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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan MEANS (Media Informasi Analisa dan Sistem) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer BEES: Bulletin of Electrical and Electronics Engineering Journal of Artificial Intelligence and Engineering Applications (JAIEA) JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Teknik Mesin, Industri, Elektro dan Informatika Journal of Informatics, Electrical and Electronics Engineering Infolitika Journal of Data Science Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Irfan Sudahri Damanik
STIKOM Tunas Bangsa
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Journal : JURIKOM (Jurnal Riset Komputer)

 1 
Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner Irnanda, Khairunnissa Fanny; Windarto, Agus Perdana; Damanik, Irfan Sudahri
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3836

Abstract

Backpropagation is a method of Artificial Neural Networks that is quite reliable in solving prediction problems (forecasting). However, in its application, this algorithm still has weaknesses such as optimizing the artificial neural network weights to avoid local minimums, the problem of long training times to achieve convergence and the process of determining the right parameters (learning rate and momentum) in the training process. The purpose of this research is to solve this problem by using Particle Swarm Optimization (PSO) which is a simple and reliable optimization algorithm to solve optimization problems. The data source is obtained from the site sumut.bps.go.id. There are 5 network architecture models used in this study, including 2-5-1, 2-7-1, 2-9-1, 2-11-1 and 2-13-1. The results of trials conducted with Rapid Miner software, the best architectural model is the 2-9-1 model with a total RMSE of 0.056 +/- 0.000 in the implementation of Backpropagation, while in the implementation of Backpropagation + particle swarm optimization the amount of RMSE is 0.055 +/- 0.000. The smaller the RMSE (Root Mean Squared Error), the better the model
Pemilihan Model Arsitektur Terbaik Dengan Mengoptimasi Learning Rate Pada Neural Network Backpropagation Cici Astria; Agus Perdana Windarto; Irfan Sudahri Damanik
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3834

Abstract

Backpropagation is one of the methods contained in a neural network that is able to train dynamic networks using mathematical knowledge based on architectural models that have been developed in detail and systematically. Backpropagation itself is able to accommodate a lot of information that serves as a useful experience. However, the Backpropagation Algorithm tends to be slow to achieve convergence in obtaining optimum accuracy and requires large training data and the optimization used is less efficient. The purpose of this research is to optimize the learning rate on backpropagation neural networks. Source of data obtained from CV. Bona Tani Hatonduhan. There are 3 network architecture models used in this study, namely 2-51, 2-6-1, and 2-7-1 with learning rates of 0.1, 0.2, and 0.3. the results of trials carried out with MATLAB software produced the best architectural model, namely the 2-7-1 model with a learning rate of 0.3 with an accuracy of 83%. Based on this background, it is hoped that the results of the research can help in the process by optimizing the learning rate of the backpropagation Neural Network on the selection of the best architecture.
Analisis Jaringan Saraf Tiruan dengan Backpropagation pada korelasi Matakuliah Pratikum Terhadap Tugas Akhir Hanifah Urbach Sari; Agus Perdana Windarto; Irfan Sudahri Damanik
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3835

Abstract

Backpropagation is one of the methods contained in a neural network that is able to train dynamic networks using mathematical knowledge based on architectural models that have been developed in detail and systematically. Backpropagation itself is able to accommodate a lot of information that serves as a useful experience. The purpose of this research is to make it easier for AMIK Tunas Bangsa Pematangsiantar students to determine the topic of their final project with practical value so that they can do their final project quickly. So the authors conducted research using correlation in determining the topic of the final project. The data in this study were obtained directly from the AMIK Tunas Bangsa Education academics in Pematangsiantar City. The data used uses data on practical grades of AMIK Tunas Bangsa Stambuk students 2017 from semester 4 to semester 6. There are 5 network architecture models used in this study, namely 5-1-2, 5-6-2, 5-8 -2, 5-10-2, and 5-12-2. From the results of trials conducted with MATLAB software, the best architecture is the 5-1-2 model with an accuracy of 47%. Based on this background, it is hoped that the research results can help students in determining the topic of the final project
Co-Authors Abdi Rahim Damanik Achmad Noerkhaerin Putra Agus Perdana Windarto Agustinus Liberty Pasaribu Anjelita, Mawaddah Azi Arisandi Azi Guntur Chairul Fadlan Chintya Carolina Situmorang Cici Astria Dea Dwi Rizki Tampubolon Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Deny Franata Pasaribu Dermawan, Sabaruddin Dewi, Rafiqa Dewinta Marthadinata Sinaga Dinda Nabila Batubara Eka Irawan Eka Irawan Eka Irawan Eka Irawan Eka Irawan Eka Irawan Eka Irawan F Fauziah Fajar Rudi Sartomo Samosir Fikri Wicaksono Frskila Parhusip Guntur, Azi Hadinata, Edrian Hanifah Urbach Sari Hanne Lore Br Siagian Hartama, Dedy Hasudungan Siahaan Hendry Qurniawan Heru Satria Tambunan Heru Satria Tambunan Heru Satria Tambunan, Heru Satria Hutasoit, Rahel Adelina Ika Okta Kirana Ilham Syahputra Saragih Ilham Syaputra Saragih Indah Pratiwi M.S Indra Gunawan Ira Audita Irawan Irawan Irnanda, Khairunnissa Fanny Irvanizam, Irvanizam Jaya Tata Hardinata Laila Kumalasari M Fauzan M Fauzan M Fauzan M FAUZAN M. Fauzan Manurung, Hotben Marina Rajagukguk Masduki Nizam Fadli Masitha Masitha Masitha, Masitha Mawaddah Anjelita Mian Manimpan Siahaan Mira Ariffiani Mita Ariffiani Muhammad Aliyul Amri Muhammad Fachrur Rozy Muhammad Ifnu Suhada Muhammad Ifnu Suhada Napitupulu, Flora Sabarina Nasution, Rizki Alfadillah Ningsih, Sri Rahayu Nur Arief Nur Hasanah Lubis Nurhidayana Nurhidayana Okprana, Harly P, Dini Rizky Sitorus Paulus Hendrico Silalahi Primatua Sitompul Rahel Nita Trides Siahaan Ria Annisa Saragih Ridho Hayati Alawiah Roni Kurniawan S Saifullah Sabaruddin Dermawan Safii, M. Sahendra Fahreza Saifullah Saifullah Sandy Putra Siregar Saputra, Widodo Saragih, Ilham Syaputra Saragih, Ria Annisa Sari, Andini Fadila Sari, Hanifah Urbach Sari, Winda Permata Sepridho, Jaka Siahaan, Mian Manimpan Sinaga, Dolli Sari Sinaga, Waris Pardingatan Siregar, Sandy Putra Siti Hadija siti rodiah Solikhun Solikhun Solikhun SRI RAHAYU Sri Rahayu Sri Rahayu Ningsih Sri Wulandari Suhada Suhada Suhada Suhada Suhada, Suhada Suhada, Muhammad Ifnu Suhendro, Dedi Sumantri Sihombing Sundari Retno Andani Susiani Susiani Susiani, Susiani Taufiq Hidayat Theresia Siburian Vikki, Zakial Wanayumini Wanto, Anjar Widodo Saputra Winanjaya, Riki Yumni Syabrina Agustina Lubis Zulia Almaida Siregar