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INCODING: Journal of Informatics and Computer Science Engineering
Published by Mahesa Research Center
ISSN : -     EISSN : 2776432X     DOI : 10.34007
Core Subject : Science,
Computer Science & IT
INCODING: Journal of Informatics and computer science engineering, is a journal of informatics is the study of the structure, behavior, and interactions of natural and engineered computational systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
 1 
Search results for , issue "Vol 5, No 1 (2025): INCODING APRIL" : 10 Documents clear
Data Security Application using Rivest Cipher 6 (RC 6) Algorithm Rahim, Robbi
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.775

Abstract

The prevention of misuse of data by other parties requires a good data security system. Cryptography is a means of securing data with a view to maintaining confidentiality of information contained in the data, so that information cannot be known to unauthorized parties. RC6 is a cryptographic block cipher algorithm that can be used to secure data or files from irresponsible parties. This study found that the data secured using the RC6 algorithm was quite good and required a long time for some parties to get the plain text.
Aspect Based Sentiment Analysis on Hotel Reviews Using Gated Recurrent Unit Lubis, Fahrurrozi; Sitompul, Dhea Novianty
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.710

Abstract

The rapid growth of online platforms has enabled users to share their experiences about various products and services, including hotels. Hotel reviews are crucial in understanding customer perceptions and preferences in the tourism sector. Tiket.com, a web and mobile-based online travel agent, allows users to book hotels and submit reviews, which can be positive, negative, or neutral. These reviews provide valuable insights into the strengths and weaknesses of hotel services and can serve as evaluation material for improvements. This study extracts meaningful information from user reviews through an aspect-based sentiment analysis approach. It categorizes sentiments into specific aspects such as price, cleanliness, service, location, and facilities, ensuring the feedback is more structured and actionable. The research utilizes a Gated Recurrent Unit (GRU) model combined with fastText word embedding to analyze sentiment. A dataset of 6512 hotel reviews was collected through web scraping. The resulting model achieved an accuracy of 91%, evaluated using a confusion matrix. The approach enhances understanding of customer satisfaction by presenting sentiments based on targeted service aspects, making the analysis more concise and relevant for hotel management.
Pendekatan Arsitektur Eficientnet Pada CNN Untuk Meningkatkan Pengenalan Tulisan Tangan Angka: Studi Kasus Dataset MNIST Amin Matondang, Rahmadani Syahriful; Susilawati, Susilawati
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.824

Abstract

This study aims to evaluate the performance of the EfficientNetB1 architecture in recognizing handwritten digits using the MNIST dataset, which consists of 60,000 training images and 10,000 testing images in 28×28 grayscale format. The methodology includes preprocessing steps such as image resizing, grayscale to RGB conversion, pixel normalization, and data augmentation. EfficientNetB1 is used as a feature extractor, followed by dense layers and a softmax output layer for classification. The model is trained using three optimizers—Adam, SGD, and RMSprop—with varying learning rates (0.001, 0.01, and 0.1). Experimental results indicate that the combination of RMSprop and a 0.001 learning rate yields the highest validation accuracy of 97.9%. Classification errors mostly occur on digits with similar visual structures, such as 2 and 5. This research contributes valuable insights into the effective use of EfficientNetB1 and hyperparameter optimization for handwritten digit classification tasks.
Penerapan Algoritma K-Means dalam Segmentasi Pelanggan untuk Meningkatkan Strategi Pemasaran di E-Commerce Andrian, Yohannes; Susilawati, Susilawati
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.825

Abstract

This study aims to implement the K-Means Clustering algorithm for customer segmentation in e-commerce to enhance marketing strategy effectiveness. By utilizing customer transaction data such as purchase frequency, product quantity, and total spending, the study classifies customers into three main segments: high, medium, and low transaction groups. The research method includes data preprocessing, cluster center initialization, Euclidean distance calculation, and iterative clustering to achieve optimal segmentation. The segmentation results are integrated into a web-based system, facilitating interactive customer data management. Testing on application features, including login, data input, clustering process, and reporting, confirms that the application functions as expected. These findings reinforce the role of K-Means-based segmentation in supporting more targeted marketing decision-making in the e-commerce sector.
Meningkatkan Deteksi Email Phising Melalui Pendekatan SVM yang Dioptimalkan NLP Tanjung, Rino Nurcahyo Fauzi; Rahman, Sayuti
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.831

Abstract

Phishing email attacks are a serious threat in the digital ecosystem because they can trick users into leaking sensitive information or accessing malicious links. This study aims to develop a phishing email classification model based on the Support Vector Machine (SVM) algorithm combined with Natural Language Processing (NLP) techniques to improve detection accuracy. The process begins with the tokenization, text cleansing, and feature extraction stages using the TF-IDF approach, which is further used as input into the classification model. Various SVM kernels, including linear, radial basis function (RBF), and polynomial, are tested through the grid search method with parameter tuning such as C, gamma, and degree. The results showed that SVMs with polynomial kernels produced the highest accuracy of 97.85%, surpassing other algorithms such as Naïve Bayes, Random Forest, and Logistic Regression. These findings indicate that the integration of NLP and SVM with proper parameter tuning provides an effective solution in mitigating phishing email attacks. This model can be the foundation for the development of a more adaptive and efficient cybersecurity system.
Pengenalan Tulisan Tangan Angka menggunakan CNN dengan Arsitektur DenseNet-201 pada Dataset MNIST Fadillah Lubis, Muhammad Fajril; Susilawati, Susilawati
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.826

