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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
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Development of a 2D Educational RPG on the 10th November Battle Alif Wisam Desanta Fitrianto; Chrystia Aji Putra; Andreas Nugroho Sihananto
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.v8i2.3344

Abstract

The advancement of digital technology provides opportunities to innovate learning methods, especially for subjects like history that are often considered less engaging. This study develops a 2D Adventure RPG educational game themed on the November 10 Battle, integrating branching narrative and lane tower defense minigames to enhance students’ historical understanding. This integration addresses a research gap, as few studies have combined branching narrative and lane tower defense in a historical educational RPG. The game allows players to make choices affecting the storyline while facing strategic challenges through interactive mechanics. The study employed a single-group pre-test–post-test design involving 23 students from SMP Negeri 6 Surabaya. Results showed a significant improvement in learning outcomes, with the average score increasing from 53.48 to 94.78 and standard deviation decreasing from 19.21 to 5.93. The Wilcoxon Signed Rank test yielded p = 0.000 (< 0.05), confirming statistical significance. User experience evaluation using GUESS-18 indicated positive responses (mean scores 3.78–4.02), with branching narrative (4.06) and lane tower defense (3.94) receiving favorable feedback. Validity tests confirmed all items were valid (r > 0.413; p < 0.05), and reliability was high (Cronbach’s Alpha = 0.867). However, the small sample size (23 students) may limit generalizability of the findings. These findings suggest the November 10th educational game effectively improves historical understanding, cognitive and affective engagement, learning motivation, and strategic thinking. The study highlights gamified learning as an engaging alternative for history education in the digital era.
Medan Area Battle Educational Game Using Branching Narrative and Turn-Based Tactical Combat Maulana Fauzan; Chrystia Aji Putra; Andreas Nugroho Sihananto
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.v8i2.3345

Abstract

The rapid advancement of digital technology has created new opportunities for integrating interactive media into history education, a subject often perceived as passive and less engaging. Addressing the lack of educational games that combine narrative with strategic simulation, this study develops and evaluates Medan Area Battle, a 2D action role-playing game that introduces a dual-pedagogical mechanism through branching narrative and turn-based tactical combat. This integration is designed to allow students to experience historically grounded decision-making while simultaneously engaging in strategic reasoning with the events of the Medan Area Battle. A design-based research framework guided the development process through requirement analysis, design, implementation, and user testing. The evaluation involved twenty-five junior high school students, providing insight into the game's effectiveness. Learning performance was measured through pre-test and post-test assessments, while user experience was examined using Game User Experience Satisfaction Scale-18 (GUESS-18). Two additional dimensions Branching Narrative and Tactical Combat were incorporated to capture the unique interaction patterns introduced by the game’s narrative and strategic systems. Statistical analysis employed the Shapiro–Wilk test, Wilcoxon Signed Rank test, and Cronbach’s Alpha reliability testing. Results indicated a significant improvement in learning outcomes, with mean scores rising from 60.80 to 85.60 (p = 0.000767). The overall GUESS-18 rating of 4.15 (“Very Good”), alongside high scores in Educational (4.30) and Tactical Combat (4.27), suggests strong user engagement. These findings demonstrate that integrating narrative choice with tactical gameplay offers effective and theoretically grounded approach enhancing students’ cognitive understanding and motivation in history learning.
Rapid Application Development Method for Web-Based Shallot Price Prediction Using Machine Learning Model Rafani Bardatus Salsabilah; Yisti Vita Via; Eva Yulia Puspaningrum
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.v8i2.3348

