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KLASTERISASI POLA PERMINTAAN PADA UMKM MENGGUNAKAN DATA TRANSFORMATION DAN K-MEANS CLUSTERING
The consumer electronics retail industry is characterized by short product life cycles and intermittent demand patterns at the stock keeping unit (SKU) level. This study performs demand pattern segmentation of SKUs at an electronics retail store using the Average Demand Interval (ADI) and Squared Coefficient of Variation (CV²) features extracted from three years of transaction data. Both features were transformed using log1p and Z-score standardization to address the skewed data distribution, and then clustered using the K-Means algorithm with candidate values of K ranging from 2 to 4. The optimal number of clusters was selected based on the Silhouette Score and Davies-Bouldin Index (DBI), while segment labeling was performed using empirical median thresholds derived from the research data, rather than the standard Syntetos-Boylan-Croston cut-off values. Results on 1,342 valid SKUs show that K=2 is the optimal number of clusters (Silhouette Score 0.6380; DBI 0.7034), which were interpreted as the "routine-stable demand" segment (1,055 SKUs; 78.61%) and the "routine but fluctuating" segment (287 SKUs; 21.39%), with the separation being more dominated by ADI values than by CV². These findings indicate that the demand structure is more accurately represented by two transaction-frequency-based groups, and can serve as a basis for inventory management policies tailored to the characteristics of each segment.
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PENGEMBANGAN ANTARMUKA WEBSITE E-COMMERCE TOKO-TOPIA BERBASIS KOMPONEN MENGGUNAKAN REACTJS
This project aims to design and develop a responsive and interactive e-commerce website user interface (UI) named Toko-Topia using ReactJS. By utilizing this modern JavaScript library, the development focuses on performance, user experience (UX), and ease of navigation for customers. The developed website features functionalities such as product search, a shopping cart system, and API integration for checkouts, providing an optimal online shopping experience. This technology also enables efficient component management, supports real-time data updates, and enhances application scalability. The implementation of ReactJS in this e-commerce website development offers a modern solution for businesses to enhance their digital presence. The results of this project are expected to contribute to improving user experience in online shopping while supporting the growth of the digital economy in Indonesia.
INOVASI APLIKASI MOBILE BERBASIS KECERDASAN BUATAN PADA LAYANAN DINAS SOSIAL PROVINSI JAWA TIMUR MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT
Delivering public services and social aid frequently faces hurdles regarding data precision and information access. This research focuses on the creation of a React Native-based mobile app prototype equipped with an AI chatbot, functioning as a digital information hub for the East Java Provincial Social Services. The study utilizes the Rapid Application Development (RAD) methodology, encompassing Requirements Planning, User Design (UI/UX with Figma), Construction (frontend with React Native, backend via Supabase, and AI training), and Cutover phases. This rapid iterative process produces the E-JSC application, a versatile platform featuring an AI chatbot ("Tanya JSC"), social aid transparency dashboards ("SapaBansos"), and online permit services. By incorporating Artificial Intelligence, the system autonomously handles frequent public questions, thereby minimizing queues at service centers. Ultimately, this mobile solution effectively upgrades public service operations, guaranteeing that social aid details remain precise, reachable, and well-managed.
PERAMALAN PENJUALAN AKI MENGGUNAKAN ARIMA UNTUK REKOMENDASI SAFETY STOCK
Inventory uncertainty is one of the main operational challenges faced by retail businesses, including automotive battery stores. Inaccurate inventory planning can lead to overstock and stockout conditions, both of which negatively affect operational efficiency and customer satisfaction. This study aims to forecast battery sales using the Autoregressive Integrated Moving Average (ARIMA) model as the basis for determining Safety Stock and Reorder Point (ROP) at Toko Aki Restu. The research adopts the CRISP-DM framework consisting of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment stages. Historical monthly sales data from October 2024 to October 2025 were analyzed using the ARIMA method. The results showed that ARIMA(1,0,2) was the best forecasting model based on the smallest Akaike Information Criterion (AIC) value. The evaluation process generated MAE of 9.85, RMSE of 12.27, and MAPE of 5.3%, indicating that the forecasting model had very high accuracy. Forecasting results were then utilized to calculate Safety Stock and Reorder Point values. The study produced a Safety Stock recommendation of 10 units and a Reorder Point of 54 units. Furthermore, the forecasting and inventory recommendations were visualized through a web-based dashboard to support operational decision-making. The implementation of this study is expected to help the company minimize inventory risks and improve stock management efficiency.
