cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sabam Simbolon
Contact Email
sabam.simbolon@ubd.ac.id
Phone
+628983591589
Journal Mail Official
yusuf.kurnia@ubd.ac.id
Editorial Address
Imam Bonjol No.41 Karawaci Ilir
Location
Kota tangerang,
Banten
INDONESIA
RUBINSTEIN
Published by Universitas Buddhi Dharma
ISSN : 29854520     EISSN : 29854512     DOI : -
Core Subject : Economy, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other
RUBINSTEIN juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora adalah jurnal yang menerbitkan artikel penelitian yang meliputi bidang ilmu multidisiplin, yang mencangkup Bisnis, Sains & Teknologi dan Sosial & Humaniora. Jurnal RUBINSTEIN menerima manuskrip atau naskah artikel dalam bidang riset, mencakup: Ekonomi publik Ekonomi industri Ekonomi intemasional Manajemen bisnis Manajemen keuangan Manajemen pemasaran Manajemen sumber daya manusia Administrasi niaga Akuntansi keuangan Perpajakan Pemeriksaan akuntansi Akuntansi manajemen Hukum Sosial Datamining Cloud computing Robotics and sensor Expert system IoT Decision support system Bussiness intelegent Linguistics Literature Culture studies with english requirements
Arjuna Subject : Umum - Umum
Articles 42 Documents
 1   2   3   4   5 
Comparative Analysis of Support Vector Machine, Decision Tree, and Naive Bayes in Evaluating Machine Learning Effectiveness Hariyanto, Susanto; Indah Fenriana; Desiyanna Lasut; Febrian
RUBINSTEIN Vol. 4 No. 1 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i1.4041

Abstract

This study aims to analyze and compare the performance of three widely used machine learning algorithms for data classification: Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. These algorithms employ distinct approaches in handling data, making it essential to evaluate their effectiveness and efficiency in classification tasks. In the digital era characterized by massive data growth, the selection of an appropriate classification algorithm is a critical determinant for accurate and efficient data-driven decision-making. The main contribution of this research is to provide a comprehensive understanding of the relative strengths and limitations of each algorithm under varying data conditions. This study not only highlights comparative performance outcomes but also emphasizes practical implications for researchers and data science practitioners in selecting algorithms suited to specific needs. In doing so, it addresses a research gap concerning integrated evaluations of data characteristics and algorithmic performance. The methodology adopts a quantitative approach through computational experiments using standardized datasets (Titanic, Spam Email, and Wine). The datasets were divided into training and testing sets and analyzed using Python with the scikit-learn library. Performance evaluation was conducted based on accuracy, precision, recall, and F1-score, validated through cross-validation techniques to ensure reliability of results. The findings indicate that SVM outperforms in terms of accuracy and recall on complex datasets, Naïve Bayes is more efficient in computational time particularly for text data, while Decision Tree stands out for model interpretability despite slightly lower accuracy. These results are expected to serve as a practical reference for selecting suitable algorithms according to data characteristics, thereby supporting more targeted and intelligent modeling strategies in the era of digital transformation.
PERAN KELURAHAN DALAM VERIFIKASI DAN VALIDASI DATA KEMISKINAN EKSTREM DI KELURAHAN MARGASARI KOTA TANGERANG Foharato Gulo; Udin, Wawan
RUBINSTEIN Vol. 4 No. 1 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i1.4059

Abstract

Accurate data on extremely poor households determines the effectiveness of extreme poverty alleviation programs, so the process of verifying and validating (verval) extreme poverty data needs to be carried out.   This study aims to determine the role of the sub-district in verifying and validating extreme poverty data in Margasari Sub-district, Karawaci District, Tangerang City, and to identify the obstacles encountered in the verification and validation process. The background of this research focuses on the importance of accurate poverty data for more targeted policy planning, but there are still challenges in the process of verifying and validating data in the field. The method used in this study is a qualitative approach, conducting interviews and observations in Margasari Sub-district to explore information related to the implementation of verification and validation as well as the obstacles that arise. The results of this study show that the role of the sub-district in the implementation of extreme poverty data verification and validation includes coordination with RT/RW, community leaders, and related parties, conducting field verification, validation through sub-district deliberations, and reporting the results of verification and validation to higher authorities. This shows that the sub-district does not only act as an administrative implementer, but also as a facilitator and liaison between government policies and the community. The obstacles encountered in the implementation of verification and validation include limited human resources, inaccuracy of initial data from the central government, resistance from some members of the community to the data collection process, and limited information technology facilities.

Page 5 of 5 | Total Record : 42

 1   2   3   4   5 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora) Vol. 3 No. 2 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora) Vol. 3 No. 1 (2024): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora) Vol. 2 No. 2 (2024): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora) Vol. 2 No. 1 (2023): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora) Vol. 1 No. 2 (2023): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora) Vol. 1 No. 1 (2022): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)