IDEALIS : InDonEsiA journaL Information System
Indonesia Journal Information System (IDEALIS) adalah Jurnal ilmiah yang diterbitkan secara berkala oleh Program Studi Sistem Informasi, Program Studi Manajemen Informatika dan Komputerisasi Akuntansi di Fakultas Teknologi Informasi Universitas Budi Luhur. Jurnal Idealis mulai terbit pada tahun 2018 dan terbit sebanyak 5 kali dalam setahun namun pada tahun 2020 direvisi menjadi terbit 2 kali dalam setahun yaitu bulan Januari dan Juli. Jurnal IDEALIS mendapatkan akreditasi SINTA 4 (Vol 3 No. 1 Tahun 2020-Vol 8 No. 1 Tahun 2025) dan naik akreditasi SINTA 3 (Vol 8 No. 1 Tahun 2025 - Vol 12 No. 2 Tahun 2029), sesuai dengan Keputusan Direktur Jenderal Pendidikan Tinggi, Riset, Dan Teknologi Kementerian Pendidikan, Kebudayaan, Riset, Dan Teknologi Republik Indonesia, nomor 225/E/KPT/2022 Tentang Peringkat Akreditasi Jurnal Ilmiah Periode III Tahun 2022 Direktur Jenderal Pendidikan Tinggi, Riset, Dan Teknologi. Topik pada Jurnal ini adalah topik-topik penelitian yang berasal dalam cakupan rumpun ilmu Komputer khususnya studi penelitian dasar dan terapan dalam sistem informasi (SI) more info Pemesanan jurnal dialamatkan ke kantor redaksi. E-ISSN : 2684-7280 IDEALIS telah terindeks: Google Scholar, Portal Garuda, Road
Designing a Cost of Production Information System for Restaurants Using the Recipe-Based Full Costing Method
Calculating the Cost of Production (CoP) is a crucial factor in determining selling prices and controlling costs within the restaurant business. The primary issue that frequently arises is cost distortion (under-costing distortion variance) caused by unstructured and conventional record-keeping, which can cause losses of up to 15% of net profit. This study aims to design a CoP information system utilizing the Recipe-Based Full Costing method for culinary Micro, Small, and Medium Enterprises (MSMEs) in Pontianak, and to evaluate its accuracy in comparison to the conventional method. The Rapid Application Development (RAD) method was employed for software development to enable rapid iteration with the users. The developed information system integrates raw material data, recipes, labor costs, and production overhead costs. The production cost for each menu item is calculated based on the quantity of raw materials specified in each recipe alongside the allocation of other production costs. Evaluation results show that the system is capable of correcting the under-costing problem, with a CoP discrepancy of IDR 1,300 per serving compared to the conventional method. Functionality evaluation using Black Box testing revealed a 100% functional success rate. System Usability Scale (SUS) testing yielded a score of 85, indicating a high level of user acceptance. The contributions of this study are divided into a methodological contribution through a cost comparison model and a technical contribution in the form of a proportionally automated operational cost allocation algorithm.
Copyright (c) 2026 Kartono Kartono, Sandi Tendean, Riyadi Jimmy Iskandar http://creativecommons.org/licenses/by-sa/4.0
Development and Functional Evaluation of a Web-Based Electronic Device Repair Service Information System Using React JS
Electronic device repair services still commonly use manual booking processes, such as visiting the service location directly or communicating through WhatsApp. Based on the observation results, the service process is still carried out manually, and system testing was simulated using 5 service types, 10 user accounts, and 20 dummy order data. This condition causes ineffective communication, customer queues, and limited information regarding repair status and cost estimates. Based on these problems, this study aims to implement React JS in the frontend development of an electronic device repair service application. The system development used the Waterfall method, which includes requirement, system design, implementation, testing, and maintenance. The application was developed using React JS with the support of React Router DOM, React Hooks, Fetch API, Tailwind CSS, and PostgreSQL as the database. The system produces 10 user features and 8 admin features. Functional testing using Black Box Testing on 15 scenarios showed that all system functions worked according to the specified requirements with a success rate of 100%, while User Acceptance Testing (UAT) obtained a user acceptance rate of 86.40%. The main contribution of this study is the implementation of React JS to build a component-based frontend interface that supports navigation management, Single Page Application (SPA), state management, and structured user interaction in the repair service system.
