cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Budi Hermawan
Contact Email
-
Phone
+62081703408296
Journal Mail Official
info@kdi.or.id
Editorial Address
Jl. Flamboyan 2 Blok B3 No. 26 Griya Sangiang Mas - Tangerang 15132
Location
Kab. tangerang,
Banten
INDONESIA
bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 642 Documents
 41   42   43   44   45 
Integrating Experience and Complexity into the Technology Acceptance Model for Health Information Systems Muhammad Akil Hi Umar; Bangkit Sitohang
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3086

Abstract

The adoption of hospital management information systems (SIMRS) has become a strategic necessity to improve service efficiency and data accuracy. Yet, user acceptance does not always align with the classical Technology Acceptance Model (TAM), especially in complex environments with diverse technological experiences. This study aims to extend TAM by introducing experience and complexity as contextual variables to assess the acceptance of the SIMRS Transmedic system at Bandung Adventist Hospital. A quantitative explanatory design was applied, and data from active SIMRS users were analyzed using Structural Equation Modeling (SEM). The findings reveal that perceived ease of use significantly influences perceived usefulness, while attitude toward use shapes behavioral intention, and both behavioral intention and perceived usefulness drive actual system use. In contrast, experience and complexity show no significant effects on perceived usefulness or ease of use. The novelty of this study lies in demonstrating that psychological factors and direct interaction with the system outweigh traditional TAM determinants in hospital settings. This challenges prevailing assumptions in prior studies that emphasize usefulness as the primary predictor of adoption. The results highlight the importance of strengthening user attitudes and experience through socialization, targeted training, and improved interface design. By providing empirical evidence on an extended TAM in healthcare, this study contributes theoretically to refining acceptance models and offers practical guidance for enhancing the effectiveness of SIMRS implementation.
Analyzing User Acceptance Factors in Digital E-Ticketing Systems Using Extended UTAUT2 Framework Achmad Rafi Argya Rasya; Anita Wulansari; Eristya Maya Safitri
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3089

Abstract

Digital ticketing platforms are reshaping fan engagement in Indonesia’s football ecosystem. This study examines acceptance of PSSI’s Kita Garuda platform by testing how instrumental benefits (usefulness and ease), social cues, enjoyment, price value, and enabling conditions relate to users’ intention to keep using the service. We extend UTAUT2 with two well-motivated additions: Service Satisfaction, which captures whether the end-to-end experience meets expectations, and Psychological Discomfort, which indexes anxiety or unease when transacting online. Using survey data and Partial Least Squares Structural Equation Modeling with bootstrapping, we find that Performance Expectancy (t=4.58, p<0.001), Effort Expectancy (t=3.92, p<0.001), Hedonic Motivation (t=5.03, p<0.001), Price Value (t=4.20, p<0.001), and Social Influence (t=3.12, p=0.002) significantly predict continued intention, whereas Facilitating Conditions (t=1.45, p=0.144) and Habit (t=1.60, p=0.110) do not. Measurement quality met conventional thresholds (α≥0.85; CR>0.90; AVE>0.50). Service Satisfaction helps explain why benefits translate into ongoing use higher satisfaction strengthens the link between perceived benefits and intention (partial mediation). Psychological Discomfort weakens the positive association between intrinsic drivers especially enjoyment and intention (interaction significant, p<0.05). Results are robust to alternative specifications and item-deletion diagnostics; bootstrapped confidence intervals exclude zero across significant paths. Practical implications are clear: reduce sources of anxiety (transparent pricing, reliable payment and QR validation, responsive helpdesk), and invest in a seamless, enjoyable flow to elevate satisfaction and loyalty. By reporting effect magnitudes and significance, this study offers transparent account of drivers continued use for sports ticketing in an emerging market context and points to actionable levers for platform design and fan experience.
ARIMA-TGARCH Model for Return Prediction and Risk Estimation with VaR Imanta Ginting; Trimono Trimono; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3090

