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INDONESIA
Prosiding Seminar Nasional Indonesia
Published by CV. Adiba Aisha Amira
ISSN : -     EISSN : 30265169     DOI : -
Core Subject : Humanities, Education, Social,
Humanities Social Sciences Other
Prosiding Nasional Adisam dapat menerima naskah dalam bidang-bidang seperti pendidikan, kesehatan, hukum, ekonomi, teknologi informasi (Teknik Informatika), teknik sipil, teknik elektro, teknik mesin, perikanan, pertanian, ilmu sosial-humaniora, dan bidang-bidang ilmu lainnya.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 128 Documents
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IMPLEMENTASI METODOLOGI AGILE-SCRUM DALAM PENGEMBANGAN WEBSITE EDUKASI MANGROVE Zahrah Hayat Arka Putri; Nayli Amirah Firdaus; Najoan Rizki Pradana; Satrio Agna Gemintang
Prosiding Seminar Nasional Indonesia Vol. 3 No. 3 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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.
KOMPARASI MODEL DEEP LEARNING PADA ULASAN APLIKASI DRAMABOX DI GOOGLE PLAY STORE Adam Idhofi Rakasiwi; Arsa Cahaya Pradipta; Al-Faiz Azzam Aryaputra
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309693

Abstract

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.
ANALISIS NIAT PEMBELIAN KONSUMEN PADA KONTEN AFFILIATE TIKTOK SHOP DENGAN PENDEKATAN SYSTEMATIC LITERATURE REVIEW Devita Fahliza Ulfa; Arista Pratama; Siti Mukaromah
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309726

Abstract

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 MACHINE LEARNING UNTUK PREDIKSI PENERIMAAN DEPOSITO BERJANGKA PADA KAMPANYE TELEMARKETING BANK Marcellio Aurel Christian
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309740

Abstract

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.
OBESITY RISK CLASSIFICATION BASED ON GRADIENT BOOSTING USING MEDICAL DATA Achmad Lukman Prayogidianto
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309761

Abstract

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.
MODEL SISTEM DINAMIK KETERSEDIAAN MINYAK GORENG BERBASIS CPO UNTUK MENDUKUNG KETAHANAN PANGAN Faradiba Aurel Yasmin; Mifa Amira Dewi; Keysha Naila Chadijah; Mafda Khoirotul Fathah
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309776

Abstract

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.
IMPLEMENTASI METODE DESIGN THINKING DALAM PERANCANGAN UI/UX PADA TAMPILAN WEBSITE DAN MOBILE Ilham Takbir Al Azhiim; Vera Rizki Yuniar
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309793

Abstract

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.
KLASIFIKASI RISIKO OBESITAS BERBASIS GRADIENT BOOSTING PADA DATA MEDIS Wildan Hafiz Firmansyah; Nala Widyadhana
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309818

Abstract

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.

Page 13 of 13 | Total Record : 128

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