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BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer
Published by Sinergis Asa Professional
ISSN : -     EISSN : 27220850     DOI : https://doi.org/10.37148/bios
Core Subject : Science,
Computer Science & IT
BIOS: Jurnal Teknologi Informasi dan Rekayasa Komputer (e-ISSN. 2722-0850) is a scientific journal in the field of information technology and computer engineering managed by the Asa Professional Research & Development Center (PUSLITBANG), Jember, East Java, Indonesia. This journal is managed by lecturers and practitioners who come from various university backgrounds in Indonesia, especially Jember, East Java.The BIOS journal is published 2 (two) times a year, namely every March and September. The BIOS journal published in each edition consists of 5-10 articles per volume. The focus and scope of this journal are in the field of Information Technology and others that are still knowledge related, including: Databases System Data Mining / Web Mining Data Warehouse Artificial Intelligence Business Intelligence Cloud & Grid Computing Decision Support System Human-Computer Interaction Mobile Computing & Application E-System Machine Learning Deep Learning Information Retrieval (IR) Computer Network Multimedia System Information System Geographic Information System (GIS) Accounting information system Database Security System & Network Security Cryptography Fuzzy Logic Expert System Image Processing Computer Graphic Computer Vision Semantic Web e-Health and others related to Information Technology and Computer Engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 74 Documents
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Perancangan Model (Blue Print) Aplikasi Penghitung Sitasi dan ArticleHub Pada Publikasi Jurnal Ilmiah Yunus, Muhammad; Pratama, Mudafiq Riyan
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 4 No 2 (2023): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v4i2.108

Abstract

The number of scientific journals, especially in Indonesia, is not commensurate with their quality. This can be seen from the number of journals accredited by SINTA which is not comparable to the number of journals indexed by Garuda. One indicator of a quality journal that is highly useful is the number of citations (scientific impact). In the substantive elements of journal accreditation assessment in Arjuna, the number of citations includes points that have a high score (maximum 8) if those who have cited them over the last 3 years are high (>30 citations). However, to find out the number of citations, the journal must be indexed in dimensions which are crossref products. In accordance with the latest journal accreditation guidelines released in 2021, citations that are recognized only use dimensions. But the problem is that the dimensions calculation process is not real-time, where the citation calculation results will be displayed four times a year. This is certainly not effective considering that scientific articles in journals can be cited by other articles at any time. It is mandatory to use dimensions because currently the Garuda and Sinta portals do not accommodate the needs related to citation calculations. The next problem is that there is no platform in one application that can bring together writers and journal managers, so up to now writers have been looking for just any journal for publication, on the other hand, journal managers have been aggressively promoting through email broadcasts and calls for papers, but sometimes it doesn't feel optimal. So the solution to this problem is the need for a multifunctional platform in the form of a scientific journal citation calculation application and article hub. The results of the research are in the form of a blueprint for the application of citation calculations and articlehub in scientific journal publications. Where the blueprint shows that the citation process is real time or up to date so that the latest number of citations in scientific journals can be known. ​
Klasifikasi Tingkat Kecemasan Atlet Sebelum Bertanding Menggunakan Algoritma K–Nearest Neighbor (KNN) Berbasis Website Munawaroh, Sulistyowati; Rosyidah, Ulya Anisatur; Yanuarti, Rosita
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.120

Abstract

Anxiety experienced by an athlete before a match often affects their performance, so it is important for the coach to know the athlete's anxiety level before competing in order to provide appropriate mental training and make decisions that will affect the outcome of the match. However, not all coaches can know the level of anxiety of athletes; therefore, it is necessary to build a web-based system to classify the anxiety level of athletes before competing. The system can be built using one of the data mining methods, namely KNN (K-Nearest Neighbour), where this method can be used to classify the anxiety level of athletes based on a dataset of 364 futsal athlete data participating in the Mechanical Futsal Competition, which will be classified into 3 anxiety categories, namely low, medium, and high, from 17 attributes. From the tests carried out on the dataset using the confusion matrix method using the ratio of testing data: 80:20 training data with K = 5, accuracy, precision, and recall values of 100% were obtained. So we successfully built a website that can be used by a coach to classify athletes based on their anxiety level.
Penerapan Data Mining Klasifikasi Lahan Tanam Buah Alpukat dengan Algoritma Naïve Bayes Fidiyanto, Nur; Izzati, Afifah Nurul
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.125

