Article Per Year (5 Year)
p-Index From 2020 - 2025
4.359
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Jurnal Simantec Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOIV : International Journal on Informatics Visualization INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] PROMINE Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Telematika Jurnal Teknologi Terpadu TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Tika Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Muchamad Kurniawan, Muchamad
Institut Teknologi Adhi Tama Surabaya
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : TIN: TERAPAN INFORMATIKA NUSANTARA

 1 
Penerapan Metode CNN (Convolutional Neural Network) dalam Mengklasifikasi Uang Kertas dan Uang Logam Rewina, Anggita Eka; Sulistyowati, Sulistyowati; Kurniawan, Muchamad; N, Muhammad Dinarta; Yunanda, Sita Fara
TIN: Terapan Informatika Nusantara Vol 4 No 12 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i12.5128

Abstract

Banknotes and coins are valuable assets that are used as legal means of payment in everyday life. The value of these two types of money has been determined and is printed on each piece of banknote when used in transactions and trade. Even though currently banknotes can be recognized using technology such as ATM machines, these machines are only able to recognize the value of the largest currency owned by a country. Computers require digital images as input to display the information contained therein because computers do not have the ability of the human eye to directly recognize or calculate the objects they see. Therefore, techniques or methods are needed that aim to obtain information from digital images to facilitate human interpretation. This research aims to design a system for detecting banknotes in images using the Convolutional Neural Network (CNN) architecture, which is a form of deep learning. . The system also integrates image pre-processing using user-based manual annotation techniques in Python program code. Using the CNN method, a test was carried out to detect the nominal amount of money in the input image. Test results using 29 banknote dataset samples and 31 coin money dataset samples show that the two types of money are divided into two classes, namely paper and coins. From the training carried out on banknotes and coins, an average accuracy of 98% was obtained, showing good results. Repetition of the detection process also shows consistent output probabilities. However, there are several denominations of money that show high accuracy values, so it can be concluded that the labeling annotation method is thought to be less effective.
Klasifikasi Jenis Jerawat pada Data Citra Jerawat Wajah Menggunakan Convolutional Neural Network Putri, Chatarina Natassya; Qornain, Wafi Dzul; Bamahri, Fakhirah; Yuliastuti, Gusti Eka; Kurniawan, Muchamad
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5231

Abstract

Acne is a condition caused by pilosebaceous inflammation which affects 85% of skin conditions in adolescents and adults. Acne has an impact on the psychological and social health of sufferers. To treat acne, it is necessary to know the right type of acne so that sufferers can treat the type of acne according to how they are treated. This research was carried out to classify the types of acne in facial acne images using the Convolutional Neural Network (CNN) method. Based on previous research, it shows that the use of CNN is considered effective and appropriate in increasing classification accuracy. This research uses a dataset of acne types from Kaggle with a total of 351 data, divided into 5 classes, namely acne fulminans, acne nodules, fungal acne, papules and pustules which will be tested using 2 different optimizers, namely Adam and RMS- prop. From the results of this test, the highest accuracy was 100% using the Adam optimizer and the RMS-prop optimizer test obtained the highest accuracy value of 80%.
Co-Authors Abdullah, Ryan Gading Andy Rachman Ar, Nanda Hafiz Arinal Haq, Fatahillah Arum Indah Sari Bagus S, Dadang Bamahri, Fakhirah Budi, Adib Pakar Fahrezi Kusuma, Andra Fanani, Yazid Farida Farida Farida Farida Faruq, Umar Al Fawaz, Ahmad Gusti Eka Yuliastuti Habibah, Maryam Ummul Hadimarta, Tommy Ferdian Hendro Nugroho, Hendro Ilmi, Hilmi Maulana Istighfariansyah, Risaldi Maftahatul Hakimah, Maftahatul Misbahul Munir Moch. Kalam Mollah Moneter, Mochamad Rizky Muhammad Aditya Kushardiawan Muhima, Rani Rotul N, Muhammad Dinarta Nabila Dayu Mega Anjani Naili Saidatin Nanik Suciati Nurlaily, Firdausiyah O.T., Pambudi Pakarbudi, Adib Pambudi, Oktavian Tegar Pratikto, Dimas Priska Amelia de Jong Putri, Chatarina Natassya Qornain, Wafi Dzul Rewina, Anggita Eka Ritonga, Mario Franko Ezra Hasiholan Rosetya Wardhana, Septiyawan Sandi Alala, Pratama Setyawati, Indah Siti Agustini, Siti Sulistyowati Sulistyowati Ubaidillah, M. Yahya Wachid, Aldi Wardhana, Septiyawan Rosetya Wicaksono, Mukhlis Adi Yunanda, Sita Fara Zaqiyah, Hafida Nur