Abstract

Fluctuations in shallot prices in Indonesia create uncertainty within the agricultural supply chain and affect farmers, traders, and policymakers. This condition highlights the need for analytical mechanisms capable of accurately monitoring and predicting price dynamics. This study develops a web-based shallot price prediction system using the Rapid Application Development (RAD) method, with the best-performing model obtained from the training process being a combination of Long Short-Term Memory (LSTM) and CatBoost. The model is designed to process historical data along with non-sequential variables including price, production, rainfall, inflation, the Consumer Price Index (CPI), and seasonal indicators using a five-year dataset compiled from various official government sources. The trained model is integrated into a Flask-based backend to generate the next 7-day price forecasts. The system allows users to upload datasets, execute prediction processes, and analyze outputs through interactive charts and prediction tables. The evaluation shows that the model achieves strong performance, indicated by a MAPE of 6.71% and an RMSE of 0.029120, reflecting good accuracy and alignment with the seasonal patterns of shallot prices. Black-box testing confirms that all system functions operate as expected. The RAD method contributes to accelerating the development process through continuous iteration, resulting in a lightweight, responsive, and user-friendly system for non-technical users. Consequently, this system has the potential to serve as a decision-support tool for monitoring and anticipating shallot price dynamics at both regional and national levels.
Application of SARIMA and XGBoost Models in Forecasting International Tourist Arrivals at Ngurah Rai Maisie Yunita Malva; Anggraini Puspita Sari; Eva Yulia Puspaningrum
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.v8i2.3352

Abstract

The tourism sector constitutes a vital component of Indonesia's economic growth, especially in Bali Province, where Ngurah Rai International Airport functions as the principal entry point for international travelers. Precise prediction of tourist arrivals is critical for strategic planning, resource distribution, and infrastructure development. Nevertheless, conventional statistical techniques often struggle to adequately capture the intricate patterns in tourism data, which exhibit both periodic regularities and non-linear characteristics shaped by external influences, including global economic fluctuations, travel regulations, and the COVID-19 pandemic. This research proposes a hybrid SARIMA-XGBoost framework that combines traditional statistical modeling with machine learning techniques to simultaneously capture linear temporal dependencies and non-linear residual patterns—an integration not previously explored for Bali's tourism forecasting. The study employs 204 monthly records of international tourist arrivals spanning January 2008 to December 2024, integrating seasonal indicators and the COVID-19 pandemic period as external covariates. The SARIMA component extracts linear temporal trends and seasonal structures, whereas XGBoost captures non-linear dynamics embedded in the residuals. The hybrid model achieves substantially higher forecasting precision with MAPE of 3.22%, MAE of 0.0492, and RMSE of 0.0597, outperforming standalone SARIMA (MAPE 25.02%, MAE 0.4305, RMSE 0.5035) and XGBoost (MAPE 7.36%, MAE 0.0736, RMSE 0.0995). These results validate that integrating statistical and machine learning methodologies significantly enhances predictive accuracy. The proposed model offers airport management, tourism boards, and policymakers a robust forecasting instrument for capacity planning and strategic decision-making, facilitating sustainable tourism development and enhancing Bali's competitiveness as an international destination.
Optimizing Plantation Production Prediction Using Category Boosting with Random Search and Walk-Forward Validation Faishal Fernando Hutama; Eva Yulia Puspaningrum; Afina Lina Nurlaili
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.v8i2.3354

Abstract

The plantation subsector is a cornerstone of the national economy, yet its productivity is increasingly volatile due to climate change. Predicting production yields remains challenging as traditional models often fail to capture complex nonlinear temporal dependencies and seasonal cycles. This study aims to improve the prediction accuracy of five major plantation commodities, namely palm oil, rubber, coffee, tea, and sugarcane, by optimizing the Category Boosting (CatBoost) algorithm. The analysis uses monthly data from 2009 to 2024, combining official production and land statistics from the Central Bureau of Statistics (BPS) with national temperature and rainfall records from the Meteorology, Climatology, and Geophysics Agency (BMKG) to ensure transparency. Unlike standard approaches that rely on default parameters and random data splitting, this research applies a rigorous optimization pipeline. Random Search is used for hyperparameter tuning, supported by lag features to capture short term dynamics and sinusoidal transformations to represent seasonal cycles. A Walk Forward Validation technique with an expanding window is employed to prevent look ahead bias and ensure realistic evaluation. The optimized model significantly outperforms the baseline. Sugarcane (R² 0.95) and Coffee (R² 0.97) show excellent accuracy, while Palm Oil improves markedly (R² 0.80) as more historical patterns are learned. Rubber and Tea remain difficult to predict, indicating insufficient explanatory features rather than model limitations. The study concludes that combining hyperparameter optimization with temporal feature engineering enables CatBoost to effectively model agricultural time series data and provides a solid foundation for strategic production planning.
A Rule-Based Data-Driven Framework for Partner Selection in Digital Agribusiness Zahra Azizah; Iik Muhamad Malik Matin; Okta Gabriel Sinsaku Sinaga; Faiz Akbar; Asiwidia Simanjuntak
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.v8i2.3359

