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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JurTI (JURNAL TEKNOLOGI INFORMASI) IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer IJISTECH Reputasi: Jurnal Rekayasa Perangkat Lunak PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Digital Transformation Technology (Digitech) Jurnal Pengabdian Pada Masyarakat
Indra Riyana Rahadjeng
Universitas Bina Sarana Informatika
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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat Ramadhani, Cerah Fitri; Siregar, Muhammad Noor Hasan; Rahadjeng, Indra Riyana; Windarto, Agus Perdana
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2676

Abstract

The use of the artificial neural network (Backpropagation) method can be used in determining the best architectural model for predicting the number of elementary school students in the Siantar Barat District. The dataset used is a dataset on the number of Elementary School (SD) students in West Siantar District, Pematang Siantar City in 2017-2021 obtained from the Website of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia (https://dapo.kemdikbud.go.id /pd/3/076303). The dataset is then divided into 2 parts, namely the training and testing dataset. In the training datasets, attribute X1 is a dataset for 2017, X2 is the dataset for 2018, X3 is a dataset for 2019, and attribute Y (target) is the dataset for 2020. For the test datasets, attribute X1 is the dataset for 2018, attribute X2 is a dataset for 2019, attribute X3 is a dataset for 2020 and attribute Y (target) is a dataset for 2021. The results obtained from the analysis of the Backpropagation and virtualization methods using the MatLab application can be generated with a valid dataset and produce an accuracy rate of 87.5% in architectural models 3-9-1. So that the Backpropagation method can be used as a prediction method that makes it very easy to find predictions.
Optimisasi Fungsi Aktivasi pada Arsitektur LeNet untuk Meningkatkan Akurasi Klasifikasi Citra Tumor Otak Harliana, Harliana; Rahadjeng, Indra Riyana; Winanjaya, Riki
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7108

Abstract

Brain hemorrhage is a critical medical condition that requires early and accurate detection to improve patient recovery outcomes. However, conventional image classification methods for brain hemorrhage still face limitations in terms of accuracy and efficiency. To address this issue, this study proposes optimizing the LeNet model using various activation functions—ReLU, Sigmoid, Tanh, and Swish—to enhance classification performance. Several optimization strategies were applied, including data augmentation techniques (flipping, rotation, shearing, rescaling) and fine-tuning of hyperparameters, to improve model generalization. Experimental results indicate that the model utilizing the Swish activation function achieves the most stable overall performance, with an accuracy of 55%, recall of 54%, precision of 54%, F1-score of 54%, and a ROC AUC value of 0.45. Although this performance is still below clinical application standards, the findings serve as an initial step toward exploring activation function optimization in CNN architectures. Further research is needed to significantly enhance classification accuracy and enable clinical viability.
Co-Authors Achmad Ihsan Fajrin Adi Supriyatna Agus Perdana Windarto Alkhairi, Putrama Deleaniara. M, Chyntia Marianna Endang Wahyudi Fatmah Citra R Fintri Indriyani Harliana Harliana Heny Sumarno, Heny Herdian, Barlin Lydia Salvina Helling Muhammad Noor Hasan Siregar Ramadhani, Cerah Fitri Ririn Restu Aria Ritapuspitasari Ritapuspitasari Sidik Sidik Susi Susilowati Susliansyah, Susliansyah Winanjaya, Riki Yasmin, Ghaisani Yuhandri, Muhammad Habib