Copyright (c) 2026 Tatas Buana, Yeremia Alfa Susetyo http://creativecommons.org/licenses/by-sa/4.0
K-Means Clustering for Grouping Elementary School Facilities and Infrastructure in Indonesia
Equitable access to quality basic education remains a national priority in Indonesia, yet the condition of elementary school facilities and infrastructure still varies considerably across regions. This research applies the K-Means Clustering algorithm to classify the condition of elementary school facilities and infrastructure at the regency/city level in Indonesia, addressing disparities in basic education facilities through a more objective, data-based mapping approach. Data were obtained from the 2025 Data Portal of the Ministry of Primary and Secondary Education, covering 514 regencies/cities and six indicators: classrooms, libraries, laboratories, school health units, boys’ toilets, and girls’ toilets. Each indicator was converted into a numerical score based on four condition categories and normalized using Min-Max Normalization prior to clustering. The number of clusters was determined using the Elbow Method and validated using the Davies-Bouldin Index, both supporting K=3. The clustering grouped the 514 regencies/cities into 117 regions with relatively low facility conditions, 302 with relatively moderate conditions, and 95 with relatively high conditions, with the low cluster concentrated in eastern Indonesia, particularly Papua and Maluku. The main contribution of this research is a data-driven cluster map that education authorities can use as an evidence-based reference for prioritizing budget allocation and infrastructure development toward regencies/cities in the low cluster, as well as a replicable scoring-normalization-clustering pipeline adaptable for future studies with additional indicators.
Copyright (c) 2026 OK Muhammad Zikri Fadillah, Abdul Halim Hasugian http://creativecommons.org/licenses/by-sa/4.0
Sentiment Classification of Indonesian PayLater App Reviews Using Random Forest and Logistic Regression
The increasing use of paylater services in various regions of Indonesia has given rise to a large collection of user reviews that contain meaningful information about how users evaluate and perceive the experience of using the service. This research focuses on the classification of sentiment contained in paylater app reviews using two machine learning approaches, namely Random Forest and Logistic Regression, which are tested both with and without the application of the Synthetic Minority Oversampling Technique (SMOTE) to address class inequality. Review information is obtained from the Google Play Store and goes through a series of initial steps involving text cleaning, case processing, standardization of non-standard words, splitting sentences into tokens, removing meaningless words, and also stemming. Subsequent characteristic extraction is carried out using the TF-IDF method (Term Frequency-Inverse Document Frequency), before the data is divided into training and testing sets using three split configurations: 80:20, 70:30, and 60:40, to evaluate model consistency across varying training data sizes. The 80:20 split consistently produced the highest performance across all models. The results of all tested configurations, the combination of Logistic Regression with SMOTE provided the best results, achieving 88.38% accuracy, 88.37% precision, 88.38% recall, and 88.37% F1-score. Unlike previous studies that analyzed sentiment from a single paylater application using a single algorithm without class balancing, this study contributes by simultaneously collecting data from three major paylater applications and empirically comparing the effect of SMOTE on two algorithms across three data split configurations, providing a more comprehensive and generalizable benchmark for paylater sentiment classification in Indonesia. This finding indicates that the application of SMOTE also strengthens the model's performance by addressing the imbalance between sentiment classes, while Logistic Regression is proven to be able to recognize patterns related to sentiment in review text. In general, this study shows that combination of TF-IDF, Logistic Regression, and SMOTE builds an effective system for classifying sentiment in paylater application reviews.
Copyright (c) 2026 Muhammad Ardi Hermansyah, Muhammad Arifin, Pratomo Setiaji http://creativecommons.org/licenses/by-sa/4.0
Measurement and Optimization of TikTok Ads Effectiveness Using Bayesian Marketing Mix Modeling and Optimization
The rapid growth of digital advertising has intensified the need for accurate measurement of advertising effectiveness and efficient budget allocation. However, many businesses still struggle to quantitatively link marketing efforts to sales outcomes due to data uncertainty, channel interactions, and dynamic consumer behavior. This study proposes an integrated framework that combines Bayesian Marketing Mix Modeling (BMMM) with Bayesian Optimization (BO) to simultaneously measure advertising contribution and optimize budget allocation under uncertainty. The proposed framework was applied to a dataset of 456 daily TikTok advertising records collected from January 2024 to March 2025. Bayesian Ridge Regression was employed for model estimation and evaluated using 5-Fold Cross-Validation, yielding strong predictive performance with an R² of 0.8649 and MAPE of 0.55%. Optimization results indicate that Visual Shopping Ads (VSA) and Live Shopping Ads (LSA) deliver the highest marginal returns on sales, while Product Showcase Ads (PSA), Awareness, and GMV Max exhibit relatively lower contributions. Unlike previous studies that typically address marketing mix modeling or budget optimization separately, this research introduces integrated probabilistic framework that simultaneously measures advertising effectiveness and optimizes budget allocation by fully utilizing posterior distributions. This approach offers a more robust alternative to traditional Marketing Mix Modeling and manual allocation methods, particularly in fast-changing digital advertising environments such as TikTok Ads. The findings provide practical guidance for retailers in making data-driven marketing decisions and demonstrate the significant potential of Bayesian approaches in digital marketing analytics, especially for emerging markets like Indonesia.
