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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 277 Documents
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Analisis Usability Dashboard Manajemen CABOR Bulutangkis POPDA Banten 2026 Berbasis REACTJS Pada Lingkungan Multi-Role User Gibran Hafizh; Nurhasan Nugroho; Ali Rohman
BETRIK Vol. 17 No. 02 (2026): 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/vzz02h66

Abstract

Digital transformation in sports administration management is a crucial need to improve the effectiveness, efficiency, and accuracy of match data management. In the implementation of POPDA Banten, especially the badminton sport branch, the administrative process still faces problems in the form of unintegrated data management, potential data duplication, input errors, and delays in the validation process. This study aims to develop and evaluate the usability of the POPDA Banten 2026 Badminton Sport Branch Management Dashboard based on ReactJS in a multi-role user environment. The study uses a Research and Development (R&D) approach with a Prototyping development model consisting of the stages of needs analysis, prototype design, prototype evaluation, system implementation, and testing. The dashboard was developed using ReactJS on the frontend side, Golang with Gin Framework as the backend, RESTful API as the data communication mechanism, and MySQL as the database. Usability evaluation was conducted using the System Usability Scale (SUS) method involving 27 respondents consisting of Superadmin, Admin, and Field Staff users according to system access rights. The implementation results show that the dashboard is able to support the process of participant administration, data validation, match management, and information presentation in an integrated manner through a multi-role user mechanism. The usability test results obtained a SUS score of 70.65, which is included in the Acceptable category with a good level of user acceptance. These results indicate that the dashboard has an adequate level of ease of use and is suitable for use as a supporting system for managing the administration of the POPDA Banten badminton sports branch. This research contributes through the development of a web-based sports information system that integrates ReactJS technology, role-based access rights management, and SUS-based usability evaluation as an approach to producing more effective and easy-to-use digital sports administration services
Penerapan Algoritma K-Means Berdasarkan Data Tracer Study Di Institut Teknologi Pagar Alam Sasmita; Inda Anggraini ANGGRAINI; Siti Muntari
BETRIK Vol. 17 No. 02 (2026): 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/axa60d38

Abstract

Tracer studies serve as an essential instrument for higher education institutions to evaluate the alignment between academic curricula and the competencies required in the workforce by systematically tracking alumni data. However, at the Institut Teknologi Pagar Alam, the tracer study process is still conducted manually, resulting in inefficiencies, limited data accuracy, and constraints in performing comprehensive analytical evaluations. This research aims to develop a web-based tracer study system integrated with the K-Means clustering algorithm to automate the grouping of alumni data and enhance the effectiveness of institutional evaluations. The system was developed using the Rapid Application Development (RAD) methodology, which includes requirements planning, prototype design, and system implementation. System validation was carried out through black-box testing using Alpha testing, involving four experts who evaluated the algorithm, database, interface, and system functionality. The implementation results show that the K-Means algorithm successfully categorized alumni into three primary clusters based on employment status and income, namely high-income employed alumni (195 individuals), medium-income employed alumni (45 individuals), and unemployed or low-income alumni (36 individuals). Furthermore, the Alpha testing yielded an average score of 4.673, equivalent to 93.46%, which is classified as Highly Feasible. These findings demonstrate that the system performs effectively in collecting, processing, and analyzing tracer study data. In conclusion, the developed system not only improves the efficiency of data acquisition but also provides analytical insights that support curriculum evaluation, graduate quality mapping, and strategic academic decision-making at the Institut Teknologi Pagar Alam.
Integrasi Explainable AI (SHAP) Pada Model Machine Learning Untuk Analisis Faktor Penentu Kualitas Fasilitas Kesehatan Berbasis Web Riduan Syahri; Fitria Rahmadayanti; Alfis Arif
BETRIK Vol. 17 No. 02 (2026): 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/ndmny220

Abstract

The quality and equitable distribution of healthcare facilities are vital indicators of regional public service development. Although modern Machine Learning models such as Extreme Gradient Boosting (XGBoost) achieve exceptionally high predictive performance in classifying medical facility quality levels, their black-box nature often limits decision-making transparency for policymakers. This study aims to integrate an Explainable Artificial Intelligence (XAI) approach using SHapley Additive exPlanations (SHAP) into an XGBoost model developed in Google Colab and deployed as an interactive web dashboard via Streamlit. The dataset comprises aggregated BPJS Healthcare Facility data across Indonesian regencies/cities supplemented with operational indicators. The experimental results demonstrate superior modeling performance, achieving an accuracy of 95.15% and an F1-Score of 97.30%. Through SHAP Summary Plot analysis, Skor_Fasilitas_UGD and Total_Faskes were identified as the primary dominant factors driving high-quality facility classifications. The Streamlit web application successfully visualizes individual feature contributions (SHAP Waterfall Plot) in real-time, providing intuitive clinical and managerial transparency for public health planning
Multimedia Pembelajaran Interaktif Perangkat Keras Komputer Berbasis AR DI SMK Muhammadiyah Pagar Alam Nanda S Prawira
BETRIK Vol. 17 No. 02 (2026): 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/pwg20848

