan Material Requirement Planning (MRP). Metode yang digunakan adalah deskriptif kuantitatif dengan teknik pengumpulan data berupa observasi, wawancara, dan dokumentasi. Tahapan analisis meliputi peramalan permintaan, penyusunan Master Production Schedule (MPS), Bill of Materials (BOM), perhitungan kebutuhan bersih (MRP), serta penentuan ukuran pemesanan melalui metode lot sizing. Hasil penelitian menunjukkan bahwa metode regresi linier menghasilkan tingkat kesalahan peramalan terendah sehingga mampu memproyeksikan kebutuhan produksi dengan lebih akurat. Penerapan MRP menghasilkan perencanaan kebutuhan bahan baku yang lebih terarah dan sesuai dengan jadwal produksi. Pada tahap lot sizing, metode Lot For Lot (LFL) menjadi yang paling efisien dengan total biaya persediaan sebesar Rp108.669.000. Dalam penerapannya, jumlah pemesanan bahan baku mengikuti kebutuhan bersih tiap periode, misalnya kebutuhan kain katun berkisar 5-7 roll per minggu dan dipesan dalam jumlah yang sama tanpa kelebihan stok. Pola ini mampu menekan penumpukan persediaan dan mengurangi risiko stok mati karena bahan baku langsung digunakan sesuai kebutuhan. Dengan demikian, tujuan penelitian untuk menentukan kebutuhan material yang optimal telah terjawab melalui penerapan metode Lot For Lot yang mampu menghasilkan kuantitas pemesanan yang tepat dan efisien.

Administrative letter services at the village (nagari) level are still largely conducted using conventional methods, resulting in various issues such as limited service hours, slow processing times, and risks of document loss. This study aims to develop a web-based letter service system with the implementation of digital signatures in Nagari Bukit Bais to improve efficiency, security, and transparency of public services. The research adopts the Agile Development method with an iterative approach, including requirement analysis, system design, implementation, and testing. The developed system enables citizens to submit requests online and is equipped with features such as officer verification, digital signing by the village head, automatic notifications, digital archiving, and document verification using QR Codes. Security mechanisms are implemented using SHA-256 cryptographic hashing and RSA-2048 digital signature algorithms, supported by X.509 digital certificates. Functional testing using end-to-end methods shows that all system features operate successfully without failures, while non-functional testing confirms the reliability of document security and integrity. The resulting system is able to automate the entire service process, reduce processing time, and ensure document authenticity and security. Therefore, this system can serve as a solution to support the digital transformation of public services at the village level.

The rapid development of information technology has transformed social media into a digital space that plays a crucial role in shaping the mindset, behavior, and lifestyle of young generations. TikTok is one of the most dominant platforms used by Generation Z, particularly students, due to its ability to present algorithm-based content that aligns with user interests. This condition has the potential to cause psychological stress in the form of Fear of Missing Out (FOMO), which reflects social anxiety due to the perception of being late in keeping up with trends, information, or experiences that hold a dominant position in the social landscape. This research initiative aims to examine the influence of TikTok usage on the lifestyle configurations of Generation Z students, with FOMO serving as a determinant that strengthens this relationship. This study is designed within the quantitative tradition with a positivist epistemological orientation. The study population includes 250 students from the Faculty of Computer Science at the National Pasim University in Bandung, with a sample size of 72 respondents derived using Slovin's formula. The analysis results indicate that TikTok usage and the level of FOMO have a positive and significant impact on changes in students' lifestyles. The regression coefficient estimates are in the positive direction, with a significance value exceeding the critical threshold of 0.05, indicating a unidirectional relationship between the variables. The model determination estimate, at around 57.5%, confirms that TikTok and FOMO are able to explain a significant portion of the variation in students' lifestyles, while the remainder is influenced by factors outside the scope of this study. This finding confirms that TikTok functions not only as a medium of entertainment but also as an agent shaping lifestyles influenced by social and psychological pressures. Therefore, strengthening digital literacy is crucial so that students can use social media more rationally and responsibly.

