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Indra, Jamaludin
Universitas Buana Perjuangan Karawang
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Journal : Building of Informatics, Technology and Science

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Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Kecacatan Pada Proses Welding di Perusahaan Manufacturing Saefulloh, Nandang; Indra, Jamaludin; Rahmat, Rahmat; Juwita, Ayu Ratna
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5321

Abstract

Manufacturing industry has become one of the largest sectors in Indonesia, driven by increasing demand from the public. A primary concern to meet both local and international market needs is product quality. In ensuring high-quality standards, production processes require strict quality control. One common issue in quality control is defects occurring during the welding process, which significantly affects inspection cycle times. To address this, the Convolutional Neural Network (CNN) approach with VGG-16 architecture can help classify defects in the welding process. This method not only expedites the defect classification process but also enhances the accuracy of identifying product defects. The stages of implementing this method include dataset preparation, data preprocessing, CNN model design, model training, and performance evaluation. Evaluation results demonstrate that the use of automatic defect detection technology, especially with balanced data scenarios, can significantly improve quality control performance. Accuracy, precision, recall, and F1-score achieve excellent levels, reaching 92%. Thus, this research provides a significant contribution to enhancing production efficiency and improving product quality in the motorcycle manufacturing industry in Indonesia. It is hoped that the use of this technology will assist manufacturing companies in identifying and addressing production defects more effectively, thereby enhancing the overall competitiveness of Indonesia's manufacturing industry.
Implementasi Metode Resampling Dalam Menangani Data Imbalance Pada Klasifikasi Multiclass Penyakit Thyroid Nugraha, Najmi Cahaya; Hikmayanti, Hanny; Indra, Jamaludin; Juwita, Ayu Ratna
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5652

Abstract

It is estimated that at least 17 million Indonesians suffer from thyroid disorders. Interestingly, nearly 60% of those living with a thyroid disorder do not receive a diagnosis. Thus, it is necessary to carry out research that applies methods to predict thyroid disease. Before applying prediction methods, it is crucial to implement classification methods to obtain an accurate prediction model. However, to achieve optimal classification results and to avoid inaccuracies, a balance in the used data is required. Data imbalance is a condition where the ratio between classes in the data is uneven, which can result in the generated model becoming biased. The main objective of the research is to present a solution that can improve the accuracy of early detection of thyroid diseases through addressing data imbalance and implementing appropriate classification algorithms. The research methodology began with the collection and analysis of a dataset consisting of 9172 data points. Preprocessing was then performed, resulting in 5321 training data points and 1331 test data points. The testing phase employed 7 different classification algorithms with 7 different resampling methods and evaluation using a confusion matrix. This research achieved the highest accuracy rate of 98%, obtained from the combination of the Random Forest Algorithm and the Random Over Sampling method. It can be concluded that the combination of the Random Forest Algorithm with the Random Over Sampling resampling method can improve early detection accuracy for thyroid diseases.
Co-Authors AA Sudharmawan, AA Abdul Gapur Achmad, Syifa Latifah Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Afifur Rahman Ahmad Fauzi Ahmad Fauzi Ahmad Rahman Al Fathir Rizal Januar Alif Kirana Amansyah, Ilham Anton Romadoni Junior Apriade Voutama April Hananto Ardiansyah, Fikri Arif Nurman Arip Solehudin Aris Martin Kobar Arum Puspita Lestari, Santi Asep Jamaludin Aviv Yuniar Rahman Awal, Elsa Elvira Ayu Juwita Azis Saputra Azzahra, Wava Lativa Baihaqi, Kiki Ahmad Cici Emilia Sukmawati Dadang Yusup Deden Wahiddin Deny Maulana Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Eko Pramono Fadmadika, Fadilla Faisal, Sutan Fauzi Ahmad Muda Fauzi, Ahmad Firdaus, Thoriq Janati Firmansyah Maulana Fitri Nur Masruriyah, Anis Garno . Garno, Garno Gugy Guztaman Munzi Hanny Hikmayanti Handayani Hanung Pangestu Rahman Hilda Fitriana Dewi Hilda Novita Hilda Yulia Novita Holila, Holila Irma Putri Rahayu Juwita, Ayu Ratna Karyanto, Dony Dwi Khoirull Munazzal Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M Andrian Agustyan Maharina, Maharina Maliah Andriyani Mudzakir, Tohirin Al Muhammad Cesar Afriansyah Arief Muhammad Deden Miftah Fauzi Muhammad Imam Naufal Muhammad Khoiruddin Harahap Muhammad Raja Nurhusen Muhammad Romadhon Nazori AZ Novalia, Elfina Nugraha, Najmi Cahaya Nurdin, Cherry Januar Nurlaelasari, Euis Nursyawalni, Reva Paryono, Tukino Pratama, Adi Rizky Purnama, Ariya Purnomo, Indarto Aditya Rahmat Hidayat Rahmat Rahmat Rahmat Rahmat Rifaldi, Rizky Rija Nur Hijriyya Rissa Ilmia Agustin Rizki, Lutfi Trisandi Robinson Nababan Rohana, Tatang Romlah Saefulloh, Nandang Sandi Susanto Santi Lestari Sihabudin Sihabudin, Sihabudin Siregar, Amril Mutoi Siti Robiah Suparno Sutan Faisal Syahrul Azis Tatang Rohana Tia Astiyah Hasan Tohirin Al Mudzakir Tohirin Mudzakir Toif Muhayat Tri Vicika, Vikha Ulfa Amelia Wahiddin, Deden Wildan Amin Wiharja Yana Cahyana Yogi Firman Alfiansyah