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Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,150 Documents
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Desain dan Implementasi Smart Contract untuk Pengelolaan Persetujuan Akses Data Pasien Berbasis Blockchain Anggie Ciecilia Saragih; Bayu Angga Wijaya; Jon Kevin Sihombing; Febryco Rives; Soeli Yanto Rotua Marbun
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3421

Abstract

This study aims to design and implement blockchain-based smart contracts for secure, transparent, and patient-oriented patient data access consent management. The research method employed a systems approach combining qualitative and quantitative methods through waterfall development stages. The system was developed using the Ethereum Sepolia Testnet blockchain and Solidity-based smart contracts. The implementation results demonstrate that blockchain technology is capable of permanently and transparently recording all patient data consent transactions. The smart contract successfully implemented a Role-Based Access Control (RBAC) mechanism, allowing patients to grant and revoke access permissions for doctors or healthcare institutions. The testing results indicate that the access validation mechanism functioned properly, although there are limitations related to scalability and gas costs on public blockchains. Security evaluation was limited to functional testing and access validation, indicating the need for further testing such as penetration testing and smart contract vulnerability analysis. Overall, this study proves that blockchain technology and smart contracts are capable of improving security and trust in digital healthcare data management, while also supporting the future development of artificial intelligence-based Decision Support Systems.
Penerapan Algoritma XGBoost dengan SMOTE untuk Klasifikasi Kanker Payudara pada Dataset Wisconsin Adrianus Anggoro; Imam Tahyudin; Ades Tikaningsih
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3423

Abstract

Penelitian ini bertujuan untuk mengembangkan model deteksi kanker payudara menggunakan algoritma Extreme Gradient Boosting (XGBoost) pada Breast Cancer Wisconsin Diagnostic Dataset. Dataset terdiri dari 569 sampel dengan 30 fitur medis yang merepresentasikan karakteristik morfologi tumor, dengan dua kelas target yaitu benign (jinak) dan malignant (ganas). Tahapan penelitian meliputi pembersihan data, imputasi nilai hilang, normalisasi fitur, serta penerapan teknik Synthetic Minority Over-sampling Technique (SMOTE) untuk menangani ketidakseimbangan kelas. Model XGBoost dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 94,55%, dengan nilai recall kelas malignant sebesar 95,24%, yang mengindikasikan kemampuan tinggi dalam mendeteksi kanker ganas. Confusion matrix menunjukkan hanya 2 kasus false negative, menandakan sensitivitas model yang sangat baik terhadap kelas minoritas. Dibandingkan dengan model tanpa SMOTE, penerapan SMOTE terbukti meningkatkan recall pada kelas malignant secara signifikan. Hasil penelitian ini menunjukkan bahwa algoritma XGBoost dengan penanganan imbalance class efektif digunakan sebagai sistem pendukung keputusan dalam diagnosis kanker payudara dan berpotensi membantu deteksi dini secara lebih akurat.
Perbandingan Apriori dan FP-Growth dalam Association Rule Pola Pembelian Sparepart Preventive Maintenance Anita; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3427

Abstract

Spare part inventory management is an important aspect of preventive maintenance activities. This study aims to analyze the performance comparison between the Apriori and FP-Growth algorithms in identifying spare part purchasing patterns for preventive maintenance activities. The main problem in spare part management is the lack of optimal inventory planning, which can lead to overstock or stock shortages. The method used in this study is Association Rule Mining with two algorithms, namely Apriori and FP-Growth, applied to spare part purchasing transaction data. The analysis process was conducted through data preprocessing, frequent itemset generation, and association rule formation using minimum support and confidence parameters. The results indicate that the FP-Growth algorithm performs more efficiently than Apriori in terms of computation time and the ability to handle large datasets. Meanwhile, the Apriori algorithm is easier to implement and understand. The resulting association patterns can be used as a basis for decision-making in more effective and efficient spare part inventory management. Therefore, this study is expected to contribute to improving data-driven preventive maintenance strategies.
Analisis Keamanan Website UPT RSUD RAA Soewondo Pati Berdasarkan Hasil Penetration Testing Menggunakan Owasp Dani Yudanta Prapaskia; Chaerul Umam
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3429