Copyright (c) 2026 Fajar Legianto Asmin, Alz Danny Wowor http://creativecommons.org/licenses/by-sa/4.0
Evaluation of ARIMA, ARIMA-LSTM, LSTM, and CNN-LSTM Models for Daily Air Quality Index Forecasting in Jakarta
Increasing air pollution in urban areas, particularly in DKI Jakarta, has made a reliable air quality prediction system increasingly essential for environmental control and public health risk management. The Air Quality Index (AQI) exhibits complex and fluctuating patterns, requiring forecasting methods capable of capturing both linear and non-linear. This study aims to conduct a comparative analysis of statistical, deep learning, and hybrid models for AQI forecasting using daily AQI data from Jakarta during the 2023–2025 period. The dataset includes polutant parameters such as , , , , , and CO. The proposed models consist of Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM), and a hybrid ARIMA-LSTM model. The research methodology includes data preprocessing, normalization using Min-Max Scaling, sequence generation using the sliding window approach, model training, and evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results show that the CNN-LSTM model achieved the best forecasting performance with MAE, RMSE, and MAPE values of 4.54, 6.03, and 13.83%, respectively, followed closely by the LSTM model. Meanwhile, the ARIMA model produced the lowest performance, and the hybrid ARIMA-LSTM model did not outperform the standalone deep learning models. These findings indicate that deep learning approaches, particularly CNN-LSTM, are more effective in capturing the complex dynamics of urban air pollution data and have strong potential to support air quality forecasting and pollution control systems in Jakarta.
Copyright (c) 2026 Mushliha, Nisrinah http://creativecommons.org/licenses/by-sa/4.0
Decision Support System for Wedding Package Selection Using AHP and MOORA Methods at Adhinatha Wedding Organizer
Wedding organizer (WO) package selection is a complex multi-criteria decision-making process that has long relied on subjective consultations. At Adhinatha Wedding Organizer, this process is still carried out manually through WhatsApp-based consultation, producing recommendations that tend to be subjective and fail to accommodate customer preferences across eight competing criteria. Previous decision support system (DSS) studies in the wedding organizer domain generally rely on a single ranking method, so results have never been cross-checked or validated by domain experts. The contribution of this research is to fill that gap by developing a web-based DSS integrating the Analytical Hierarchy Process (AHP) for criteria weighting with three ranking methods, namely Multi-Objective Optimization by Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), applied to eight criteria and thirty-six package alternatives. This research aims to design and build a web-based DSS through data collection, criteria weighting with AHP, alternative ranking, expert validation, and weight sensitivity testing. The three rankings are compared, validated against an expert consensus using Spearman correlation, and tested for stability via ±5% weight sensitivity analysis. The results show MOORA produces the ranking most consistent with expert judgment (rho = 0.998) and the most stable underweight change and is therefore selected as the system's primary ranking engine. The system is developed using the waterfall method, modeled with UML, and implemented using PHP and MySQL. It is expected to provide faster, more transparent, and more accountable package recommendations for Adhinatha Wedding Organizer customers.
Copyright (c) 2026 Muhammad Wifqi Aufal Maulana, Anteng Widodo, Zainur Romadhon http://creativecommons.org/licenses/by-sa/4.0
Subdistrict Welfare Level Ranking in Semarang City Using Hybrid Entropy-EDAS Method
Sustainable regional development in Semarang City faces challenges regarding unmapped social welfare disparities and uneven infrastructure distribution across its 16 districts. Conventional assessment methods often suffer from subjective bias and fail to capture multidimensional data complexity, potentially leading to misallocated resources. This research aims to objectively analyze and rank district welfare levels using a Decision Support System (DSS) approach, assisting local government in accurately prioritizing development strategies. The systematic research stages encompass problem identification, dataset collection, objective weighting using the Entropy method, alternative ranking using the Evaluation Based on Distance from Average Solution (EDAS) method, and final result analysis. The hybrid framework utilizes Entropy to determine objective weights for five critical criteria (number of households, population size, healthcare facilities, stunting cases, and school facilities) based on data variation, eliminating human error. Meanwhile, EDAS evaluates district performance by calculating positive and negative distances from the average solution. Results reveal sharp inequality; Pedurungan District achieved the top rank (0.935), reflecting an optimal balance between public facilities and population needs. Conversely, Semarang Utara District ranked last (0.058), primarily driven by high stunting incidence as a major cost factor. The integrated Entropy-EDAS model provides a robust scientific basis for policymakers to effectively target budget allocation and stunting reduction programs in disadvantaged areas.
