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BIOS: Jurnal Informatika dan Sains
Published by SEAN INSTITUTE
ISSN : -     EISSN : 29886910     DOI : 10.54209
Core Subject : Science,
Computer Science & IT
BIOS: Jurnal Informatika dan Sains is a journal managed by the Sean Institute, which serves as a dissemination medium for research findings from scientists and engineers in the fields of computer science and science. Bios Journal is a bi-annual journal aimed at exploring, developing, and explaining knowledge about Computer Science and science, in order to keep practitioners and researchers informed about current issues and best practices, and to serve as a platform for exchanging ideas, knowledge, and expertise among researchers and practitioners in computer science.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 35 Documents
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Data Mining Analysis of the Influence of Social Media on Students' Sleep Hours and GPA Using the Cluster Method Ibrahim Ibrahim; Sri Wahyuni
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 02 (2024): BIOS: Jurnal Informatika dan Sains, October 2024
Publisher : Sean Institute

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Abstract

This study aims to analyze the effect of social media usage and sleep duration on students' Grade Point Average (GPA) using the clustering method in data mining. Social media has become an integral part of students' lives, but excessive use can have a negative impact on sleep time and, indirectly, on academic achievement. Using the clustering method, this study groups students based on their social media usage patterns, sleep hours, and GPA to identify groups with certain characteristics. Data collected from 520 students were analyzed using the K-Means clustering algorithm, which resulted in three main groups: a group with high social media usage and low GPA, a group with a balanced sleep pattern and moderate GPA, and a group with adequate sleep time and high GPA. The results of the analysis showed that students with high social media usage tend to have lower sleep hours and lower GPA than students who have sufficient sleep duration. This study is expected to be a basis for campuses to develop programs to improve student welfare, especially in regulating social media usage and improving sleep quality. The data used in this study uses data from the Kaggle.com platform which provides various types of data worldwide. This research is expected to provide insight for students, lecturers and people with regard to this method.
Implementation of Paperless System for Documentation Process Efficiency at PT Everbright Irwan Syahputra; Muhammad Iqbal
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 02 (2024): BIOS: Jurnal Informatika dan Sains, October 2024
Publisher : Sean Institute

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Abstract

PT Everbright faces challenges in managing documentation that still relies on a paper-based system, resulting in inefficiencies in storing, searching, and distributing documents. This system also increases operational costs and increases the risk of document loss or damage. Therefore, this study aims to implement a paperless system at PT Everbright to improve the efficiency of the documentation process. By utilizing digital technology, such as document management software and electronic filing systems, companies can reduce dependence on paper, accelerate information access, and optimize internal workflows. This study evaluates the effectiveness of implementing a paperless system in reducing the time required for document searches, minimizing document management errors, and reducing operational costs. The expected results are increased efficiency, reduced costs, and contributions to environmental sustainability through reduced paper use. Thus, the paperless system is expected to provide a solution for PT Everbright to achieve a more efficient, safe, and environmentally friendly documentation process.
Career Pattern Analysis of SMKN 1 Stabat Graduates Using K-Means Clustering Algorithm on Tracer Study Dataset Ibrahim Ibrahim; Muhammad Iqbal
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 01 (2024): BIOS: Jurnal Informatika dan Sains, April 2024
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Abstract

Tracer study is a method commonly used to determine the condition of graduates of an educational institution, including the career patterns they pursue. This study aims to analyze the career patterns of SMKN 1 Stabat graduates by utilizing the K-Means clustering algorithm. The dataset was obtained from the results of a tracer study of 287 alumni of SMKN 1 Stabat. The dataset used came from a tracer study conducted on graduates in the last five years. By grouping data using K-Means, it is hoped that specific patterns can be found that can help schools improve the quality of learning and student work readiness.[4] The results of the analysis show several dominant career pattern groups, such as the industrial sector, entrepreneurship, and further education.
Application of Data Mining to Predict Best-Selling Stationery Sales at Blessing Stationery Store Using the K-Nearest Neighbor (K-NN) Method Maida Indrayani
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 01 (2024): BIOS: Jurnal Informatika dan Sains, April 2024
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Abstract

