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All Journal Paspalum: Jurnal Ilmiah Pertanian Jurnal Teknik Sipil Cendekia (JTSC) Journal of Mechanical and Manufacture
ari Wibowo
universitas subang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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KLASIFIKASI DAN DETEKSI KERETAKAN PADA TROTOAR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK: Classification and Detection of Cracks on Sidewalks Using the Convolutional Neural Network Method ari Wibowo; E Setiyadi
JURNAL TEKNIK SIPIL CENDEKIA (JTSC) Vol 4 No 1 (2023): February
Publisher : Departement of Civil Engineering, Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51988/jtsc.v4i1.116

Abstract

Trotoar adalah bagian dari jalan raya yang khusus disediakan untuk pejalan kaki dimana trotora pada umumnya terletak di daerah manfaat jalan untuk memudahkan ketika berjalan kaki. Hal ini agar pejalan kaki tidak bercampur dengan kendaraan yang tentunya dapat memperlambat arus lalu lintas dan dapat membahayakan pejalan kaki itu sendiri. Namun pada kenyataannya permukaan trotoar memiliki kondisi yang beragam. Oleh karena itu perbaikan trotoar merupakan solusi tepat agar kerusakan trotoar tidak semakin memburuk dan tidak mengganggu para pengguna trotoar. Langkah pertama dalam permukaan trotoar adalah mendeteksi kerusakan yang ada di permukaan. Salah satu metode yang dapat dipakai dalam mendeteksi kerusakan pada trotoar adalah menggunakan teknologi terkini salah satunya adalah memanfaatkan deep learningdengan metode CNN. Tujuan penelitian ini adalah menyusun algoritma yang secara khusus digunakan untuk membedakan trotoar yang retak dan tidak retak. Adapun dataset latih yang digunakan sebanyak 3200 citra gambar dan 800 citra untuk data uji. Dimana gambar gambar ini kami ambil dari katalog kaggle. Dari penelitian yang kami lakukan hasil pengujian menunjukkan bahwa model berhasil membedakan permukaan trotoar yang retak maupun yang tidak retak dengan akurasi yang cukup tinggi, dimana nilai akurasi rata-rata yang dihasilkan di atas 96% dan nilai loss yang mendekati 1,5%.
Implementasi Algoritma Deep Learning You Only Look Once (YOLOv5) Untuk Deteksi Buah Segar Dan Busuk Lusiana Lusiana; Ari Wibowo; Tika Kartika Dewi
Paspalum: Jurnal Ilmiah Pertanian Vol 11, No 1 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35138/paspalum.v11i1.489

Abstract

Fruit is one of the nutritional needs for the body that must be met. But with a note, these nutrients will be obtained from fruit that is still fresh. The definition of fresh fruit itself is fruit that can be consumed directly and does not require any further processing. There are many ways to select and differentiate between fresh fruit and bad fruit and in general direct observations can be made. But over time, there are several other ways to observe fruit freshness using existing technology. Where one of them is by optimizing deep learning and machine learning. This detection and classification system was created using a deep learning method using the YOLOv5 algorithm which can detect in real-time the types of apples, bananas and oranges. We use image datasets for each of these fruits for fresh fruit and rotten fruit, a total of 1200 images for train data and 330 images for validation data and 6 images for test data. Based on the tests that have been carried out with training data, along with validation data, and test data using the YOLOv5 algorithm, it can be concluded that this detection method can recognize objects consistently with a high degree of accuracy. This can be proven at the level of accuracy which reaches an accuracy rate of 90%.
DETEKSI KERETAKAN JALAN ASPAL MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK ari Wibowo; yusuf yulianto
JURNAL TEKNIK SIPIL CENDEKIA (JTSC) Vol 4 No 2 (2023): July
Publisher : Departement of Civil Engineering, Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51988/jtsc.v4i2.132

Abstract

Road conditions determine the comfort of road users, the comfort of these road users is the responsibility of the Public Works and Spatial Planning Office in each region. Roads are of course an important aspect because roads are the main supporting factor in the social, cultural, environmental fields which are developed in order to achieve an equitable distribution of development between regions and sustainability with regional and economic development approaches. The first step that must be taken by policy makers in seeking comfort for users is to evaluate the quality of roads, including in Indonesia. The evaluation in question includes estimating repairs, required construction, estimating quality. The strategic step in making road quality evaluation steps is to detect road cracks on the surface. One of them is by implementing an intelligent system method in detecting road damage using the Convolutional Neural Network (CNN) algorithm. The input is an image of the road surface in RGB format. The image is obtained from kaggle as many as 2074 images. Based on the results of the tests and evaluations that have been carried out, it can be concluded that the system built has succeeded in producing very good data as evidenced by an accuracy rate of 92.9%.
FLUID DYNAMIC SIMULATION ON THE FLARE OF COMBUSTION OF GAS FROM BIOMASS GASIFICATION dian susanto; Muhtar Kosim; Ari Wibowo
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.5682

