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Jurnal Informatika & Teknologi Cerdas (JITC)
Published by Universitas Paramadina
ISSN : -     EISSN : 31097677     DOI : https://doi.org/10.51353/b2cvvm55
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Jurnal Informatika & Teknologi Cerdas (JITC) dikelola dan diterbitkan oleh Program Studi Teknik Informatika, Universitas Paramadina. Jurnal ini memuat artikel hasil penelitian di bidang ilmu komputer dan informatika, mencakup topik seperti pengembangan perangkat lunak, aplikasi multimedia, jaringan komputer, sistem cerdas, dan sistem informasi manajemen. JITC terbit dua kali dalam setahun, yaitu pada bulan Juni dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 15 Documents
 1   2 
Applications of WDM System in US Patents Mhnd Farhan
Jurnal Informatika & Teknologi Cerdas Vol 2 No 1 (2026): Jurnal Informatika & Teknologi Cerdas (JITC)
Publisher : Program Studi Teknik Informatika Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/c7yp9z53

Abstract

In fiber-optical telecommunications, wavelength division multiplexing (WDM) is a multiplexing and transmission scheme in which distinct wavelengths, each emitted by multiple lasers, carry specific information. WDM filters are used to multiplex these wavelengths. Similarly, at the receiving end, they are demultiplexed or separated using coherent detection with tunable local oscillators or similar filters. This paper explains the most recent applications of the WDM System in US patents. A succinct explanation of the WDM System is also given.
Tinjauan Literatur: Penerapan Teknologi Blockchain dalam Sistem Verifikasi Sertifikat Akademik Wisnu Rizki Ferdiansyah; Safani Nur Awal; Muhamad Tsaqif
Jurnal Informatika & Teknologi Cerdas Vol 2 No 1 (2026): Jurnal Informatika & Teknologi Cerdas (JITC)
Publisher : Program Studi Teknik Informatika Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/6sdya968

Abstract

This study evaluates the application of blockchain technology to address academic certificate forgery using a Systematic Literature Review (SLR) based on PRISMA guidelines (2022–2025). Analysis of seven selected studies reveals that the most effective architecture is a hybrid model combining public blockchain (Ethereum) and off-chain storage (IPFS) integrated with QR Codes. This implementation is proven to guarantee data immutability and reduce verification time to seconds. However, due to high transaction costs and privacy issues, future research is recommended to focus on consortium blockchain or Layer-2 solutions.
Prediksi Prognosis Kanker Payudara Menggunakan Hybrid Artificial Neural Network Dan Gaussian Naïve Bayes Jesika Octavia Hutagaol; Margaretha Yohanna; Harlen Gilbert Simanullang
Jurnal Informatika & Teknologi Cerdas Vol 2 No 1 (2026): Jurnal Informatika & Teknologi Cerdas (JITC)
Publisher : Program Studi Teknik Informatika Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/hf480q42

Abstract

Breast cancer is one of the types of cancer that causes the most deaths in women in the world. Breast cancer prognosis is important to assist medical personnel in predicting the possibility of recurrence so that treatment can be provided more effectively. This study aims to implement a hybrid Artificial Neural Network (ANN) and Gaussian Naïve Bayes method for breast cancer prognosis prediction using the Breast Cancer Wisconsin Prognostic (WPBC) dataset. The dataset consisted of 198 patient records with 35 numerical features. The research stages included data preprocessing, normalization, splitting the dataset into training and testing data using an 80:20 ratio, feature extraction using ANN, and classification using Gaussian Naïve Bayes. Unlike previous studies that generally used single methods, this study utilizes ANN as a feature extractor before the classification process using Gaussian Naïve Bayes. ANN was used with one hidden layer containing 16 neurons to learn non-linear relationships among features before the classification process. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The experimental results showed that the hybrid ANN-Gaussian Naïve Bayes method achieved an accuracy of 90%, precision of 72.73%, recall of 88.89%, and an F1-score of 80%. These results indicate that the hybrid method provides better classification performance compared to single methods in breast cancer prognosis prediction.
Sistem Pendukung Keputusan Seleksi Penerima Beasiswa KIP Kuliah Menggunakan Metode SAW dengan Pembobotan Kriteria Akademik dan Kondisi Ekonomi Mahasiswa Laura; Yaslinda Lizar
Jurnal Informatika & Teknologi Cerdas Vol 2 No 1 (2026): Jurnal Informatika & Teknologi Cerdas (JITC)
Publisher : Program Studi Teknik Informatika Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/0t6wwh51

Abstract

The scholarship recipient selection process requires an assessment mechanism capable of accommodating multiple criteria in a fair and measurable manner. The large number of applicants and the variety of factors considered often make the selection process complex when conducted manually. Such conditions may lead to subjectivity and inconsistencies in decision-making. This study aims to develop a Decision Support System (DSS) to assist in selecting scholarship recipients by applying the Simple Additive Weighting (SAW) method. The SAW method was chosen because it can process multiple criteria that have been assigned weights according to their level of importance, thereby generating preference values that can be used as the basis for determining scholarship recipients. The criteria used in this study include Grade Point Average (GPA), parents’ income, number of dependents in the family, student achievements, and attendance records. Data were collected through the distribution of questionnaires to 30 respondents to obtain information regarding the relevance and importance of each criterion. The results indicate that the implementation of the SAW method can systematically rank scholarship candidates based on the highest preference values. Therefore, the developed system can support a more efficient, objective, and accountable scholarship selection process.
Klasifikasi Sampah Berbasis Citra Menggunakan Metode CNN: Studi Komparatif dengan Decision Tree, Random Forest, dan SVM untuk Pengelolaan Sampah Berkelanjutan Nabila Carrissa Dewi; Gus Nanang Syaifuddiin
Jurnal Informatika & Teknologi Cerdas Vol 2 No 1 (2026): Jurnal Informatika & Teknologi Cerdas (JITC)
Publisher : Program Studi Teknik Informatika Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/wdxwt968

Abstract

The increasing volume of municipal waste has intensified the need for accurate and efficient automated waste-sorting technologies to support sustainable waste management. Although Convolutional Neural Networks (CNNs) have become the dominant approach in image classification due to their ability to learn feature representations automatically, their effectiveness under limited-data conditions remains insufficiently explored. This study investigates the performance of CNNs in comparison with conventional machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), for image-based waste classification. The experiments were conducted using the Garbage Classification Dataset consisting of 4,133 images distributed across seven waste categories. The proposed framework involved image preprocessing, Histogram of Oriented Gradients (HOG) feature extraction for machine learning models, stratified data partitioning with a 70:20:10 ratio, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM achieved the highest accuracy of 67.15%, followed by Random Forest (65.70%), Decision Tree (39.61%), and CNN (23.67%). A notable finding of this study is that the CNN model, despite its superior theoretical capacity for automatic feature learning, produced the lowest classification performance among the evaluated approaches. This outcome suggests that training a CNN from scratch on a relatively limited dataset with considerable inter-class visual similarity is insufficient to learn highly discriminative feature representations. In contrast, HOG-based feature engineering provided more structured and stable visual descriptors, enabling conventional machine learning algorithms to achieve better generalization performance. These findings indicate that deep learning models do not necessarily outperform traditional machine learning approaches in all scenarios and that dataset characteristics play a critical role in determining model effectiveness. This study contributes empirical evidence that, in resource-constrained environments and limited-data settings, the combination of HOG and SVM can serve as a more accurate and computationally efficient alternative to CNN-based approaches for automated waste classification. The findings provide valuable insights for the development of practical intelligent waste-sorting systems that support sustainable waste management initiatives.

Page 2 of 2 | Total Record : 15

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