Abstract

Digital transformation has reshaped partner evaluation in agribusiness business-to-business (B2B) networks, shifting decision-making from intuition-based judgments to transparent, data-driven assessments. Addressing the need for scalable and trustworthy selection mechanisms, this study introduces a novel hybrid anomaly detection framework that sequentially combines rule-based z-score normalization with the Local Outlier Factor (LOF) algorithm to evaluate digital business credibility. The framework leverages Google Maps data, a widely accessible, user-generated information source that reflects real customer experiences, to assess 6,237 hospitality, restaurant, and café (HORECA) businesses in Indonesia’s Jabodetabek region, a growing hub in the agribusiness supply chain. Using structured data collected through the Google Places API, the rule-based method identified 47.06% of businesses as anomalies, predominantly those with disproportionately high ratings relative to customer engagement. Meanwhile, LOF detected 5.02% of density-based outliers, capturing irregularities that only emerge in local spatial and contextual comparisons. A statistical comparison (χ² = 195.10, p < 0.001) revealed a 56.52% overlap between the two methods, emphasizing their complementary strengths: rule-based thresholds provide interpretability and efficiency, whereas LOF offers sensitivity to nuanced, neighborhood-level deviations. These findings show that no single technique fully captures the complexity of digital credibility anomalies; however, their combination enables more balanced and context-aware evaluations. This approach enhances the accuracy and fairness of credibility assessments, which is crucial for partner selection in digital agribusiness ecosystems. Overall, the study provides practical and methodological contributions for building transparent, reproducible, and equitable anomaly-detection systems for emerging digital markets
UI/UX Design for a Makeup Artist App Using Design Thinking Rizvina Hadi Imani; Abdul Rezha Efrat Najaf; Prasasti Karunia Farista Ananto
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.v8i2.3368

Abstract

This study aims to develop the UI/UX design for a Makeup Artist (MUA) application using a Design Thinking approach, focusing on the service provider’s perspective. The research followed the five stages of Design Thinking Empathize, Define, Ideate, Prototype, and Test to explore the operational challenges MUAs face in managing bookings, service information, and scheduling. Insights gained through interviews and observations of MUAs revealed key needs such as clear booking information, structured service presentation, and more efficient scheduling. These findings formed the basis for developing a solution concept, which was then realized in the form of a high-fidelity prototype using Figma. The design focused on intuitive workflows, simple navigation, and a more organized display of service information. The usability of the prototype was evaluated using the System Usability Scale (SUS), with MUAs as the primary respondents. The first iteration resulted in an average SUS score of 82, categorized as "Good," meaning the design is intuitive, easy to use, and aligned with the MUA’s work processes. This study demonstrates that the Design Thinking method is highly effective in producing user-centered UI/UX designs. The results highlight improvements in operational efficiency, clarity, and professionalism in managing MUA services, providing a solid foundation for future application development and further refinement.
ARAS Method for Ranking Vocational High School Students Achmad Andrian Maulana; Muhammad Muharrom Al Haromainy; Afina Lina Nurlaili
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.v8i2.3369

