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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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Implementation of New Student Registration Information System and Web-Based Tuition Fee Administration Management Marzaq, Anas Al; Novita, Dien
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3488

Abstract

The advancement of information technology has encouraged educational institutions to adopt digital transformation in various administrative processes, including the New Student Admission (PPDB) system. Manual PPDB procedures often lead to issues such as data entry errors, delays in document verification, and limited transparency for parents and school administrators. These challenges highlight the need for an automated system that streamlines the entire registration workflow. Therefore, this study aims to develop a web-based PPDB system that can address administrative problems encountered at SDIT Kamiliyah Palembang. The objective of this research is to design and implement an integrated information system that effectively supports online registration, document verification, and payment management. The Rational Unified Process (RUP) was applied, comprising the Inception, Elaboration, Construction, and Transition phases, to ensure systematic development aligned with user requirements. The results indicate that the system successfully facilitates online registration, document uploading, administrative verification, payment processing, and real-time information presentation through a dashboard. Black-Box Testing confirms that all functionalities operate as defined. Furthermore, the simplified user interface enhances usability and makes it easier for users to understand the registration flow. In conclusion, the web-based PPDB system effectively improves administrative efficiency and enhances transparency in the school's admissions process. This implementation contributes to the digitalization of educational services and may serve as a practical model for other schools with similar needs.
Machine Learning Classification of Liver Disease Using Clinical Data with SVM and PCA Johan, Daniel; Yoannita, Yoannita
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3494

Abstract

Liver disease remains a major global health problem that requires early and accurate diagnosis to prevent severe clinical complications and mortality. In recent years, Support Vector Machine (SVM) combined with Principal Component Analysis (PCA) has been widely applied for liver disease classification. However, existing studies are often limited by small or moderately sized datasets, a lack of systematic comparison among SVM kernel functions, and insufficient discussion of clinical relevance and data representativeness. These limitations restrict model generalizability and hinder practical clinical adoption. To address these gaps, this study evaluates a PCA–SVM classification framework using a large-scale Liver Disease Patient Dataset comprising 30,691 clinical records, thereby improving robustness and population representativeness. The main contribution of this research lies in a systematic and controlled comparison of four SVM kernel functions linear, radial basis function (RBF), polynomial, and sigmoid—under identical preprocessing and dimensionality reduction conditions. PCA is applied to reduce feature redundancy while preserving over 97% of clinically relevant information, supporting efficient learning without increasing model complexity. Experimental results indicate that kernel selection has a substantial impact on diagnostic performance. The RBF kernel consistently outperforms other kernels, achieving an accuracy of 83.63% and an area under the ROC curve of 92.09%, while maintaining strong generalization on unseen data. From a clinical perspective, these findings demonstrate that the proposed PCA–SVM framework has significant potential as a clinical decision support tool for early liver disease screening based on routine laboratory data, offering a balance between predictive performance, computational efficiency, and practical applicability.
Design Local E-Commerce Application Oli Gholi Pay for Online Buying and Selling Transactions Mardiana, Mardiana; Sari, Andira; Nurddin, Dina Firliana
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3497

Abstract

The limited digitalization of buying and selling activities remains a significant challenge  for micro, small, and medium enterprises (MSMEs), particularly at the local level. Although national e-commerce platforms continue to grow, many local MSMEs still face obstacles related to accessibility, usability, and system relevance to their business context. This condition underscores the need for localized e-commerce solutions designed to support MSME digital transformation. This research aims to design and develop an Android-based e-commerce application for Baubau City, named Oli Gholi Pay. The main objective is to produce an e-commerce application design that is easy to recognize, user-friendly, and relevant for MSME actors, local government stakeholders, and the general public. The study employs a descriptive qualitative approach combined with application Development methods. Data were collected through observations, interviews, and focus group discussions involving MSME actors. The prototype Development method was applied through the following stages: requirement identification, initial prototyping, prototype evaluation, application coding, testing, final evaluation, and implementation. The research scope includes participant recruitment analysis, system requirement analysis, system architecture and interface design, Development, functional testing, and application implementation and maintenance. The study successfully produced a functional design of a local Android-based e-commerce application that supports online transaction processes and aligns with MSME operational needs. The results indicate that Oli Gholi Pay can support MSME digitalization by expanding market access and facilitating secure, sustainable digital transactions, while also serving as a reference model for future local e-commerce application Development.
Comparison of the Effectiveness IndoBERT and mBERT for Sentiment Analysis of SME Customer Reviews Afandy, Selena Nurmanina; Hindrayani, Kartika Maulida; Damaliana, Aviolla Terza
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3501