ANALISIS KOMPARASI ALGORITMA RANDOM FOREST, XGBOOST, DAN MULTILAYER PERCEPTRON (MLP) DALAM PREDIKSI RISIKO GAGAL BAYAR KREDIT
Credit risk prediction is a critical task for financial institutions in identifying customers at risk of default. This study compares the performance of three machine learning and deep learning algorithms as in Multilayer Perceptron (MLP), Random Forest, and XGBoost in predicting credit card defaults using the “Default of Credit Card Clients” dataset from the UCI Machine Learning Repository. The dataset consists of 30,000 records with 23 features covering demographic information, payment history, bills, and payment amounts over a six-month period. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE), which was applied only to the training data. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The experimental results show that Random Forest achieved the best overall performance with an F1-score of 0.5275 and an AUC-ROC of 0.768, outperforming MLP (F1-score 0.5182, AUC-ROC 0.7610) and XGBoost (F1-score 0.5080, AUC-ROC 0.7616%). These findings indicate that ensemble-based methods remain competitive compared to deep learning approaches for tabular credit data, and provide valuable insights for financial institutions in implementing data-driven risk management.
PENDETEKSIAN MANIPULASI LAPORAN KEUANGAN MENGGUNAKAN BENEISH M-SCORE PADA PERUSAHAAN KONSTRUKSI BUMN YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2021–2023
This study aims to detect the potential of fraudulent financial statements in state-owned construction companies listed on the Indonesia Stock Exchange during the 2021–2023 period using the Beneish M-Score method. This research employed a quantitative method with a descriptive approach. The data used in this study were secondary data obtained from the companies’ financial statements and annual reports. The research sample consisted of PT Waskita Beton Precast Tbk, PT Wijaya Karya (Persero) Tbk, PT PP (Persero) Tbk, and PT Adhi Karya (Persero) Tbk selected using purposive sampling. The analysis was conducted by calculating the Beneish M-Score through eight financial ratios, namely DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, and TATA. The results indicate that several state-owned construction companies were suspected of having the potential to manipulate financial statements. PT Waskita Beton Precast Tbk was indicated in 2022 and 2023, PT Wijaya Karya (Persero) Tbk in 2021 and 2022, PT PP (Persero) Tbk in 2022 and 2023, and PT Adhi Karya (Persero) Tbk in 2021 and 2023. The findings suggest that state-owned construction companies have a relatively high risk of fraudulent financial statements due to financial pressure, project complexity, and high leverage. Therefore, the Beneish M-Score method can be used as an early detection tool to identify potential financial statement manipulation in state-owned construction companies.
PERBANDINGAN ALGORITMA XGBOOST DAN RANDOM FOREST DALAM PERAMALAN HARGA EMAS ANTAM MENGGUNAKAN INDIKATOR TEKNIKAL
Gold is a widely used investment instrument due to its characteristics as a store of value and a hedge. However, gold prices are volatile, necessitating a forecasting approach that can help predict future price movement trends. This study aims to compare the performance of the XGBoost and Random Forest algorithms in forecasting Antam gold prices using technical indicators. The data used is historical Antam gold price data for the 2020–2025 period with 2,048 observations. Predictor features are formed through a feature engineering process based on historical price data, including close, lag price, Exponential Moving Average (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and daily return. The prediction target used is the Antam gold price one day ahead or T+1. Model evaluation is carried out using the Walk Forward Validation scheme so that the testing process maintains the time sequence in the time series data. Model performance is measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that Random Forest achieved the best performance with an RMSE of 21,503.71, an MAE of 15,020.22, and a MAPE of 0.780%. Meanwhile, XGBoost obtained an RMSE of 23,896.92, an MAE of 16,940.59, and a MAPE of 0.878%. These results indicate that Random Forest is superior to XGBoost in forecasting Antam gold prices in this research scenario. Feature importance analysis shows that the close feature, EMA 21, and EMA 9 are the most influential features on the prediction results.
