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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah FIFO Akuntabel : Jurnal Ekonomi dan Keuangan Jurnal Sisfokom (Sistem Informasi dan Komputer) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN TAMBUSAI Jurnal Algoritma, Logika dan Komputasi Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Telematika MKOM Media Abdimas Journal Collabits Jurnal Ilmiah Universitas Satya Negara Indonesia
Sari, Yunita Sartika
Program Studi Sistem Informasi, Fakultas Ilmu Komputer Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : journal collabits

 1 
Use and Application of Virtual Reality (VR) in Learning Optimization and Effectiveness to Increase Student Interest in Learning: A Study Using Systematic Literature Review (SLR) Method Sari, Yunita Sartika; Santoso, Hadi; Rahmah, Nia; Ibrahim, Maulana; Imaman, Rahardian Aulia
Journal Collabits Vol 1, No 2 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i2.27264

Abstract

Adopsi media pembelajaran digital telah merevolusi cara kita belajar dan mengajar. Dalam konteks sistem pendidikan, sering ada kebutuhan untuk media baru. Media inovatif ini diharapkan dapat menyulut motivasi belajar siswa. Selain itu, integrasi mereka ke dalam sistem pendidikan bertujuan untuk meningkatkan prestasi siswa baik secara akademis maupun di luar. Media digital memiliki potensi yang signifikan untuk meningkatkan pengalaman belajar siswa. Penelitian ini berfokus pada kajian pustaka terkait pemanfaatan lingkungan belajar digital dalam pendidikan. Dalam lanskap yang berkembang pesat saat ini, banyak materi pembelajaran telah beralih dari format berbasis cetak ke digital. Meluasnya adopsi media digital dalam pendidikan mencakup teknologi mutakhir seperti virtual reality (VR). VR berfungsi sebagai alat pembelajaran yang kreatif dan mendalam, membawa siswa ke dunia baru yang menantang untuk dijelajahi melalui metode pengajaran tradisional. Penelitian secara konsisten menekankan pentingnya lingkungan belajar berbasis teknologi di abad ke-21. Media ini memfasilitasi keterlibatan siswa, membuat pembelajaran lebih interaktif dan efektif. Pada akhirnya, media pembelajaran mencakup media apa pun yang secara efektif menyampaikan pesan atau informasi antara siswa dan pendidik, menangkap perhatian, minat, pikiran, dan emosi mereka untuk mencapai tujuan pendidikan.Kata kunci: virtual reality (VR), lingkungan belajar digital, optimasi pembelajaran. 
Implementation Analytical Hierarchy Process Method to Improve the Effectiveness of Social Assistance Distribution Sari, Yunita Sartika; Hakim, Lukman
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25490

Abstract

Based on the report of the central statistics agency, the number of poor people in Indonesia reached 26.16 million people. The government has made efforts to provide assistance to overcome this problem, one of which is beneficiaries. The distribution of beneficiaries which is being held is still not optimal because of the uneven distribution of aid to underprivileged communities. The purpose of this research is to implement a Decision Support System (DSS) to determine the right community to receive beneficiaries which will be given based on several criteria used, namely: education, employment, and place of residence. In this study, proposes to build a model that has a decision-making concept. The method used in this Decision Support System is the Analytical Hierarchy Process (AHP). The expected results in this study are a decision support system that can assist in determining beneficiaries.
Comparative Analysis of Google Dialogflow and Rule-Based NLTK Chatbots for Application FAQ Yasin, Raihan Nur; Cherid, Ali Hadi; Prihandi, Ifan; Sari, Yunita Sartika
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27345

Abstract

This study presents a comparative analysis of two chatbot frameworks, Google Dialogflow and rule-based NLTK (Natural Language Toolkit), for the development of chatbots to handle frequently asked questions (FAQ) in applications. The study focuses on Blender, a popular 3D modeling software, as a case study. Ten testing questions were used to evaluate the chatbots' accuracy, precision, recall, and F1-score. The results showed that Dialogflow achieved an accuracy of 80%, precision of 80%, recall of 100%, and an F1-score of 88.9%. In contrast, the rule- based NLTK chatbot achieved an accuracy of 60%, precision of 66.7%, recall of 80%, and an F1-score of 72.8%. The study concluded that Dialogflow is a more effective and reliable chatbot for handling Blender FAQs due to its ability to retrieve relevant information from a large knowledge base and its use of machine learning algorithms to improve its performance over time. However, the rule-based NLTK chatbot may still be useful in certain situations where a more simple and customizable chatbot is required.
A Data Science Approach to Cancer Patient Classification Using Support Vector Machine and Random Forest Anggraini, Devi Dwi; Salsabila, Mutiara Rizky; Kamila, Keisya Rizkia; Sari, Yunita Sartika
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37642

Abstract

The increasing availability of healthcare data has encouraged the application of data science and machine learning techniques in medical research. Cancer patient datasets contain numerical demographic and clinical attributes that can be utilized for classification tasks; however, complex feature relationships and limited feature relevance remain key challenges. This study aims to analyze cancer patient data and compare the performance of Support Vector Machine and Random Forest algorithms for gender classification. The dataset used in this study consists of numerical features, including patient age, tumor size, number of examined lymph nodes, number of positive lymph nodes, body mass index, and survival duration measured in months. The research methodology includes data preprocessing, exploratory data analysis, model development, and performance evaluation. Feature normalization and data splitting are applied to ensure a fair comparison between models, while exploratory analysis is conducted to examine data distribution and relationships among variables. Both classification models are trained under identical experimental settings and evaluated using accuracy as the primary performance metric. The results indicate that both algorithms can classify cancer patients with satisfactory accuracy. Support Vector Machine demonstrates slightly better performance compared to Random Forest, suggesting its effectiveness in handling numerical data with complex decision boundaries. The findings highlight the importance of appropriate algorithm selection and feature utilization in healthcare data analysis.
Co-Authors Anggraini, Devi Dwi Ardi Cahyadi Yudistira Ayu Oktaviani, Ayu Bagus Priambodo Cherid, Ali Hadi Danny Yudin Djahidin Deny Friyansyah Ema Utami Fadli Fairuz Ramadhan Ferry Wahyu Wibowo Firdaus, Yaumil Hadi Santoso Hidayat, Rahmat Rian Ibrahim, Maulana Ifan Prihandi Ika Agustina Imaman, Rahardian Aulia Indah Fitriana Hapsari Inge Handriani Irma Hamidah Nasution Jumaryadi, Yuwan Kaburuan, Emil Robert Kamila, Keisya Rizkia Kristanto, Dedy KUSRINI Kusumaningsih, Dewi Lukman Hakim Misbahul Fajri muhamad nashar Muhammad Nashar Nia Rahma Kurnianda Nia Rahma Kurnianda, Nia Rahma Nicolaus Pramono Hardosubroto Nurjanah, Amelia Purnama, Nurlela Rahmah, Nia Ratna Mutu Manikam Salsabila, Mutiara Rizky Setiawan, Fredi Wellhard Halomoan Simamora Yasin, Raihan Nur Yudo Devianto