The architecture of consumer decision-making has completely changed due to the quick development of recommendation systems based on artificial intelligence (AI). The majority of earlier studies saw algorithms as instruments for forecasting and maximizing preexisting preferences. This study, however, makes a different claim: algorithmic curation actively shapes preferences rather than just reflecting them. This study creates and evaluates a structural model that examines the impact of algorithmic curation intensity on perceived search autonomy, identity resonance, affective evaluation, and the development of initial preferences. The model is based on identity-based consumption theory and the literature on human-AI interaction. The study's findings, which are based on survey data from Generation Z consumers and Structural Equation Modeling (SEM) analysis, demonstrate a contradictory dynamic: algorithmic curation improves identity resonance and directly influences initial preferences while simultaneously decreasing feelings of autonomy. The primary mediating mechanism that links algorithmic exposure to emotional assessment and preference creation is identified as identity resonance. In addition to introducing the concept of algorithmic consumer formation as a new conceptual framework for comprehending consumer behavior in the AI-based digital era, our findings expand the notion of bounded rationality toward algorithmically bounded agency.

This study analyzes the performance of the Dual Modulus RSA algorithm in securing text data using Python. The rapid growth of digital technology has increased the risk of data security threats, making efficient and secure encryption essential. Dual Modulus RSA is a modification of the classic RSA algorithm that uses two different moduli in the encryption and decryption process, thus increasing security levels because attackers must factorize two moduli simultaneously. This research uses an experimental quantitative approach by measuring the execution time of encryption and decryption processes with variations in plaintext length (5, 10, and 15 characters). Implementation was carried out using Python 3 with the time.perf_counter() function for microsecond-precision measurement. The results show that the Dual Modulus RSA algorithm successfully encrypts and decrypts all test plaintexts correctly. Encryption time ranged from 0.0212 ms to 0.0823 ms, while decryption time ranged from 0.0422 ms to 0.0955 ms. There is a positive linear relationship between plaintext length and processing time. Decryption is consistently slower than encryption due to the larger private key exponent (d1=2753, d2=3533) compared to the public exponent (e=17). The main factors affecting performance are exponent size, dual modulus overhead, CPU caching effects, and Python interpretation overhead. This study recommends using Dual Modulus RSA with hybrid encryption for practical implementation to balance security and performance.

The Fréchet distribution is one of the commonly used Extreme Value Distributions (EVDs) in statistical modeling and heavy-tailed data analysis, where it plays an important role in describing product lifetimes as well as climatic and financial phenomena. The estimation of its two parameters, namely the shape parameter and the scale parameter, is traditionally based on the Maximum Likelihood Estimation (MLE) method. However, maximizing the likelihood function for this distribution involves numerical difficulties, which necessitates the use of numerical optimization methods. In this study, we propose the use of the Aquila Optimizer (AO), a recent metaheuristic algorithm inspired by the hunting behavior of eagles, as an efficient numerical tool for maximizing the likelihood function of the Fréchet distribution. The objective function was formulated as the negative log-likelihood function (-LogL), and the Aquila Optimizer was employed to obtain the optimal estimates of the distribution parameters. Several simulation experiments with different sample sizes were conducted to compare the performance of the proposed method with a conventional approach represented by the Nelder–Mead method, using the Mean Squared Error (MSE) criterion. The simulation results demonstrated that the Aquila Optimizer outperformed the Nelder–Mead algorithm in many cases, although the superiority was slight. The results also showed that both algorithms were consistent, as their MSE values decreased with increasing sample size. In addition, a practical application was carried out using real data, and the results of the survival function estimation indicated a good fit.

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

The digital era demands a fundamental transformation in the role of early childhood educators, shifting from passive technology consumers to active architects of digital literacy. However, the dominance of technocentric views often acts as a substantial psychological and pedagogical barrier for teachers in regional areas. This collaborative community service project aims to reconstruct the paradigm of 50 kindergarten teachers in Bulukumba Regency by integrating "unplugged coding" logic and deep learning into play-based learning. Utilizing a Product-Based Intensive Training method with a "Logic over Laptop" strategy, the program focused on deconstructing technology-related stigmas and reconstructing teachers' ability to transform abstract concepts into safe, concrete media for children. Data analysis revealed a significant shift in teacher paradigms; while the majority were initially in the "less successful" category, 100% of participants reached positive categories (successful and very successful) post-intervention. Statistically, the program's effectiveness was evidenced by a dramatic increase in mean scores from 18.04 to 31.24 (p < 0.05) and an N-Gain score of 0.778, classified as highly effective. Furthermore, the partner satisfaction index reached 4.82 (very satisfied), confirming that the tri-campus collaboration model (STAI Al-Gazali, UNM, and Unismuh) is highly relevant to the implementation of the Merdeka Belajar curriculum. This project concludes that strengthening digital literacy through non-digital algorithmic reasoning effectively dismantles technical barriers for teachers while ensuring the safety of child development in the digital age.