KLASIFIKASI RISIKO OBESITAS BERBASIS GRADIENT BOOSTING PADA DATA MEDIS
Obesity is a growing health concern that can lead to various chronic diseases, making accurate risk identification an important preventive effort. The development of machine learning techniques enables the utilization of medical data to support intelligent decision-making in the healthcare domain. This study aims to apply the Gradient Boosting algorithm as a classification method to predict obesity risk based on medical data. The dataset used contains information related to eating habits, physical activities, and individual characteristics. The research process includes data preprocessing, data transformation and normalization, class mapping, and data partitioning into training and testing sets with a ratio of 70:30. The Gradient Boosting model is constructed using multiple decision trees with specific parameter settings to classify obesity risk into two categories, namely obese and non-obese. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that the proposed model achieves good classification performance with an accuracy exceeding 90%, while the performance gap between training and testing data remains relatively small. This indicates that the model has strong generalization capability and does not suffer from overfitting. Therefore, the application of Gradient Boosting on medical data proves to be an effective approach for obesity risk classification and has the potential to support intelligent health information systems in assisting medical practitioners with more precise obesity prevention and management strategies.
IMPLEMENTASI METODE DESIGN THINKING DALAM PERANCANGAN UI/UX PADA TAMPILAN WEBSITE DAN MOBILE
This study discusses the implementation of the Design Thinking method in UI/UX design in two digital product contexts, namely information system websites and mobile applications. The focus of the research is directed at the use of Design Thinking as a human-centered design framework, rather than on testing instruments or detailed feature discussions. This study uses a mapping of the design process of the main Design Thinking activities, namely understanding users, formulating problems, developing solution ideas, creating prototypes, and making design improvements based on feedback. The results of the discussion show that Design Thinking provides a systematic yet flexible workflow in translating user problems into UI/UX designs. In the website context, this method emphasizes information architecture, navigation hierarchy, and workflow organization. In the mobile context, this method emphasizes concise interaction flows, visual clarity, and simplicity of use. Thus, Design Thinking can be applied adaptively in website and mobile UI/UX design.
MODEL SISTEM DINAMIK KETERSEDIAAN MINYAK GORENG BERBASIS CPO UNTUK MENDUKUNG KETAHANAN PANGAN
Cooking oil is a basic necessity that plays an important role in supporting food security in Indonesia. Although Indonesia is one of the largest producers of crude palm oil (CPO) in the world, the availability of cooking oil in the domestic market still faces various problems, such as price fluctuations and supply imbalances. These problems are influenced by various interrelated factors, including production, exports, consumption, distribution, and government policies. This study aims to analyze the availability of CPO-based cooking oil using a system dynamics approach, the method used is a causal loop diagram (CLD) to systematically map the cause-and-effect relationship among variables in the system. The results of the analysis indicate that the cooking oil availability system is complex and dynamic, with the presence of a feedback relationship in the form of reinforcing and balancing loops. Variables such as CPO production, exports, consumption, and prices play an important role in influencing cooking oil availability. The resulting model can be used as a tool to comprehensively understand system behavior, as well as a basis for policy formulation to maintain supply stability and support national food security.
OBESITY RISK CLASSIFICATION BASED ON GRADIENT BOOSTING USING MEDICAL DATA
Obesity is a growing global health issue that significantly contributes to the development of chronic diseases such as diabetes, cardiovascular disorders, and metabolic syndromes, making early detection essential to support preventive healthcare strategies. This study aims to implement the Gradient Boosting algorithm to classify obesity risk based on medical data obtained from the UCI Machine Learning Repository, which includes information on eating habits, physical activity, and individual characteristics. The research methodology involves several stages, including data preprocessing, transformation, normalization, class mapping, and dataset splitting into training and testing sets with a ratio of 70:30. The Gradient Boosting model is constructed using multiple decision trees in an iterative manner to improve classification performance, categorizing individuals into obese and non-obese classes. Model evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the model achieves an accuracy of over 90%, with a relatively small gap between training and testing performance, demonstrating good generalization capability without overfitting. These findings confirm that Gradient Boosting is an effective approach for obesity risk classification and has strong potential to support intelligent healthcare systems in enabling data-driven decision-making for early prevention and treatment.