Copyright (c) 2026 Tan Bagas Endrihartono, Isa Ghani Al-Hadid, Prind Triajeng Pungkasanti http://creativecommons.org/licenses/by-sa/4.0
Knowledge-Based Decision Support System for Hotel Room Recommendation and Maintenance Prioritization Using Mamdani Fuzzy Logic
To improve service quality, hotel management must make decisions quickly and accurately, particularly in providing hotel room recommendations to guests and determining room maintenance priorities by reception staff. However, these decisions are often challenged by uncertainty and subjective considerations. This research is primarily driven by the goal to construct a knowledge-based decision support system. at Hotel Pusaka Mulya that not only facilitates hotel administrative management but also supports accurate room recommendation decisions based on guest preferences and effective prioritization of room maintenance activities. A knowledge-based decision support system leveraging the Mamdani fuzzy reasoning technique is developed as the core of this proposed framework.. The input variables for room recommendation include price, facilities, comfort level, and number of occupants, while the output variable is the room type, consisting of Standard , Standard 1 , Superior 1 , Superior 2 , and Superior 3 . Meanwhile, the input variables for maintenance priority determination are cost, time, and level of damage, with output categories classified as low, medium, and high priority. The Mamdani fuzzy inference process consists of four stages: fuzzification, implication, aggregation using the MAX operator, and defuzzification using the centroid method. The software development process follows the waterfall model, encompassing the phases of analysis, design, implementation, and testing. The testing results demonstrate that the Mamdani Fuzzy is capable of generating recommendations efficiently and accurately in accordance with the defined decision criteria based on an 80% validation accuracy for room recommendations and an 85% accuracy for determining improvement priorities.
Copyright (c) 2026 Sri Widaningsih, Agus Suheri, Mohammad Hasnan Ali http://creativecommons.org/licenses/by-sa/4.0
Intelligent One-Gate System Based on Natural Language Processing for Enhancing Academic Information Services
The rapid digitalization of higher education requires academic information services that are fast, integrated, and accessible. Fragmented service channels, high administrative workloads, and slow response times remain persistent operational challenges. This study develops an Intelligent Transformer-Based One-Gate System that centralizes academic services by integrating text classification, abstractive summarization, and chatbot modules. Using a Research and Development approach, the system was built from 1,000 academic documents and service queries collected from FAQs, academic regulations, guides, and digital service archives. The corpus was cleaned, tokenized, encoded, and divided into training, validation, and testing subsets. BERT was applied for document and query classification, while BART and PEGASUS were evaluated for abstractive summarization using ROUGE metrics. The chatbot was assessed through a Likert-scale user acceptance survey. The results of this research show that the BERT classifier achieved 88% accuracy and an F1-score of 0.875, PEGASUS outperformed BART with ROUGE-1 = 0.74, ROUGE-2 = 0.66, and ROUGE-L = 0.71, and the chatbot achieved an average user satisfaction score of 82%. The main contribution of this research is an integrated transformer-based one-gate architecture that combines document routing, academic document summarization, and conversational assistance in a single service platform, offering practical value for reducing fragmented academic information services and methodological value as a reference model for higher education NLP implementation.
Copyright (c) 2026 Jaka Purnama, Yayuk Ike Meilani http://creativecommons.org/licenses/by-sa/4.0
Explainable Phishing Website Detection Using Comparative Machine Learning and SHAP
Phishing attacks distributed through fraudulent URLs remain one of the most damaging cyber threats, including in Indonesia where malicious links spread widely through messaging applications and e-mail. Blacklist-based approaches cannot recognize newly created phishing URLs, while accurate machine learning models are often difficult to interpret. This study aims to compare six machine learning algorithms, namely XGBoost, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes, for phishing website detection based on lexical URL, page content, and external reputation features, while providing model transparency through Explainable Artificial Intelligence (XAI). Experiments were conducted on the Web Page Phishing Detection Dataset containing 11,430 URLs with 87 features and a balanced class distribution. The research stages include exploratory data analysis, feature selection analysis using Chi-Square, Mutual Information, and Recursive Feature Elimination, an 80:20 data split, model training, hyperparameter optimization using RandomizedSearchCV, and interpretation of the best model using SHapley Additive exPlanations (SHAP). The results show that XGBoost delivers the best performance with 96.50% accuracy, 96.26% precision, 96.76% recall, 96.51% F1-score, and an AUC of 0.9943. SHAP analysis identifies google_index, page_rank, and nb_hyperlinks as the most influential features, dominated by external reputation-based features. Under this experimental setting, the findings indicate that an accurate phishing detection model can be equipped with interpretable explanations of its feature contributions. The main contribution of this study is an integrated comparative evaluation that combines six-algorithm benchmarking, leakage-free hyperparameter optimization, and SHAP-based interpretation on a public phishing dataset, offering practical guidance for security analysts.