Abstract

Student performance assessment is a crucial component in strengthening the quality of vocational education. At SMK Muhammadiyah 2 Jogoroto, Kab. Jombang, the evaluation process is still conducted manually and relies primarily on report card scores, leading to subjectivity, inconsistency, and a limited representation of students’ competencies. This study develops a Decision Support System (DSS) by integrating the Rank Order Centroid (ROC) weighting technique and the Additive Ratio Assessment (ARAS) method to provide a clearer and more systematic multicriteria evaluation framework. The analysis involves ten student alternatives evaluated using six criteria: average report card score, attitude, absenteeism, extracurricular activities, achievements, and industrial internship performance. ROC is applied to generate proportional criterion weights based on ranked priority, while ARAS is used to execute the core computational stages, including normalization of each criterion, application of weighted values, calculation of the optimal function score (Si), and determination of utility values (Ui) to rank student performance. The results indicate that the system yields consistent outcomes, with Nikmatuz achieving the highest utility value of 2.65224526 and identified as the top-performing student. These findings show that combining ROC and ARAS enhances assessment accuracy, reduces evaluator bias, and improves transparency in the ranking process. Beyond this case study, the proposed model demonstrates potential for broader application in vocational institutions seeking structured, data-driven mechanisms to evaluate academic and non-academic competencies more comprehensively.
Real-Time Identification of Potholes and Road Damage Using Yolov11 Muh Ryamizard Albasith; Muhammad Fachrie
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.v8i2.3371

Abstract

Road surface deterioration poses increasing risks to transportation safety and operational efficiency, prompting the need for automated, real-time detection systems suitable for mobile deployment. This study develops and evaluates a YOLOv11-based road damage detection model implemented on Android devices, targeting four defect classes: potholes, cracks, surface waviness, and patched roads. Unlike previous YOLO-based approaches (e.g., YOLOv5, YOLOv8), YOLOv11 integrates a C2PSA attention mechanism and an anchor-free architecture, offering enhanced detection accuracy and computational efficiency critical for resource-limited environments. A total of 2,989 images were collected and annotated from public datasets and organized in standard YOLO format. Model evaluation was conducted using metrics such as AP@0.5, confidence curves, confusion matrix analysis, and latency benchmarks. YOLOv11 achieved high AP@0.5 scores of 0.849 and 0.850 for cracked and patched roads, with a real-time inference latency of 2.6 ms per image and an end-to-end latency of 3.8 ms faster than YOLOv8 in comparable mobile settings. The model was successfully integrated into an Android application, demonstrating robust performance during real-time deployment. However, the system showed reduced accuracy in detecting subtle or low-contrast defects such as shallow potholes and wavy surfaces, often due to background-texture similarity. These limitations suggest the need for improved data diversity and feature refinement. Overall, the findings confirm YOLOv11’s suitability for mobile-based road monitoring, combining speed, accuracy, and lightweight deployment. Future research should address generalization challenges under varied lighting and environmental conditions.
Student Violation Point Information System Using The Web-Based Waterfall Method Dani Dani; Reza Saputra; A Nurul Anwar
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.v8i2.3372

Abstract

Juvenile delinquency remains a growing concern in school environments, where ineffective manual documentation often slows disciplinary decision-making and delays communication with parents. This study aims to design and develop a web-based student violation information system that integrates WhatsApp notifications to support real-time parental engagement. Using the Waterfall methodology, the system was built with PHP and MySQL and evaluated in a school setting involving administrators, teachers, and parents as primary users. The analysis stage identified recurring inefficiencies in existing disciplinary workflows, including incomplete records and an average communication delay of two to three days between violation occurrence and parental notification. The developed system streamlines these processes by enabling structured data entry, automated point calculation, and instantaneous message delivery. Functional testing across twelve core features showed 100% alignment between expected and actual outputs, while user feedback from initial deployment indicated faster access to violation records and improved traceability of student behavior. Preliminary observations also suggested a reduction in documentation time, from approximately five minutes per case to under two minutes. These findings demonstrate the system’s potential to enhance accuracy, accelerate information flow, and strengthen disciplinary governance within schools. The implications highlight the value of integrating real-time communication technologies into school information systems to support timely intervention and increased parental involvement. Overall, this study provides an empirically grounded model that can be adapted by educational institutions seeking more transparent, efficient, and accountable mechanisms for managing student violations.

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