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Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Published by Institut Teknologi Pagar Alam
ISSN : 23391871     EISSN : 27157369     DOI : https://doi.org/10.36050/betrik.v10i03
Core Subject : Science,
Computer Science & IT
Besemah Teknologi Informasi dan Komputer (BETRIK) is a national journal published by Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M), Institut Teknologi Pagar Alam (ITPA). This scientific work was published in 3 editions, with topics related to Computers, Technology, and Science. Topics related to this field can be information systems, informatics, computer science, IT business, IT Governance, enterprise architecture planning, software engineering, modeling and simulation, Data Mining, Artificial Neural Network, Digital Image Processing, Algorithm and Programming, Internet of Things (IoT), artificial intelligence, information security, social networking, cloud computing, science, engineering and related topics. The Scientific Journal BETRIK is a peer journal -National review dedicated to the exchange of high-quality research results in all aspects of education and teaching. This journal publishes the latest works in basic theory, experiments and simulations, as well as applications, with systematically proposed methods, adequate reviews of previous works, extended discussions and conclusions. As our commitment to the advancement of education and teaching, the BETRIK Journal follows an open access policy that allows published articles to be available online for free without subscribing.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 239 Documents
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Perancangan Sistem Prediksi Deteksi Alzheimer Berbasis Random Forest Menggunakan Metode Scrum Daira Syahfitri; Dian Rahayuningtyas; Raihano Garcia; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/gkep7058

Abstract

Alzheimer's disease is a neurodegenerative characterized by a gradual decline in memory and cognitive function, with a prevalence that continues to increase globally and in Indonesia. Constraints in early detection, such as limited healthcare facilities and the high cost of conventional diagnosis, drive the need for easily accessible technology-based solutions. This research aims to develop a web system named MindCare that integrates the Random Forest algorithm to predict the risk of Alzheimer's based on clinical and lifestyle data. The system development method uses the Agile Scrum approach with four sprint cycles, covering needs analysis, model training, web system integration, as well as testing and refinement. The model was trained using Alzheimer's and mental health datasets from Kaggle, with evaluation results showing perfect accuracy and AUC (100%). The features FamilyHistoryAlzheimers, Age, and PhysicalActivity proved to be the most influential in prediction. The resulting web system provides risk prediction features, result visualization, personalized prevention recommendations, and education about Alzheimer's. Black-box testing showed all functions worked as expected. The conclusion of this research is that the MindCare system is suitable for use as an easily accessible medium for early detection and education on Alzheimer's, with recommendations for further development through database expansion, exploration of other algorithms, and the addition of consultation and monitoring features
Pengembangan Sistem Prediksi Risiko Gangguan Mental Remaja Menggunakan Support Vector Machine (SVM) Anisya Septianur; Elsya Bani Aulia; Nugroho Fathul Aziz; Findi Ayu Sariasih; Syifa Nur Rakhmah; Imam Sutoyo
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zh47p731

Abstract

Adolescent mental health has become an increasingly critical issue due to the rising prevalence of emotional and behavioral disorders among young individuals. Social pressure, academic demands, and psychological changes often trigger stress, anxiety, and even depression, which affect learning activities and social interactions. This study aims to develop a web-based system to detect mental disorder risk in adolescents using a machine learning approach with the Support Vector Machine (SVM) algorithm. Three open datasets from the Kaggle platform—Big Five Personality Test Dataset, Symptom2Disease Dataset, and Mental Health in Tech Survey Dataset—were utilized to integrate personality traits, physical conditions, and mental health indicators. The data underwent preprocessing involving duplicate removal, missing value imputation, standardization, and categorical-to-numerical transformation before being split into 70% training and 30% testing sets. The system was developed using the Agile Scrum methodology in an iterative and adaptive manner based on user feedback. The experimental results show that the SVM model with an RBF kernel achieved 91.3% accuracy, 89.7% precision, and 91.9% F1-score. The resulting system, can classify mental disorder risk levels and provide prevention recommendations according to the assessment results. With an interactive interface, this system is expected to assist adolescents in recognizing their mental conditions early, increase awareness of psychological well-being, and serve as a technologybased educational tool for mental health prevention. 
Optimasi Model Deep Learning EfficientNet Berbasis Citra Digital untuk Deteksi Penyakit Padi Nurmaleni; Alfis Arif
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/tp7s2t87

Abstract

This study optimized the EfficientNet deep learning model based on digital images for rice disease detection, focusing on two main classes: Brown Spot and Leaf Scald, which are constraints to farmer productivity in Pagar Alam City. The dataset consisted of 780 images (476 Brown Spot, 304 Leaf Scald) processed through 224×224 resizing, normalization, data cleaning, and augmentation (rotation, flip, shear, shift, zoom) to improve generalization and reduce overfitting. The model was initialized with transfer learning from ImageNet, trained and fine-tuned at the final layer, and then evaluated using accuracy, precision, recall, and F1-score metrics. EfficientNet B0 showed a high training accuracy of up to 95% with a validation accuracy of around 80%, indicating good detection performance although there are still symptoms of overfitting that need further optimization. The model was then integrated into a web-based expert system for automatic diagnosis from leaf images and presentation of knowledge-based treatment recommendations, thereby accelerating early identification and supporting decision-making in the field. These results confirm EfficientNet's potential as the foundation for a practical, accurate, and applicable rice disease diagnosis system for local agriculture.
Aplikasi Mobile Padi Kita Berbasis Rapid Application Development Untuk Digitalisasi Pertanian Desa Rias Muhammad Raihan Pasha; Linda Fujiyanti; Bradika Almandin Wisesa
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zan4db85