Abstract

This study presents a structured comparative evaluation of IndoBERT and Multilingual BERT (mBERT) for three-class sentiment classification of customer reviews from Pawonkoe Banyuwangi, an Indonesian small and medium-sized enterprise (SME). Motivated by the limited transferability of IndoNLU-style benchmarks to real SME feedback, the central question is whether monolingual versus multilingual transformers remain reliable when fine-tuned on small, domain-specific, and operationally noisy datasets. A total of 365 survey-based reviews (January–December 2024), which is substantially smaller than typical transformer fine-tuning corpora, served as the empirical basis. Models were fine-tuned under matched hyperparameters and evaluated using a single stratified hold-out train–test split (not cross-validation), reporting accuracy, precision, recall, and F1-score. To reflect the deployed pipeline, mBERT additionally incorporates the original 1–5 rating as an auxiliary numeric signal alongside the review text, whereas IndoBERT is trained on text only. The results reveal a substantial performance gap: mBERT achieved 81% test accuracy, whereas IndoBERT reached 48% under the same evaluation setting. Because the label distribution is strongly imbalanced (with very few negative instances), these aggregate scores should be interpreted as overall effectiveness rather than minority-class robustness. Overall, the findings indicate that multilingual representations combined with auxiliary rating information can generalize more effectively in low-resource SME scenarios, while IndoBERT appears more sensitive to data scarcity in this context. The study offers practical guidance for model selection in resource-constrained Indonesian sentiment analytics and contributes evidence on transformer behavior beyond curated benchmarks.
Decision Support System for Food Menu Selection at Engineering Faculty Canteen 3 Using the SAW Method: Decision Support System for Food Menu Selection Using the SAW Method Ningrum, Dwi Aura; Ramadhan, M Rizky Subagia; Nursari, Sri Rezeki Candra
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3505

Abstract

Students often face challenges when selecting food from cafeteria menus due to the need to consider multiple criteria simultaneously, including price, taste, and portion size. To address this issue, this study develops a Decision Support System (DSS) for menu selection at Canteen 3 of the Faculty of Engineering, Universitas Pancasila, using the Simple Additive Weighting (SAW) method. The primary contribution of this research lies in enhancing the objectivity and consistency of daily food selection decisions among students through a quantitative, criteria-based approach. The SAW method is employed due to its simplicity and effectiveness in multi-criteria decision-making problems. Three main criteria are applied: price (C1), taste (C2), and portion size (C3). Each menu alternative is evaluated, normalized, and weighted to obtain a final preference score. The results indicate that Indomie achieves the highest score of 100, primarily due to its favorable balance between affordable price, acceptable taste, and sufficient portion size, followed by Spaghetti with a score of 88.875. These findings demonstrate that the SAW-based DSS is capable of producing objective and efficient menu recommendations that align with student preferences. Furthermore, the proposed system has the potential to be applied in other cafeterias or faculties within the campus environment, thereby supporting broader decision-making processes. Future research is encouraged to incorporate additional criteria and more diverse menu alternatives to further improve system accuracy and applicability.
Klasifikasi Kualitas Rasa Kopi Menggunakan Sistem Web Berbasis Naïve Bayes: Implementasi dalam Sistem Berbasis Web Baharianto, Ahnaf Irfan; Dani, Dani
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3510