Abstract

Handwritten digit recognition using the MNIST dataset is one of the applications in digital image processing. The selection of hyperparameters in the CNN architecture for handwriting recognition presents a challenge in achieving better recognition accuracy. This research focuses on the implementation of the DenseNet-201 architecture for recognizing handwritten digits in the MNIST dataset. The research stages include dataset preprocessing, model training, model testing, and model evaluation. The MNIST dataset consists of 60,000 training data and 10,000 testing data. Dataset preprocessing involves resizing the images to a larger size. The model training applies the DenseNet-201 architecture with selected hyperparameters such as activation functions (Softmax and ReLU), optimizers (Adam, RMSprop, and SGD), and learning rates (0.1, 0.01, and 0.001). The model testing uses one of the nine best-performing trained models. Model evaluation uses a confusion matrix to assess the accuracy and recognition performance on the MNIST dataset. The results show that the DenseNet-201 architecture with the RMSprop optimizer and a learning rate of 0.001 achieved a handwritten digit recognition accuracy of 99.49%. This study provides insights into CNN architectures and optimal hyperparameter selection for digital image processing
Pengembangan Hybrid App Arsip Ijazah dan SKHUN di SMK Pembangunan Bukittinggi Khomarudin, Agus Nur; Novita, Rina; Aulia, Romy; Putri, Ega Evinda; Jamaluddin, Jamaluddin
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.770

Abstract

The importance of fast, precise, and accurate information is highly essential for every institution or organization, particularly in archival management. SMK Pembangunan Bukittinggi is currently making continuous efforts to support the "Innovative Vocational School" program. However, the institution faces several challenges in managing the archiving of diplomas and SKHUN (School Leaving Certificates). The current archiving process is still carried out using conventional methods that are not yet digital-based, which presents several weaknesses, such as the risk of damage, fire, and difficulties in retrieving required documents. This research follows the development steps outlined in the Agile methodology. The research has produced a hybrid application for diploma and SKHUN archiving at SMK Pembangunan Bukittinggi, which has undergone several tests, including system validity testing, which resulted in a score of 0.83, indicating a valid system; practicality testing with a score of 0.90, categorized as very practical; and effectiveness testing with a score of 0.82, indicating high effectiveness. Therefore, it can be concluded that the hybrid archiving application for diplomas and SKHUN developed in this study is feasible to be implemented at SMK Pembangunan Bukittinggi..
Klasifikasi Penyakit Tanaman Cabai Menggunakan Googlenet Pada Citra Daun Harahap, Jaffar Siddik; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.832

Abstract

Red chili pepper (Capsicum annuum L.) is a horticultural commodity that has high economic value, but its production is often hampered by plant disease attacks. To automatically detect diseases in chili leaves, this study uses a deep learning approach with GoogLeNet architecture and transfer learning techniques. This study aims to classify five types of chili leaf diseases, namely Healthy, Leaf Curl, Leaf Spot, Whitefly, and Yellowish, using a model initialized with pretrained weights from ImageNet. Three types of optimizers (Adam, RMSprop, and SGD) were tested to evaluate their effect on classification accuracy. The results showed that Adam performed best with a validation accuracy of 98.80%, followed by RMSprop (98.40%) and SGD (94.00%). The confusion matrix shows that misclassification occurs mainly in the Leaf Curl class, which is often confused with Yellowish, due to visual similarities. Although the classification results were excellent, limitations such as the small size of the dataset (500 images) and the need for further augmentation techniques to address prediction errors remained challenges. This research contributes to the development of an efficient and accurate computer vision-based plant disease classification system.
Analisis Klustering Menggunakan Algoritma DBSCAN untuk Deteksi Anomali dalam Data Transaksi Keuangan Alwi, Buchori; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.827

Abstract

Anomaly detection in financial transaction data is a crucial aspect due to the increasing use of e-money, which raises the risk of suspicious activities such as fraud and money laundering. This study applies the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster transaction data and identify anomalies based on three main variables: transaction amount, transaction frequency, and final balance. The optimal parameters were determined by evaluating various combinations of epsilon (ε) and minPts values using the Davies-Bouldin Index (DBI) as a clustering quality indicator. The analysis results indicate that the optimal parameters are ε of 0.2727 and minPts of 6, with a DBI score of 1.1753. DBSCAN successfully formed six main clusters and detected 138 data points as noise, indicating potentially abnormal transactions. These findings demonstrate that DBSCAN can effectively distinguish between normal and suspicious data without requiring prior assumptions on the number of clusters, contributing to the development of more accurate and adaptive digital transaction anomaly detection systems.
Klasifikasi Tumbuhan Obat Berdasarkan Citra Daun Menggunakan Algoritma CNN Sinaga, Nicolas Novelico; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.833

Abstract

This study aims to classify various types of medicinal plants based on leaf images by utilizing the Convolutional Neural Network (CNN) algorithm. The model used is the MobileNetV2 architecture because of its ability to balance accuracy and computational efficiency. The leaf images dataset is divided into training and validation data, then processed through several stages such as augmentation, fine-tuning, and regularization. The evaluation results show that the model successfully achieved the highest validation accuracy of 98,43%, proving that this approach is effective in identifying types of medicinal plants.

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