Abstract

The agricultural sector, especially the rice commodity in Rias Village, is a strategic pillar still facing significant challenges, namely the manual nature of harvest recording and data management processes, limited access to accurate agroclimatology information, and suboptimal market access. This research aims to overcome these constraints through the development of an integrated agricultural information system. The proposed solution is the Padi Kita mobile application, which was designed to support agricultural digital transformation. The development method applied is the Rapid Application Development (RAD) model, chosen for its effectiveness in accelerating the design cycle and rapidly accommodating functional adjustments based on user needs. The application facilitates digital harvest recording, provides real-time weather information, and enables more transparent monitoring and marketing of sales results, including a direct ordering feature for buyers. The results of the study indicate that the implementation of the Padi Kita application successfully realized the digitalization of the agricultural business flow, providing time efficiency and improving data accuracy at the farmer and milling administrator levels. It is concluded that this RAD-based application development is capable of creating a more integrated agricultural ecosystem, directly contributing to increased productivity and potential welfare for farmers in Rias Village
Prediktor NPK Berbasis AI untuk Budidaya Kopi dengan Whale Optimization Algorithm Firza Septian; Agustian Prakarsya
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/6t030w66

Abstract

Coffee is one of Indonesia’s major agricultural commodities, yet its productivity is often limited by inefficient fertilizer management, particularly in determining nitrogen (N), phosphorus (P), and potassium (K) requirements. Although conventional soil and leaf analyses are reliable, they are time-consuming and less practical for smallholder farmers. This underscores the need for an accurate, scalable, and cost-effective solution to optimize fertilizer usage. To address this issue, the study introduces an AI-based predictor for assessing NPK sufficiency in coffee plants. The research integrates computer vision and metaheuristic optimization to form a practical decision-support system. A dataset containing 12,000 images of coffee leaves was classified into three categories: Deficient, Sufficient, and Excessive. Image preprocessing involved resizing, grayscale conversion, HSV transformation, and normalization. Feature extraction utilized Histogram of Oriented Gradients (HOG) and HSV Color Histograms, followed by classification using a Support Vector Machine (SVM) optimized with the Whale Optimization Algorithm (WOA). The model achieved an accuracy exceeding 97%, effectively recognizing Deficient and Sufficient categories, with most misclassifications occurring in the Excessive class due to visual similarities. Model performance was validated using a confusion matrix, learning curve, and PCA visualization, confirming efficient convergence. The study highlights the promise of AI-driven solutions in enhancing precision agriculture and promoting sustainable coffee farming practices.
Perancangan Sistem Informasi Pembinaan Atlet Dojang Taekwondo Pada Jasdam II Sriwijaya elitaputri10_ putri; Tata Sutabri
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zm214650

Abstract

The development of information technology has made a significant contribution to the world of sports, especially in improving the effectiveness of the athlete development process. At the Jasdam II Sriwijaya Taekwondo Dojang, the assessment and monitoring of athlete development remains a challenge, leading to inaccurate and inefficient coaching evaluations. This study aims to design an Athlete Development Information System based on the Extreme Programming (XP) method, allowing application development to run iteratively and in accordance with user needs. With this system, coaches, athletes, and parents can record training progress by providing assessments, and manage training schedules automatically and in an integrated manner. The system implementation can improve the quality of athlete development, facilitate more systematic and accurate progress monitoring, and increase efficiency in managing scores and schedules. Ultimately, the application of this technology supports the improvement of athlete performance through more structured and targeted evaluations. The results of application testing using the black-box testing method indicate that all system features function according to their functions without errors in the operational process. The testing covers all menus for coaches and athletes, from the login process, inputting scores, displaying progress charts, managing training schedules, to updating profiles, and all of which function as expected (successful).
Meningkatkan Kinerja WordPress Melalui Optimasi Arsitektur Memori: Analisis Kuantitatif Guntoro Barovih Guntoro; Molavi Arman Molavi
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/5ezap146

