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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kurawal - Jurnal Teknologi, Informasi dan Industri MULTINETICS JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sisfotek Global Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Digital Business SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) ECONEUR (Journal of Economics and Entrepreneurship) COSMOS Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Sri Farida Utami
Politeknik Sukabumi
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Strategi Pemasaran Digital Pada PT. Setiabudi Persada Sukabumi Hendra Ningsih, Ratih; Farida Utami, Sri; Liliawati, Lia
COSMOS : Jurnal Ilmu Pendidikan, Ekonomi dan Teknologi Vol 1 No 4 (2024): Juni - Juli
Publisher : PUSDATIN Institut Agama Islam Sultan Muhammad Syafiuddin Sambas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37567/cosmos.v1i4.157

Abstract

A house is one of the basic needs of humans, it will definitely meet its basic needs, as well as a place to live. This research is to find out the Digital Marketing Strategy at PT. Setiabudi Persada, Sukabumi Regency in marketing Setiabudi Estate housing as well as the obstacles faced and solutions that have been carried out. The Digital Marketing Strategy used in this study is Market Segmentation (Segmenting), Target Market (Targeting), and Market Positioning (Positioning) as well as the stages of digital marketing. The research method used by the author in writing the Final Project report is a descriptive qualitative method by analyzing data on PT. Setiabudi Persada Sukabumi Regency is seen from Market Segmentation (Segmenting), Target Market (Targeting), and Market Position (Positioning). The data collection techniques in this study include interview methods, observation methods and literature methods. The results of the research from Marketing Strategy at PT. Setiabudi Persada of Sukabumi Regency stated that market segmentation (Segmenting) with consumers who have behavior in harmony with the nature of Sukabumi is also with a modern and practical lifestyle, Target market (Targeting) with the target market prioritized for permanent employees, Market Positioning (Positioning) as urban living housing with a modern minimalist style using the current market and considering air health. Then the stages of digital marketing carried out are market research, branding development, creative content, Social Media Marketing, paid advertising sponsorship, SEO optimization and email marketing.
The Application of Machine Learning in Liver Disease Diagnosis: Analysis of Algorithm Performance and Axiological Implications Utami, Sri Farida; Patmanthara, Syaad
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.253

Abstract

Liver disease remains a significant global health challenge, requiring accurate and timely diagnosis to improve patient outcomes and reduce healthcare costs. This study investigates the application of four machine learning classification algorithms—Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN)—to predict the presence of liver disease using a dataset sourced from Kaggle. These algorithms were evaluated based on performance metrics such as accuracy, precision, recall, and F1 score. Both Decision Tree and Random Forest achieved the highest accuracy rate of 72.41%, demonstrating their robustness in classifying liver disease cases. However, these models showed some limitations in identifying patients without liver disease. Naïve Bayes, with an accuracy of 60.34%, exhibited an impressive recall rate of 96.97%, indicating its potential in detecting liver disease cases, though at the cost of lower precision. KNN, with an accuracy of 70.69%, proved to be a competitive option in the classification task. Beyond technical performance, the study also explores the ethical and axiological implications of using machine learning in healthcare, emphasizing the importance of fairness, transparency, and human oversight. The research highlights the need for responsible deployment of machine learning technologies, ensuring they are aligned with ethical standards to avoid biases and enhance healthcare outcomes. This study demonstrates that machine learning can significantly support liver disease diagnosis, though it must be integrated with a comprehensive ethical framework to ensure equitable and transparent decision-making in clinical practice.
Literature Review: Ethical Perspectives in the Development of Artificial Intelligence and Recommender Systems Utami, Sri Farida; Elmunsyah, Hakkun; Widiyaningtyas, Triyanna
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.4364

Abstract

The development of Artificial Intelligence (AI) and recommendation systems has had a major impact on various digital service sectors, including social media, e-commerce, and healthcare. Although this technology offers efficiency and personalised services, its implementation also raises complex ethical challenges, such as privacy protection, algorithmic bias, and system accountability. This study aims to analyse ethical perspectives in the development of AI and recommendation systems through a literature review approach. The methods used include analysis of various scientific articles classified into several main ethical dimensions, namely data privacy and security, algorithmic fairness, social media and recommendation systems, digital communication, and educational and publication ethics. The results of the study show that issues of privacy, transparency, and the potential for user behaviour manipulation are dominant issues. Ethics in AI no longer functions solely as an individual value, but as the foundation of responsible and sustainable institutional governance.
K-Nearest Neighbor Performance Optimization for Multiclass Imbalance of Intrusion Detection Data Using SMOTE and Distance Variation-Based Parameter Tuning Hairani Hairani; Christopher Michael Lauw; Sri Farida Utami; Afrig Aminuddin; Abu Tholib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7489

Abstract

The increasing use of computer networks and internet-based services has made cybersecurity threats more complex. Intrusion Detection Systems (IDS) play a crucial role in identifying network attacks; however, conventional signature- or rule-based approaches are limited in handling novel attacks and dynamically changing attack patterns. Therefore, machine learning approaches are applied to enhance the adaptive capabilities of IDS. Nevertheless, the use of machine learning in IDS still faces a major challenge: data imbalance, where normal traffic significantly outweighs attack traffic. This condition biases models toward the majority class, leading to suboptimal detection of minority attacks. Based on this issue, this study aims to improve the performance of the K-Nearest Neighbor (KNN) method in network attack detection by applying the Synthetic Minority Over-sampling Technique (SMOTE) and parameter tuning. The study employs KNN with parameter tuning and SMOTE to address multiclass data imbalance in network attack detection. Parameter tuning is conducted to determine the optimal value of k and distance functions, including Euclidean, Manhattan, and Cosine Similarity. The results show that KNN with k = 3 and Manhattan distance on SMOTE-balanced data achieves the highest accuracy of 96.51%, outperforming Euclidean and Cosine Similarity distances. These findings conclude that applying SMOTE and appropriately selecting k and distance metrics significantly improve KNN performance in network attack detection and increase overall detection accuracy.
Co-Authors Abu Tholib Afrig Aminuddin Agustiansyah ajiz, Muhammad Risman Aulia, Syaira Christopher Michael Lauw Faustina Gunadi Hairani Hairani Hakkun Elmunsyah Hendra Ningsih, Ratih Liliawati, Lia Mauludyansah, Widhy Al Mohamad Alif Dzikry Mohamad Alif Dzikry Nadira, Aghniya Rahayu, Samirah Septian Rheno Widianto Syaad Patmanthara Tohirin Tohirin Triyanna Widiyaningtyas Tutu Gondewa Willy Prihartono Willy Prihartono