Stationery stands for office stationery that is used by various groups. Examples of stationery such as pencils, pens, erasers and so on. Toko Berkah ATK is one of the stores that sells office supplies. Sales of stationery at Toko Berkah ATK often decrease and increase every month. Depending on the needs of the people who buy at the ATK Blessing Store. To avoid the accumulation of ATK items that are not sold out, the application of data mining using the K-Nearest Neighbor (K-NN) method is carried out to predict the best-selling ATK sales at Toko Berkah ATK.
APPLICATION OF DECISION SUPPORT SYSTEM WITH SIMPLE ADDITIVE WEIGHTING METHOD TO DETERMINE STUDENT ACHIEVEMENTS AT SMA MAHATMA GADING JAKARTA Maria Korsini; Ismailah Ismailah; Erlin Windia Ambarsari
BIOS: Jurnal Informatika dan Sains Vol. 1 No. 02 (2023): BIOS: Jurnal Informatika dan Sains, October 2023
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Abstract

This study aims to determine outstanding students using a decision support system with the simple additive weighting method at SMA Mahatma Gading Jakarta. The research method used is descriptive qualitative, data collection with interviews, literature analysis and questionnaires. The calculation stages with the simple additive weight method consist of identifying criteria, normalization, weighting criteria and calculating final scores with four assessment criteria for outstanding students, namely academic grades, attendance, attitudes and non-academics. The results of the analysis obtained that the implementation of the decision support system with the simple additive weighting method ran well, indicated by giving a ranking to each alternative in determining outstanding students by looking at the ranking process carried out with the highest score results. The final result of the 3 (three) best rankings, namely Ardiansa Martawijaya got the first rank with a score of 0.921, then Revan Sahal got the second rank with a score of 0.902 and the third rank was a student named Owen Cornelius with a score of 0.893.
Artificial Intelligence-Assisted IoT Model for Water Level Monitoring and Prediction Systems: A Review and Analysis I Gede Iwan Sudipa; I Dewa Gede Agung Pandawana; I Made Subrata Sandhiyasa
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 02 (2024): BIOS: Jurnal Informatika dan Sains, October 2024
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Abstract

Flood disasters have become increasingly frequent and severe due to climate change and urban expansion. Traditional water level monitoring systems often lack real-time data processing and predictive capabilities. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) presents a promising solution for enhancing water level monitoring and flood prediction systems. This paper provides a comprehensive review and analysis of AI-assisted IoT models for water level monitoring and prediction. It examines system architectures, sensor networks, and the application of AI algorithms such as Fuzzy Logic and Long Short-Term Memory (LSTM) networks. The study highlights the benefits of combining real-time IoT data with AI-based predictive models to improve the accuracy and responsiveness of flood early warning systems. Challenges related to data quality, sensor network infrastructure, and model optimization are also discussed. This review aims to inform future research and development in intelligent disaster mitigation systems.
Application of PROMETHEE Method to Support the Best Flourist Ni Kadek Shely Prastikayani; Ni Ketut Maria Suryani; Ni Luh Sri Deviyanti; I Gede Iwan Sudipa; I Nyoman Tri Anindia Putra
BIOS: Jurnal Informatika dan Sains Vol. 3 No. 1 (2025): BIOS: Jurnal Informatika dan Sains, April 2025
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Abstract

Determining the best flower shop in the INSTIKI campus area is a challenge because it involves various criteria that affect each other. This research applies the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) method, a multicriteria decision-making method, to broadcast three alternative flower shops based on ten criteria, namely accessibility, price, product quality, product availability, service, store reputation, promotions and discounts, availability, operating hours, and transaction security. Data were obtained from a survey to university students. The analysis results show that Poppy Florist Bali is the best choice with the highest net flow value, followed by Flora Flower Boutique and Anya Florist. The PROMETHEE method is proven to provide a transparent, structured, and effective approach in supporting strategic decision making, thus providing relevant recommendations for flower shop business development.
Management Audit and Information Technology Governance at Village-Owned Enterprises (BUMDesa) Catu Kwero Sedana Pecatu I Kadek Budi Sandika; Ni Wayan Eka Wijayanti; Ni Luh Ayu Prima Dania; Putu Adreal Candranatha; I Gede Iwan Sudipa
BIOS: Jurnal Informatika dan Sains Vol. 3 No. 2 (2025): BIOS: Jurnal Informatika dan Sains, October 2025
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BUMDesa Catu Kwero Sedana Pecatu was formed through Pecatu Village Regulation Number 10 of 2014 as a strategic effort to strengthen the local economy. Operations began in 2017 with a waste management business unit, and in 2022 expanded with a trade and services unit. As legality is strengthened (legal entity 2022, NIB 2024), the need for information technology (IT) governance becomes increasingly important to support efficiency, transparency and accountability. However, there has never been an in-depth study of IT readiness, effectiveness, and risk, so an IT management and governance audit is needed. This research uses a descriptive qualitative approach with a case study method at BUMDesa Catu Kwero Sedana Pecatu. Data was obtained through document analysis, interviews, and observations. The evaluation framework refers to COBIT and the maturity model to assess five main aspects: IT policies and procedures, organizational structure and IT HR capabilities, IT maturity level, IT risk identification, and internal control effectiveness. The audit showed that the IT maturity level was at Level 1 - Initial (average score 0.6) with practices that were ad hoc, lacked documentation, and were not integrated. Significant risks were found such as delays in application development, HPP calculation errors due to system limitations, absence of backup policies, dependence on vendors, and potential data privacy violations. Internal controls are still weak in the aspects of system security, data backup, access authorization, and transaction monitoring. Recommendations include the preparation of an IT Master Plan, establishment of a specialized IT unit, HR training, implementation of information security policies, and implementation of an integrated information system aligned with BUMDesa's business strategy.
Maximization of Material Removal Rate in the Machining of A356/Cow Horn Particle Composites Using Response Surface Methodology Sunday Chimezie Anyaora; Francis Chukwunonso Okeke; Ikenna Theophilus Odoh; Onyeka Noel Anyali; Chibuzo Ndubuisi Okoye
BIOS: Jurnal Informatika dan Sains Vol. 3 No. 2 (2025): BIOS: Jurnal Informatika dan Sains, October 2025
Publisher : Sean Institute