Abstract

The performance of a web page plays a crucial role in ensuring a smooth user experience and directly influences the success of digital services. This study aims to understand how a web service responds when subjected to varying traffic loads and to identify the technical factors that most significantly shape service quality. The main focus is to evaluate response time, system stability, and server resource utilization as demand gradually increases, both during peak traffic and lighter load conditions.The research applies an experimental approach using load-testing techniques with a cascading scenario, where a predefined number of HTTP requests is sent and measured at each stage. The observed variables include CPU and memory consumption, latency values (minimum, average, maximum, and standard deviation), response time, round trip time (RTT), and throughput measured through average and peak HTTP requests per second (RPS). Additionally, two user-experience indicators—Largest Contentful Paint (LCP) and Page Load Time—are analyzed to understand their relationship with perceived speed and potential changes in bounce rate. The findings show notable improvements after optimization was applied. The server’s TTFB decreased significantly from 300–399 ms to 239–247 ms. Network RTT also improved, dropping from 64 ms to between 6 and 40 ms. Overall latency declined by about 6–7% from the initial range of 757–759 ms. Even under a test scenario involving 20 virtual users and 1,200 total requests, CPU and memory usage remained stable, while peak load decreased to about 49–50% based on p95 and p99 metrics. These results indicate that implementing Memcache and MySQLTuner contributes substantially to improving application responsiveness and enhancing users’ perception of system performance.
Pengaruh Konfigurasi Hyperparameter Pada Kinerja YOLOv11 Dalam Deteksi Objek Pohon Kelapa Sawit Fernandi Indi Nizar G; Eka Puji Widiyanto
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/j4khdz72

Abstract

Oil palm (Elaeis guineensis) is a strategic commodity for Indonesia’s economy, however, tree inventory processes in plantation areas are still predominantly manual, requiring considerable time and cost, and posing a high risk of human error. This study analyzes the effect of hyperparameter variations on the performance of the YOLOv11 algorithm for automated oil palm tree detection using UAV imagery. Four key hyperparameters batch size (16 and 32), number of epochs (100 and 150), learning rate (0.01 and 0.001), and optimizer (SGD and AdamW) were evaluated, resulting in 16 training configurations. The dataset, obtained from Roboflow, underwent annotation, augmentation, and preprocessing prior to model training. Model performance was assessed using precision, recall, and mean Average Precision (mAP), followed by additional evaluation at varying confidence and Intersection over Union (IoU) thresholds. Experimental results show that the optimal configuration batch size 16, 100 epochs, a learning rate of 0.001, and the SGD optimizer achieved an mAP50 of 98.3%, with precision and recall values of 95.3% and 94.1%, respectively. The model also demonstrated stable detection performance at a confidence threshold of 0.5 and an IoU threshold of 0.5. These findings highlight the significant effect of hyperparameter tuning on YOLOv11 detection performance and offer insights for enhancing automated tree-counting systems in the plantation sector, enabling more efficient and accurate operational workflows.
Analisis Reaksi Emosional Siswa terhadap Pembelajaran SAVI Menggunakan Support Vector Machine Yadi; Siti Aminah
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/msbn6g22

Abstract

The development of information and communication technology has enabled the implementation of more interactive and personalized learning methods, one of which is the SAVI (Somatic, Auditory, Visual, Intellectual) learning model. This study aims to analyze students' emotional responses to SAVI-based learning using the Support Vector Machine (SVM) algorithm for sentiment classification. The study involved 100 student respondents who provided opinions regarding their learning experiences. Prior to analysis, the opinion data underwent preprocessing, including case folding, cleaning, tokenization, stopword removal, and stemming, to ensure high-quality features for the SVM model. The classification results indicate that the majority of students (65%) showed positive sentiment, while 20% expressed negative sentiment and 15% neutral. Integration of the SAVI model revealed that students with Somatic learning styles tended to show positive sentiment, whereas students with Visual and Intellectual learning styles exhibited a more varied sentiment, including negative and neutral. The SVM model performance evaluation demonstrated high precision, recall, and F1-score, particularly for the majority class, indicating the model's accuracy in classifying student opinions. These findings highlight the importance of considering students' emotional responses in SAVI-based learning, as emotional factors significantly influence motivation and learning outcomes. The integration of SVM and SAVI provides comprehensive insights for designing adaptive, responsive, and data-driven learning strategies to enhance learning effectiveness.
Peningkatan Literasi Siswa Sekolah Dasar Melalui Pengembangan Media Interaktif Berbasis Web Lesi Anggraini; Yadi
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/yhvedf03

Abstract

Literacy is a key aspect in the learning process because all students need to have good literacy skills to be able to process information optimally. Literacy is not only limited to reading ability, but also includes the ability to analyze, understand, and translate information based on written text. Improving student literacy in schools is crucial to support the achievement of literacy competencies as a whole. However, the limited learning media that can attract students' interest in literacy remains an obstacle, as seen from the learning process that is still predominantly using the lecture method. This study aims to determine the improvement of student literacy through the use of web-based interactive media. Media development was carried out using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The results of the study showed an increase in student literacy scores based on a comparison of pretest and posttest scores given to 35 respondents, with an increase of 35%. In addition, the practicality test showed a score of 93.47% with a practical category. Thus, web-based interactive media can be an effective approach in improving student literacy because it can stimulate optimal cognitive development.

Page 20 of 24 | Total Record : 239

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