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All Journal Indonesian Journal of Electronics and Instrumentation Systems Jurnal Sarjana Teknik Informatika
Zandy Yudha Perwira
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Computer Science & IT Electrical & Electronics Engineering

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Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG Catur Atmaji; Zandy Yudha Perwira
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 7, No 2 (2017): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.868 KB) | DOI: 10.22146/ijeis.22893

Abstract

In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.
Optimasi Electronic Nose Menggunakan Sensor Subset Selection untuk Deteksi Asap Kebakaran Hutan Zandy Yudha Perwira; Danang Lelono; Andi Dharmawan; Nur Achmad Sulistyo Putro
Jurnal Sarjana Teknik Informatika Vol. 13 No. 3 (2025): Oktober
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v13i3.31427

Abstract

Pengembangan sistem deteksi kebakaran hutan telah banyak dilakukan dengan berbagai pendekatan, salah satunya menggunakan electronic nose (e-nose) berbasis larik sensor gas. Namun, penggunaan larik sensor menimbulkan tantangan baru pada sistem, yaitu meningkatnya konsumsi daya, bobot berlebih, redundansi, dan risiko overfitting. Tantangan tersebut dapat diminimalisir dengan mereduksi sensor yang memiliki kontribusi rendah terhadap performa klasifikasi tanpa mengorbankan akurasi sistem. Reduksi sensor pada penelitian ini dilakukan menggunakan metode sensor subset selection dengan konfigurasi baseline yang terdiri atas enam kanal sensor gas (CO, NO₂, MQ7, MQ9, MQ135, dan TGS2600). Proses penelitian meliputi tahapan pra-pemrosesan, ekstraksi ciri, sensor subset selection, serta klasifikasi menggunakan algoritma Random Forest (RF) dan Support Vector Machine (SVM). Hasil evaluasi menunjukkan bahwa tiga sensor inti, yaitu CO, MQ135, dan TGS2600, secara konsisten memberikan kontribusi signifikan terhadap akurasi sistem. Konfigurasi optimal diperoleh dengan lima sensor (CO, MQ7, MQ9, MQ135, dan TGS2600) yang menjaga keseimbangan antara akurasi (±85%) dan efisiensi sistem. Penerapan metode sensor subset selection ini mampu mengoptimalkan larik sensor gas pada e-nose, sehingga dihasilkan sistem deteksi asap kebakaran hutan yang lebih efisien, portabel, dan adaptif sebagai payload UAV.
Co-Authors Andi Dharmawan Catur Atmaji Nur Achmad Sulistyo Putro