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All Journal JURNAL PENDIDIKAN TAMBUSAI Transformasi Fuse-teknik Elektro
Khoerunnisa, Ica
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Perancangan dan Analisis Antena Yagi-Uda pada Frekuensi 433 Mhz untuk Sistem Komunikasi Radiosonde dan Ground Control Station (GCS) Khoerunnisa, Ica; Ikhsan, Akhmad Fauzi; Hasyim, Ahmad
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.v1i1.1152

Abstract

Antena Yagi-Uda adalah antena yang bersifat directional, artinya antena tersebut hanya dapat mengambil atau menerima sinyal pada satu arah. Tujuan dari pembuatan antena adalah menjamin hubungan komunikasi antara Ground Control Station (GCS) dan muatan balon atmosfer yang diterbangkan radiosonde. Perancangan dilakukan menggunakan perangkat lunak Computer Simulation Technology (CST) dan direalisasikan untuk dilakukan pengukuran dan pengujian. Dari hasil simulasi, antena Yagi-Uda memiliki nilai gain 11,09 dBi, VSWR bernilai 1,46 dan return loss bernilai 14,442 dB. Sedangkan dari hasil pengukuran antena Yagi-Uda pada frekuensi 433 MHz menggunakan Advantest R3770 Network Analyzer didapat nilai gain 13,63 dBi, VSWR bernilai 1,46 dan return loss bernilai 14,471 dB.
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.
Perbandingan Pemeriksaan Feses Metode Natif dengan Sedimentasi Menggunakan NaCl 0,9% dalam Mendeteksi Telur Cacing Soil Transmitted Helminth (STH) Khoerunnisa, Ica; Solikah, Monika Putri; Ismarwati, Ismarwati
Jurnal Pendidikan Tambusai Vol. 8 No. 3 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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

Abstract

Soil transmitted helminth (STH) merupakan cacing parasit yang dapat menginfeksi manusia. Status kecacingan seseorang dapat dipastikan dengan menemukan telur cacing pada pemeriksaan laboratorium. Metode pemeriksaan feses dapat dilakukan dengan beberapa metode diantaranya metode sedimentasi menggunakan NaCl 0,9% dan metode natif. Sampel yang digunakan pada penelitian ini adalah sampel feses positif sebanyak 15 sampel yang diperoleh dari UPT Laboratorium Kesehatan Daerah kota Magelang. Teknik pengambilan sampel dalam penelitian ini adalah Total sampling. Analisis data dilakukan dengan uji normalitas Shapiro-Wilik dan dilanjutkan dengan menggunakan uji Wilcoxon untuk mengetahui perbandingan metode natif dan metode sedimentasi menggunakan NaCl 0,9%. Hasil uji normalitas didapatkan nilai sebesar 0,000 dimana data tersebut dinyatakan tidak terdistribusi normal. Sedangkan pada uji Wilcoxon didapatkan hasil nilai p value sebesar 1,000 dimana nilai tersebut lebih besar dari pada a yaitu 0,05 jadi 1000 > 0,05 maka tidak terdapat perbandingan yang signifikan antara metode natif dan metode sedimentasi dalam mendeteksi telur cacing STH.
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 Ahmad Hasyim Helfy Susilawati Ikhsan, Akhmad Fauzi Ismarwati Rendra Widyatama I.M. Sunarsih Sutaryo Nurfalah, Rifki Solikah, Monika Putri