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All Journal International Journal of Advances in Applied Sciences Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Informatika Prosiding Semnastek Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA INTECOMS: Journal of Information Technology and Computer Science Zero : Jurnal Sains, Matematika, dan Terapan KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik dan Informatika Jurnal Elektro dan Telkomunikasi Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Bulletin of Computer Science Research Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) Jurnal Minfo Polgan (JMP) TECHSI - Jurnal Teknik Informatika Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Nasional Teknologi Komputer Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Jurnal Pengabdian Masyarakat International Journal of Industrial Innovation and Mechanical Engineering International Journal of Computer Technology and Science Bulletin of Engineering Science, Technology and Industry Jurnal Bisantara Informatika Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
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Journal : Bulletin of Information Technology (BIT)

Analysis of Inpatient Data Using Cluster Analysis on Simulation Dataset Putera Utama Siahaan , Andysah; Azizah Harahap, Nur; Yuni Simanullang, Rahma; Khairunnisa; Wanny, Puspita; Utari
Bulletin of Information Technology (BIT) Vol 6 No 1: Maret 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1830

Abstract

This study aims to analyze inpatient data using the K-Means Clustering method on a simulated dataset. The dataset includes various patient-related attributes such as age, billing amount, length of stay, medical condition, and type of admission. Several preprocessing steps were applied, including date conversion, duration calculation, numerical normalization, and one-hot encoding for categorical attributes. The Elbow Method was used to determine the optimal number of clusters, and clustering quality was evaluated using both the Silhouette Score and Davies-Bouldin Index. The analysis results show that the patients can be segmented into three major clusters, each exhibiting distinct characteristics—for example, younger patients with short and low-cost stays, and elderly patients with prolonged and more expensive hospitalizations. The resulting Silhouette Score of 0.14 and Davies-Bouldin Index of 1.74 reflect a moderate clustering performance, yet the model remains informative and meaningful. These clusters provide actionable insights that hospitals can use to optimize their service strategies, improve resource allocation, and enhance operational efficiency. Moreover, the study illustrates the practical application of unsupervised learning techniques in healthcare settings, contributing to data-driven decision-making practices and offering a foundation for further research into patient segmentation.
Sentiment Analysis Classification of E-commerce User Reviews Using Natural Language Processing (NLP) and Support Vector Machine (SVM) Methods Iqbal Wiranata Siregar, Jimmy; Putera Utama Siahaan, Andysah; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2018

Abstract

In the swiftly changing digital age, e-commerce has become a vital component of everyday living. Individuals actively share product reviews, whether favorable or unfavorable, which companies can utilize to grasp users' views on their services. An efficient approach for evaluating and categorizing user sentiments is required to aid in analyzing these reviews. In this scenario, the Support Vector Machine (SVM) and Natural Language Processing (NLP) methods offer the appropriate answer. This research intends to develop a classification model capable of sorting e-commerce user feedback into positive, negative, or neutral sentiments. Utilizing NLP methods to analyze the review text and SVM as the classification approach, this model aims to achieve high accuracy in identifying user sentiment. Words that do not affect sentiment analysis, like "and," "that," "for," are eliminated, and SVM is utilized once the review data is converted into vectors via the TF-IDF method. The labeled sentiment training data will be used to train the SVM model.
Co-Authors . Zulfan A. Khalid, Noor Aldeen Abda Abda Afandi Syahputra Alex Siregar Alfiandi, Alfiandi Andreas Ghanneson Nainggolan Anwar, Dede Utari Anzas, Anzas Ibezato Zalukhu Aprijal, Rendi Arahman Harahap Arif Rahman Asyifa, Nathania Aulia, Popi Aulia, Wina Ayu, Ayu Ofta Sari Azizah Harahap, Nur Baehaqi Bambang, Bambang Sugito Barus, Efriansyah Putra Bahari beckham pratama, arya Binti Saari, Erni Marlina Chairul Indra Angkat Darmeli Nasution Datin, Maha Valne Dewi Sartika Didi Riswan Dika, Dika Dina Marsauli Sibarani Efendi, Syahril Ehkan, Phaklen Eko Hariyanto Eko Hariyanto EKO WAHYUDI Farta wijaya, Rian Fawaz, Muhammad Ayyas Fawaz, Muhammad Ayyasi Ghanneson Nainggolan, Andreas Hafizhah Sufina Azzahra Hasibuan, Peronika Br Hassan, Moustafa Hussein Ali Hendra Harnanda Hermansyah Hermansyah Hermawan, Bagus Ibrahim Ibrahim Imam Solihin Iqbal Wiranata Siregar Iqbal Wiranata Siregar, Jimmy Izhari, Fahmi Juliyandri Saragih Kariyani Khairil Putra Khairul Khairul , Khairul Khairul Khairul, Khairul Khairunnisa Kiki Artika Leni Marlina Leni Marlina Leni Marlina Manurung, Monica M Marsauli Sibarani, Dina Melva Sari Panjaitan Mesran, Mesran Muham, Dinda Novita Sari Muhammad Akbar Syahbana Pane Muhammad Hasanuddin, Muhammad Muhammad Indra Muhammad Iqbal Muhammad Irsyad Muhammad Syahputra Novelan Muhammad Wahyudi Muhammad Zarlis Nahampun, Natalia Natalia Nahampun Nurwijayanti Putra, Khairil Rabe, Siska Mayasari Ramatika, Desy Rambe, Rezkinah Rendi Aprijal Rian Farta Wijaya Rizaldi, Fakhri Rizky Rinaldi Simamora, Siska Simorangkir, Elsya Sabrina Asmita Sinyo Andika Nasution, Ahmad Siregar, Iqbal Wiranata Sitepu, Nabila Putri Br Sitorus, Zulham Solihin, Imam Sony Putra Sri Wahyuni Suheri Supiyandi Supiyandi Swandi Dedi Arnold Pardede Syafran Panggabean, Edwin Syahputri, Maulisa Syahri, Rahma Syamsiar, Syamsiar Syamsul Arifin Trisnani, Anis A Ullah, Insaf Utari Wanny, Puspita Wiko Pratama Wina Aulia Wulan Ramadhani Yuni Simanullang, Rahma Zuhri Ramadhan Zulfan Zulham, Zulham Sitorus