Abstract

Coffee taste quality is a key factor influencing customer satisfaction and reflecting the professionalism of baristas in coffee service operations. However, manual coffee taste assessment remains highly subjective and often leads to perceptual differences between professional assessors and consumers, resulting in inconsistent flavor quality. To address this challenge, this study aims to develop and evaluate a machine learning-based system for predicting coffee taste quality, which supports the sensory assessment process in operational coffee business environments. The proposed system utilizes the Naive Bayes algorithm and is implemented as a web-based application to facilitate structured data management, automated classification, and informed decision-making during the espresso dialing-in process. The research data were obtained from coffee taste assessments involving baristas as trained panelists and customers as consumer panelists. Sensory attributes, including coffee bean origin, brewing time, resting period, water temperature, and extraction results, were collected and processed as classification input features. The evaluation involved comparing assessment patterns between baristas and customers to analyze consistency across key sensory attributes. The results show high agreement between barista and customer assessments for attributes such as body and balance, while noticeable differences were observed in acidity and sweetness. At the system evaluation stage, the model was tested on a limited dataset consisting of seven samples and correctly classified six instances, achieving an accuracy of 85.71%. These findings indicate that the proposed system has strong potential to support objective and consistent coffee taste quality evaluation in real-world operational settings.
Predicting Moral Degradation Using Tree-Ensemble Machine Learning Methods Fajirulhabshah, Rindha; Astuti, Femi Dwi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3514

Abstract

Moral degradation poses significant challenges across social, organisational, and digital environments, yet empirical tools for predicting individual vulnerability to unethical behaviour remain limited. This study develops an interpretable machine learning-based predictive model to estimate tendencies toward moral degradation using multidimensional moral domain scores derived from the Moral Perspectives and Foundations Scale (MPFS), with a specific focus on the Perpetrator Relevance (PR01) block. The final analytical sample consisted of 2,130 respondents after data filtering. Two tree-ensemble algorithms, Random Forest (RF) and Gradient Boosting (GB), were implemented and compared using an 80:20 train-test split. Model performance was evaluated using the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The results demonstrate that both models achieved strong predictive performance across all PR01 moral domains, with GB consistently outperforming RF. The highest predictive accuracy was observed in the Loyalty (GB R² = 0.616) and Authority (GB R² = 0.595) domains, accompanied by lower MAE and MSE values, indicating stable predictive tendencies rather than deterministic moral behaviour. To enhance interpretability, SHAP analysis was applied, revealing that binding moral dimensions, particularly Loyalty and Authority across multiple moral perspectives, exert the strongest influence on predicted moral degradation tendencies. Overall, the findings highlight the value of integrating ensemble learning with explainable AI techniques in moral psychology. Given the cross-sectional nature of the data, the proposed framework is intended as a risk-detection tool rather than a diagnostic or causal model, while future research should incorporate longitudinal and behavioural data to strengthen generalisability and inference.
Analisis Peramalan IHSG Menggunakan Model ARIMA-ARCH untuk Mengatasi Efek Heteroskedastisitas Adinda, Nadia Puspita; Salamy, Fairuz Izzaty; Subagyo, Salsabilla Fatika; Sari, Dewi Puspita; Zuhdi, Shaifudin
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3515