Abstract

The development of technology in the healthcare sector has encouraged the utilization of web-based platforms to comprehensively support hospital service operations. This requires the implementation of strict security standards to protect the privacy of patients’ medical data. This study focuses on evaluating the security level of the official website of UPT RSUD RAA Soewondo Pati through penetration testing based on the OWASP framework. The evaluation stages included web infrastructure identification using Wappalyzer and vulnerability scanning using OWASP ZAP. Based on the testing results, several security vulnerabilities with varying levels of risk were identified, including SQL Injection, Cross-Site Scripting (XSS) threats, and vulnerabilities related to session management and authentication systems. In general, the system’s security profile falls into the medium-risk category, indicating that further improvements are required to reduce cyber threats. The use of OWASP guidelines in this study proved effective in identifying system weaknesses while also formulating mitigation strategies, such as optimizing server configuration, implementing secure coding practices, and improving authentication workflows.
Transfer Learning VGG16 untuk Deteksi Kanker Otak MRI: Analisis Komparatif CNN, FNN, LSTM Nesa Puspitasari; Imam Tahyudin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3431

Abstract

Brain cancer has a high mortality rate due to delayed diagnosis, making accurate early detection systems an urgent necessity. This study proposes a two-stage transfer learning approach (initial training and fine-tuning) using VGG16 as a feature extractor, combined with three classification architectures—CNN, FNN, and LSTM—for brain cancer detection in MRI images. The novelty of this study lies in the systematic comparison of the three architectures within a transfer learning framework on a small-scale MRI dataset (818 images with an 80:20 ratio) enhanced through data augmentation. The VGG16+LSTM model achieved the highest accuracy (96.38 percent), followed by VGG16+FNN (96.21 percent) and VGG16+CNN (94.74 percent). The best-performing model was integrated into a web application as a clinical decision support system for early screening. These results confirm the effectiveness of the two-stage transfer learning approach in overcoming data limitations while improving MRI-based classification performance.
Pengembangan Sistem Manajemen Stok dan Penjualan Berbasis Web Dalam Mendukung Transformasi Digital Menggunakan Metode Prototype Pada Butik Merry’s Fashion Lampung Dhella Samputri; Halimah
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3432

Abstract

This study aims to develop a web-based inventory and sales management information system to address the issues of manual record-keeping, which lead to data discrepancies and low operational efficiency. The Prototype method was used to enable iterative system development based on user needs. The developed system includes features for inventory management, sales transactions, purchase orders, and integrated reports, supported by barcode technology and real-time data. Test results show that the system is capable of improving operational process efficiency, speeding up transactions, and reducing recording errors compared to manual methods. Additionally, the system also enhances the ease of inventory monitoring and decision-making. Thus, the developed system is effective in supporting the digital transformation at Merry’s Fashion Boutique in Lampung.
Analisis Kesenjangan Pendidikan dan Usia Kerja Masyarakat Kabupaten Garut dengan K-Means Clustering Hamzah Nurrifqi Fakhri Fikrillah; Fahmi Fadillah Septiana
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3433

Abstract

This study aims to analyze educational disparities and working-age populations in Garut Regency using the K-Means Clustering algorithm. The research data comes from the 2024 verification results of the Garut Regency Social Service, comprising 912,419 individuals aged 15 to 60 years. The primary attributes used include age, highest level of education, and occupation, with the optimal number of clusters determined using the Elbow Method (K=3). The analysis results show three main clusters: Cluster 0 (32.4%) consists of individuals in the late productive age group with low education levels and a predominance of informal employment; Cluster 1 (43.6%) consists of young individuals with secondary education, the majority of whom are unemployed; and Cluster 2 (24.0%) consists of individuals in the middle productive age group with secondary education and diverse employment. Model validation yielded a Silhouette Score of 0.5855 and a Davies-Bouldin Index of 0.5188, indicating that the cluster quality is quite good. These findings confirm that education is a key factor in social mobility and access to employment, and can serve as the basis for strategic policies to reduce socioeconomic inequality in Garut Regency.
Perencanaan Sistem Informasi Pelaporan Kasus Kekerasan Berbasis Constraint: Studi pada Yayasan Sanggar Suara Perempuan Jeny Maryana Nuban; Eko Sediyono; Evi Maria
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3434