Abstract

The avocado plant is a plant that came into Indonesia in the 18th century. It originated in Central America under the Latin name Persea Americana Mill. Avocado plants have many different varieties and the majority grow fertile in the tropics. Nevertheless, there are differences in growing needs between different types of avocado crops when planted on different crops. As in this study where in the observation of the research on the growth differences between avocado plants of type miki and shepard on the grown land of KTH Pedunung Lestari Welfare Village Purworejo Prefecture Pungging district of Mojokerto. In this case, it is necessary to determine exactly what type of avocado plants are suitable to be planted on the land of KTH Pedunung Lestari Sejahtera. The research was conducted using the Naïve Bayes algorithm method. Based on observations, interviews with sources and library studies, the most influential variables are ground height (Mdpl), Temperature (°C), Rainfall (mm/day) and Soil type. In this study, the results were obtained on the land of KTH Pedunung Lestari Sejahtera with a land height of 250 Mdpl, temperature 18°C, rainfall 25 mm/day and humus soil type, more suitable for planting avocado type miki than shepard type. Based on the calculations on miki avocado, the value of "Yes" is 0.75 and "No" is 0,25, while shepard type has a value of “Yes” of 0 and “No” of 1. The value of accuracy is 50%, Precision is 43% and Recall is 100%.
Implementasi YOLOv5 untuk Deteksi Objek Mesin EDC: Evaluasi dan Analisis Hesananda, Rizki; Noviani, Irma Ayu; Zulfariansyah, Muhammad
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.127

Abstract

The Electronic Data Capture (EDC) machine is essential for facilitating non-cash transactions, yet its efficient detection remains a challenge. This study explores the implementation of the You Only Look Once (YOLOv5) algorithm to enhance EDC machine detection. The objective is to improve accuracy and efficiency in detecting EDC machines in various environments, thereby enhancing transaction security and efficiency. The research methodology involved acquiring a diverse dataset from social media platforms and the internet, comprising 396 images after augmentation. Using Roboflow, the dataset was annotated and divided into training, validation, and testing sets. The YOLOv5 model was trained on Google Colab, achieving a Precision of 97.1%, Recall of 86.4%, and mean Average Precision (mAP50) of 92.0% on the validation set. The results demonstrate that YOLOv5 effectively detects EDC machines with high accuracy across different scenarios, validating its robustness in real-world applications. This research suggests that YOLOv5 can significantly improve transaction security and efficiency in retail and service industries. The implications of this research are substantial for industry stakeholders and decision-makers, offering a reliable solution to enhance transaction security and streamline non-cash payment processes. By integrating YOLOv5, businesses can optimize operational efficiency and customer service, paving the way for broader adoption of advanced computer vision technologies in commercial applications
Implementasi Ensemble Learning Metode XGBoost dan Random Forest untuk Prediksi Waktu Penggantian Baterai Aki Rayadin, Muhamad Amhar; Musaruddin, Mustarum; Saputra, Rizal Adi; Isnawaty, Isnawaty
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.128

Abstract

In motor vehicles, including cars, the battery plays an important role, namely as a place to store electrical energy and as an electric voltage stabilizer when the engine is turned on. In general, motorized vehicle users do not know the condition of the battery in their vehicle. Even though the use of battery batteries that are already in poor condition can interfere with vehicle performance. In battery replacement services such as after-sales service, the process of checking and replacing battery batteries takes a relatively long time. This can be caused by high service volume, lack of worker reliability, lack of responsiveness to the complexity of the inspection. This research aims to build a prediction model for battery battery replacement time quickly. To meet these needs, a Machine Learning approach can be used. Machine Learning uses historical replacement data to make predictions of replacement time. Machine Learning algorithms that can be used for prediction are XGBoost and Random Forest. This research uses ensemble learning techniques to combine the two models. Based on the evaluation results, it can be concluded that the model built with ensemble learning has better prediction results than a single model. Evaluation results with MSE on the ensemble bagging model have the lowest error values of 145,448. The MAPE, MAE, and RMSE evaluations on the ensemble boosting model have the lowest error values of 11.56 %, 43.80 and 38,760.
Implementasi Manajemen Bandwidth Hierarchical Token Bucket (HTB) Menggunakan Metode Network Development Life Cycle (NDLC) Prayitno, Moh. Asy Syam Iriansyah; Rahman, Miftahur; Pater, Dewi Lusiana
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.131

Abstract

SMKN 1 Klakah is one of the educational institutions in Kab. Lumajang requires an internet network to support the learning process for teachers or students. With adequate internet, it can make it easier for teachers and students to access learning materials. There are times when using the SMKN 1 Klakah Internet network causes poor Internet performance when users access the Internet simultaneously. In addition, the download and upload volume for each user is not distributed evenly, so bandwidth management is required. One method that can stabilize the distribution of bandwidth is the Hierarchical Token Bucket (HTB) method. The research was conducted based on the Network Development Life Cycle (NDLC) model with 6 stages, namely: analysis, design, simulation prototyping, implementation, monitoring and management. Resulting in research that the implementation of the HTB method for bandwidth management in the SMKN 1 Klakah Computer Network Lab was successfully implemented. It was proven that when testing the bandwidth it was in accordance with the specified limit. Also when the QoS test was carried out it was in the good category, as evidenced by the QoS test results on the delay parameters with a value of 5,6 ms, jitter with a value of 4,04 ms, throughput with an average value of 0,921 Mbit, and packet loss with a value of 0%.
Analisis Prediksi Kebutuhan Kapasitas Media Penyimpanan RME dengan Metode Least Square RSUPN Dr. Cipto Mangunkusumo Jakarta Wulandari, Savira Puteri; Rahagiyanto, Angga; Nuraini, Novita
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.133