Copyright (c) 2026 Juni Ismail, Raja Anan Nasution, Muhammad Nasri Gea http://creativecommons.org/licenses/by-sa/4.0
Design of a Trans Palu Bus Schedule Monitoring System for Public Service Improvement
Public transportation services in Palu City have adopted digital features through Sangu Palu and Mitra Darat, but still face unstable GPS data, weak real-time notifications, limited local data control, and fragmented coordination between government and field personnel. This study aims to design a Trans Palu bus schedule monitoring system that provides real-time passenger information and strengthens local e-government transport governance. Design Science Research was used. Data were collected through interviews with agencies and bus officers, field observations, user surveys, literature review, and system analysis. The artifact is a multilayer software architecture with four modules: passenger, administrator, operator, and field officer. The design focuses on the passenger and administrator modules, while the operator and field officer modules are formulated conceptually to support data recording and operational integration. A survey of 67 respondents identified schedules, estimated arrival times, real-time bus positions, nearest stops, fastest routes, and notifications as user priorities. The artifact includes route and schedule management, fleet monitoring, estimated arrival times, a journey planner, incident reporting, dashboard analytics, and role-based access. Evaluation yielded a model consistency score of 3.65, a technical feasibility score of 3.0, and an expert validation score of 3.96, indicating that the design is feasible with improvements. This study contributes an architectural foundation for local governments to develop locally grounded public transport monitoring systems. Its novelty lies in integrating passengers, local-government administrators, operators, and field officers in one ecosystem combining passenger information services with oversight, operational management, and local data control.
Copyright (c) 2026 Muthiah Azzahrah, Andi Hendra http://creativecommons.org/licenses/by-sa/4.0
Application of the Simple Additive Weighting Method for Selecting Vacant Stalls in Markets Using Tag-Location
As a public facility, traditional markets play an important role in supporting the economic activities of the community, particularly for micro and small business actors. However, kiosk management in traditional markets is still carried out manually, making it prone to delayed data updates and data loss. This research is directed toward designing and implementing a tag-location-based kiosk monitoring information system integrated with the Simple Additive Weighting (SAW) method to support the prioritization of vacant kiosks at Mayong Market and Kalinyamatan Market, Jepara Regency. The novelty of this research lies in the application of a tag-location feature integrated with the Simple Additive Weighting (SAW) method to support kiosk monitoring and vacant kiosk prioritization within a single system. The system was developed using the Waterfall method by utilizing latitude and longitude coordinates integrated with a digital map to support visual and real-time monitoring of kiosk location and occupancy status. The SAW method was applied to 163 vacant kiosks, consisting of 69 kiosks at Mayong Market and 94 kiosks at Kalinyamatan Market. The results show that at Mayong Market, kiosk LT2.IV/10 obtained the highest SAW score of 0.96, while at Kalinyamatan Market, kiosk PKL.U/15 obtained the highest score of 1.00. Black Box Testing on 10 test scenarios showed that all system functions ran according to user requirements. The integration of the tag-location feature with the Simple Additive Weighting (SAW) method can serve as an approach in developing a location-based decision support system for the management of traditional market kiosks.
Copyright (c) 2026 Syafira Salsabila, Diana Laily Fithri, Supriyono Supriyono http://creativecommons.org/licenses/by-sa/4.0
A Static Analysis of Privacy by Design Compliance in Indonesian Quick-Service Restaurant Applications
The adoption of Quick-Service Restaurant (QSR) apps in Indonesia tripled from 2020 to the following three years. However, technical privacy audits of local apps remain very limited, and no research has systematically evaluated QSR apps in Indonesia using technical methods. To address this gap as the study's objective, the privacy compliance of QSR applications was systematically evaluated. Methodologically, the PbD-MASVS-MobSF evaluation framework was implemented, wherein Privacy by Design (PbD) principles, OWASP MASVS privacy controls, and Mobile Security Framework (MobSF) processes were integrated. As a sample, seven QSR apps were analysed, and their privacy findings were compared to the provisions of Law No. 27 of 2022 on Personal Data Protection (UU PDP). Regarding the key results, it was found that the apps were at a MEDIUM to HIGH risk level, with MobSF scores of 39-54 out of 100. The compliance analysis found that the apps consistently failed to meet six of the ten MobSF subprocesses. Meanwhile, strong compliance was only identified in the absence of API access for device identification. These findings indicate that privacy risks in the Indonesian QSR sector are structural and sectoral. Crucially, because only static analysis was utilised in this assessment, the findings are considered indicative rather than conclusive, and legal non-compliance with the PDP Law cannot be independently established. As a primary contribution, an operational framework that connects static analysis results, privacy design principles, and national regulatory requirements is provided as a reference for application developers, privacy auditors, and regulators.