KOMPARASI MODEL MACHINE LEARNING UNTUK PREDIKSI PENERIMAAN DEPOSITO BERJANGKA PADA KAMPANYE TELEMARKETING BANK
Telemarketing campaigns are widely used by banks to promote term deposit products, yet their success rate is often low because the offers are not targeted to the right customers. This study aims to compare several machine learning models for predicting customers’ acceptance of term deposit offers, so that banks can conduct more effective and efficient campaigns. The dataset used is the Bank Marketing Dataset, which contains 45,211 customer records with demographic, socio-economic, and campaign-related attributes. The research stages include exploratory data analysis to understand the data characteristics and class imbalance, followed by data preprocessing such as handling “unknown” values, encoding categorical variables using one-hot encoding, transforming the target label into binary classes, and splitting the data into training and test sets using a stratified scheme. The models evaluated in this study are Decision Tree, Random Forest, and XGBoost, which are further optimized using Grid Search Cross-Validation. Model performance is measured using accuracy, precision, recall, and F1-score. The experimental results show that the tuned XGBoost model achieves the best performance with accuracy above 90% and stable results across different data subsets. This model can be utilized as a decision support tool to prioritize customers with a high probability of accepting term deposit offers and to improve the efficiency of telemarketing campaigns.
ANALISIS NIAT PEMBELIAN KONSUMEN PADA KONTEN AFFILIATE TIKTOK SHOP DENGAN PENDEKATAN SYSTEMATIC LITERATURE REVIEW
This study aims to analyze factors influencing purchase intention in affiliate content on TikTok Shop using a Systematic Literature Review (SLR) approach. The review examines 30 empirical articles published between 2022 and 2026, sourced from scientific databases such as Google Scholar, Scopus, ScienceDirect, and Emerald Insight. The SLR process was conducted systematically through planning, execution, and reporting stages, with research questions formulated using the PICOC framework. The findings indicate that trust, habit, perceived value, hedonic motivation, and performance expectancy are the most influential factors affecting purchase intention in the context of social commerce. Among these, trust and habit emerge as the most dominant variables. The study also reveals that the UTAUT2 model is the most widely used theoretical framework, often extended with additional variables to better explain consumer behavior in digital environments. In terms of methodology, most studies employ a quantitative approach using Structural Equation Modeling (SEM). Furthermore, the results show a growing trend of research in social commerce over recent years. However, studies specifically focusing on affiliate content on TikTok Shop remain limited, indicating opportunities for future research. This study provides a systematic overview of existing literature and contributes to a better understanding of purchase intention in social commerce.
KOMPARASI MODEL DEEP LEARNING PADA ULASAN APLIKASI DRAMABOX DI GOOGLE PLAY STORE
The rapid growth of streaming platforms and the increasing popularity of micro-drama have led to the emergence of applications such as DramaBox, which generate a large number of user reviews as a source of public opinion. However, sentiment analysis on streaming application reviews is still limited, particularly in comparing the performance of deep learning models. This study aims to compare the performance of Bi-LSTM and IndoBERT models in conducting sentiment analysis on user reviews of the DramaBox application. The data were collected through web scraping from the Google Play Store, resulting in a total of 13,701 user reviews. The data then underwent preprocessing before being divided into data train and data test. Model evaluation was carried out using accuracy, precision, recall, F1-score, confusion matrix, and 5-fold cross-validation. The results show that the IndoBERT model achieved better performance with an accuracy of 0.90 compared to 0.87 for Bi-LSTM, and also demonstrated higher stability based on cross-validation results. Therefore, IndoBERT is considered more effective in understanding the context of the Indonesian language and producing more accurate sentiment analysis on the DramaBox user review dataset.
