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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 Ton Blockchain-Based Online Donation System for Enhanced Transparency Zaenal Alamsyah; Somantri Somantri; Yoviar Pauzi
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.v8i2.2984

Abstract

Online donation platforms in Indonesia despite the nation’s leading position in the 2024 World Giving Index continue to suffer from low public trust due to insufficient transparency in fund management. This lack of verifiability creates opportunities for fraud and misuse, ultimately deterring donor participation. Addressing this challenge, the present study proposes a novel blockchain-based donation system built on The Open Network (TON), specifically designed to embed transparency, accountability, and trust directly into the donation process through decentralized mechanisms. Unlike previous platforms that rely on centralized fund tracking, this system integrates smart contracts programmed in the Tact language to automate and enforce donation logic without human intervention. Core functionalities include campaign creation, donation receipt, fund withdrawal, and balance transfers all of which are transparently recorded and publicly accessible on the blockchain. The system achieved 100% functional suitability and a 94% usability score, based on evaluations involving 36 participants, while all 14 smart contract unit test scenarios returned successful outcomes. These results highlight the platform’s robustness and user acceptance. By enabling tamper-proof transaction records and public verifiability through the TON Explorer, the proposed platform demonstrates strong potential for restoring donor trust and setting new standards for transparency in digital philanthropy. The study contributes both a practical system prototype and theoretical insights into blockchain’s role in reengineering trust within charitable ecosystems, offering a replicable model for future philanthropic technologies in Indonesia and beyond.
Predicting Precious Metal Prices Using the Long-Short-Term Memory (LSTM) Method Marshanda Amalia Vega; Rendra Gustriansyah; Indah Permatasari
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.v8i2.2985

Abstract

Gold price fluctuations pose significant challenges for investors in determining accurate investment strategies. The volatility is strongly influenced by inflation, exchange rates, and global economic dynamics, making reliable forecasting increasingly important. Although various statistical and machine learning models have been applied, many are limited in capturing complex temporal dependencies, especially in the context of Indonesia’s ANTAM gold prices. This study addresses that gap by applying the Long Short-Term Memory (LSTM) method, a deep learning approach designed to model sequential patterns in time series data. The novelty of this research lies in the application of LSTM specifically for ANTAM gold price forecasting in Indonesia, which has received limited attention in previous studies. Unlike conventional approaches, LSTM is capable of preserving long-term dependencies, thereby improving predictive accuracy for volatile commodities. Using historical daily data from November 2023 to March 2025, the model was trained to recognize price dynamics and evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate high predictive accuracy, with a MAPE of 1.39% and RMSE of 0.0137. These findings confirm the suitability of LSTM for gold price prediction and underline its potential contribution to both theoretical advancements in time series forecasting and practical decision-making in investment management. Thus, this study not only strengthens evidence of LSTM’s effectiveness but also offers valuable insights for investors and policymakers in managing risks associated with commodity price volatility.
Analysis of Acceptance of Digital Population Identity Applications Using The UTAUT Model Nissa Adwitiya Aji; Asif Faroqi; Tri Lathif Mardi Suryanto
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.v8i2.2986

Abstract

Digital transformation requires public services to operate more efficiently, transparently, and inclusively. In Indonesia, this shift is embodied in the Digital Population Identity (IKD) system, developed to modernize demographic administration and provide citizens with easier access to identity services. Despite achieving millions of downloads, IKD adoption still encounters notable challenges, including technical issues, complex interfaces, limited internet access in rural areas, and low levels of digital literacy. This study explores the factors influencing IKD adoption through the Unified Theory of Acceptance and Use of Technology (UTAUT). Using a quantitative design, data were collected from 408 valid respondents through purposive sampling and analyzed with Structural Equation Modeling–Partial Least Squares (SEM-PLS). The findings reveal that performance expectancy, effort expectancy, and social influence significantly affect user behavior, while facilitating conditions show no significant impact. The results emphasize that social support and perceived usability are stronger drivers of digital identity adoption compared to technical support, highlighting the importance of addressing user experience and societal influence. The study contributes theoretically by extending UTAUT in an e-government context, methodologically through SEM-PLS application, and practically by offering recommendations for improving system design, strengthening digital literacy initiatives, and ensuring IKD reliability. Future research is encouraged to incorporate additional constructs, such as habit or hedonic motivation, and to investigate adoption patterns across diverse regional settings.
Automatic Classification of Public Complaints Using Naive Bayes Rico Andrean Hardiansyah; Jamaludin Indra; Dwi Sulistya Kusumaningrum; Tohirin Al Mudzakir
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.v8i2.2992