Abstract

Investment activity in the Indonesian capital market has experienced significant growth, driven by increasing public awareness and accessibility to financial instruments. Stocks remain the most favored investment tool due to their potential for high returns, though they come with higher risks. Accurate modeling of return dynamics and risk estimation is thus crucial for informed investment decisions. This study analyzes the return and volatility of PT Telekomunikasi Indonesia Tbk (TLKM) stock using a hybrid time series approach that combines the Autoregressive Integrated Moving Average (ARIMA) model and the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) model. The analysis uses daily closing price data from 2020 to 2024, with 1,210 observations. The best-fitting model, ARIMA(2,0,2)–TGARCH(1,1), resulted in low Root Mean Squared Error (RMSE) values of 0.0188 for both training and testing datasets, indicating strong prediction accuracy. Forecasting over a five-day horizon revealed fluctuating returns and a decreasing trend in volatility, from 0.0230 to 0.0198. Additionally, the study utilized the Value at Risk (VaR) method to estimate potential losses under normal market conditions. At a 95% confidence level, the predicted daily loss for a capital investment of IDR 50,000,000 ranged between IDR 1,633,108 and IDR 1,859,355. The combination of ARIMA and TGARCH, integrated with VaR, provides a comprehensive framework for capturing both linear return trends and asymmetric volatility, offering investors a robust quantitative tool for managing risks and optimizing strategies.
Application Of Hybrid ARIMAX-ANN In Forecasting The Price Of Chili Bird's Eye Dina Magdalena Manurung; Aviolla Terza Damaliana; Dwi Arman Prasetya
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3091

Abstract

Chili peppers are a vital horticultural commodity in Indonesia, especially within the culinary industry, due to their high economic value and demand. In Medan, the demand for chili peppers is notably high, yet production limitations often lead to significant price fluctuations. These price variations are influenced by multiple factors, including weather conditions, such as rainfall, and increased demand during national holidays. This study focuses on predicting the prices of both green and red bird's eye chili, which are widely consumed for their distinct spicy flavor. The data used in this study consists of daily chili prices spanning from January 1, 2019, to February 28, 2025, along with external variables such as precipitation and national holiday weeks. To predict the price fluctuations, a Hybrid ARIMAX-ANN model was employed, combining the linear ARIMAX model and the non-linear ANN model to better capture the complex price patterns. The findings revealed that the optimal model for green bird's eye chili was Hybrid ARIMAX(4,0,0)-ANN(6,64,1) with a MAPE of 3.98%, while for red bird's eye chili, the Hybrid ARIMAX(4,0,0)-ANN(6,64,1) model achieved a MAPE of 4.15%. This model was then applied to forecast the chili prices for the next 5 days, and the predictions demonstrated similar price trends for both green and red bird's eye chili. The results highlight the effectiveness of the Hybrid ARIMAX-ANN model in providing accurate chili price forecasts, which could be useful for better price management and planning in the agricultural sector.
Heckman Probit Two-Step Regression Approach for Analyzing Open Unemployment Factors in West Java Province Holly Patrycia; Dwi Arman Prasetya; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3096

Abstract

Open unemployment remains a major socio-economic challenge in Indonesia, with West Java recording the highest national rate in August 2024 at 6.75%. This study investigates the determinants of open unemployment using the Heckman Probit Two-Step model, an approach rarely applied in Indonesian labor market research. Unlike conventional regression methods, this model corrects for sample selection bias by simultaneously estimating labor force participation and unemployment status. Data are drawn from the 2024 Survei Angkatan Kerja Nasional (SAKERNAS) conducted by Badan Pusat Statistik (BPS), covering working-age individuals in West Java Province. The first stage models labor force entry, while the second stage incorporates the Inverse Mills Ratio (IMR) to adjust for selection effects. Results show that the IMR coefficient (–0.3100, p = 0.0412) is statistically significant, confirming the necessity of the two-step correction. The explanatory power of the model is substantial, with Pseudo-R² values of 0.385 for labor force participation and 0.381 for open unemployment. Marginal effects indicate that being married reduces unemployment probability by 5.50%, each additional year of age decreases it by 2.79%, whereas a longer job search increases it by 3.35%. Training experience lowers unemployment risk, while disabilities and larger household size increase vulnerability. Methodologically, the study demonstrates the advantages of Heckprobit in producing unbiased estimates compared to descriptive or conventional probit approaches previously used in Indonesia. Nonetheless, the cross-sectional design and focus on a single province limit generalizability. Findings provide valuable evidence for policymakers to design targeted, inclusive employment strategies aligned with regional development goals
AHP and SAW Based Decision Support for Indonesia Smart Program Beneficiaries Imam Halim Mursyidin; Doni Prasetyo; Dede Irawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3097