IMPLEMENTASI METODOLOGI AGILE-SCRUM DALAM PENGEMBANGAN WEBSITE EDUKASI MANGROVE
This study aims to analyze the implementation of Agile methodology using the Scrum framework in the development of a Mangrove Education Website. The research applies an applied research approach within the field of software engineering, focusing on the practical application of Scrum in a real system development project. The development process was conducted iteratively through several sprints, involving a cross-functional team consisting of a Project Manager, UI/UX Designer, Front-End Developer, and Back-End Developer. Data were collected qualitatively through observation, documentation of sprint activities, backlog records, sprint reviews, and sprint retrospectives. The findings indicate that the implementation of Scrum, as defined in The Scrum Guide, effectively supports transparency, inspection, and adaptation throughout the development lifecycle. Iterative increments enabled continuous improvement of system features, including educational content modules, planting progress dashboards, and carbon absorption visualization. The study concludes that Agile-Scrum is an effective and adaptive approach for developing dynamic, stakeholder-oriented web-based environmental education systems.
RANCANG BANGUN QUESTION ANSWERING SYSTEM LAYANAN INFORMASI AKADEMIK BERBASIS RAG
The complexity of academic information in the New Student Admissions (PPMB) process often overwhelms conventional helpdesk services. This study aims to design and build a prototype Question Answering (QA) System based on Retrieval-Augmented Generation (RAG) that can automate information services accurately. The system is built using a three-layer architecture: Presentation Layer (Gradio UI), Application Layer (Python/LangChain), and Data Layer (ChromaDB). A key focus of the development is the data pipeline strategy, specifically handling "Indivisible Information Units" in PDF tables by setting a dynamic chunking limit of 3000 tokens. The prototype features a Knowledge Base Manager for dynamic document updates and a multilingual Chat Interface. Testing demonstrates the system's ability to process heterogeneous data from 30 sources and successfully retrieve specific procedural information, such as the "Golden Ticket" requirements, with high precision. The system is deployed using a reasoning model engine to ensure logical answer synthesis.
PERKEMBANGAN SISTEM HUKUM DAGANG INDONESIA DALAM KONTEKS BISNIS GLOBAL
Indonesia's trade law system has undergone significant changes in line with the increasing integration of the global economy and the transformation of modern business, including the development of digital trade. This research aims to analyze the development of Indonesia's commercial law system in the context of global business and its implications for modern business activities. The research method is a qualitative approach based on library research, including the review of scientific literature, regulations, and reports from relevant international institutions. The results of the study show that trade regulation reform, business licensing simplification, and adaptation to the digital economy are important factors in increasing national business competitiveness. However, there are still challenges in the form of regulatory complexity, overlapping policies, and the effectiveness of legal implementation that need improvement. This research emphasizes the importance of strengthening legal institutions and harmonizing trade regulations with international standards to support sustainable business growth in the era of economic globalization.
PERANCANGAN WEB PEMESANAN TIKET PESAWAT BERBASIS REACTJS
An airline ticket booking website is a modern digital solution that provides convenience in air travel ticket purchasing. This study presents the development of a web-based airline ticket booking system using the ReactJS framework to create a responsive, dynamic, and intuitive user interface, combined with the Scrum methodology to support flexible and adaptive project management. ReactJS was selected for its component-based architecture, which supports reusable UI components, strong application performance, and an interactive user experience. The Scrum framework facilitates iterative development through structured sprint cycles, encouraging team collaboration, clear communication, and continuous system refinement in response to changing user requirements. The developed system offers key features such as flight search, seat selection, and a secure and user-friendly payment process. In addition, the integration between front-end and back-end systems through APIs supports data exchange and system maintainability. The results show that the integration of ReactJS and Scrum can produce a reliable and adaptable airline ticket booking system that aligns with current technological developments and user needs in the aviation industry.