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.

The development of digital media has transformed Islamic da’wah into a digitally mediated form of religious communication, reshaping structures of authority, engagement, and community formation. However, existing research remains fragmented and lacks a comprehensive structural map. This study conducted a Systematic Literature Network Analysis (SLNA) of Scopus-indexed publications on Islamic da’wah via digital platforms from 2016 to 2026. Using PRISMA-based screening techniques and bibliometric methods—including co-authorship, co-citation, and co-occurrence analyses with VOSviewer—the study analyzed 74 peer-reviewed journal articles. The results indicate a significant increase in the number of publications, particularly following the COVID-19 pandemic. The field is structured around four main themes: platform-based engagement strategies, youth identity formation, digital authority and ethics, and the adaptation of prophetic communication principles to the online context. Although Indonesia and Malaysia dominate the research output, the collaborative network remains relatively dispersed. New topics such as the influence of algorithms and AI-supported da‘wah suggest an expansion toward interdisciplinary directions. This study provides the first comprehensive map based on Scopus for research on Islamic digital da‘wah and offers a foundation for future theoretical and empirical development.

Dengue fever is a disease commonly found in tropical and subtropical regions. This disease can cause severe symptoms, such as very high fever, accompanied by nausea, vomiting, headache, abdominal pain, and leukopenia (decrease in white blood cells). This infectious disease, known as dengue hemorrhagic fever (DHF), is a viral infection transmitted by the Aedes Aegyppti mosquito. This study aims to classify dengue-prone areas using the K-Means Algorithm, and to classify the factors that cause dengue in Purwodadi District, Grobogan Regency. The clustering results using the K-Means algorithm with Rapidminer tool from 266 data produced 3 clusters: cluster 0 (blue) with 138 patients dominated by Kuripan, Purwodadi, Ngambak villages, cluster 1 (green) with 31 patients in Ngraji, Nambuhan, Cingkrong villages, and cluster 2 (orange) with 97 patients in Danyang, Kalongan, Pulorejo villages. This study is expected to provide additional information for stakeholders in controlling dengue cases and increase awareness of the importance of environmental cleanliness as a preventive measure.

Social media has brought significant changes in the way humans interact, construct meaning, and shape social identities. In Indonesia, TikTok is the most widely accessed social media platform, reaching 32% of users. TikTok is not only a medium of entertainment but also a space for the production and reproduction of values, tastes, and social standards. The TikTok algorithm presents personalized content according to user preferences, creating a continuous flow of information that is visually and repetitively consumed. From an anthropological perspective, this article aims to analyze how algorithms influence the formation of digital identity and generate new status symbols in virtual spaces. This research uses a qualitative approach with ethnographic methods and literature studies on TikTok usage practices. The results of the study show that indicators of popularity such as the number of views, followers, likes, and interaction rates become new forms of status symbols that represent users’ social influence in the digital world. From an anthropological perspective, TikTok can be understood as a new cultural space where algorithms act as structural agents that influence social practices, identity production, and symbolic hierarchies within digital society. This study emphasizes that algorithms function not only as technical technologies but also as cultural forces that shape how individuals understand themselves and their social positions within the social media ecosystem.

