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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,135 Documents
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Sistem Pendukung Keputusan Penentuan Kebijakan Strategi Promosi Kampus Dengan Metode Weighted Aggregated Sum Product Assesment (WASPAS) Sri Sugiarti; Dormauli K Nahulae; Syafrizal Syafrizal; Tongam E Panggabean; Maringan Sianturi
JURIKOM (Jurnal Riset Komputer) Vol. 5 No. 2 (2018): April 2018
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v5i2.611

Abstract

Decision support system is an application used to facilitate campus promotion team members to determine the strategy policy that will be used in the campus promotion process. This author discusses Decision Support System policy determination campus promotion strategy with WASPAS method. The WASPAS method to be used in this decision support system aims to facilitate the campus promotion team to make good and wise decisions. One of the supporting problems faced by STMIK Budi Darma is the process of determining the location of promotion. From the results of interviews with teams from the field of cooperation, business and marketing STMIK Budi Darma found that the problem of promotion on STMIK Budi Darma because it has not had the right way to choose an effective way to choose a potential school. This potential school can be interpreted as a school that has good financial and academic. To achieve and answer these things one of them can be solved by WASPAS method.
Information Security Risk Analysis and Identification in the Tulang Bawang Data Portal Application Using the OCTAVE Allegro Method Wahyu Aji Pulungan; Allwine
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9335

Abstract

Information technology has become an essential component in supporting organizational and governmental operations. In line with the implementation of E-Government and the Satu Data Indonesia initiative, the Tulang Bawang Regency Government, through its Communications and Information Office (Diskominfo), developed the Tulang Bawang Data Portal to facilitate centralized data management. Despite its strategic role, the system is exposed to various information security risks that may threaten the confidentiality, integrity, and availability of data if not properly addressed. This study aims to systematically identify, assess, and prioritize information security risks within the Tulang Bawang Data Portal using the OCTAVE Allegro method. A qualitative risk assessment approach was employed, incorporating asset identification, threat analysis, impact evaluation, and risk prioritization based on defined risk measurement criteria. The findings indicate the presence of four significant risks, with Risk Relative Scores (RRS) ranging from 14 to 23. Notably, 75% of the identified risks are classified as high priority. The most critical risk is associated with database misuse, which poses a substantial threat to sensitive government data. To address these risks, several mitigation strategies are recommended, including the enhancement of access control mechanisms, periodic security audits, user awareness programs, and the establishment of comprehensive data governance policies. This study contributes to the field by providing a structured and practical risk assessment framework tailored to government-based data portal systems, thereby supporting more effective and informed decision-making in information security management.
Implementation of YOLO11 for Disease Detection in Strawberry Plants Based on Android Application Yosea Mervandy Sugiarto; Aditya Dwi Putro W; Abednego Dwi Septiadi
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9542

Abstract

A Diseases in strawberry plants, particularly leaf spot and powdery mildew, represent major challenges that can diminish fruit quality and production quantity. Manual diagnosis by farmers is often subjective, time-consuming, and prone to error. This research aims to develop an automated strawberry disease detection system by implementing and comparing three variants of the latest deep learning architecture, YOLO11n (Nano), YOLO11s (Small), and YOLO11m (Medium), into an Android-based application. The results indicate that the YOLO11n (Nano) variant, as the baseline, provides the most optimal performance for mobile use, achieving a mean Average Precision (mAP@50) of 91.7%, a precision of 0.888, and a recall of 0.841. After integration into Android devices using the TensorFlow Lite format, the model recorded a real-time inference time ranging from 104-125 ms at a speed of 8 FPS. This study contributes an empirical framework for deploying cutting-edge deep learning models on resource-constrained edge devices, establishing that YOLO11n effectively bridges the gap between state-of-the-art detection accuracy and mobile operational efficiency. Furthermore, it provides a practical roadmap for the digital transformation of early-stage crop monitoring, enabling farmers to perform reliable, real-time diagnostics directly in the field.
Optimasi Jadwal Tanam Padi di Kabupaten Tuban melalui Prediksi Curah Hujan Menggunakan Random Forest Naili Nafa Khatirokimmah; Mula Agung Barata; Sahri
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9635

Abstract

Weather uncertainty due to climate change increasingly threatens rice harvest success, especially when farmers still rely on traditional forecasts that are not always accurate. This study developed a decision support system to determine the timing of rice planting based on daily rainfall predictions using Random Forest Regression. Daily climate data from the BMKG in Tuban, East Java, for the period 2022–2025 was used as the basis for training, with the addition of time features such as month, day of the year, and season to capture seasonal patterns. In East Java, the rainy season usually lasts from October to April and the dry season from May to September, but climate change has caused shifts in the timing, duration, and intensity of rainfall, making traditional seasonal classifications less reliable for determining the optimal planting time. The model was tested on 2025 data and showed improved performance compared to the baseline model. The tuned model produced an MAE of 5.78 mm, an RMSE of 9.75 mm, and a coefficient of determination (R²) of 0.177, an improvement over the baseline, which had an MAE of 6.02 mm, an RMSE of 10.16 mm, and an R² of 0.107. Although the R² value is still relatively low, the decrease in MAE and RMSE indicates that the tuned model is more accurate in predicting daily rainfall, especially in the light to moderate range, which is most relevant for planting decisions
Perbandingan Model Deep Learning DenseNet121, EfficientNetB0 dan Resnet-50 pada Klasifikasi Anemia Citra Telapak Tangan Muhammad Ihksan; Dede Fauzi
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9638