Abstract

The Indonesia Smart Program (PIP) is a government initiative that provides cash assistance to help underprivileged children access education. SD Negeri Cikuya 4, located in the Solear subdistrict, is one of the schools implementing PIP. However, the school has struggled to decide which students should receive the aid because there is no systematic weighting of the criteria and the scoring has been subjective. This study asks whether combining AHP and SAW can produce objective weights and a transparent ranking of recipients. To address this, we developed a Decision Support System (DSS) that uses the Analytical Hierarchy Process (AHP) to set the relative weights for six criteria: ownership of KIP/KKS/PKH/SKTM, student active/enrollment status, child status, parents’ occupation, parents’ income, and number of dependents. The pairwise-comparison consistency ratio met the validity threshold (CR = 0.0803 < 0.10), indicating consistent judgments. These AHP weights were then integrated into Simple Additive Weighting (SAW) to normalize scores, calculate preference values, and prioritize eligible recipients. The results show that the selection process becomes more objective, transparent, and systematic than the traditional manual approach. A system quality test using ISO 9126 produced an average score of 86.67% (“Very Good”). This provides a replicable decision framework at the school level to improve the targeting of PIP assistance.
Analysis and Design Web-Based Computer Service Management Information System Prayoga Kurniawan; Verri Kuswanto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3108

Abstract

Computer systems are essential to modern business operations but often encounter issues such as slow performance, system crashes, and virus infections due to inadequate maintenance practices. Although computer repair service companies are available to address these problems, they frequently experience inefficiencies caused by limited information about hardware or software damage, poor communication between technicians, and ineffective service reporting systems. These issues reduce the overall quality of service and customer satisfaction. To overcome these challenges, this study developed a web-based service management system designed to streamline repair workflows, enhance coordination among technicians, and improve communication with customers. The system integrates a diagnostic module that applies a simplified Backward Chaining reasoning method, enabling users to identify the causes of computer faults by testing possible hypotheses based on observed symptoms similar to the reasoning process of an expert technician. The system was evaluated using the Blackbox testing method, focusing on input accuracy, feature functionality, and overall system performance. The testing results showed that all system features operated correctly (100%), and the diagnostic module effectively simulated expert reasoning in identifying computer faults. User feedback also indicated noticeable improvements in operational efficiency, technician collaboration, and customer satisfaction. Overall, the proposed system demonstrates its potential as a reliable tool for optimizing computer repair business processes, improving diagnostic accuracy, and enhancing communication and service delivery
Design of a Real-Time IoT-Based Air Quality, Temperature, and Humidity Monitoring System Rommy Firmansyah; Muhamad Ariandi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3114

Abstract

This study aims to design and implement a novel Internet of Things (IoT)-based monitoring sysatem for air quality, temperature, and humidity specifically tailored for industrial-scale tissue manufacturing. Unlike previous works that focused on single-sensor applications or small-scale prototypes, this system integrates multiple sensors MQ135 for air quality, DHT11 and BME280 for temperature and humidity into a unified platform controlled by an ESP32 microcontroller. The integration enables simultaneous multi-parameter monitoring with higher accuracy and reliability, while real-time data transmission via Wi-Fi to the Blynk platform allows remote accessibility through mobile devices. Testing results demonstrate strong performance: the DHT11 sensor shows a deviation of only ±0.1 °C and ±0.1% RH compared to standard instruments, the BME280 sensor exhibits an error margin of 0.3–0.5 °C, and the MQ135 successfully detected pollutant concentration increases from 9% to 14%, consistently triggering automated alerts. Voltage measurements across components revealed low error rates of 0.8–2.5%, validating the system’s electrical stability. The novelty of this research lies in the integration of cost-efficient sensors with a dual-alert mechanism (buzzer and mobile notifications) for real-time environmental control, tested and validated in an actual industrial production environment. These findings confirm that the system not only ensures accurate real-time monitoring but also enhances occupational health, operational efficiency, and workplace safety, offering a scalable model for industrial IoT-based environmental monitoring.
Comparison of BARS and SKP Methods in Evaluating Lecturer Performance in Higher Education Marta Ardiyanto; Ridwan Dwi Irawan; Kresna Agung Yudhianto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3116

