cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
SAFITRI JUANITA
Contact Email
idealis.fti@budiluhur.ac.id
Phone
+6283898928000
Journal Mail Official
idealis.fti@budiluhur.ac.id
Editorial Address
Jl. Ciledug Raya, Petukangan Utara, Jakarta Selatan, 12260. DKI Jakarta, Indonesia. Telp: 021-585 3753 Fax: 021-585 3752.
Location
Kota adm. jakarta selatan,
Dki jakarta
INDONESIA
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 
Analisis Penerimaan Sistem Self-Service Kiosk McDonald's Menggunakan Technology Acceptance Model Berdasarkan Kelompok Usia Revalina Saputera; Yessica Nataliani
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.v9i2.3807

Abstract

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.
SENTIMEN ANALISIS MOBILE BANKING MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE PADA GOOGLE PLAY STORE Dewi Masitoh; R. Rhoedy Setiawan; Soni Adiyono
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.v9i2.3811

Abstract

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.
Optimasi Hiperparameter Extreme Learning Machine Menggunakan Particle Swarm Optimization dan Indikator Teknikal untuk Peramalan Bitcoin Bagus Putra Sulung; Wiwit Agus Triyanto; 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.v9i2.3812

Abstract

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.
Analysis of BCA and Mandiri User Satisfaction Based on Google Maps and Google Play Store Reviews Using the Support Vector Machine Method Alfianas Shofi Tafta Mahendra; Fajar Nugraha; Andy Prasetyo Utomo
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.v9i2.3817

Abstract

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.
Analisis Sentimen Ulasan Mobile Legends: Bang Bang dalam Bahasa Indonesia Menggunakan Random Forest, KNN, TF-IDF, dan SMOTE Ahmad Alif Candra Selamet; Pratomo Setiaji; Wiwit Agus Triyanto
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.v9i2.3819

Abstract

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.
Application Of K-Means Clustering To Group Deforestation Areas In Indonesia Nasrul Mahruf Aznawi; Abdul Halim Hasugian
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.v9i2.3836

Abstract

Deforestation remains one of the major environmental challenges in Indonesia because the rate of tree cover loss varies considerably among regions, making it difficult for stakeholders to identify priority areas for forest monitoring and management using conventional descriptive analysis. This study aims to identify spatial patterns of deforestation by clustering Indonesian districts/cities based on multi-year Tree Cover Loss and to evaluate the effectiveness of the K-Means clustering algorithm for supporting data-driven environmental analysis. The dataset was obtained from Global Forest Watch (GFW) and consists of Tree Cover Loss data for 402 districts/cities in Indonesia during 2023–2025, represented by three numerical attributes measured in hectares. The research methodology includes data cleaning, attribute selection, Min-Max normalization, determination of the optimal number of clusters using the Elbow Method, K-Means clustering, and cluster evaluation using the Davies–Bouldin Index (DBI). Experimental results show that the optimal number of clusters is three, producing 333 districts (82.84%) in the low-loss cluster, 61 districts (15.17%) in the moderate-loss cluster, and 8 districts (1.99%) in the high-loss cluster, with a DBI value of 0.6599, indicating good clustering quality. The findings reveal that tree cover loss is unevenly distributed across Indonesia and provide a data-driven regional categorization that can support priority setting for forest monitoring and conservation. The scientific contribution of this study lies in utilizing multi-year Tree Cover Loss data (2023–2025) at the district/city level across Indonesia to characterize regional deforestation patterns using K-Means clustering.
Perbandingan YOLO26 dan RT-DETR-L pada Deteksi Elemen Visual Manga Menggunakan Dataset Manga109 Daniel Sande Bona; Tindia Febriyati
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.v9i2.3842

Abstract

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.
Sistem Informasi Manajemen Terintegrasi: Pengelolaan Order, Arus Kas, dan Otomatisasi Penggajian pada UMKM Konveksi Slamet Rahayu; R. Rhoedy Setyawan; Yudie Irawan
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.v9i2.3844

Abstract

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.
Sistem Informasi Berbasis WebGIS untuk Data Spasial Akademik Pada Program Studi Penginderaan Jauh dan Sistem Informasi Geografis Hidayatul Fikri; Eva Purnamasari
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.v9i2.3847

Abstract

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.
Machine Learning Prediction of Concrete Compressive Strength: Model Comparison, CatBoost Optimization, and SHAP Interpretation Musthafa 'Abduh Fakhruddin; Sri Winarno; Acun Kardianawati
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.v9i2.3848

Abstract

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.

Page 94 of 95 | Total Record : 942

 91   92   93   94   95 

Filter by Year

2018 2026


Reset
Filter By Issues
All Issue Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026 Vol. 9 No. 1 (2026): Jurnal IDEALIS Januari 2026 Vol. 8 No. 2 (2025): Jurnal IDEALIS Juli 2025 Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025 Vol. 7 No. 2 (2024): Jurnal IDEALIS Juli 2024 Vol. 7 No. 1 (2024): Jurnal IDEALIS Januari 2024 Vol 6 No 2 (2023): Jurnal IDEALIS Juli 2023 Vol 6 No 1 (2023): Jurnal IDEALIS Januari 2023 Vol 5 No 2 (2022): Jurnal IDEALIS Juli 2022 Vol 5 No 1 (2022): Jurnal IDEALIS Januari 2022 Vol 4 No 2 (2021): Jurnal IDEALIS Juli 2021 Vol 4 No 1 (2021): Jurnal IDEALIS Januari 2021 Vol 3 No 2 (2020): Jurnal IDEALIS Juli 2020 Vol 3 No 1 (2020): Jurnal IDEALIS Januari 2020 Vol 2 No 6 (2019): Jurnal IDEALIS November 2019 Vol 2 No 5 (2019): Jurnal IDEALIS September 2019 Vol 2 No 4 (2019): Jurnal IDEALIS Juli 2019 Vol 2 No 3 (2019): Jurnal IDEALIS Mei 2019 Vol 2 No 2 (2019): Jurnal IDEALIS Maret 2019 Vol 2 No 1 (2019): Jurnal IDEALIS Januari 2019 Vol 1 No 5 (2018): Jurnal IDEALIS November 2018 Vol 1 No 4 (2018): Jurnal IDEALIS September 2018 Vol 1 No 3 (2018): Jurnal IDEALIS Juli 2018 Vol 1 No 2 (2018): Jurnal IDEALIS Mei 2018 Vol 1 No 1 (2018): Jurnal IDEALIS Maret 2018 More Issue