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Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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Journal : Building of Informatics, Technology and Science

 1 
Analisis Kinerja Model Support Vector Machine dalam Prediksi Kasus HIV di Indonesia Berdasarkan Data Time Series Erza, Muhammad Al-Ghifari; Prasetyaningrum, Putri Taqwa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7365

Abstract

Accurate predictions of HIV cases are crucial in efforts to control the epidemic effectively in Indonesia. As the number of cases and the complexity of transmission factors increase, machine learning-based prediction methods are becoming increasingly relevant. This study analyzes the performance of the Support Vector Machine (SVM) model in forecasting the number of HIV cases in Indonesia using time series data from 2012 to 2024. The CRISP-DM methodology is used as the framework for the analysis process, starting from business understanding to model deployment. The dataset used includes secondary data from the Ministry of Health, such as SIHA, national surveillance, and reports from the Directorate General of Disease Prevention and Control (Ditjen P2P). The SVM model is selected due to its ability to handle non-linear data and limited data sizes, as well as its resilience to overfitting. Model evaluation is performed using MAE, RMSE, and MAPE metrics. The results of the study show that the SVR model with an RBF kernel provides good prediction accuracy, with MAE values of 691.34, RMSE of 823.11, and MAPE of 13% on the test data. Therefore, SVM can be an effective tool to support data-driven decision-making in HIV control efforts in Indonesia.
Segmentasi Produk Pakaian Menggunakan Algoritma K-Means Clustering dan Particle Swarm Optimization untuk Strategi Pemasaran Putra, Rio Aji Hadyanta; Prasetyaningrum, Putri Taqwa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7367

Abstract

This research aims to analyze product segmentation in the apparel industry using the K-Means Clustering algorithm optimized with Particle Swarm Optimization (PSO) to generate accurate product segmentation that can support more effective marketing strategies for a company. The data used in this analysis were obtained from sales transactions of a clothing manufacturing company that offers various categories of apparel products. The dataset consists of 333 rows and includes transaction numbers, product types, quantities sold, and total sales values. The data were processed using the Python programming language via Visual Studio Code. The segmentation process was initially performed using the K-Means algorithm to group products, and the Elbow method was applied to determine the optimal number of clusters. The number of clusters obtained from the Elbow method was then optimized using PSO to find more optimal cluster counts and centroids. Cluster evaluation was conducted by comparing the values of several metrics, including the Davies-Bouldin Index (DBI), Silhouette Score, Sum of Squared Error (SSE), and the SSW/SSB ratio. Although the DBI increased slightly from 0.6690 to 0.6878, indicating greater similarity between clusters, the improvement in the Silhouette Score from 0.5513 to 0.5569 suggests better internal consistency within the clusters. Furthermore, the reduction in SSE from 418.52 to 313.25 indicates a tighter distribution of data within clusters, while the significant decrease in the SSW/SSB ratio from 0.4582 to 0.3075 demonstrates more clearly defined cluster boundaries and improved separation. The results of the study produced four distinct product clusters, enabling the company to implement more targeted and differentiated marketing strategies.
Co-Authors Adi Ronggo Wicaksono Affandi Putra Pradana Agung Supoyo Agustin, Isnaini Ahmad Iwan Fadli Ahmad Mukhlasin Ahsan, Moh Ajisari, Lanang Dian Albert Yakobus Chandra Albert Yakobus Chandra Alphi Mukti Anggie Kurniawati Anggo Luthfi Yunanto Ari Wibowo Arita Witanti Aritonang, Roselina Artika Sari Arwa Ulayya Haspriyanti Ati, Gresensia Rosadelima Azzahra, Bernica Bagus Nur Solayman Bambang Setio Purnomo Bambang Setio Purnomo Budianto, Alexius Endy Cindy Okta Melinda Dapit Virdaus Denny Jean Cross Sihombing Devi Febrianti dewi, Ine shinta Dhana Sudana Eka Aryani, Eka Erza, Muhammad Al-Ghifari Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia Ibnu Rivansyah Subagyo Ibrahim, Norshahila Irfan Pratama Irya Wisnubhadra Julius Bata Jumiyati Juwita Juwita Karlina, Leni Khalifah Samiih Sya'bani Sya'bani Khoirut Tamimi Kris Rahayu Kristina Andryani Larasaty, Raditha Latifah, Retno Leni Karlina Lewoema, Scholastica Larissa Zefira luky kurniawan, luky M. Anjas Leonardi M. Irfan Bahri Mita Oktafani Mu'ti, Dewi Lestari Mukti, Alphi Rinaldi Nalendra Mutaqin Akbar Nadeak, Puja Waldi Nanda, Tietan Geovanka Ningsih, Rully Ningsih, Ruly Norshahila Ibrahim Nuning Rusmilawati Nur Sholehah Dian Saputri Nuri Budi Hangesti Nurul Tiara Kadir Okta, Sri Oktafani, Mita Ozzi Suria Ozzi Suria Ozzi Suria Pipin Yuliyanto Pratama, Bagus Wahyu Ari Pratama, Harfin Ibna Pratama, Irfan Puja Waldi Nadeak Puja Putra, Rio Aji Hadyanta Putry Wahyu Setyaningsih Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Rustiawan, Muhammad Rizqi Akfani saka, Hildegardis Kristina Santoso Pamungkas Sari, Artika Scholastica Lewoema Setiyani, Santi Setyaningsih, Putry Wahyu Simarmata, Penni Wintasari Subagyo, Ibnu Rivansyah Suria, Ozzi Suyoto Suyoto Viony Julianti Sipayung Wahyuningsih Wahyuningsih