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All Journal Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Kitektro JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Rekayasa elektrika
Fitri Arnia
Universitas Syiah Kuala
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Sistem Multi-Sensor Nirkabel Berbasis RFID Untuk Pemantauan Keaktifan Siswa Zakiah Zakiah; Yuwaldi Away; Fitri Arnia; Andri Novandri
Jurnal Rekayasa Elektrika Vol 15, No 3 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i3.14107

Abstract

This study aims to design a multisensor observation system, develop a microcontroller-based prototype with ESP8266 and RFID modules, and analyze the performance of the prototype. From the testing carried out by tagging each time students do activities, the client tag data will be sent to the server, and the results are displayed PLX-DAQ. With the 50 tag cards that have registered the IDs of each student, it shows that those who attend the schedule will be given the logic "1" (otherwise logic "0"), and attendance data will be obtained by calculating the number of attendance of students in four types of activities in four locations. The data is transmitted to Data-loggers through two configurations, namely Data-logger as the client (indirect), and Data-logger as a server (direct). From the two configurations, it was found that the configuration of Data-logger as a server had a performance of 19.08% better than Data-logger as a client. From the data processing, it was found that the highest activity of students was the interest in the activities of language institutions (95.92%), followed by religious activities (95.83%), teaching and earning activities (93.88%), and reading (79.59 %)
Unjuk Kerja Pendeteksian Dhamir Raf’a Munfasil pada Citra Al-Qur’an dengan Penggabungan Algoritma Adaboost dan Tranformasi Slant Juanda Nargaza; Yuwaldi Away; Fitri Arnia
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i4.15486

Abstract

Dalam penelitian ini pendeteksian Pola karakter Dhamir Raf’a Munfasil (DRM) pada citra Al-Qur’an menggunakan metode Transformasi Slant, Adaboosting dan gabungan Slant - Adaboosting yang kemudian di ukur unjuk kerja pendeteksian DRM pada setiap metode. Hasil penelitian menunjukkan bahwa sistem pendeteksian pola Dhamir Raf’a Munfasil pada citra Al-Qur’an menggunakan Transformasi Slant memiliki prescision sebesar 50% dan Recall 90%. Dengan menggunakan Algoritma Adaboosting memiliki prescision sebesar 71% dan Recall 92%. dengan menggunakan gabungan Algoritma tersebut Slant-Adaboost memiliki prescision sebesa 86% dan Recall 93%. Dari hasil perbandingan antara Adaboost dan Gabungan Slant-Adaboost, Slant-Adaboost memiliki tingkat akurasi lebih baik dari pada Adaboost sendiri.
Klasifikasi Kanker Payudara Menggunakan Citra Termal Berdasarkan Filter Gabor Listia Sukma Putri; Fitri Arnia; Rusdha Muharar
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i4.15487

Abstract

Penelitian ini bertujuan untuk mengambil nilai fitur dari citra termal payudara melalui ekstraksi fitur Filter Gabor, dengan fokus pada mean, variance, kurtosis, skewness, dan entropi, serta untuk mengevaluasi kinerja tiga metode klasifikasi, yaitu K-Nearest Neighbor (KNN), Support Vector Machine (SVM), dan Artificial Neural Network (ANN). Kanker payudara merupakan masalah kesehatan yang serius, terutama bagi perempuan, karena potensial menyebabkan kematian. Dalam upaya mengurangi risiko kematian, penelitian dilakukan untuk mendeteksi kanker secara dini, termasuk menggunakan termografi. Metode ini memanfaatkan suhu dari objek untuk mendeteksi kanker, dimana pola suhu yang berbeda di area payudara yang terkena kanker dapat diamati karena peningkatan aliran darah. Penelitian menggunakan citra termal dari Database for Mastology Research (DMR) sebanyak 150 citra, dengan 108 citra sehat dan 42 citra sakit. Fitur tekstur diekstraksi menggunakan Filter Gabor dengan variasi skala dan sudut orientasi tertentu. Hasilnya diuji dengan beberapa metode klasifikasi, dimana ANN menunjukkan akurasi tertinggi yaitu 88.88%, diikuti oleh KNN dengan 86.66% dan SVM dengan 84.44%. Hasil ini menegaskan bahwa termografi bersama dengan ekstraksi fitur tekstur dan algoritma pembelajaran mesin dapat efektif dalam mendeteksi kanker payudara secara dini, menawarkan potensi diagnosis dini dan manajemen penyakit yang efektif.
Improved Histogram of Oriented Gradient (HOG) Feature Extraction for Facial Expressions Classification Luthfiar Ramiady; Fitri Arnia; Maulisa Oktiana; Andri Novandri
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.34044

Abstract

Facial expression classification system is one of the implementations of machine learning (ML) that takes facial expression datasets, undergoes training, and then utilizes the trained results to recognize facial expressions in new facial images. The recognized facial expressions include anger, contempt, disgust, fear, happy, sadness, and surprise expressions. The method employed for facial feature extraction utilizes histogram-oriented gradient (HOG). This study proposes an enhancement method for HOG feature extraction by reducing the feature dimension into multiple sub-features based on gradient orientation intervals, referred to as HOG channel (HOG-C). Classifier testing techniques are divided into two methods for comparison—support vector machines (SVM) with HOG features and SVM with HOG-C features. The testing results demonstrate that SVM with HOG achieves an accuracy of 99.9% with an average training time of 18.03 minutes, while SVM with HOG-C attains a 100% accuracy with an average training time of 18.09 minutes. The testing outcomes reveal that the implementation of SVM with HOG-C successfully enhances accuracy for facial expression classification.
Co-Authors Akhyar Bintang Hayatun Maghfirah Hendrik Leo Ilal Mahdi Juanda Nargaza Kahlil Muchtar Khairul Munadi Khairul Munadi Khusnul Azima Listia Sukma Putri Luthfiar Ramiady Maulisa Oktiana Muhammad Haries Novandri, Andri Nur Amalia Hasma Nuriza Pramita Oktiana, Maulisa Raihan Islamadina Ramadhani Ramadhani Rizal Fikri Rizka Ramadhana Rusdha Muharar Rusdha Muharrar Saddami, Khairun Safrizal Razali Syamsul Bahri Tia Ernita TWK Muhammad Iqbal Yustina Dhyanti Yuwaldi Away Zakiah Zakiah