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All Journal Transformasi Fuse-teknik Elektro
Nurfalah, Rifki
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics

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Prototype Sistem Pengeringan Gabah Padi Berbasis Logika Crisp dengan Arduino Uno Nurfalah, Rifki; Susilawati, Helfy; Nurpadillah, Sifa
Fuse-teknik Elektro Vol 1 No 1 (2021): Fuse-teknik Elektro
Publisher : Fakultas Teknik Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/jft.v1i1.1164

Abstract

Dari banyak macam jenis komoditi produksi pangan, untuk penelitian ini dipilih gabah sebagai objek uji pengeringan. Gabah memiliki ketentuan kadar air pada 14% untuk kondisi kering optimal sesuai dari data Badan Standar Nasional Indonesia (BSNI). Dengan menggunakan metode prototyping sesuai kebutuhan kondisi gabah dan lingkungan produksi sistem prototype dirancang dengan memperhatikan elemen elemen tersebut, digunakan Heater PTC dan Heater “Selimut” sebagai sumber panas utama, fan blower luar dan fan dalam untuk kontrol suhu, sensor DHT22 untuk mengetahui kondisi tabung pengeringan (suhu dan kelembapan), dan sensor YL-69 untuk mengetahui kondisi objek pengeringan. Dilakukan uji kemampuan mekanikal, algoritma, dan pengujian keseluruhan dengan data uji coba sebagai berikut, sensor suhu (DHT22) berpotensi error 0,00~1,74%, sensor kadar air (YL-69) dengan potensi error 0,00~9,14%, dan suhu kerja mulai dari 23°C~77°C. Waktu yang dibutuhkan untuk proses pengeringan agar gabah mencapai kadar air 14% yaitu ~53 menit untuk gabah baru panen 200ml, ~15 menit untuk 100ml, dan ~102 menit untuk gabah terkondisi basah 200ml.
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 Helfy Susilawati Khoerunnisa, Ica Sifa Nurpadillah