Abstract

This study aims to develop and evaluate interactive Augmented Reality-based learning multimedia for computer hardware topics at SMK Muhammadiyah Pagar Alam. The initial problems include the dominant use of two-dimensional images, limited practice equipment, and students’ difficulty in understanding the form, function, and relationship of computer components. The study applied a Research and Development approach using the Multimedia Development Life Cycle model, which consists of concept, design, material collecting, assembly, testing, and distribution. The trial involved 30 students. Data were collected through observations, interviews, expert validation sheets, black-box testing, student response questionnaires, and pretest-posttest assessments. The simulated results showed media feasibility of 92.50%, material feasibility of 90.00%, functional testing success of 100%, and student response of 88.47%. The average score increased from 56.13 in the pretest to 84.70 in the posttest. The class N-Gain score was 0.65, which was categorized as moderate, while learning mastery increased from 0% to 93.33%. The paired-samples t-test produced t = 33.219 with p < 0.001, indicating a significant difference between learning outcomes before and after using the media. These findings indicate that the developed media is feasible, practical, and potentially effective in improving students’ understanding of computer hardware. Future studies should involve a control group, a larger sample, and learning retention measurements.
Implementasi Machine Learning Untuk Klasifikasi Penyakit Tanaman Lada Berdasarkan Karakteristik Daun Hairil Novansyah; Sigit Candra Setya
BETRIK Vol. 17 No. 02 (2026): 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/gmx4pp29

Abstract

Black pepper (Piper nigrum L.) is one of Indonesia's leading plantation commodities with significant economic value and an important contribution to the agricultural sector. However, its productivity is frequently reduced due to leaf diseases such as Foot Rot, Pollu Disease, and Slow Decline. Conventional disease identification relies on visual observation by experts, which is time-consuming, subjective, and may delay appropriate disease management. This study aims to develop an image-based classification model for black pepper leaf diseases using the EfficientNetB0 deep learning architecture. The research methodology consists of image dataset collection, image preprocessing, data augmentation, dataset partitioning into training and validation sets, model training, and performance evaluation using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results demonstrate that the proposed model achieved a training accuracy of 98.17% with a training loss of 0.0856, while the highest validation accuracy reached 88.33% at epoch 18 with a validation loss of 0.3568. These results indicate that EfficientNetB0 is capable of effectively learning the characteristics of black pepper leaf diseases, although slight overfitting was observed. Overall, the proposed model demonstrates strong potential for accurate and efficient disease classification and can serve as a decision-support tool for early disease detection in black pepper cultivation. The implementation of this model is expected to contribute to the advancement of smart agriculture by enabling farmers to identify plant diseases more quickly, accurately, and efficiently.
Evaluasi Mutual Information dalam Klasifikasi Dokumen Ilmiah Menggunakan Random Forest dan CBOW Mufidah Karimah; Dendi Sunardi; Ikhwan Fauzi
BETRIK Vol. 17 No. 02 (2026): 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/aqfp3679

Abstract

Scientific document classification in large-scale digital repositories requires an efficient process because manual categorization is time-consuming and may introduce inconsistencies. This study evaluates the performance of a Random Forest Classifier (RF) using Continuous Bag-of-Words (CBOW) for feature representation and Mutual Information (MI) for feature selection in scientific document classification. The dataset contains 5,560 titles and abstracts of nuclear-related scientific documents distributed across 10 categories. After data cleaning, 5,374 documents were used for experiments with an 80:20 training-test split. Experiments were conducted using CBOW vector dimensions of 100, 200, 300, 400, and 500 under two scenarios: RF+CBOW and RF+CBOW+MI. Performance was evaluated using precision, recall, F1-score, and accuracy. The RF+CBOW scenario achieved the highest accuracy of 73% at a vector dimension of 100, while the scenario with MI reached a maximum accuracy of 71% across several dimensions. Adding MI increased recall to 73% at dimension 100 but did not improve overall performance and reduced all reported macro metrics to 69% at dimension 300. These findings indicate that feature selection does not necessarily improve classification performance; dataset characteristics and representation dimensionality also influence model effectiveness.
Perancangan Sistem Informasi Penerimaan Santri Baru pada Pesantren Maskanul Huffadz Rizky Fauzi; Rian Mohamad Said; Nur Aisyah
BETRIK Vol. 17 No. 02 (2026): 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/fhea2w03

Abstract

The New Student Admissions Process (PSB) at Pesantren Maskanul Huffadz previously utilized separate media platforms, including Google Forms, Google Spreadsheets, Google Drive, WhatsApp, and physical documents. This setup resulted in fragmented data management, creating potential difficulties in searching for data, verifying documents, monitoring the selection process, and compiling reports. This Project Work aims to design and develop the Web-Based PSB System called Maskanul Huffadz Admission (MASHAD) as a solution to integrate all new student admission processes into a single unified system. System development followed the Agile methodology, comprising the stages of planning, design, implementation, testing, deployment, and evaluation. The system was developed using the Laravel framework with a centralized database and implemented role-based access control for Admins, Committees, Ustadz (instructors), and Prospective Students. Key features of the system include account registration, application submission, bio-data and document management, payment integration via the Xendit payment gateway, interview scheduling and evaluation, admission determination, selection result announcements, re-registration, financial monitoring, and report generation. System testing was conducted using the Black Box Testing method to ensure that all core functions operate according to requirements. The development results indicate that MASHAD effectively integrates the new student admission process in a more structured, effective, efficient, and centralized manner. This system is expected to improve the quality of administrative services and simplify every stage of the New Student Admission process for both the Islamic boarding school management and prospective students.

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