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Wandi Syahindra
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Arcitech: Journal of Computer Science and Artificial Intelligence
Published by Institut Agama Islam Negeri Curup
ISSN : 29623669     EISSN : 29622360     DOI : http://dx.doi.org/10.29240/arcitech
Core Subject : Science,
Computer Science & IT
Arcitech: Journal of Computer Science and Artificial Intelligence, is an Open Access and peer-reviewed journal published by the State Islamic Institute (IAIN) Curup. This journal focuses on the field of computer science and artificial intelligence covering all aspects of information technology, computer science, computer engineering, information systems, Software Engineering and its development, software engineering Computer networks, IoT, security systems, Simulation Modeling and Applied Computing, Computing High Performance, Image and speech processing, big data and data mining, and artificial intelligence. The journal is published by Institut Agama Islam Negeri (IAIN) Curup, online and printed twice a year, in June and December.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 59 Documents
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Implementasi dan Evaluasi Sistem Failover Otomatis Berbasis Netwatch pada Router Mikrotik Dual-ISP untuk Meningkatkan Ketersediaan Jaringan Apotek Farmaku Yuliadi, Boy; Rasto
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.14551

Abstract

This study contributes by proposing and implementing an automated failover–failback mechanism based on Mikrotik Netwatch for multi-homed Internet connectivity, which has not been widely explored in the context of pharmacy information systems, and by validating its effectiveness through empirical testing in a production-like environment. Unlike prior work that is largely conceptual or simulation-oriented, this research evaluates the system in an end-to-end manner at both the control plane (router-level monitoring and switching) and the data plane (client-side traffic continuity). The system is developed following the Network Development Life Cycle (NDLC) methodology, encompassing analysis, design, simulation, simulation testing, and deployment. A dual-ISP architecture is implemented, in which Netwatch continuously probes upstream reachability and triggers automated route switching upon link degradation or failure, as well as automatic restoration upon link recovery. Experimental results indicate that the system achieves an average failover time of approximately 5 seconds, with minimal packet loss and no perceptible service interruption for end users. The failback mechanism also operates autonomously and stably. Overall, router- and client-side performance measurements confirm that the proposed solution effectively enhances network resilience and service availability in environments with high availability requirements.
Perancangan dan Evaluasi Sistem Informasi Manajemen Aset Printer dan Toner Berbasis Web untuk Operasional Perusahaan Sukerman; Ramayanti, Desi
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.14651

Abstract

Printer asset and toner management in enterprises is often inefficient due to fragmented, manual procedures, which reduce data accuracy and slow operational decisions. Addressing this gap, this study designs and evaluates a web-based printer asset and toner management information system that integrates asset records, toner stock and request monitoring, and incident logging within a single platform with role-based access control. The system also provides dashboard summaries and reporting features to support near real-time monitoring and asset audits. Development followed an Agile methodology to enable rapid iterations driven by user feedback. Evaluation employed black-box testing and User Acceptance Testing (UAT) to verify functional correctness and workflow fit. Results indicate that the system operates as required and improves traceability and operational efficiency compared with the prior manual approach. The study contributes a replicable design and evaluation framework for integrated printer–toner asset management systems in organizations with similar operational characteristics.
Klasifikasi Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak md, Ramanda; Hartati, Ery
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15122

Abstract

Early detection of Alzheimer's disease is crucial for effective treatment, and the use of brain MRI images has become a common method for diagnosis. However, many previous studies have faced challenges in addressing class imbalance in their datasets, leading to lower accuracy for minority classes. This study aims to address this issue by using a pretrained CNN architecture, VGG19, combined with the SMOTE method to address class integration and improve classification accuracy. This study contributes by introducing SMOTE to the Alzheimer's MRI image dataset to achieve a more balanced class distribution, which has not been fully explored in previous studies. The evaluation results show that the classification accuracy reaches 95%, higher than previous studies using VGG-19 with an accuracy of 77.66%. These results confirm that the use of VGG19 with SMOTE produces better performance, especially in addressing class representation, which is a key contribution of this study. This research has the potential to be applied in more efficient and accurate automated image-based detection systems, especially for the early diagnosis of Alzheimer's disease.
Klasifikasi Kerusakan Uang Rupiah Menggunakan CNN Dengan Arsitektur VGG16 Roshan, Muhamad Rizvi; Irsyad, Hafiz
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15125

Abstract

This study developed a deep learning model using a Convolutional Neural Network (CNN) architecture with VGG16 to classify the level of damage to rupiah banknotes. Previous studies have focused more on recognizing denominations and detecting counterfeit money using CNN and transfer learning, while the classification of physical damage to rupiah banknotes is still limited, both locally and internationally, and often relies on special acquisition devices or template registration. The dataset used consists of images of rupiah banknotes grouped into three damage categories: >20%, >40%, and >50%. This dataset is divided into 80% for training data (537 images) and 20% for test data (135 images). To enrich the data variety, this study applied on-the-fly data augmentation techniques with rotation, zoom, and flipping during the training process. The experimental results show that this model achieves an accuracy of 93.33%, with excellent precision, recall, and F1-score values, especially in the >50% damage category. The use of the ADAM optimizer with a learning rate of 1e-3 proved to provide more stable and efficient training. Overall, this study shows that the application of CNN with the VGG16 architecture is effective in classifying rupiah currency damage and can contribute to the development of image processing technology, particularly for evaluating currency feasibility in real-world scenarios.
Pengembangan dan Evaluasi Usability Aplikasi Penjualan Rumah Berbasis Web terintegrasi Payment Gateway untuk Digitalisasi KPR In-House menggunakan Model Waterfall Patria, Muhammad; Wardhana, Mahardhika Dava
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15155

