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Idealis : Indonesia Journal Information System
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 26847280     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Indonesia Journal Information System (Idealis) adalah jurnal penelitian Program Studi Informasi, Fakultas Teknologi Informasi, Universitas Budi Luhur. Topik pada Jurnal ini adalah Decision Support System, E-Commerce/E-Business, Datawarehouse/BI, Enterprise System, Data Mining, Sistem Penunjang Keputusan selamat membaca,  Admin Jurnal Idealis
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 942 Documents
 91   92   93   94   95 
Pengukuran dan Optimasi Efektivitas Iklan Menggunakan Bayesian Marketing Mix Modeling dan Bayesian Optimization Fajar Legianto Asmin; Alz Danny Wowor
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3753

Abstract

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.
Intelligent One-Gate System Based on Natural Language Processing for Enhancing Academic Information Services Jaka Purnama; Yayuk Ike Meilani
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3772

Abstract

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.
Perancangan Sistem Monitoring Jadwal Bus TransPalu Sebagai Solusi E-Government Untuk Meningkatkan Layanan Publik Muthiah Azzahrah; Andi Hendra
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3775

Abstract

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.
Evaluation of ARIMA, ARIMA-LSTM, LSTM, and CNN-LSTM Models for Daily Air Quality Index Forecasting in Jakarta Mushliha; Nisrinah
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3799

Abstract

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.
Sistem Pendukung Keputusan Berbasis Pengetahuan untuk Rekomendasi Kamar Hotel dan Prioritas Perawatan Menggunakan Fuzzy Mamdani Sri Widaningsih; Agus Suheri; Mohammad Hasnan Ali
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3802

Abstract

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.
Deteksi Phishing URL Menggunakan XGBoost dengan Explainable AI pada Web Page Phishing Detection Dataset Juni Ismail; Raja Anan Nasution; Muhammad Nasri Gea
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3810

Abstract

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.
Klasifikasi Sentimen Ulasan Pengguna Aplikasi Paylater di Indonesia Menggunakan Mesin Pembelajaran Muhammad Ardi Hermansyah; Muhammad Arifin; Pratomo Setiaji
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3814

Abstract

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.
Penerapan Metode Simple Additive Weighting untuk Pemilihan Kios Kosong di Pasar Tradisional Berbasis Tag-Lokasi Syafira Salsabila; Diana Laily Fithri; Supriyono Supriyono
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3820

Abstract

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.
A Static Analysis of Privacy by Design Compliance in Indonesian Quick-Service Restaurant Applications Emanuel Ristian Handoyo; Flourensia Sapty Rahayu
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3825

Abstract

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.
DECISION SUPPORT SYSTEM FOR WEDDING PACKAGE SELECTION USING AHP AND MOORA METHODS AT ADHINATHA WEDDING ORGANIZER Muhammad Wifqi Aufal Maulana; Anteng Widodo; Zainur Romadhon
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3835

Abstract

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.

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