TRANSFORMASI AKUAKULTUR MELALUI REKAYASA GENETIK DAN BIOTEKNOLOGI: IMPLIKASI TERHADAP PRODUKSI DAN KUALITAS IKAN
The rapid growth of the aquaculture sector, the leading provider of global animal protein, demands the application of technological innovations that sustainably improve the productivity, health, and quality of farmed fish. Genetic engineering and biotechnology have become strategic tools for transforming modern aquaculture systems by improving fish genetic traits, increasing disease resistance, and optimizing product quality. This study aims to conduct a comprehensive literature review of the role of genetic engineering and biotechnology in aquaculture transformation, focusing on its implications for the production, health, and quality of farmed fish. The research method is a qualitative approach based on library research, analyzing reputable scientific articles indexed in the Scopus, Web of Science, and Google Scholar databases. The analysis used thematic content analysis to identify patterns, conceptual frameworks, research trends, and key research gaps. The study found that marker-assisted genetic selection, genomics, and CRISPR-Cas9-based gene editing significantly improved growth rate, feed efficiency, and disease resistance in fish. In addition, the development of biotechnology-based recombinant vaccines, probiotics, and biosensors contributes to improving fish health, food safety, and the quality of aquaculture products. However, the study also identified a range of challenges, including ecological risks, biosecurity issues, regulation, and social acceptance of genetically engineered organisms. Therefore, the transformation of biotechnology-based aquaculture needs to be balanced with a strong policy framework, a prudential approach, and sustainable governance. This research is expected to make a theoretical contribution to the development of modern aquaculture science and to have practical implications for the formulation of innovative, inclusive, and environmentally friendly fisheries industry strategies.
PERANCANGAN UI/UX APLIKASI PET CARE UNTUK PELANGGAN MENGGUNAKAN METODE DESIGN THINKING
Pets play an important role in human life, yet owners often face difficulties in accessing complete and efficient pet care services. This study aims to design a mobile-based pet care application with a focus on user experience (UI/UX) using the Design Thinking method. The research process involves the stages of empathize, define, ideate, prototype, and test. Data were collected through interviews with 5 customers and 15 pet care owners and analyzed using empathy maps, user personas, and wireflows. The prototype was tested using the System Usability Scale (SUS) to assess usability and user comfort. The SUS test results showed an average score of 73.5, categorized as good and acceptable, indicating that the application has good usability. This study demonstrates that proper UI/UX design can enhance accessibility, efficiency, and user satisfaction in pet care services.
KLASIFIKASI TINGKAT NILAI MATEMATIKA SISWA MENGGUNAKAN METODE MACHINE LEARNING BERBASIS FAKTOR SOSIAL DAN PERILAKU
The use of data in education is increasingly important to support data-driven decision making, particularly in monitoring students’ learning progress in mathematics. This study aims to classify students’ mathematics achievement levels based on social, behavioral, and academic factors using machine learning methods. The dataset used is the Student Performance Data Set from the UCI Machine Learning Repository, specifically the student-mat.csv file, which contains 395 student records with 33 attributes. The final mathematics grade (G3) is grouped into three categories: low, medium, and high. The research methodology follows the CRISP-DM approach, which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. Model development is carried out using Logistic Regression, Random Forest, and XGBoost algorithms. The evaluation results show that the Random Forest algorithm achieves the best performance, with an accuracy of 0.5570 and an F1-score of 0.4975, outperforming the other algorithms. Feature analysis indicates that prior academic failures, school support, social activities, and students’ absenteeism levels have a significant influence on mathematics achievement. This study is expected to help schools identify at-risk students earlier and support the planning of more targeted learning interventions.