Copyright (c) 2026 Emanuel Ristian Handoyo, Flourensia Sapty Rahayu http://creativecommons.org/licenses/by-sa/4.0
Comparison of Yolo26 and RT-DETR-L for Manga Visual Element Detection on The Manga109 Dataset
Automatic comic element detection is a critical prerequisite for manga digital analysis applications such as indexing, translation, and accessibility enhancement. Manga109 is a widely used benchmark for this task, yet the rapid progress of real-time object detectors has not been matched by a systematic comparison among state-of-the-art models in this domain, particularly between YOLO-family detectors employing Small-Target-Aware Label assignment (STAL) and transformer-based detectors such as RT-DETR. This study benchmarks YOLO26 (nano, small, and medium variants) against RT-DETR-L for detecting four comic element classes (panel, character, text, and face) on Manga109. All models were trained for 100 epochs at 1024×1024 pixels and evaluated using mAP and per-size AP following COCO conventions. YOLO26m achieves the best performance (mAP@0.5:0.95 of 0.7471), while RT-DETR-L obtains the lowest (0.7001) despite the largest parameter count and slowest inference. For small text detection, YOLO26m outperforms RT-DETR-L by 42.7% relatively, and the AP gap across object sizes narrows monotonically as model size grows, supporting the STAL design claim. RT-DETR-L also exhibits significant training instability. The main contribution is a systematic, multi-dimensional benchmark of YOLO26 against RT-DETR-L on Manga109 that evidence STAL effectiveness for small-text detection and offers practical guidance for selecting real-time detectors in manga image analysis.
Copyright (c) 2026 Daniel Sande Bona, Tindia Febriyati http://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of Mobile Banking Reviews Using Naïve Bayes and Support Vector Machine on Play Store
The increasing adoption of mobile banking has generated a large volume of user reviews on the Google Play Store, providing valuable insights into customer satisfaction with digital banking services. This study analyzes user sentiment toward four mobile banking applications BRimo, Livin' by Mandiri, BCA Mobile, and SeaBank—and compares the performance of the Naïve Bayes and Support Vector Machine algorithms for sentiment classification. A dataset of 40,000 Play Store reviews was collected through web scraping. Reviews were labeled based on user ratings, followed by preprocessing, TF-IDF feature extraction, and sentiment classification. Model performance was evaluated using the train-test split method and 5-fold cross-validation. The results indicate that Naïve Bayes outperformed and demonstrated greater stability than SVM across all evaluation metrics, achieving an accuracy of 89.23%, precision of 89.76%, recall of 89.23%, and F1-score of 89.37%, while SVM achieved an accuracy of 88.39%. Sentiment analysis revealed that SeaBank received the highest number of positive reviews, whereas BCA Mobile recorded the highest proportion of negative sentiment. These findings provide a comparative evaluation of Naïve Bayes and SVM for mobile banking sentiment classification and offer practical guidance for selecting appropriate classification algorithms while supporting the continuous improvement of digital banking services through user feedback.
Copyright (c) 2026 Dewi Masitoh, R. Rhoedy Setiawan, Soni Adiyono http://creativecommons.org/licenses/by-sa/4.0
Location-Based Facility Management in a Study Program Information Portal Using WebGIS
Study-program portals commonly present academic profiles, lecturer data, news, galleries, and facility information without linking facilities to geographic positions; consequently, academic information loses its spatial context. This study designed, implemented, and evaluated a WebGIS portal for the Remote Sensing and Geographic Information Systems Study Program. An R&D workflow covered problem identification, literature review, field acquisition, requirements analysis, system design, implementation, and evaluation. Coordinates and photographs were collected with KoboToolbox, verified in QGIS, and stored as point geometries in PostgreSQL/PostGIS; nonspatial content was managed through an administrator dashboard. The portal combines public information pages, an interactive map, facility markers, search, layer controls, categories, object details, and centralized content management. Evaluation involved one expert reviewing 35 functions, User Acceptance Testing with 30 students using 16 Likert items, and a limited security inspection. Thirty-three functions met the scenarios (94.29%), while UAT reached 2,045 of 2,400 points (85.21%; mean 4.26/5). No critical or high vulnerabilities were identified within the tested scope. Two WebGIS functions require improvement: HTTPS-based geolocation and complete object details. The research contribution is an integration model combining coordinate-based facility records, nonspatial academic content, and centralized administration in one study-program portal.