Abstract

The evaluation of lecturer performance in higher education is vital for maintaining the quality of teaching, research, and community service. In Indonesia, civil servant lecturer assessments rely heavily on the Sasaran Kinerja Pegawai (SKP), which emphasizes quantitative indicators such as teaching load, publications, and community engagement. However, this approach often overlooks qualitative aspects, including teaching innovation, professional ethics, and student interaction. To address these gaps, this study proposes integrating the Behaviorally Anchored Rating Scale (BARS) with SKP to create a more comprehensive evaluation framework. The research, conducted at Universitas Duta Bangsa Surakarta in the 2024/2025 academic year, applied the Rapid Application Development (RAD) method to design a web-based prototype. Key stages included instrument design, weighting mechanisms, and reliability testing using Cronbach’s Alpha, which yielded a strong coefficient of 0.850. Evaluation involved six faculty leaders and eight rectorate leaders (bureau heads, vice rectors, and the rector), while the foundation board acted as validators to ensure objectivity. System testing demonstrated promising results: blackbox testing confirmed 100% functional accuracy, and usability testing showed an average satisfaction score of 4.2 out of 5 (84%). Findings indicate that combining SKP and BARS enhances accountability, transparency, and professional feedback. This model contributes practically to higher education human resource management by enabling data-driven decision-making and fostering lecturer development. While further validation across diverse institutions is needed, the integrated framework offers a more holistic and adaptive approach to lecturer performance evaluation.
Comparison of Naïve Bayes and SVM Methods in Detecting Hoax News Pedi Irawan; Asrul Abdullah; Istikoma Istikoma
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3122

Abstract

This study aims to detect hoax news in Indonesian-language media by comparing two popular text classification methods: Naïve Bayes and Support Vector Machine (SVM). Unlike most prior studies that focus on English-language datasets, this research addresses a significant gap by analyzing hoax detection in the Indonesian context. The growing spread of misinformation online has made it increasingly difficult for the public to distinguish between factual and false information, often leading to anxiety, confusion, and social unrest. To tackle this issue, a dataset of 2,010 news headlines comprising 1,005 hoax and 1,005 factual titles was collected through web scraping from verified news portals and fact-checking websites. After undergoing text preprocessing and feature engineering using TF-IDF and N-Gram models, the data was classified using Naïve Bayes and SVM. Performance was evaluated in terms of accuracy, precision, recall, and computation time. The SVM model achieved 93% accuracy, 94% precision, and 93% recall, whereas the Naïve Bayes model yielded 93% across all three metrics. Notably, Naïve Bayes required only 5.2 seconds for classification, significantly faster than SVM's 15.7 seconds, highlighting a trade-off between speed and precision. A web application was developed using Streamlit to make the models publicly accessible, enabling users to test news headlines directly. This practical tool can assist journalists, fact-checkers, and policymakers in verifying information more efficiently. The findings confirm that both models are effective, with distinct advantages depending on the context of use.

Page 43 of 65 | Total Record : 642

 41   42   43   44   45 

Filter by Year

2018 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 3 (2026): bit-Tech Vol. 8 No. 2 (2025): bit-Tech Vol. 8 No. 1 (2025): bit-Tech Vol. 7 No. 3 (2025): bit-Tech Vol. 7 No. 2 (2024): bit-Tech Vol. 7 No. 1 (2024): bit-Tech Vol. 6 No. 3 (2024): bit-Tech Vol. 6 No. 2 (2023): bit-Tech Vol. 6 No. 1 (2023): bit-Tech Vol. 5 No. 3 (2023): bit-Tech Vol. 5 No. 2 (2022): bit-Tech Vol. 5 No. 1 (2022): bit-Tech Vol. 4 No. 3 (2022): bit-Tech Vol. 4 No. 2 (2021): bit-Tech Vol. 4 No. 1 (2021): bit-Tech Vol. 3 No. 3 (2021): Remote Delivery Vol. 3 No. 2 (2020): Datamining Vol. 3 No. 1 (2020): Distance Learning Vol. 2 No. 3 (2020): Pandemik ICT Vol. 1 No. 3 (2019): Learning Synchronous and Asynchronous in the Industry 4.0 Vol. 2 No. 2 (2019): Support System Vol. 2 No. 1 (2019): Data Mining and Green Technology Vol. 1 No. 2 (2018): Data and Information Quality Vol. 1 No. 1 (2018): First Impression on Computing More Issue