Abstract

The Indeks Harga Saham Gabungan (IHSG) is widely recognized as a central indicator of the Indonesian capital market and reflects overall market performance, investor sentiment, and macroeconomic conditions. Accurate forecasting of the IHSG is essential for investors, financial institutions, and policymakers; however, financial time series data are often characterized by non-stationarity and volatility clustering, which limit the effectiveness of conventional forecasting models. This study applies a hybrid Autoregressive Integrated Moving Average–Autoregressive Conditional Heteroskedasticity (ARIMA–ARCH) model to forecast the IHSG by simultaneously modeling the conditional mean and time-varying volatility. The ARIMA model is used to capture linear temporal dependence in the mean process, while the ARCH component addresses heteroskedasticity in the residuals by allowing conditional variance to change over time. Daily IHSG closing price data from September 2024 to September 2025 are analyzed using the Box–Jenkins methodology, including stationarity analysis, model selection, parameter estimation, and diagnostic validation. The empirical results indicate that the hybrid ARIMA–ARCH model provides improved forecasting accuracy compared to a standalone ARIMA model, particularly in periods of heightened market volatility. The ARCH component successfully captures volatility clustering and enables the construction of dynamic volatility-based prediction intervals, offering additional risk-related insights beyond point forecasts. These findings demonstrate that the ARIMA–ARCH framework is effective for modeling IHSG dynamics and can support better risk management, portfolio optimization, and decision-making processes in the Indonesian capital market.
Modeling and Forecasting World Stock Market Price Volatility Using ARIMA, GARCH, and EGARCH Models Dinata, Alfansyah Putra Raja; Paskalin, Graciella; Yogatama, Ikhsan Tri; Tsaqif, Regina Aurellia; Fransischa, Tyara Avriliany
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3518

Abstract

This study investigates the comparative performance of symmetric and asymmetric GARCH-family models in capturing volatility dynamics and forecasting stock market volatility, using S&P 500 index data spanning 2023–2025. The primary objective is to evaluate whether asymmetric models that account for leverage effects whereby negative shocks exert disproportionately larger impacts on volatility than positive shocks of comparable magnitude offer superior in-sample fit and out-of-sample predictive accuracy relative to symmetric specifications. Methodologically, daily closing prices are transformed into logarithmic returns, with the conditional mean modeled using ARIMA and the conditional variance estimated through GARCH, GJR-GARCH, and EGARCH specifications. Model selection is based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), while out-of-sample forecasting performance is assessed using MSE, RMSE, MAPE, and R² measures. Empirical results reveal that asymmetric models, particularly GJR-GARCH, achieve superior in-sample performance according to information criteria, reflecting the presence of leverage effects in stock market volatility. However, the standard GARCH model delivers more consistent and accurate out-of-sample volatility forecasts. This finding highlights a critical distinction: models achieving the lowest AIC or BIC values do not necessarily provide the most accurate volatility predictions, particularly over extended forecasting horizons. From a practical standpoint, these results carry important implications for risk managers and portfolio analysts. When the primary objective is volatility prediction for hedging or risk assessment purposes, simpler symmetric models may be preferable due to their forecasting stability. Conversely, asymmetric models remain valuable for understanding market dynamics and the differential impact of positive versus negative shocks on volatility behavior.
Point-of-Sale System Architecture Design To Support the Digital Transformation of MSMEs (F&B) Damayanti, Lily; Wijaya, Andri
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3522

Abstract

The rapid development of information technology has encouraged Micro, Small, and Medium Enterprises (MSMEs) in the Food and Beverage (F&B) sector to digitalize their business processes to improve efficiency and competitiveness. However, most existing Point-of-Sale (POS) solutions adopted by MSMEs remain fragmented and application-centric, focusing primarily on transaction processing while lacking architectural integration across business processes, data management, application services, and supporting technology infrastructure. This architectural gap limits system scalability, interoperability, and long-term alignment with MSME operational growth. This study aims to design an integrated POS system architecture that positions POS development as an architectural transformation framework rather than a standalone system implementation. A descriptive qualitative, problem-solving approach is employed, involving requirement identification from multiple F&B MSMEs, enterprise architecture modeling using the TOGAF Architecture Development Method (ADM), and iterative refinement through Agile development cycles. The proposed architecture integrates business, data, application, and technology domains, producing a cohesive architectural blueprint and traceable design artifacts adapted to MSME operational characteristics and resource constraints. System validation is conducted through User Acceptance Testing (UAT) involving MSME end users. The results indicate high usability and functional adequacy for core operational activities, including sales transactions, inventory control, reporting, and petty cash management. These findings suggest that the proposed architecture is practically feasible for real-world adoption and can serve as a scalable foundation for future digital integration. Overall, this study contributes a structured and adaptable POS architectural framework that enhances integration, digital readiness, and sustainable transformation in the F&B MSME sector.

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