Copyright (c) 2026 Hidayatul Fikri, Eva Purnamasari http://creativecommons.org/licenses/by-sa/4.0
Integrated Management Information System for Order Management, Cash Flow, and Payroll Automation in Garment MSMEs
Order tracking, cash flow monitoring, and payroll calculation are still handled manually at many garment Micro, Small, and Medium Enterprises (MSMEs), a practice that leaves considerable room for human error, lost records, and slower administrative processes. This study designs and builds an integrated, web-based management information system tailored for Dims.Collection, examined through a single case study. Development followed the Waterfall model, covering requirements gathering, system design with Unified Modeling Language (UML), coding, verification, and maintenance planning. User permissions for five roles - Administrator, Owner, Sales Staff, Finance Staff, and Human Resources Staff - are managed by a Role-Based Access Control (RBAC) framework to maintain secure system authorization. Ten functional test scenarios were run using Black Box Testing, spanning login authentication, order handling, cash recording, attendance tracking, and automated payroll computation, with every scenario passing (100% success rate). Beyond a working application, this research contributes an RBAC-based business process model that abstracts how order, cash, and payroll functions can be tied together within one centralized-access architecture. Rather than a solution built for one case only, the model is intended to transfer to other MSMEs sharing comparable roles and workflows, functioning as a reusable reference for building access-controlled information systems in the garment sector.
Copyright (c) 2026 Slamet Rahayu, R. Rhoedy Setyawan, Yudie Irawan http://creativecommons.org/licenses/by-sa/4.0
Extreme Learning Machine Hyperparameter Optimization Using Particle Swarm Optimization and Technical Indicators for Bitcoin Forecasting
Highly volatile price changes in crypto assets like Bitcoin complicate precise forecasting. The Extreme Learning Machine (ELM) artificial neural network approach offers high computational speed but is prone to performance instability due to random weight initialization and manual hyperparameter determination. To overcome this problem, this study proposes a hybrid PSO-ELM algorithm to automate the search for optimal parameters, namely the number of hidden neurons and the regression regularization penalty. Evaluated using a five-fold Walk-Forward Validation to prevent data leakage, the model comparatively tested five input feature scenarios based on technical indicators, which are mathematical calculations from historical prices to identify market patterns. Results demonstrate the hybrid PSO-ELM outperforms conventional static models, reducing average error (MAPE) by 18.67 percent. The cross-scenario comparison reveals that applying the Simple Moving Average technical indicator yields the best forecasting model, achieving a 26.65 percent error reduction and a final MAPE accuracy of 2.15 percent. The contribution of this research is providing empirical evidence that automatic parameter optimization combined with a random fluctuation filtering feature is proven to be more robust and accurate in responding to extreme volatility compared to the stacking of various complex derivative momentum indicators.
Copyright (c) 2026 Bagus Putra Sulung, Wiwit Agus Triyanto, Pratomo Setiaji http://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of BCA and Mandiri Branch and Mobile Banking User Satisfaction Using Support Vector Machine
The development of digital banking services encourages banks to improve service quality through branch offices and mobile banking applications. User reviews on Google Maps and Google Play Store can be used to identify users’ assessments of these services. However, previous studies have generally focused on a single banking application or one review platform, so comparisons of branch and mobile banking services across two banks remain limited. This study aims to analyze sentiment, compare user satisfaction with BCA and Mandiri, and evaluate the performance of the Support Vector Machine (SVM) based on reviews from both platforms. The research stages include data collection, preprocessing, feature extraction using Term Frequency-Inverse Document Frequency, and sentiment classification using SVM. The dataset consists of 1,934 Google Maps reviews and 5,786 Google Play Store reviews. Testing results on Google Maps data achieved an accuracy of 91.99%, precision of 93.09%, recall of 98.25%, and F1-score of 95.60%. Meanwhile, testing on Google Play Store data achieved an accuracy of 83.85%, precision of 87.17%, recall of 77.96%, and F1-score of 82.31%. Sentiment analysis showed that positive sentiment toward BCA and Mandiri on Google Maps reached 87.79% and 88.96%, respectively. On Google Play Store, BCA Mobile obtained 43.68% positive sentiment, while Livin’ by Mandiri obtained 52.74%. Therefore, Mandiri had a higher proportion of positive sentiment on both platforms. This study contributes an integrated comparison of branch and mobile banking services and an evaluation of SVM performance across two different review sources.
