Article Per Year (5 Year)
p-Index From 2021 - 2026
7.899
P-Index
This Author published in this journals
All Journal Jurnal Computech & Bisnis (e-Journal) Nuansa Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Technologia: Jurnal Ilmiah Journal of Empowerment Community Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian dan Edukasi Sekolah (Jubaedah) International Journal of Quantitative Research and Modeling Joutica : Journal of Informatic Unisla Formosa Journal of Applied Sciences (FJAS) Paradigma International Journal of Ethno-Sciences and Education Research Journal Of Information System And Artificial Intelligence Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Computer Technology and Science International Journal of Information Engineering and Science Bulletin of Intelligent Machines and Algorithms Jurnal Informatika
Budiman
Universitas Informatika Dan Bisnis Indonesia, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Transportation Other

Published : 74 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 74 Documents
Search

 4   5   6   7   8 
Fine-Tuned Autoencoder Neural Network for Anomaly Detection in Accounting Transactions Nur Alamsyah; Budiman, Budiman; Rahmani, Hani Fitria; Erpurini, Wala
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8697

Abstract

Anomaly detection in accounting transactions plays a crucial role in identifying irregularities that may signal fraud, errors, or unusual financial behavior. Traditional rule-based and statistical methods often struggle to detect complex and hidden patterns in large-scale financial datasets. This paper presents a fine-tuned Autoencoder Neural Network for detecting anomalies in structured accounting records. The model processes feature such as date, account type, debit, credit, transaction category, and payment method. Preprocessing includes handling missing values, encoding categorical data, and extracting temporal features. The Autoencoder architecture was optimized using multiple hidden layers and dropout regularization to prevent overfitting. Reconstruction errors were used to determine anomaly scores, with a dynamic threshold set at the 98th percentile. Experimental results show that the model accurately distinguishes normal and anomalous transactions, identifying 2,000 outliers from a total of 100,000 records. Additional analysis indicates that anomalies often occur during weekends or holidays and involve unusual payment methods. These findings demonstrate the potential of the fine-tuned Autoencoder as a scalable and intelligent anomaly detection framework to support auditors and financial analysts in proactive fraud prevention.
OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING Jennifer Kaunang, Valencia Claudia; Alamsyah, Nur; Parama Yoga, Titan; Hendra, Acep; Budiman, Budiman
Jurnal Techno Nusa Mandiri Vol. 20 No. 2 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.6912

Abstract

The increasing complexity of 6G network slicing introduces new challenges in identifying abnormal behavior within highly virtualized and dynamic network infrastructures. This study aims to address the anomaly detection problem in 6G slicing environments by comparing the performance of three models: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques. The optimized autoencoder is trained to reconstruct normal data patterns, with anomaly detection performed using a threshold- based reconstruction error approach. Reconstruction errors are evaluated across different percentile thresholds to determine the optimal boundary for classifying abnormal behavior. All models are tested on a publicly available 6G Network Slicing Security dataset. Results show that the optimized autoencoder outperforms both the baseline autoencoder and the random forest in terms of anomaly sensitivity. Specifically, the optimized model achieves an F1- score of 0.1782, a recall of 0.2095, and an accuracy of 0.714. These results indicate that introducing regularization and dropout significantly improves the ability of autoencoders to generalize and isolate anomalies, even in highly imbalanced datasets. This approach provides a lightweight and effective solution for unsupervised anomaly detection in next- generation network environments.
A Metaheuristic Wrapper Approach to Feature Selection with Genetic Algorithm for Enhancing XGBoost Classification in Diabetes Prediction Alamsyah, Nur; Budiman; Danestiara, Venia Restreva; Yoga, Titan Parama; Nursyanti, Reni; Kaunang, Valencia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2366