Abstract

Anemia is a global health problem affecting approximately 1.2 billion people worldwide, with the highest prevalence among pregnant women and adolescent girls. Conventional diagnosis through laboratory blood tests is invasive, requires trained medical personnel, and is unaffordable for communities in remote areas. This study aims to evaluate and compare the performance of three deep learning architectures, namely DenseNet121, EfficientNetB0, and ResNet-50, in classifying anemia and non-anemia conditions non-invasively based on palm images. The dataset used is a public dataset called anemiatangan from the Kaggle platform, consisting of 10,200 images with two classes, Anemia and Non-Anemia, divided into 80% training data (8,200 images), 10% validation (1,000 images), and 10% testing (1,000 images). All three models were trained using a transfer learning approach with pre-trained weights from ImageNet, accompanied by preprocessing and data augmentation. Evaluation was performed based on accuracy, precision, recall, F1-Score, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve) metrics. The test results indicate that DenseNet121 and EfficientNetB0 achieved the highest accuracy of 99% with precision, recall, and F1-Score values approaching perfection at 0.99, while ResNet-50 recorded an accuracy of 97%. Therefore, DenseNet121 and EfficientNetB0 are proven to be the most optimal architectures for implementing a non-invasive anemia screening system based on palm images, with the potential to be integrated into mobile applications and telemedicine systems to support early detection of anemia in remote areas.
Implementasi dan Analisis Kinerja Komputasi Paralel Menggunakan Ray pada Lingkungan Multi-Core untuk Pemrosesan Audio Phie Chyan; Sean Coonery Sumarta
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9649

Abstract

Parallel computing has become an effective approach to improving data processing efficiency, particularly for CPU-bound applications. This study aims to implement parallel computing using the Ray framework for audio feature extraction in a multi-core single-node environment. The dataset consists of 1000 .wav audio files from the RAVDESS dataset, which are processed through feature extraction in the time, frequency, and time–frequency domains using the Librosa library. The extracted features include zero-crossing rate, spectral features, and Mel-Frequency Cepstral Coefficients (MFCC), which provide a comprehensive representation of audio signal characteristics. Each audio file is treated as an independent task and distributed across multiple workers for parallel processing. This approach allows data processing to be performed independently without inter-task dependencies. Experiments are conducted by varying the number of workers from 1 to 4 to observe their impact on execution time. The primary parameter observed is the total execution time required to complete the feature extraction process. The results show that the implementation of parallel computing reduces execution time compared to serial processing. However, the performance improvement is not strictly linear due to system overhead and hardware resource limitations. These findings indicate that a task parallelism approach using Ray can serve as a practical solution to improve audio data processing efficiency in resource-constrained environments without requiring complex distributed computing infrastructure.
Optimasi Strategi Inventory dan Mitigasi Knowledge Loss pada Industri Otomotif Melalui Integrasi Algoritma K-Means Clustering dan Framework SECI Juseia Wulandari; Violin Juneyla Nandita; Khairunnisa’ Almaududy; Rafi Herdian; Ken Ditha Tania; Ahmad Rifai; Dedy Kurniawan
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9650

Abstract

Digital transformation within used automotive industry today demands paradigm shift from intuitive decision-making towards data-driven approach to face increasingly intense market competition dynamics. The primary problem identified in this research is high level of subjectivity in stock management and dependence on individual experience triggering organizational knowledge loss risks or knowledge loss. This study aims to optimize stock management strategy and mitigate these risks through integration of K-Means clustering algorithm and Socialization, Externalization, Combination, Internalization framework. The research method involves in-depth analysis of five hundred fifty-eight thousand eight hundred thirty-seven vehicle transaction data using data mining techniques to discover hidden patterns from automotive market behavior. Research results show that the algorithm successfully classified stock into three optimal clusters, where symbol k represents cluster number of three, with high performance proven by Calinski-Harabasz Index score of 283,364.95. These clusters differentiate assets into medium, high-risk, and premium categories based on physical condition and mileage, which allows companies to determine liquidation or retention strategies accurately. Integration with the framework ensures that data mining findings are transformed into permanently documented organizational knowledge management. The implementation of this model provides a significant impact for companies in improving operational efficiency and reducing dependence on individual memory. This research study provides a real contribution in creating an objective foundation for more measurable, systematic, and sustainable managerial decision-making for national industry sectors and other related complex business environment systems.
Pemanfaatan Yolo11 dan Bytetrack untuk Penghitungan Telur Berbasis Visi Komputer pada Konveyor Secara Real-time Nurmahendra Harahap; Jhoni Hidayat; Paula Risten Butarbutar; Akbar Dandi Aljaba
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9664