Copyright (c) 2026 Alfianas Shofi Tafta Mahendra, Fajar Nugraha, Andy Prasetyo Utomo http://creativecommons.org/licenses/by-sa/4.0
Self-Service Kiosk Acceptance at McDonald's: A TAM-Based Analysis Across Age Groups
The fast-food industry continues to adopt digital technology, one example being the implementation of self-service kiosks at McDonald's outlets in Bandung. Although these kiosks are intended to improve service efficiency and reduce dependence on cashiers, their acceptance by users requires further evaluation. This study examines factors influencing user acceptance of self-service kiosks using the Technology Acceptance Model (TAM) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model comprises Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), and Behavioral Intention (BI), while Multi-Group Analysis (MGA) compares users aged ≤35 years and >35 years. Data were collected from 100 kiosk users in Bandung through purposive sampling. The findings indicate that PU (β = 0.314) and ATU (β = 0.596) positively and significantly affect BI, whereas PEOU influences BI indirectly through PU and ATU. The structural model explains 78.6% of the variance in behavioral intention. In addition, MGA reveals no significant differences between the two age groups. These findings suggest that age does not substantially influence user acceptance of self-service kiosks in Indonesian fast-food restaurants.
Copyright (c) 2026 Revalina Saputera, Yessica Nataliani http://creativecommons.org/licenses/by-sa/4.0
Machine Learning Prediction of Concrete Compressive Strength: Model Comparison, CatBoost Optimization, and SHAP Interpretation
Accurate prediction of concrete compressive strength is vital for structural design, yet conventional testing is constrained by lengthy curing requirements. Machine learning offers an alternative by modeling non-linear mix-performance interactions. This study presents a comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset (1,005 samples). Performance was assessed via 10x5 repeated cross-validation with 95% confidence intervals, and statistical significance was evaluated using a Linear Mixed-Effects Model with Holm-Bonferroni corrected pairwise t-tests. Tree-based ensembles outperformed linear approaches, with CatBoost yielding the highest baseline cross-validation R² of 0.931 (95% CI: 0.927 to 0.935). Subsequent Bayesian hyperparameter optimization via Optuna’s Tree-structured Parzen Estimator (400 trials) improved the final CatBoost model’s performance to a test of 0.943, RMSE of 4.142 MPa, and MAE of 2.616 MPa. SHAP analysis indicated that curing age, the water-to-binder ratio, and cement are the dominant predictors, while the model exhibited physically consistent behavior aligned with concrete hydration kinetics and Abrams' law. This work's key contribution is jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-strength studies. The framework offers an accurate, interpretable tool for preliminary concrete mix design.
Copyright (c) 2026 Musthafa 'Abduh Fakhruddin, Sri Winarno, Acun Kardianawati http://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of Indonesian Mobile Legends: Bang Bang Reviews Using Random Forest, KNN, TF-IDF, and SMOTE
Mobile Legends: Bang Bang (MLBB) is one of the most popular mobile games, generating a large number of user reviews on the Google Play Store. The large volume of reviews makes manual sentiment analysis impractical, requiring an automated machine learning approach. This study compares the performance of Random Forest and K-Nearest Neighbors (KNN) for classifying sentiment in Indonesian-language MLBB reviews. A total of 6,994 reviews were obtained through web scraping and preprocessing. The proposed framework includes text preprocessing, rating-based sentiment labeling, TF-IDF feature extraction, SMOTE-based class balancing, model training, and evaluation using Accuracy, Precision, Recall, F1-score, Macro-F1, Weighted-F1, and 5-fold cross-validation. Random Forest with SMOTE achieved the best performance, with an Accuracy of 84.0% and a Macro-F1 score of 80.8%, outperforming KNN with SMOTE, which achieved 73.8% Accuracy and 71.9% Macro-F1. The ablation study demonstrates that the effectiveness of SMOTE is model-dependent, improving Random Forest but degrading KNN performance. This study provides empirical evidence of SMOTE impact on different classifiers and employs cross-validation-based K selection to prevent test data leakage.
Copyright (c) 2026 Ahmad Alif Candra Selamet, Pratomo Setiaji, Wiwit Agus Triyanto http://creativecommons.org/licenses/by-sa/4.0