Abstract

This study develops a web-based home sales application to support In-House Mortgage (KPR In-House) financing at PT Nirwana Propertindo Sejahtera. The previous manual process led to administrative delays, limited transaction traceability, and a high risk of data loss. However, studies addressing an end-to-end digital KPR In-House system that integrates online applications, payment gateway–based installment payments, and standardized usability evaluation remain limited. The system was developed using the Waterfall model within the System Development Life Cycle (SDLC) and includes housing catalogs, installment simulation, online KPR applications, and integrated installment payments. Evaluation was conducted using Black Box Testing and the System Usability Scale (SUS). The results show that all system functions operate as expected and achieved a SUS score of 88.25, indicating high usability. The implementation improves transaction traceability, reduces the risk of document loss, and supports payment transparency. This study contributes an end-to-end digital KPR In-House process model, payment gateway integration, and empirical usability evidence.
Implementasi Customer Relationship Management Digital pada Sektor Jasa Kebersihan Menggunakan Metode Waterfall Fadilah, Denny; Patria, Muhammad
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15156

Abstract

The customer complaint management system in cleaning service companies still relies heavily on manual processes, while previous studies have focused more on the implementation of general CRM without focusing on structured ticketing mechanisms and system usability evaluation. This study aims to design and implement a web-based Customer Relationship Management system in the cleaning services sector using the Waterfall method, with an emphasis on single ticket ownership, clear resolution flows, and usability evaluation. The research uses a mixed method approach through observation, interviews, and documentation studies, as well as system testing using User Acceptance Test and System Usability Scale. The SUS test involved 15 respondents and produced an average score of 75.83, which is in the good category, indicating an adequate level of system usability. The UAT results show that all the main functions of the system run according to user operational needs. This research provides practical contributions in the form of a measurable CRM ticketing implementation model for cleaning service companies and academic contributions through empirical evidence of Waterfall-based CRM usability in the context of cleaning services.
Optimasi Hyperparameter CNN dengan Arsitektur VGG16 Menggunakan Grid Search Untuk Klasifikasi Penyakit Buah Delima Fawzan, Muhammad; Udjulawa, Daniel
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15175

Abstract

Early detection of pomegranate fruit diseases is crucial to reduce yield losses and improve harvest quality; however, visual identification in the field is often subjective and difficult due to the similarity of symptoms among different diseases. This study aims to develop a pomegranate fruit disease classification model using a Convolutional Neural Network (CNN) based on the VGG16 architecture, optimized through the Grid Search method. The dataset consists of five classes: 886 Alternaria samples, 116 Anthracnose samples, 966 Bacterial Blight samples, 631 Cercospora samples, and 1,450 Healthy samples, resulting in a total of 5,099 images. The dataset underwent preprocessing and data augmentation to increase variability and prevent overfitting. After balancing the dataset, it was split into 70% training data, 20% validation data, and 10% testing data. Hyperparameters such as epoch, batch size, learning rate, and optimizer were evaluated using Grid Search to determine the optimal configuration. The results indicate that the best performance was achieved using 100 epochs, a batch size of 32, a learning rate of 0.0001, and the Adam optimizer. The proposed model achieved a testing accuracy of 99.59%, with precision, recall, and F1-score values of 0.996. These findings demonstrate that the optimized VGG16-based CNN model is highly effective in accurately classifying pomegranate fruit diseases.
Klasifikasi Motif Kain Jumputan Palembang Menggunakan Metode CNN dengan Arsitektur Resnet-50 Mauladi, Muhammad; Hermanto, Dedy
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15310

Abstract

This study develops an automated classification system for Palembang jumputan textile motifs based on computer vision to address inter-motif pattern similarities that often challenge non-expert users and hinder the digital documentation of textile cultural heritage. Unlike traditional textile studies that typically employ generic Convolutional Neural Networks (CNNs), this research applies transfer learning using the ResNet-50 architecture on a primary dataset consisting of five motif classes: lilin, titik 7, titik 9, bunga tabur, and akoprin daun. The dataset is divided into training, validation, and testing sets, followed by preprocessing and image augmentation to enhance data variability. The model is trained with learning rate tuning, and the best configuration achieves a training accuracy of 95.57%, a validation accuracy of 87.33%, and a testing accuracy of 88%. Evaluation using a classification report and confusion matrix indicates excellent performance for the titik 9 and bunga tabur motifs, with precision and recall values approaching 1.00, while misclassifications still occur in the lilin motif due to visual similarity. These results confirm the effectiveness of ResNet-50 for jumputan motif classification and support cultural preservation through faster and more consistent motif identification.
Klasifikasi Sentimen Komentar Youtube Demonstrasi DPR RI Menggunakan Support Vector Machine Rahmadhani, Siti Aulia; Rusanti, Lia Dwi; Rosyid, Harun Al
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15316

Abstract

Demonstrations against the Indonesian House of Representatives (DPR RI) have triggered extensive public opinion flows on social media; however, sentiment mapping of Indonesian-language comments on YouTube live broadcasts of political issues still requires more structured methodological reporting and evaluation. This study aims to classify public sentiment from 1,493 YouTube comments related to DPR RI demonstrations using the Support Vector Machine (SVM) algorithm. Data were collected via the YouTube Data API and subsequently processed through text cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using an Indonesian lexicon-based approach to generate three sentiment classes (positive, negative, and neutral), with neutral sentiment being dominant. Feature representation was constructed using CountVectorizer, and the SVM model was trained using an 80:20 split for training and testing data. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics, achieving an accuracy of 92.4% (weighted performance of 0.924). Word frequency analysis was also employed to identify dominant terms within each sentiment class. These findings demonstrate the effectiveness of SVM in mapping digital public perceptions on political issues and highlight its potential to support data-driven policy evaluation.

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