Abstract

Public complaint services are essential for improving government service quality by providing a direct channel for citizens to report issues. In Karawang Regency, the Tanggap Karawang (TANGKAR) platform serves this function; however, the manual classification of complaints causes delays and potential misrouting, especially due to the highly imbalanced distribution of complaint categories. This study develops an automatic classification model for public complaints in eight categories economy, education, health, social, infrastructure, security, environment, and transportation by integrating Term Frequency–Inverse Document Frequency (TF–IDF), Multinomial Naive Bayes, and Synthetic Minority Oversampling Technique (SMOTE). This integration addresses domain-specific class imbalance challenges, combining the computational efficiency of Naive Bayes, the feature representation strength of TF–IDF, and the improved minority class recognition from SMOTE. A dataset of 800 complaint records from TANGKAR underwent preprocessing, including cleaning, case folding, normalization, tokenizing, stemming, and stopword removal. TF–IDF with unigram and bigram features was used for feature extraction, followed by classification under two scenarios: original unbalanced data and balanced data via SMOTE. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The model achieved 85.09% accuracy without SMOTE and 83.40% with SMOTE, with notable improvement in detecting minority categories after balancing. Although overall accuracy slightly decreased, SMOTE enhanced equitable prediction across all categories. This approach advances current public complaint classification methods by adapting to the linguistic diversity and uneven category distribution in actual e-government data, supporting faster and more accurate decision-making in public complaint management systems.
Enterprise Resource Planning (ERP) Performance and Hardware Requirements in Manufacturing Andi Romansyah; Emi Sita Eriana
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.v8i2.2998

Abstract

Despite the critical role of Enterprise Resource Planning (ERP) systems in manufacturing competitiveness, Indonesian companies frequently encounter significant performance bottlenecks attributed to suboptimal hardware configurations. Limited empirical evidence exists regarding optimal hardware specifications for ERP systems within Indonesian manufacturing contexts, creating uncertainty in technology investment decisions.This study investigates the relationship between hardware specifications and ERP system performance to develop evidence-based optimization strategies for Indonesian manufacturing companies. A mixed-methods sequential explanatory design was implemented over 18 months, examining 120 Indonesian manufacturing companies. The research employed quasi-experimental quantitative analysis combined with in-depth qualitative interviews to evaluate five critical hardware components: processor, RAM, storage media, network bandwidth, and network cards. Performance metrics included response time, transaction throughput, and system stability. The optimization framework demonstrated exceptional predictive accuracy with precision of 94.2%, recall of 91.8%, F1-score of 93.0%, and overall model accuracy of 92.5% (R² = 0.892). Hardware optimization achieved performance improvements up to 268%, with storage speed contributing 38.7%, processor performance 28.5%, and RAM capacity 19.8% to overall gains.This comprehensive framework enables Indonesian manufacturing companies to make informed hardware investment decisions with ROI achievement within 3.3 months, providing concrete guidance for digital transformation initiatives and establishing benchmarks for ERP infrastructure optimization in emerging manufacturing economies
Classification Of Eligibility For Assistance Recipients Program Indonesia Pintar Using The Naïve Bayes Method Via Kris Savitri; Herri Setiawan; Zaid Romegar Mair
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.v8i2.3002

Abstract

The manual process of determining student eligibility for the Indonesia Pintar Program (PIP) often results in inefficiencies and inaccuracies. Schools are required to evaluate large volumes of socioeconomic data, and errors in judgment may lead to misallocation, where eligible students are excluded and ineligible students are included. Such inefficiencies highlight the need for objective, data-driven approaches. This study aims to evaluate the performance of the Naïve Bayes classification algorithm in classifying PIP eligibility, with a particular focus on attribute selection and its effect on classification accuracy. Historical student data from a primary school (SDN 1 Sindang Marga), which has rarely been examined in previous works and the analysis of attribute selection strategies, showing that fewer but more relevant attributes can yield better results. A dataset of 172 students was pre-processed and divided into training (80%) and testing (20%) subsets. Model evaluation was conducted using confusion matrices to calculate accuracy, precision, recall, and F1-score. The results demonstrate that using four attributes parental occupation, parental income, KPS ownership, and KIP ownership achieved the highest performance, with 85.3% accuracy, 92.0% precision, 88.5% recall, and a 90.2% F1-score. By contrast, using all seven attributes resulted in slightly lower accuracy (82.4%). These findings highlight that selective attribute use improves model efficiency and accuracy. Beyond methodological contributions, this research provides practical implications by demonstrating how machine learning can enhance fairness, transparency, and objectivity in educational aid distribution.
Application of Multivariate Singular Spectrum Analysis for Weather Prediction Abdul Mukti; Kartika Maulida Hindrayani; Mohammad Idhom
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.v8i2.3003

Abstract

Weather significantly influences various aspects of life, especially in urban areas like Surabaya, where unpredictable weather can disrupt transportation, public health, economic activities, and overall comfort. Among the key meteorological variables, air temperature and relative humidity are crucial for assessing human thermal comfort, as their interaction forms the heat index a key indicator of health risks in tropical regions. This study introduces the use of the Multivariate Singular Spectrum Analysis (MSSA) method to forecast daily weather parameters, including minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), and average relative humidity (RH_AVG). The research utilized weather data from the Perak 1 Meteorological Station in Surabaya, spanning from August 1 to December 31, 2024 (training data) and January 1 to January 14, 2025 (testing data). Unlike traditional methods, the MSSA model effectively analyzes the complex relationships between multiple weather variables, improving forecasting accuracy. The model demonstrated strong performance, with Mean Absolute Percentage Errors (MAPE) of 3.70% for TN, 5.99% for TX, 4.44% for TAVG, and 7.39% for RH_AVG. These results highlight MSSA's potential as an effective tool for short-term weather forecasting in urban tropical environments, supporting more accurate predictions that can inform early warning systems, disaster planning, and public health strategies. This work advances the state-of-the-art by offering a robust method for handling multivariate weather data, which is essential for making informed decisions in rapidly changing climates
Effectiveness of Extreme Learning Machine in Online Payment Transaction Fraud Detection Radya Ardi; Mohammad Idhom; 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.v8i2.3005