Abstract

This study addressed the problem of selecting the most relevant features for improving the accuracy of diabetes classification using health indicator data. The research focused on a binary classification task based on the Behavioral Risk Factor Surveillance System dataset, which comprised over seventy thousand records and twenty-one predictive features related to individual health behaviors and conditions. A metaheuristic wrapper approach was developed by integrating a Genetic Algorithm for feature selection with an XGBoost classifier to evaluate the predictive quality of each feature subset. The fitness function was defined as the average classification accuracy obtained through cross-validation. In addition to feature selection, hyperparameter optimization of the XGBoost model was carried out using a Bayesian-based search strategy to further enhance performance. The proposed method successfully identified a subset of fourteen optimal features that contributed most significantly to the prediction of diabetes. The final model, combining the selected features and optimized parameters, achieved an accuracy of 0.753, outperforming both the baseline models trained on all features and models using features selected through deterministic methods. These results confirmed the effectiveness of combining evolutionary feature selection with model tuning to build efficient and interpretable predictive models for medical data classification. This approach demonstrated a practical solution for managing high-dimensional data in the context of chronic disease prediction.
Optimization Of Micro, Small and Medium Enterprise Financial Management Through Android-Based Zazan Mobile Application for Efficiency of Digital Economy Sustainability Yoga, Titan Parama; Setiana, Elia; Hamzah, Encep; Budiman, Budiman; Sarifiyono, Aggi Panigoro
Jurnal Computech & Bisnis (e-journal) Vol. 18 No. 2 (2024): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/95x8j738

Abstract

In order to sustain and grow a firm, financial management is essential. This procedure is essential for obtaining profit and loss data, preventing employee and business partner fraud, and separating personal and business funds to determine the company's financial situation accurately. Informal MSME actors multitask and operate as small business owners, entrepreneurs, and managers of all business issues. So, there is not enough time to document the financials of a corporation. Making financial records requires basic knowledge, which makes the work feel challenging, intricate, and time-consuming. One way to address this issue is making the Android mobile application "Zazan" to assist in managing the finances of medium-sized, tiny, and microbusinesses. A descriptive strategy was used to gather the data required for this investigation. An integrated application that could record business activity transactions, record business activity schedules, and connect with customers was required, as determined by the analysis and implementation findings. Furthermore, clients can quickly learn more about business players by accessing location information. Interaction between customers and business transaction activities facilitates the automation of business activity recording, enabling the application to complete financial recording. Entrepreneurs will find it easier to manage their financial spending with the help of the company's financial reports. Having employee and multi-store functionalities would be preferable because business actors occasionally have multiple business branches and employees. The implications of this research extend beyond merely providing a tool for financial management; they highlight the necessity of accessible financial solutions for MSMEs. By bridging the gap in financial literacy and time constraints, the "Zazan" application not only empowers business owners to maintain accurate financial records but also fosters better decision-making and strategic planning. This can lead to enhanced business performance, improved relationships with stakeholders, and ultimately, sustainable growth in the competitive market landscape.
Anomaly Detection in Walking Data Using Isolation Forest: An Unsupervised Learning Approach Nur, Nur Alamsyah
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.235

Abstract

Detecting anomalies in walking data is crucial for ensuring data quality in wearable devices and understanding irregular physical activity patterns. Traditional methods often rely on labeled data, which is scarce in real-world applications. This study presents an unsupervised learning approach using Isolation Forest to detect anomalies in walking datasets. The data, comprising features such as step count, distance, and time, was preprocessed and analyzed to identify patterns and deviations. Isolation Forest was employed due to its efficiency in handling high-dimensional data and its ability to separate anomalies without prior labeling. The model successfully detected 5 anomalous data points out of the dataset, with anomaly scores ranging from -0.15 to 0.2. These outliers corresponded to extreme walking patterns, such as unusually high step counts with disproportionate time and distance. Visualization of anomaly scores and statistical evaluations validated the model's effectiveness, showing clear distinctions between normal and abnormal data. The proposed approach highlights the potential of Isolation Forest in improving data quality and enabling real-time anomaly detection in fitness tracking applications. This work contributes to the broader field of unsupervised anomaly detection by demonstrating a scalable and effective method for handling real-world activity data.
Usability Testing Pada APPFIN Menggunakan Metode System Usability Scale Studi Kasus PT. Maja Kerta Wang Budiman Budiman; Rd. Yadi Rakhman Alamsyah; Ahmad Fauzi Ramadhan
NUANSA INFORMATIKA Vol. 18 No. 1 (2024): Nuansa Informatika 18.1 Januari 2024
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v18i1.66