By examining how visual aesthetics play a crucial role in creating a hyperreal symbolic reality, this study critically investigates the creation and consumption of simulacra in the Instagram marketing of regional Indonesian fashion firms. This study shows that local brands actively create representational worlds that negotiate identity between claims of locality and global aesthetic standards, in addition to selling products, using an interpretative qualitative approach that combines netnography, visual semiotic analysis, and in-depth interviews with brand managers and consumers. According to the research, rigorous visual curation techniques result in what are known as ethical simulacra, in which ideals like sustainability and community support are reduced to beautiful pictures divorced from tangible behaviors. In addition to examining ethical conundrums in netnographic research within ambivalent digital settings, this analysis emphasizes the critical role that Instagram's algorithms play as non-human actors that influence aesthetic canons. By highlighting distinctive dynamics including self-exoticization tactics and modernity paradoxes that influence symbolic consumption patterns, this study theoretically advances Consumer Culture Theory and critical marketing by concentrating on the Indonesian setting. The study's findings suggest a more ethical and thoughtful approach to marketing when confronting the logic of digital hyperreality.

The digital era, with 78% internet penetration in Indonesia (2025), brings information advancement but also threats like cyberbullying, hoaxes, and SARA polarization through social media. This study aims to analyze the relevance of Pancasila values as an ethical filter in mitigating these negative digital impacts through social media usage case studies. Employing a qualitative approach based on library research, data was gathered from 18 Sinta-accredited journals (2021-2026), 2 Pancasila digital theory books, UU ITE regulations, and APJII reports. Content analysis with Miles & Huberman (2024) data reduction was applied to code the implementation of each Pancasila principle. Results show that the first principle combats religious intolerance, the second suppresses cyberbullying (25% reduction), the third reduces 2024 election polarization (40%), the fourth promotes digital deliberation, and the fifth closes rural literacy gaps through gotong royong crowdfunding (Rp1T collected). Viral disinformation and Lombok 2025 disaster cases prove Pancasila's effectiveness beyond formal regulations. It is concluded that Pancasila is adaptive as a moral algorithm in the digital era, transforming social media from conflict breeding grounds into national integration spaces. Recommendations include strengthening the "Pancasila Digital Ethics" curriculum for Gen Z/Alpha, national AI literacy applications, and platform collaboration with BPIP-Kominfo.

Implementing REST in modern applications, security will be a key foundation for its development because the REST architecture requires communication between servers. In this study, we will enhance REST request security by using SHA-1 tokens and the Keccak algorithm. Tokens are the access keys for making requests. The token generation process is carried out on the server; the client will generate a token, and the server will return a valid token. This valid token can be used to request data from the server. Adding a token will impact the security and speed of REST. The token will be verified by the server and declared valid. If valid, the server will return the data; otherwise, the server will send an error message. Compared to using a token, data security is more assured. Furthermore, adding a token parameter will increase the token verification process, thus increasing the number of processes, which will impact speed. The results of this test show that server data security is better maintained and more secure compared to using a token, because anonymous users cannot access the data. The API access speed without a token is 48.8 milliseconds, while using a SHA-1 token is 62.3 milliseconds, and the Keccak algorithm is 58.9 milliseconds. The time efficiency reduction for implementing the SHA-1 token algorithm is 27.67% or 13.5 milliseconds, and the Keccak algorithm is 26.6% or 10 milliseconds.

The phenomenon of digital da'wah has significantly transformed the ways religious knowledge is accessed, interpreted, and disseminated in contemporary society. This study aims to analyze the patterns of actions and perceptions of TikTok and YouTube users toward religious content delivered by Gus Baha within the context of the digital religious space. Using Max Weber’s theory of rationalization as an analytical framework, this research explores how religious authority, knowledge transmission, and user interpretation undergo processes of rationalization on digital platforms.This study employs a qualitative approach using virtual ethnography, content analysis, and in-depth interviews with users of both platforms. Data were collected through observation of uploaded content, analysis of user interactions and comments, and examination of engagement dynamics.The findings indicate that user responses to Gus Baha’s content reflect Weber’s four types of social action. Instrumentally rational actions are evident in the use of short videos as practical and efficient learning tools. Value-oriented rational actions appear in users’ consistent efforts to deepen religious understanding. Affective actions emerge from emotional attachment to Gus Baha’s communicative style, while traditional actions are reflected in the perception of digital da'wah as a continuation of established religious learning traditions. Furthermore, digital rationalization through algorithms, short-video formats, and platform accessibility, shapes how religious knowledge is selected, interpreted, and circulated.This study concludes that digital religious spaces function not only as channels of dissemination but also as arenas for the transformation of religious authority, meaning construction, and religious practice in the digital era.