Abstract

The rise of fintech and digital payment systems has increased efficiency but also escalated the risk of online transaction fraud, particularly under imbalanced data conditions where fraudulent cases are rare. This study addresses the limitations of traditional rule-based and machine learning models in such scenarios by proposing the use of Extreme Learning Machine (ELM) with hyperparameter tuning as a novel and efficient solution for fraud detection. Unlike most prior studies relying on default settings or data resampling, this research focuses on enhancing ELM performance purely through parameter optimization using the Optuna framework. A dataset of 20,000 real-world online transactions was used to evaluate model performance before and after tuning. In its default configuration, ELM yielded high overall accuracy (96.80%) but failed to detect fraudulent cases (0% recall and F1-score). After tuning key parameters such as the number of hidden neurons and activation function, the model achieved a significantly better balance between accuracy and fraud detection performance, with 99.53% accuracy, 98.20% precision, 86.51% recall, and a 91.98% F1-score. These results demonstrate that hyperparameter tuning alone, without resampling, can substantially improve ELM’s sensitivity to minority class detection. The findings suggest that optimized ELM offers a promising alternative for real-time fraud detection in imbalanced financial datasets, contributing to more adaptive and reliable security systems in the digital finance landscape.
Geographic Information System for Freshwater Aquaculture Using Web Platform Saumia Rahmi Putri; Asril Asril; Raimon Efendi
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.v8i2.3012

Abstract

Freshwater aquaculture in Dharmasraya Regency plays a vital role in local food security and economic development, yet data management practices remain limited to static tabular records. This lack of spatial representation hampers effective planning, monitoring, and decision-making by the Fisheries and Food Agency. To address this issue, the present study develops a web-based Geographic Information System (GIS) tailored for freshwater aquaculture that integrates spatial visualization with comprehensive attribute data, including fish species, cultivation methods, pond size, and production outputs. The novelty of this research lies in three aspects: the integration of attribute and geospatial data into a unified platform, district-level filtering and route mapping to aquaculture sites, and the use of open-source technologies such as Leaflet.js and Chart.js to ensure interactivity, accessibility, and scalability. The system was developed using the Waterfall model, covering requirement analysis, design, implementation, and black-box testing for validation. Results show that the GIS successfully displays aquaculture distribution maps, interactive production graphs, and user-friendly data management features that align with stakeholder needs. Compared to previous GIS applications, which often emphasize coastal aquaculture or rely on satellite imagery, this system directly supports inland aquaculture management at the district level. Beyond technical contributions, the platform delivers practical benefits by enabling local authorities to monitor aquaculture spatially, improving transparency for the public, and supporting sustainable fisheries development. This study thus demonstrates how a district-level WebGIS can bridge the gap between static data and actionable decision support in aquaculture management.
Performance Comparison of Gaussian Mixture Model, Hierarchical Clustering, and K-Medoids in Passenger Data Clustering Thalita Syahlani Putri; I Gede Susrama Mas Diyasa; Achmad Junaidi
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.v8i2.3013

Abstract

The rapid growth of urban populations and increasing reliance on public transportation in Indonesia present challenges in managing passenger demand effectively. In Surabaya, the steady rise in Suroboyo Bus passengers underscores the need for data-driven strategies to optimize fleet allocation, scheduling, and infrastructure development. Identifying passenger density patterns through clustering provides a systematic basis for decision-making. This study aims to address a local research gap by comparing three clustering algorithms Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Model (GMM), and K-Medoids on empirical passenger data. Unlike previous studies that emphasize route optimization or demand forecasting, this research highlights a comparative evaluation to determine the most effective method for handling fluctuating and outlier-prone transportation data. The dataset was obtained from the Surabaya City Transportation Office for the Purabaya–Perak route during a two-week period in 2024. Data preprocessing included attribute selection, transformation of time into numerical format, outlier detection using the Interquartile Range (IQR), and Z-Score normalization. Clustering results were assessed with the Silhouette Score and visualized using scatter plots and histograms. Findings show that K-Medoids achieved the highest Silhouette Score (0.4222), surpassing AHC (0.3657) and GMM (0.3024). K-Medoids produced more balanced clusters and stronger resilience to outliers, while AHC provided interpretable hierarchical structures, and GMM modeled complex patterns but with weaker separation. In conclusion, K-Medoids is recommended as the most suitable approach for passenger density clustering. Academically, this study contributes a comparative framework for clustering in transportation research, while practically offering insights to support data-driven public transport management in developing cities.

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