Abstract

Egg counting is an important stage in the laying hen farming industry because it directly affects productivity and the efficiency of production management. Manual counting methods still have several limitations, including relatively long processing time, the need for a large workforce, and a fairly high error rate due to human factors. These conditions indicate the need for the implementation of automated technology capable of improving speed, accuracy, and consistency in the egg counting process. This study aims to develop an automatic egg counting system based on computer vision by integrating YOLO11 as the object detection method and ByteTrack as the object tracking method. The egg dataset was collected through image acquisition under various conditions, followed by annotation and data augmentation processes before being used in the model training stage using Google Colab.The test results show that the developed system is capable of detecting and counting eggs with a high level of accuracy, where precision and recall values exceed 0.90, and the average counting accuracy reaches 94.4% under various testing conditions. The main factors affecting system errors are high conveyor speed, which causes motion blur, and lighting variations that can lead to false positives and false negatives. The main contribution of this research is the development of a camera-based counting system design, thereby reducing errors in counting the number of eggs. The results of this study indicate that the integration of YOLO11 and ByteTrack has the potential to improve the efficiency of automated egg counting processes and contribute to the advancement of computer vision technology in the modern poultry industry.
Analisis Komparatif Information Gain Dan Gain Ratio Pada Algoritma C4.5 Untuk Klasifikasi Produk ATK Terlaris Segmen Business-to-School Ana Billah; Dicky Nofriansyah; Ahmad Calam
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9702

Abstract

Uncertainty in stock management, such as the risk of overstock and stock-out, is a major challenge for Office Stationery (ATK) distributors in facing fluctuating seasonal demand patterns in the Business-to-School (B2S) segment. This study aims to analyze the comparative attribute selection criteria in the C4.5 algorithm, namely Information Gain and Gain Ratio, in the classification of best-selling ATK products. The dataset used consists of 647 sales transaction data from January to December 2024. The novelty of this study lies in the comparative analysis of the two criteria in a sales dataset with specific seasonal characteristics, which has not been widely discussed in previous studies that generally only focus on the application of a single algorithm. The research methodology follows the Knowledge Discovery in Database (KDD) stages systematically. The results show that Information Gain produces a slightly higher accuracy value, namely 78.98%, while Gain Ratio (77.89%) produces a model with a simpler, more stable, and easier to interpret decision tree structure. The Procurement Type attribute is identified as the most dominant factor in determining the level of product sales. As a main conclusion, this study establishes that Gain Ratio is a more optimal method for strategic business decision making because through Split Information normalization, this method successfully reduces bias towards highly variable attributes and produces a more concise decision tree structure and avoids overfitting compared to Information Gain.
Aceh Cultural Asset Data Management System Using Website-Based Business Process Reengineering Method Kairul Abdi; Samsudin
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9709

Abstract

This study aims to design and implement a cultural asset data management system in Aceh using a website-based Business Process Reengineering (BPR) approach. Aceh possesses abundant cultural assets with significant historical, traditional, and artistic value; however, their management has been constrained by manual, fragmented, and inefficient processes, resulting in difficulties in data monitoring, maintenance scheduling, and information dissemination. To address these challenges, this research applies the BPR method to analyze and redesign existing workflows, followed by the development of an integrated web-based management system. The proposed system features a user-friendly interface that enables administrators and stakeholders to manage asset records, update asset conditions, perform data searches, monitor preservation status, and generate reports efficiently. The novelty of this study lies in the integration of BPR methodology with a web-based cultural asset management platform specifically tailored to Aceh’s cultural preservation needs, combining workflow optimization, centralized data storage, and real-time monitoring within a single system. Unlike previous studies that mainly focus on digitization or database development, this research emphasizes comprehensive business process transformation to improve operational efficiency and governance quality. System evaluation was conducted through functionality testing, usability assessment, and efficiency analysis. The results show that the implementation of the proposed system reduced average data processing time by 68%, improved data retrieval speed by 74%, and increased data accuracy and consistency from 71% to 94% compared to the previous manual system. In addition, usability testing involving 30 respondents achieved a System Usability Scale (SUS) score of 86.5, indicating excellent user acceptance and system effectiveness. These findings demonstrate that the application of BPR significantly enhances cultural asset management performance while supporting more sustainable preservation and decision-making processes. This study is expected to serve as a reference for other regions seeking to implement information technology solutions for cultural heritage preservation and management.

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