Abstract

The implementation of electronic medical record services is supported by PMK RI Number 24 of 2022, Article 3, Paragraph 1, which mandates that every health service facility must organize Electronic Medical Records (EMR). Therefore, hospitals need to transition from manual to electronic records. One necessary step is determining the storage capacity needed for the server. Additionally, according to Article 39, EMRs must be stored for 25 years, requiring a prediction of storage needs over that period. This study discusses predicting the storage capacity needs for EMRs using the Least Square method, which analyzes time series data trends. Hospital data shows 130,712 medical records with a size of 261,424 MB from September 2022 to March 2023. The predicted storage needs from April to December 2023 are 781,490 MB, and for the next 25 years (until 2048) is 8,974,669 MB or 9 TB. The accuracy of the prediction, tested using MAPE, is 6.34%, which is considered very good. RSUP Nasional Dr. Cipto Mangunkusumo has provided 6 TB of server storage and 71 TB of NAS as backup. With 80 GB used per month as of March 2023, the hospital is advised to provide storage according to the prediction. Additionally, the maximum upload size in the HIS needs to be increased beyond 2 MB per medical record to maximize scanning quality and efficiency
Klasterisasi Data Rekam Medis Pasien Menggunakan Metode K-Means Clustering Di Rumah Sakit Widodo Ngawi Dilawati, Harfina; Widianto, Heru; Kuswiadji, Agustinus
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.134

Abstract

Medical record management is the most vital aspect of hospital operations. Medical records serve as a crucial source of information in patient healthcare services. Through data mining, knowledge extraction and data analysis can be carried out to find relationships, data structures, patterns, and regularities between data. The purpose of this study is to cluster using the RStudio application with the K-means clustering technique and identify disease prevalence patterns in Ngawi Regency. The methodology used is quantitative descriptive using secondary data, samples taken from hospitalization data from October to December 2023 totaling 3171 and divided into six variables, namely gender, age, sub-district, diagnosis, length of treatment and payment method. There are four clustering results, Cluster 1 amounted to 524 patients (19%), Cluster 2 amounted to 831 patients (30%), Cluster 3 amounted to 940 patients (33%) and Cluster 4 amounted to 512 patients (18%). Based on visits from each sub-district in Ngawi Regency, the total number of inpatient disease cases is fever (74), gastrointestinal diseases (64), stroke (43), and degenerative diseases such as diabetes (64), heart disease (7), neoplasms (6), asthma (4), systemic diseases (3), and external diseases (10). The study's findings can serve as a foundation for statistical management to inform decisions aimed at enhancing services and inpatient facilities
Pengembangan Media Pembelajaran Berbasis Augmented Reality (AR) pada Mata Pelajaran IPA Kelas VII Toha, Defrian Ardianto Putro Fadhlullah; Panggayuh, Vertika
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.145

Abstract

The problem in science education at SMPN 1 Karangan is the lack of interactive and technology-based learning media, especially for the complex topics of Earth and the Solar System. This study developed Augmented Reality (AR) based learning media to enhance interactivity and visualization in teaching. The method used is Research and Development (R&D) with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The analysis phase revealed the need for more interactive media. The design was developed using Unity and Canva, with content sourced from reliable science textbooks. Validation results from media and material experts showed scores of 95% and 93%, respectively. The small group trial (10 students) achieved a score of 96%, while the large group trial (30 students) reached 90.6%. The AR media proved effective in improving student understanding and motivation, despite some technical issues. The AR learning media is suitable for use and can enhance understanding of science topics. It is recommended for schools and teachers and should be disseminated through workshops. Further research is needed to test its effectiveness in various contexts and to enrich the content and features of the AR application
Python Application to SEIR Model of the Spread of Malaria Maulana, Fajar Ilham; Ramdani, Yani
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.151

Abstract

Python is the next breakthrough in natural science computing because it enables users to do more and better science. Research on the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) disease spread model has received a lot of attention, but with different factors, and the article aims to apply Python in simulating SEIRS-type Malaria spread data with handling/treatment parameters in other classes. They are exposed to the assumption that individuals who recover from Malaria may become susceptible to transmission of the disease. The data processing simulation aims to see whether Malaria will develop into an epidemic. The use of Python code will make it easier to detect outbreaks. The model for spreading Malaria involves four classes: susceptible, infected but not yet active, infected, and recovered. The simulation data is the number of malaria sufferers in 2017 from the Mimika District Health Service, Indonesia. Mimika Regency is the region with the highest number of malaria cases at 29.12% of all malaria cases in Indonesia. The equilibrium point is determined using a SEIRS-type mathematical model. Data processing with simulations in the SEIRS model obtains a primary reproduction number (Ro) of 0.078 and R0 < 1, so the disease will not become an epidemic.

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