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Abstract

Efficient machining of metal matrix composites is vital for enhancing productivity and reducing manufacturing costs in modern engineering applications. Aluminum alloy A356 reinforced with cow horn particles offers improved mechanical properties, but its machinability requires systematic optimization. The study utilized Aluminum alloy A356 reinforced with cow horn particles, fabricated via spark plasma sintering at 550 °C and 30 MPa. Composite samples (100×5 mm) were produced under vacuum with graphite dies. Machining experiments were conducted on a Universal Turning Machining Centre using HSS/HCS cutting tools, supported by equipment such as weighing balance, crucible, stirrer, hopper, mould, and lathe for dimensional accuracy. Process parameters included cutting speed (500–900 RPM), depth of cut (0.5–1.5 mm), and feed rate (0.15–0.25 mm/rev). Material Removal Rate (MRR) was measured using surface testers and weighing balance. Optimization employed Response Surface Methodology (RSM) and regression analysis for predictive modeling. Results showed that wear rate decreased with increased graphite content, with sample L having the lowest wear and sample I the highest. Response Surface Methodology (RSM) analysis revealed that material removal rate (MRR) ranged from 3.75 to 30.91 mm³/min, with a mean of 15.72. Feed rate, cutting speed, and depth of cut were significant (p < 0.05), while interaction effects were negligible. Feed rate exhibited a strong negative effect, while cutting speed and depth of cut had mixed influences. Model accuracy was validated (R² = 0.9932). Optimal conditions were found at moderate cutting speed and higher depth of cut. These findings validate RSM as an effective optimization tool for machining composites, supporting improved efficiency and performance in industrial applications.
Development of a Deep Neural Network Model for Prediction of Machine Vibration Uchendu Onwusoronye Onwurah; Chinedu Sebastian Ani; Harold Chukwuemeka Godwin; Obiora Jeremiah Obiafudo
BIOS: Jurnal Informatika dan Sains Vol. 3 No. 2 (2025): BIOS: Jurnal Informatika dan Sains, October 2025
Publisher : Sean Institute

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The reliability of induced draft (ID) fans in cement production is critical to ensuring operational continuity, energy efficiency, and cost-effective maintenance. Excessive vibration in these fans often triggered by unbalance, misalignment, and bearing defects can lead to catastrophic failures, unplanned downtime, and increased operational expenses. This study presents the development of a deep neural network (DNN) model for predicting vibration anomalies in cement mill fans. Vibration signals were collected over a 34-week period from multiple sensors installed on induced draft fans at a cement production mill in Nigeria. Statistical time-domain features, including root mean square (RMS), kurtosis, crest factor, and impulse factor, were extracted and processed through advanced feature engineering and selection techniques. A Multi-Layer Perceptron (MLP)-based deep neural network was then designed, trained, and optimized in MATLAB. The model achieved high classification accuracy and robust generalization across different operational conditions. Furthermore, a real-time monitoring application was developed in MATLAB App Designer, enabling interactive visualization and prediction from Excel-based sensor inputs. The findings underscore the significance of integrating artificial intelligence into predictive maintenance workflows, demonstrating that deep learning-driven vibration prediction can enhance machine reliability, reduce downtime, and support the industry 4.0 agenda in cement manufacturing and other process industries.

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