Abstract

Violence case reporting systems in community-based organizations are often framed as technical solutions, overlooking the social and operational conditions that shape their use. This study examines how reporting system requirements are formed in such contexts and develops an information system design that responds to these conditions. The study draws on a qualitative, design-oriented case study conducted at Yayasan Sanggar Suara Perempuan (SSP). Data were collected through interviews and observations with key informants, analyzed thematically, and mapped onto the Ward and Peppard framework to inform system design. The findings show that system requirements are not purely functional but emerge as a configuration of interacting constraints, process fragmentation, data security risks, and social barriers that simultaneously bound integration, visibility, and user access. Fragmented reporting practices across unintegrated channels lead to data inconsistencies and delays, while concerns about identity exposure constrain user participation. In response, the study proposes an integrated reporting system featuring centralized data management, anonymous reporting, and role-based access control. These findings demonstrate that system design in high-risk, socially sensitive contexts cannot be derived from functional requirements alone but must be configured within a constraint-bounded design space that directly shapes and limits design decisions.
Klasifikasi Kultivar Jambu Semarang Menggunakan MobileNetV2 dengan Pendekatan Transfer Learning Ndaru Febrian Pujo Leksono; Bagus Adhi Kusuma; Andi Dwi Riyanto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3437

Abstract

Manual identification of Semarang guava cultivars is prone to subjectivity. This study proposes a MobileNetV2 model based on transfer learning to classify 12 cultivars. As an initial study, the main limitation of this research is the very small dataset size, consisting of 192 images with a balanced distribution of 16 images per class. The data were acquired under varied in-the-wild conditions, including differences in background, lighting, and shooting angles. The dataset was divided using a 70 percent training and 30 percent validation ratio. The testing results showed that the validation accuracy reached 94 percent, with an average F1-score of 0.94. However, analysis using the confusion matrix and per-class evaluation showed that the model still experienced difficulties in fine-grained misclassification among visually similar fruits. Considering the small dataset size and the absence of testing using an independent test set or cross-validation, the model’s performance should only be regarded as an initial indication with limited generalizability. In addition, the fine-tuning stage was found to be less significant. As a recommendation, future research should increase the data volume, apply cross-validation testing, and explore architectures with attention mechanisms.
Analisis Komparatif Image-to-Video Artificial Intelligence Pada Animasi 2D Menggunakan PSNR Dan SSIM Rizki Pamuji; Imam Tahyudin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3438

Abstract

The development of generative Artificial Intelligence (AI) technology has had a significant impact on the multimedia sector, particularly in image-to-video techniques that are capable of automatically transforming static images into videos. This study aims to analyze and compare the video quality produced by four AI platforms, namely Kling, Runway, PixVerse, and Pika, in the context of 2D animation. The method used is a comparative experimental approach combining quantitative and qualitative methods. The data consist of three rendered 2D animation images from Blender that were converted into 5-second videos using identical prompts on each platform. Quantitative evaluation was conducted through measurements of processing time, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Meanwhile, qualitative evaluation involved panelists using a Likert scale to assess nine visual aspects. The results indicate that Pika and Runway excelled in processing time efficiency, with average times of 34.4 seconds and 36.3 seconds, respectively. Kling achieved the highest PSNR and SSIM values, with an average PSNR of 14.62 dB and an SSIM of 0.41, indicating the best technical quality. On the other hand, Runway received the highest ratings in terms of visual and aesthetic aspects based on respondent evaluations. Overall, no single platform outperformed the others across all aspects of the study. Therefore, the selection of a platform should be adjusted according to user needs, whether in terms of efficiency, technical quality, or visual aesthetics. This study highlights the importance of an integrated evaluation approach to produce a more comprehensive assessment of video quality.

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