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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Komputer dan Informatika (JSON)
Muhammad Ikhsan
Universitas Islam Negeri Sumatera Utara, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Jaringan Syaraf Tiruan Backpropagation Pada Klasifikasi Grade Teh Hitam Muhammad Ikhsan; Armansyah Armansyah; Anggara AlFaridzi Tamba
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5312

Abstract

Black tea is the most widely produced type of tea in Indonesia, where Indonesia itself is the 5th largest black tea exporter in the world. According to the provisions of SNI-1902-2016, the quality requirements of black tea through appearance include the shape, size and weight (density), and the color of the black tea particles themselves. This study aims to determine the workings of the backpropagation method and the implementation of python on black tea grade classification, and to determine the level MSE of accuracy in the results of black tea grade classification using backpropagation. The model used in this study uses 4 input layers, 5 hidden layers, and 3 output layers. In the input layer, 4 input variables are used, namely shape, size, density, and color. The results of the classification using backpropagation with a number of iterations of 1000 iterations on the training data obtained an error of 0.096.
Implementasi Jaringan Syaraf Tiruan Backpropagation Pada Klasifikasi Grade Teh Hitam Muhammad Ikhsan; Armansyah Armansyah; Anggara AlFaridzi Tamba
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5312

Abstract

Black tea is the most widely produced type of tea in Indonesia, where Indonesia itself is the 5th largest black tea exporter in the world. According to the provisions of SNI-1902-2016, the quality requirements of black tea through appearance include the shape, size and weight (density), and the color of the black tea particles themselves. This study aims to determine the workings of the backpropagation method and the implementation of python on black tea grade classification, and to determine the level MSE of accuracy in the results of black tea grade classification using backpropagation. The model used in this study uses 4 input layers, 5 hidden layers, and 3 output layers. In the input layer, 4 input variables are used, namely shape, size, density, and color. The results of the classification using backpropagation with a number of iterations of 1000 iterations on the training data obtained an error of 0.096.
Analisis dan Implementasi Singular Value Decomposition (SVD) untuk Sistem Rekomendasi Anime Berbasis Collaborative Filtering Muhammad Daffa Ginting; Muhammad Ikhsan
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9462

Abstract

The rapid growth of digital platforms has significantly increased the number of available anime titles, making it difficult for users to choose content that matches their preferences. In addition, the diversity of genres and content characteristics accessible to different age groups highlights the need for a system that can provide recommendations more accurately and efficiently. This study aims to develop an anime recommendation system based on collaborative filtering by applying the truncated Singular Value Decomposition (SVD) method to address the data sparsity problem in the user–item matrix. The dataset was collected from the AniList platform and consists of 30 users, 100 anime titles, and 1,230 explicit rating records. The evaluation was conducted using a hold-out scheme with an 80% training set (992 ratings) and a 20% testing set (248 ratings). Prediction performance was measured using an RMSE of 1.6464 and an MAE of 1.2370, while the quality of Top-N recommendations was assessed using Precision@10. The experimental results indicate that the best configuration was achieved at k = 4, which produced the lowest prediction error on the test set and generated relevant recommendations. The average Precision@10 obtained was 0.1897, meaning that approximately 18.97% of the Top-10 recommendations provided by the system were considered relevant.
Perbandingan CNN Dan ResNet50 Dalam Klasifikasi Tuberkulosis Pada Citra X-Ray Paru Muhammad Fathir Aulia; Muhammad Ikhsan
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9554

Abstract

Tuberculosis (TB) remains a global health problem and requires rapid and consistent early screening. Chest X-rays are widely used because they are practical and economical, but manual interpretation is highly dependent on experts, which can lead to subjectivity, fatigue, and delayed diagnosis. This study aims to compare the performance of a basic Convolutional Neural Network (CNN) and a transfer learning-based ResNet50 in classifying lung X-ray images into two classes, namely TB and Normal, as well as to assess the trade-off between accuracy and computational efficiency. The dataset used is a balanced subset of 1,000 images (500 TB and 500 Normal) divided into 70% training data, 15% validation, and 15% testing with a fixed seed to ensure reproducible experiments. Preprocessing was performed by resizing the images to 224×224 pixels and normalizing the pixel values. ResNet50 used a preprocessing scheme in accordance with the pretrained model. Evaluation was performed using a confusion matrix and accuracy, precision, recall, and F1-score metrics. The test results show that CNN achieved an accuracy of 98.00% with three classification errors, while ResNet50 achieved an accuracy of 99.33% with one classification error and average precision, recall, and F1-score metrics above 0.99. In terms of efficiency, the CNN training time was approximately 40.46 seconds, while ResNet50 took a total of approximately 226.99 seconds. In the robustness test, the CNN inference time was approximately ±100 ms/image and ResNet50 was approximately ±1,900 ms/image. These findings indicate that ResNet50 excels in accuracy and generalization stability, while CNN is more efficient for fast response and limited resource requirements.
Co-Authors Anggara AlFaridzi Tamba Armansyah Armansyah Muhammad Daffa Ginting Muhammad Fathir Aulia