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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Algoritma Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI CARADDE: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Cerdas Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Journal of Energy and Electrical Engineering (JEEE) Jurnal Teknik Informatika (JUTIF) Teknika Mosharafa: Jurnal Pendidikan Matematika Transformasi Algoritme Jurnal Mahasiswa Teknik Informatika Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Algoritma Fuse-teknik Elektro MDP Student Conference
Helfy Susilawati
Fakultas Teknik Universitas Garut
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Transformasi

 1 
KEPUTUSAN MANUSIA VS KEPUTUSAN MESIN: STUDI KOMPARATIF TERHADAP AKURASI DAN KONSISTENSI DALAM PENGAMBILAN KEPUTUSAN Nurfalah, Rifki; Susilawati, Helfy; Khoerunnisa, Ica
TRANSFORMASI Vol 20, No 2 (2024): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v20i2.414

Abstract

This study aims to analyze and compare the accuracy, consistency, and decision-making efficiency between humans and machine learning (ML) algorithms in tabular data classification tasks. The dataset comprises 50 classification cases containing both numerical and categorical features with binary decision labels. Two groups were compared: 10 human participants, and six ML algorithms—Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, k-Nearest Neighbors, and Naive Bayes. ML models were trained on 80% of the data and tested on the remaining 20%, while human participants manually classified all 50 test cases. The results showed that the average human accuracy was 76.2%, while ML algorithms achieved between 78.9% and 91.8%, with Random Forest yielding the highest performance. Human decision-making took an average of 18 seconds per case, significantly slower than the algorithmic predictions completed within milliseconds. Additionally, high variability in human responses indicated lower consistency compared to deterministic outputs from ML models. These findings support the integration of ML algorithms as a decision support or replacement tool in data-driven domains, with the potential to reduce human error in high-stakes environments. Nevertheless, human involvement remains essential in contexts requiring ethical consideration and interpretability.
KEPUTUSAN MANUSIA VS KEPUTUSAN MESIN: STUDI KOMPARATIF TERHADAP AKURASI DAN KONSISTENSI DALAM PENGAMBILAN KEPUTUSAN Nurfalah, Rifki; Susilawati, Helfy; Khoerunnisa, Ica
TRANSFORMASI Vol 20, No 2 (2024): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v20i2.414

Abstract

This study aims to analyze and compare the accuracy, consistency, and decision-making efficiency between humans and machine learning (ML) algorithms in tabular data classification tasks. The dataset comprises 50 classification cases containing both numerical and categorical features with binary decision labels. Two groups were compared: 10 human participants, and six ML algorithms—Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, k-Nearest Neighbors, and Naive Bayes. ML models were trained on 80% of the data and tested on the remaining 20%, while human participants manually classified all 50 test cases. The results showed that the average human accuracy was 76.2%, while ML algorithms achieved between 78.9% and 91.8%, with Random Forest yielding the highest performance. Human decision-making took an average of 18 seconds per case, significantly slower than the algorithmic predictions completed within milliseconds. Additionally, high variability in human responses indicated lower consistency compared to deterministic outputs from ML models. These findings support the integration of ML algorithms as a decision support or replacement tool in data-driven domains, with the potential to reduce human error in high-stakes environments. Nevertheless, human involvement remains essential in contexts requiring ethical consideration and interpretability.
Co-Authors Ade Rukmana Ade Rukmana Adhitya Yusuf Wibysono Adiatma, Dani Aeni, Dinda Noorfaidah Afina Carmelya, Anindya Ahmad Noor Jaman ahmad rizal Akhmad Fauzi Ikhsan Akhmad Fauzi Ikhsan Alfaz Arva Baihaqi Aloysius Adya Pramudita Artemysia, Khaulyca Arva Baihaqi, Alfaz Arva Dani Prasetyo Adi, Puput Dhiky Juansyah Dinda Noorfaidah Aeni Dini Fajriani Etnisa, Moch Mirza Evi Novitasari Fadillah, Ardi Fajriani, Dini Fauzi, Moch Zulfi Fazri, Nurul Firman Firman Firman Fitri Nuraeni Galura Muhammad Suranegara Ghofur, Shaefan Afuan Ginaldi Ari Nugroho Gusman, Dilla Oktaviani Hamdani, Nizar Alam Haqiqi, Mokh. Mirza Etnisa Iik Muhammad Malik Matin Iik Muhammad Malik Matin Ikhsan, Akhmad Fauzi Jaman, Ahmad Noor Juansyah, Dhiky Juniawan, Ega Rizki Khoerunnisa, Ica Latukolan, Merlyn Inova Christie Lubis, Anggi Muhammad M.Angdarun, M.Angdarun Malikmatin, Iik Muhammad Matin, Iik Muhammad Malik Mirza Etnisa Haqiqi, Mokhamamad Muhamad, Reza Muhammad Ihsan Mutmainah, Rina Nasrullah Armi Novitasari, Evi Nurdin, Agung Ihwan Nurfalah, Rifki Nurfitriani, Nabila Nurichsan, Irman Reza Muhamad Rifki Nurfalah Rina Mutmainah RR. Ella Evrita Hestiandari Rukmana, Ade Samie, Muhammad Ikbal Satyawan, Arief Suryadi Sediono, Wahju Setyawan, Arief Suryadi Shaefan Afuan Ghofur Shamie, M. Ikbal Sifa Nurpadillah Sobari, Acep Hasan Sopian, Sani Moch Sopian, Sani Moch. Sri Nuraeni, Sri Sugandi, Gandi Sunardi, Dede Suryadi Satyawan, Arief Syarif Saeful Yusup Tri Arif Wiharso TRI ARIF WIHARSO Wibysono, Adhitya Yusuf Wiharso, Tri Arif Wiwik Handayani Yusup, Syarif Saeful