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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Buletin Udayana Mengabdi Journal of Information Systems Engineering and Business Intelligence International Journal of Artificial Intelligence Research JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal Jurnal Riset Multidisiplin Edukasi INOVTEK Polbeng - Seri Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Ni Putu Sutramiani
Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover Sutramiani, Ni Putu; Arthana, I Made Teguh; Lampung, Pramayota Fane'a; Aurelia, Shana; Fauzi, Muhammad; Darma, I Wayan Agus Surya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.13-24

Abstract

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country's economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability.   Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover
Extensive Deep Learning Models Evaluation For Indonesian Sign Language Recognition Audrey Tilanov Pramasa; Ni Putu Sutramiani; I Putu Agung Bayupati; I Wayan Agus Surya Darma
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p04

Abstract

Sign language is a vital communication method for individuals with hearing loss or deafness, with variations reflecting unique cultural contexts. Real-time recognition of sign language can bridge communication gaps, yet­­ developing tools for Indonesian Sign Language (BISINDO) is challenging due to limited datasets. This research addresses these challenges by enhancing BISINDO detection and real-time rec­­ognition, focusing on flexible dataset collection and adaptation to varying lighting conditions. Three convolutional neural networks—InceptionV3, MobileNetV2, and ResNet50—are evaluated with optimizers SGD, Adagrad, and Adam to determine the best architecture-optimizer combination. Models were trained on a common dataset and analyzed for optimal performance. Real-time recognition uses MobileNetV2 SSD, integrating data augmentation to improve performance under diverse lighting. The system was deployed on a mobile device for practical use. Results showed the real-time model attained a mean Average Precision (mAP) of 90.34%. This study demonstrates significant advancements in BISINDO recognition and real-time application
Network Reduction Strategy and Deep Ensemble Learning for Blood Cell Detection I Nyoman Piarsa; Ni Putu Sutramiani; I Wayan Agus Surya Darma
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p04

Abstract

Identifying and characterizing blood cells are vital for diagnosing diseases and evaluating a patient's health. Blood, consisting of plasma and cells, offers valuable insights through its biochemical and ecological features. Plasma constitutes the liquid component containing water, protein, and salt, while platelets, red blood cells (RBCs), and white blood cells (WBCs) form the solid portion. Due to diverse cell characteristics and data complexity, achieving reliable and precise cell detection remains a significant challenge. This study presents a network reduction strategy and deep ensemble learning approaches to detect blood cell types based on the YOLOv8 model. Our proposed methods aim to optimize the YOLOv8 model by reducing network depth while preserving performance and leveraging deep ensemble learning to enhance model accuracy. Based on the experiments, the NRS strategy can reduce the complexity of the YOLO model by reducing the depth and width of the YOLO network while maintaining model performance by 4%, outperforming the baseline YOLOv8 model.
Instagram influencer classification using fine-tuned BERT model Sutramiani, Ni Putu; Dwikasari, Ni Made Dita; Trisna, I Nyoman Prayana; Darma, I Wayan Agus Surya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp1009-1018

Abstract

Influencer marketing has emerged as a powerful strategy in today’s digital world, where social media stars can influence how people think about products. However, the rapid growth of influencers and social media users presents novel challenges for brands in identifying suitable influencers for their marketing goals. Traditional approaches that rely on popularity and follower count are no longer the primary metrics for determining an influencer’s ability to affect consumer behavior. To address this gap, this study proposed an influencer classification to enhance audience targeting and marketing effectiveness. By utilizing deep learning, specifically fine tuned bidirectional encoder representations from transformers (BERT), influencer classification was carried out for Instagram users in Indonesia based on their post captions. The multilingual BERT model is optimized through hyperparameter tuning, including learning rate, batch size, and stop word removal variation. With an outstanding 80% accuracy, the model performs best in situations where stop words are not removed. This study on influencer classification using a fine-tuned BERT model has demonstrated the effectiveness of BERT in enhancing influencer selection. It contributes to the digital marketing domain by showcasing the potential of deep learning for social media analysis and content classification, paving the way for future data-driven marketing strategies.
Co-Authors A. A. N. H. Susila A.A.N.H. Susila Adiwinata, Yonatan Agus Ghana Putra Partama Anak Agung Kompiang Oka Sudana Anak Agung Ngurah Hary Susila Arsa, Dewa Made Sri Arthana, I Made Teguh Audrey Tilanov Pramasa Aurelia, Shana D.M.S. Arsa D.P. Githa D.P.S. Putri Darma, Made Dwika Junata Desak Ayu Sista Dewi Desy Purnami Singgih Putri Dwi Putra Githa Dwikasari, Ni Made Dita Gede Bagas Aritama I Gede Hendra Parwata I Gusti Agung Indrawan I Gusti Ngurah Wira Dharma I ketut Gede Darma Putra I Made Sukarsa I Made Suwija Putra, I Made I Nyoman Piarsa I Nyoman Piarsa I Putu Agung Bayupati I Putu Arya Dharmaadi I Putu Eka Giri Gunawan I Wayan Agus Surya Darma I Wayan Agus Surya Darma I. M. S. Putra I. M. S. Raharja I.M.S. Putra I.M.S. Raharja Kadek Intan Cahya Putria Kadek Suar Wibawa Kusuma, Nyoman Tri Rahaditya Lampung, Pramayota Fane'a M. Yogi Riyantama Isjoni Made Sudarma N.W.E.R. Dewi Ni Kadek Juniawatia Pertiwi, Ni Kadek Puja Ari Pramana, Made Dimas Aldi Dwi Putri, Komang Sri Yanisa Putu Oka Yudiantara Trisna, I Nyoman Prayana Wibawa, K Suar