Article Per Year (5 Year)
p-Index From 2021 - 2026
5.19
P-Index
This Author published in this journals
All Journal JPM17: Jurnal Pengabdian Masyarakat Heuristic JOURNAL OF INDUSTRIAL AND MANUFACTURE ENGINEERING Teknika: Engineering and Sains Journal Kaizen : Management Systems and Industrial Engineering Journal Jurnal Optimalisasi Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Surya Teknika JURMATIS : Jurnal Manajemen Teknologi dan Teknik Industri JISO : Journal of Industrial and Systems Optimization Among : Jurnal Pengabdian Masyarakat Industrika : Jurnal Ilmiah Teknik Industri Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research Jurnal Taguchi : Jurnal Ilmiah Teknik dan Manajemen Industri Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Serambi Engineering
Siti Mundari
Universitas 17 Agustus 1945 Surabaya
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Serambi Engineering

 1 
Comparison of Financial Distress Prediction Model Accuracy Using Support Vector Machines and Discriminant Analysis Methods Herlina; Mundari, Siti
Jurnal Serambi Engineering Vol. 10 No. 2 (2025): April 2025
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Financial distress is a stage before the company goes bankrupt. For this reason, the ability to predict financial can be useful information for companies and investors. This information is useful for companies to be able to improve their financial condition so that the company does not go bankrupt. For investors, this information is useful to avoid investor losses in capital investment. Studies on financial distress have been conducted for a long time, starting with using statistics until now being developed using artificial intelligence methods. The purpose of this study was to compare the accuracy of the financial distress prediction model for publicly traded manufacturing companies in miscellaneous industry sectors listed on the Indonesia Stock Exchange using the data mining method, namely Support Vector Machines, which is one of artificial intelligence method and the statistical method, namely Discriminant Analysis. From the research results, the two methods provide equally good accuracy. Based on the processed data, the accuracy of the two methods is 100%.
Co-Authors Adiati Trihastuti Adiati Trihastuti, Adiati Ajeng Septiana Alfiono, Bima Andika Pratama Andre Leonard Sawargo, Andre Leonard Arvy Argadya Fortunata Cahyo, Chandra Dwi Dhiya 'ul Hanifah Dhoan Prasetyo Widanarko Cahyono Erm Yuliaty Erma Yuliaty Erma Yuliaty, Erma Fakhrur Rozi, Fakhrur Farkhan, M. Firdah Lestari, Erlina Firdaus Muswati, Awanda Hanifah, Dhiya 'ul Herlina Herlina Herlina Herlina Herlina Kurniawan, David Elvando Daniel Kurniawan, Mashur Affandi Mukhammad Misbakh Saffana Nakhas, Muhammad Iqbal Nekea, Keane Kenze Nugroho, Septian Adi Octavian, Muhammad Wirawan Prima, Jupiter Yulio Rendragraha, Putra Riski Rifaldi, Moch Fiqi Roikan Sofianto Romadhon, Muhammad Guntur Santoso, Afiyah Febriana Siti Muhimatul Khoiroh Sri Hadijono Sri Hadijono, Sri Waaidzin, Zubaidu Wijaya, Aditya Wulan Ayu Permatasari Zainal Arief Zainal Arief Zainal Arief