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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmiah KOMPUTASI
Achmad Fahrurozi
Gunadarma University
Automotive Engineering Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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VEHICLE DETECTION USING PRINCIPAL COMPONENT ANALYSIS Rifki Kosasih; Achmad Fahrurozi; Iffatul Mardhiyah
Jurnal Ilmiah KOMPUTASI Vol 19, No 2 (2020): Juni
Publisher : STMIK JAKARTA STI&K

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

Abstract

The detection of a vehicle in video is a activity that is important to help the security forces keep an eye on the traffic flow. However, it is hard to security forces to keep watching the video (CCTV) of traffic flow in all day long. Artificial intelligence can be use to help the security to monitoring and analyze the traffic of vehicles, such as to know the level of vehicle traffic density at a certain time period or find out detailed information about the vehicle that want to observe. In this study, Principle Component Analysis (PCA) method used to doing background substraction process to detect vehicles in a real time. To improve the results of PCA method, morphological operation is implemented. The experiment result shown that PCA method is well used to detect the vehicle in a real time with accuracy at 95%.
Wood Classification Based on Edge Detections and Texture Features Selection Achmad Fahrurozi; Sarifuddin Madenda; Ernastuti Ernastuti; Djati Kerami
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 5: October 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (380.015 KB) | DOI: 10.11591/ijece.v6i5.pp2167-2175

Abstract

One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Co-occurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.
SISTEM PENDETEKSI PELANGGAR JARAK SOSIAL COVID-19 BERBASIS VIDEO MENGGUNAKAN ALGORITMA YOLOv3 Putri Setiya Ningsih; Achmad Fahrurozi
Jurnal Ilmiah Teknologi dan Rekayasa Vol 27, No 2 (2022)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2022.v27i2.7100

Abstract

Corona Virus Disease 2019 (COVID-19) telah dinyatakan oleh WHO sebagai pandemik dan Pemerintah Indonesia berdasarkan Keputusan Presiden Nomor 11 Tahun 2020 tentang Penetapan Kedaruratan Kesehatan Masyarakat telah menyatakan COVID-19 sebagai kedaruratan kesehatan masyarakat yang wajib dilakukan upaya penanggulangan. Untuk mengatasi pengaruh dalam banyak sektor di Indonesia, pemerintah telah melakukan tindakan pencegahan. Salah satunya yaitu dengan menjaga jarak dan menghindari kerumunan. Pencegahan ini untuk menghindari penyebaran virus Corona yang lebih luas. Organisasi Kesehatan Dunia (WHO) menyebut pencegahan ini sebagai physical distance. Namun masyarakat cenderung lalai dalam melaksanakan protokol kesehatan tersebut. Salah satu cara untuk mengatasi masalah ini adalah dengan memantau jarak fisik antar objek manusia dan membuat sistem pendeteksian otomatis yang digunakan untuk mendeteksi jumlah dan jarak dari objek manusia yang ada pada suatu area tertentu. Penelitian ini bertujuan untuk membangun sistem pemantauan jarak fisik menggunakan bahasa pemrograman Python dengan library YOLOv3. Data yang digunakan dalam penelitian ini merupakan data primer berupa video berdurasi 15 detik dengan rate 20 fps, dengan format MP4. Secara umum, sistem mendeteksi jumlah objek manusia yang terdapat dalam tiap frame dari video, untuk kemudian mendeteksi pelanggar jarak sosial dalam frame tersebut. Hasil rata - rata akurasi dari deteksi objek adalah 83,07% dan hasil  rata – rata akurasi dari deteksi pelanggar jarak sosial adalah 86,24%.
Classification of six banana ripeness levels based on statistical features on machine learning approach Rifki Kosasih; Sudaryanto Sudaryanto; Achmad Fahrurozi
International Journal of Advances in Applied Sciences Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i4.pp317-326

Abstract

Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.
Co-Authors Djati Kerami Ernastuti Ernastuti Iffatul Mardhiyah Putri Setiya Ningsih Rifki Kosasih Sarifuddin Madenda Sudaryanto Sudaryanto