Abstract

The use of energy which always comes from fossil fuels will eventually run out, so the development of renewable energy or alternative energy is very important to maintain petroleum reserves and as a substitute for fossil fuels which are the main energy source. One alternative energy is biomass which has not been widely used by the gasification method. The gas produced by the gasification process is utilized by burning it in a flare to get a flame. In this study, the 3D simulation method with Computational Fluid Dynamics (CFD) was used to determine the temperature distribution on the flare walls using CFD simulations and to compare the temperature of the flare walls from the CFD simulation results with the test results. The results of this study, the distribution of combustion occurs in the flare with a temperature of 1106°C in the upper area close to the outlet boundary. The wall temperature comparison shows that the CFD simulation tends to be similar to the test results. This shows that computational fluid dynamic simulations can be used to predict fluid flow rates and combustion reactions.
OPTIMIZATION OF PREDICTION AND PREVENTION OF DEFECTS ON METAL BASED ON AI USING VGG16 ARCHITECTURE muhtar kosim; Ari Wibowo; Novandri Tri Setioputro; Kasda; Dian Susanto
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.6542

Abstract

Manufacturing is one of the most valuable industries in the world, it can be automated without limits but still stuck in traditional manual and slow processes. Industry 4.0 is racing to define a new era in digital manufacturing through the implementation of Machine Learning methods. In this era, Machine learning has been widely applied to various fields and will certainly be very good applied in the manufacturing world. One of them is used to predict and prevent defects in metal. The process of predicting and preventing defects in metal is one of the important efforts in improving and maintaining production quality. Accuracy in predicting and preventing defects in metal can be an innovation and competitiveness in technology, both in production methods, and improving product safety and its users. Human operators and inspectors without digital assistance generally can spend a lot of time researching visual data, especially in high-volume production environments. For this reason, there needs to be research in developing Machine Learning technology in an effort to prevent the occurrence of defects in metal. And one of the development of this technology by using Convolutional Neural Network (CNN) architecture Visual Geometry Group 16 layer (VGG16). As for the metal defect dataset with 10 classes with details for training data as many as 17221, and test dataset as many as 4311, From the use of methods and datasets available, has been done training model used and produce very good accuracy, that is equal to 89% and testing with accuracy equal to 76%. And also done Interpreter process against new input data, to know metal defect type, prediction accuracy and appropriate action to prevent and overcome metal defect type result of Interpreter process application.
Implementasi Algoritma Deep Learning You Only Look Once (YOLOv5) Untuk Deteksi Buah Segar Dan Busuk Lusiana Lusiana; Ari Wibowo; Tika Kartika Dewi
Paspalum: Jurnal Ilmiah Pertanian Vol. 11 No. 1 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35138/paspalum.v11i1.489

Abstract

Fruit is one of the nutritional needs for the body that must be met. But with a note, these nutrients will be obtained from fruit that is still fresh. The definition of fresh fruit itself is fruit that can be consumed directly and does not require any further processing. There are many ways to select and differentiate between fresh fruit and bad fruit and in general direct observations can be made. But over time, there are several other ways to observe fruit freshness using existing technology. Where one of them is by optimizing deep learning and machine learning. This detection and classification system was created using a deep learning method using the YOLOv5 algorithm which can detect in real-time the types of apples, bananas and oranges. We use image datasets for each of these fruits for fresh fruit and rotten fruit, a total of 1200 images for train data and 330 images for validation data and 6 images for test data. Based on the tests that have been carried out with training data, along with validation data, and test data using the YOLOv5 algorithm, it can be concluded that this detection method can recognize objects consistently with a high degree of accuracy. This can be proven at the level of accuracy which reaches an accuracy rate of 90%.
Co-Authors dian susanto E Setiyadi Kasda, Kasda Lusiana Lusiana RR. Ella Evrita Hestiandari Setioputro, Novandri Tri Tika Kartika Dewi yusuf yulianto