LITERATURE REVIEW: PENGARUH PENERAPAN SISTEM PEMBELAJARAN ADAPTIF BERBASIS AI TERHADAP HASIL BELAJAR DAN MOTIVASI BELAJAR
Adapting teaching strategies to students' varied requirements is one of the biggest difficulties facing education today. Through the use of adaptive learning systems, which enable learning materials to be customized in real-time to students' strengths, shortcomings, and learning pace, artificial intelligence (AI) technology offers a creative answer. By offering a more individualized learning experience, this approach may enhance student motivation and learning outcomes. The purpose of this study is to examine how learning results and learning motivation are affected by the use of an AI-based adaptive learning system. This study looks at several current studies on the application of AI in education, especially in the context of adaptive learning, using the Literature Review (LR) method. The findings demonstrate that using an AI-based adaptive learning system significantly improves student motivation and learning outcomes. However, in order for the advantages of this technology to be felt equally across all educational levels, concerns pertaining to the disparity in access to technology and the preparedness of educators to use AI still need to be resolved.
PERANCANGAN UI/UX APLIKASI WEDDING ORGANIZER WILAYAH SURABAYA MENGGUNAKAN METODE USER CENTERED DESIGN
There are numerous things to consider when arranging a wedding. To solve this problem, people often choose to use wedding organizer services. However, some wedding organizer service companies still do conventional marketing, making it difficult for prospective brides to find the best vendors at the right price. The reason for this investigation is to form an interface with the User Centered Design strategy, an application plan that can get its client needs ideally. Through the stages of the method, the design that has been designed through prototypes is carried out using usability testing which is measured based on three aspects, namely effectiveness, efficiency, satisfaction. The comes about of the SUS calculation over-gets the ultimate result with a normal score of 79.25, which esteem is categorized as acceptable (good) or produces a plan with great ease of use.
PERENCANAAN STRATEGIS SISTEM INFORMASI MENGGUNAKAN METODE WARD AND PEPPARD (STUDI KASUS: SIROPEN)
The rapid development of Information Technology (IT) and Information Systems (IS) has driven companies to plan and implement appropriate IT strategies to enhance operational efficiency and competitiveness. PT Moya Kasri Wira Jatim, the company managing the Siropen syrup brand, faces challenges in optimizing IT utilization, with a heavy reliance on manual processes in production and limited financial systems. This study aims to formulate an IS/IT strategic plan to improve operational efficiency and strengthen the company’s digital position. Using the Ward and Peppard framework and analytical tools such as PEST, McFarlan’s Strategic Grid, and Value Chain, the research identifies external and internal factors influencing IS/IT implementation. Furthermore, the study proposes technology-based solutions such as an ERP system for process automation, enhanced customer feedback management, and the optimization of e-commerce and real-time inventory management systems. The results indicate that with a well-planned and integrated IS/IT strategy, Siropen can improve efficiency, accelerate data-driven decision-making, and strengthen its competitiveness in an increasingly competitive market.
PERANCANGAN WEBSITE PENDUKUNG KEPUTUSAN PENGURUS TERBAIK UKKI UPN JATIM MENGGUNAKAN MODEL WATERFALL
UKKI UPN Veteran Jawa Timur requires an objective and transparent mechanism to determine the best management board members in each organizational period. The evaluation process previously relied on manual calculations using spreadsheets, which was considered ineffective, difficult to interpret, and prone to human error and subjectivity. This study aims to design and develop a web-based Decision Support System (DSS) to assist in selecting the best management board members in a systematic and accurate manner. The research adopts a qualitative approach to identify system requirements through interviews with the general chairman, general secretary, and several relevant stakeholders. System development is conducted using the Waterfall model, which consists of requirements analysis, system design, implementation, testing, deployment, and maintenance stages. The Decision Support System applies the Analytical Hierarchy Process (AHP) to determine criteria weights and the Weighted Product (WP) method to rank the alternatives. The results show that the developed website is capable of managing member data, evaluation criteria, assessment scores, and organizational periods, as well as presenting ranking results in real time. The system enhances efficiency, accuracy, and transparency in determining the best management board members at UKKI UPN Veteran Jawa Timur.
PERAMALAN PENJUALAN MENGGUNAKAN METODE LEAST SQUARE PADA CV RAGAM JAYA
This research aims to forecast sales using the Least Square method at CV Ragam Jaya, a medium-scale enterprise engaged in the distribution of office stationery (ATK) in Surabaya. Currently, the company relies on a manual system for managing sales and inventory, leading to inefficiencies and inaccuracies in stock control. By applying the Least Square forecasting method, the company can predict future demand trends and optimize its inventory management. The study used monthly sales data from January to September 2024. Results indicate a consistent upward trend across all product categories with Mean Absolute Percentage Error (MAPE) values below 5%, demonstrating high forecasting accuracy. This method proves effective in improving sales forecasting and supports better inventory decision-making for CV Ragam Jaya.
