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

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Enhancing Breast Cancer Recognition in Histopathological Imaging Using Fine-Tuned CNN Darma, I Wayan Agus Surya; Sutramiani, Ni Putu
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 12 No 3 (2024): Vol. 12, No. 3, December 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2024.v12.i03.p04

Abstract

Global Cancer Statistics reports that of the 2.3 million cases of breast cancer worldwide, 600,000 result in death. Factors contributing to breast cancer in women include both genetic and lifestyle influences. One method for recognizing breast cancer is through histopathology images. Recently, deep learning has gained significant attention in machine learning due to its powerful capabilities in modeling complex data, such as images. In this study, we classify breast cancer by training a Convolutional Neural Network (CNN) model on a dataset of histopathology images annotated and validated by experts, containing two classes. We propose an optimization strategy for CNN models to enhance breast cancer recognition performance, applying a fine-tuning strategy to MobileNetV2 and InceptionResNetV2 to evaluate CNN performance in classifying breast cancer within histopathological images. The experimental results demonstrate that the model achieves optimal performance with an accuracy of 96.22%.
Design and Development of Customer Relationship Management in a Construction Company Pertiwi, Ni Kadek Puja Ari; Sutramiani, Ni Putu; Wibawa, K Suar
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): Juli
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qhkc3j28

Abstract

CV. Puspa Karya is a construction company that faces challenges in customer management and marketing activities. This study aims to design and implement a Customer Relationship Management (CRM) system using the Flectra framework to support marketing, sales, and customer‐service processes more effectively, with the specific objectives of improving both operational efficiency and customer satisfaction. The research employs the Accelerated SAP (ASAP) methodology, chosen for its systematic, result‐oriented approach that is well suited to projects requiring structured planning and rapid execution. ASAP was applied in five tailored phases: Project Preparation, Business Blueprint, Realisation, Final Preparation, and Go-Live & Support. The developed system was validated through User Acceptance Testing (UAT), achieving a final score of 167. User satisfaction was further assessed via the Post-Study System Usability Questionnaire (PSSUQ), yielding an overall mean score of 1.60 on a 1–7 scale (where lower scores indicate higher satisfaction): System Usefulness 1.55, Information Quality 1.66, and Interface Quality 1.61. These results exceed typical industry benchmarks for comparable systems. The implications include qualitative enhancements in customer‐service quality and quantitative gains in process speed and prospect‐tracking accuracy, leading to heightened operational professionalism, increased client trust, and stronger potential for customer loyalty.
Customer Segmentation for Optimizing Marketing Strategy at Hotel Puri Mesari Using the K-Means Clustering Method Ni Kadek Juniawatia; Ni Putu Sutramiani; Kadek Suar Wibawa
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 6 (2025): Jurnal Resit Multidisiplin Edukasi (Edisi Juni 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i6.382

Abstract

Hotel Puri Mesari Sanur menghadapi tantangan dalam mempertahankan tingkat okupansi dan retensi pelanggan, dengan tingkat retensi hanya 32,44%. Penelitian ini bertujuan untuk menganalisis segmentasi pelanggan menggunakan algoritma K-Means Clustering dan memberikan rekomendasi strategi pemasaran yang efektif. Tujuan tersebut adalah untuk memahami karakteristik pelanggan dan meningkatkan potensi pendapatan di tengah persaingan industri perhotelan yang ketat. Data reservasi hotel dari 2018 hingga 2024 dianalisis menggunakan K-Means Clustering, dengan validasi melalui Silhouette Score dan Elbow Method. Hasil analisis menunjukkan tiga cluster pelanggan: Cluster 0 mencakup pelanggan loyal dengan rata-rata 371 kunjungan dan pengeluaran moderat; Cluster 1 terdiri dari pelanggan dengan kunjungan rendah tetapi beragam asal negara; dan Cluster 2 merupakan segmen eksklusif dengan pengeluaran tertinggi dan durasi menginap terpanjang. Rekomendasi strategi pemasaran disusun menggunakan Marketing Mix 4P, lalu pihak manajemen hotel menguji rekomendasi tersebut dengan mengisi kuesioner skala Likert kepada mereka. Hasilnya, didapatkan rekomendasi strategi bagi setiap cluster yang sudah sesuai dan dapat diterapkan di hotel untuk memperkuat hubungan dengan pelanggan dan meningkatkan pendapatan.
Designing a Product Classification Dashboard for Marketing Strategy Using K-Nearest Neighbor Kadek Intan Cahya Putria; Anak Agung Ngurah Hary Susila; Ni Putu Sutramiani
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 7 (2025): Jurnal Riset Multidisiplin Edukasi (Edisi Juli 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i7.726

Abstract

The development of information technology has driven the use of sales data to support data-driven business decision-making. This study aims to design a dashboard to classify Orlenalycious Padangsambian's products using the K-Nearest Neighbor (K-NN) algorithm to determine more accurate marketing strategies. The methods used include collecting sales data from the Moka POS system, data preprocessing, classification using the K-NN algorithm with K=5, and visualizing the classification results in a Streamlit-based dashboard. The classification results divide the products into three categories: Highly Popular, Popular, and Fairly Popular. The proposed marketing strategy refers to the 4P Marketing Mix, where highly popular products are promoted intensively, popular products are pushed through advertising, and fairly popular products are evaluated or promoted through bundling. The resulting dashboard displays informative visualizations such as pie charts and bar charts to facilitate the analysis of sales trends and product performance. This study provides a solution for Orlenalycious to design more efficient and effective data-driven marketing strategies, as well as offering an easier way to monitor and evaluate product performance in real-time.
Co-Authors A. A. N. H. Susila A.A.N.H. Susila Agus Ghana Putra Partama Anak Agung Kompiang Oka Sudana Anak Agung Ngurah Hary Susila Arsa, Dewa Made Sri 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 Nyoman Piarsa I Nyoman Piarsa 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 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 Wibawa, K Suar Yonatan Adiwinata