THE ROLE OF SHARIA ECONOMICS IN REALIZING GOLD INDONESIA 2045
Backgrounds Researchers believe that the Islamic economy can have a strong contribution in supporting the creation of a Golden Indonesia 2045 with the potential for Indonesia to have the largest Muslim population in the world and a business sector that has greater growth potential. Objectives Therefore, this research aims to analyze the role of Sharia Economics in Realizing Golden Indonesia 2045. Methods: This research is qualitative research with a descriptive approach, namely describing the role of Sharia Economics in supporting the creation of a Golden Indonesia 2045 Results and Conclusions: The economy is getting serious attention with the potential for the largest Muslim population in the world. Due to the role of sharia economics in supporting the creation of a Golden Indonesia 2045 which is measured by per capita income which is equivalent to the per capita income of developed Indonesia and the poverty rate which is at 0.6% -0.8%, increasing Indonesia's position on SGIE with these six indicators will help Achieving a Golden Indonesia is based on the assumption that increasing the sharia financial market can increase employee income in the sharia financial sector, increasing the lifestyle and halal food market can increase employment opportunities and income levels.
KONTRIBUSI PEMUDA DALAM PEMBANGUNAN SOSIAL DAN INOVASI
Youth contribution to social development and innovation is the active engagement and constructive participation of young people (generally aged 15-30 years) in eflorts to improve the quality of life in society and create creative solutions to social challenges. This includes the role of youth in developing new ideas, applying technology, leading social initiatives, and engaging in decision-making processes that impact society. These contributions can take many forms, including social entrepreneurship, environmental activism, technology development, volunteering, and public policy advocacy. Through this engagement, youth not only contribute to current development but also shape the future of society by bringing fresh perspectives, energy, and innovation in addressing social, economic, and environmental issues. The study in this research uses the literature method. The results show that youth have the potential to drive innovation and positive social change. The creativity, digital skills and fresh perspectives brought by the younger generation have contributed to innovative solutions to various social and environmental challenges. However, the research also identified several barriers, such as limited access to resources and lack of platforms for meaningful participation. Therefore, to maximise the contribution of youth, supportive policies and investments in their capacity development are needed.
KERAGAMAN SOSIAL DAN TOLERANSI ANTARBUDAYA DI INDONESIA
Social diversity and intercultural tolerance in Indonesia, an archipelago with extraordinary cultural richness. With more than 17,000 islands, 1,340 ethnic groups, and 718 regional languages, Indonesia is a clear example of a multicultural society. The research method used is literature. The results show that while Indonesia's cultural diversity is a source of national wealth, it can also be a potential source of conflict if not managed properly. The research identified key strategies in building intercultural tolerance, including multicultural education, intercultural dialogue, inclusive public policies, use of media and technology, and active participation in cross-cultural activities. As such, intercultural tolerance is a vital element in maintaining the unity of the Indonesian nation, and an important foundation for sustainable national development.
PENINGKATAN KAPASITAS APARAT ADMINISTRASI NEGARA MELALUI PELATIHAN DAN PENDIDIKAN
Increasing the capacity of state administrative apparatus through training and education is an important eflort to strengthen the performance of government and public services. Well- designed training can improve the knowledge, skills, and professionalism of the apparatus, so that they are able to carry out their duties more eflectively and efficiently. However, the implementation of training and education programmes is often faced with various challenges, such as budget constraints, inflexible time, and gaps between training materials and field needs. To overcome these problems, a strategy is needed that involves regular training needs analysis, the use of e-learning technology, and programme evaluation through participant feedback. With good management and responsiveness to existing challenges, this capacity building programme is expected to produce competent administrative officers who are ready to face better public service tasks.