Abstract

Ensuring a positive user experience is essential for the success of usability testing and meeting user expectations in system development. In this study, the System Usability Scale method is employed for usability testing, utilizing a questionnaire to assess acceptance, grading, and ratings. The research involves end users of the APPFIN application across all branches and head offices, with twenty-nine respondents achieving a notably favorable acceptance rate. The System Usability Scale score of 82 places APPFIN in the "GOOD" category with a B grade, indicating a reasonably high level of user satisfaction. The majority of respondents expressed contentment with their experience using APPFIN, reflecting a positive reception and acceptance among users.
Emoji-Based Sentiment Classification Using Ensemble Learning with Cross-Validation: A Lightweight Approach for Social Media Analysis: Klasifikasi Sentimen Berbasis Emoji Menggunakan Ensemble Learning dengan Validasi Silang: Pendekatan Ringan untuk Analisis Media Sosial Nur Alamsyah; Gunthur Bayu Wibisono; Titan Parama Yoga; Budiman; Acep Hendra
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.396

Abstract

The increasing use of emojis in online communication reflects emotional expression that is often more immediate and intuitive than text. This study proposes a lightweight sentiment classification approach that utilizes only emoji features extracted from social media posts, without relying on textual content. The importance of this research lies in its relevance to short-form digital content, where textual sentiment cues are minimal or absent. To address the classification problem, we implement and compare multiple machine learning models including Random Forest (RF), Support Vector Machine, and an ensemble Voting Classifier combining both. Emoji tokens were vectorized using character-level count vectorization, and performance was evaluated using 5-fold cross-validation to ensure robustness and generalizability. Results show that the ensemble model achieved the highest average accuracy of 93.6%, outperforming the individual classifiers. These findings confirm that emojis alone can serve as reliable indicators of sentiment and support the deployment of fast, interpretable, and scalable models for social media sentiment analysis.
A Bidirectional GRU Approach with Hyperparameter Optimization for Sentiment Classification in Game Reviews : Pendekatan GRU Dua Arah dengan Optimasi Hiperparameter untuk Klasifikasi Sentimen dalam Ulasan Game Nur Alamsyah; Titan Parama Yoga; Budiman; Imannudin Akbar; Acep Hendra; Alif Januantara Prima
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.399

Abstract

Sentiment analysis plays a vital role in understanding user perspectives, especially in domains such as game reviews where user feedback influences product perception and engagement. This study presents a comparative approach using Gated Recurrent Unit (GRU), hyperparameter-tuned GRU, and Bidirectional GRU models to classify sentiments in a dataset of game reviews. The experiment begins with standard preprocessing and tokenization steps, followed by vectorization and supervised training. Hyperparameter optimization is conducted using Keras Tuner to identify the most effective configuration of embedding dimensions, GRU units, dropout rates, and learning rates. The best model, a Bidirectional GRU with tuned parameters, achieves a validation accuracy of 85.37% and shows superior performance across key metrics such as precision, recall, and F1-score. Despite the relatively small and imbalanced dataset, the Bidirectional GRU model demonstrates robust generalization. This study also highlights future directions, including class balancing techniques and the integration of pretrained word embeddings to further improve model performance.
A Data-Driven Approach to Comparative Evaluation of Regression Models for Accurate House Price Prediction: Pendekatan Berbasis Data untuk Evaluasi Komparatif Model Regresi untuk Prediksi Harga Rumah yang Akurat Tiara Permata Hati; Budiman Budiman; Imannudin Akbar; Nur Alamsyah
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.411

Abstract

This study aims to develop and evaluate a property price prediction model in Bandung by applying machine learning (ML) algorithms. The need for more accurate property price predictions is increasing due to fluctuations in the property market. This study analyzes property characteristics, including the number of bedrooms, bathrooms, land area, building area, and location, as well as their impact on house prices. The study evaluates four regression algorithms, including linear regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost. Finally, this study proposes price_per_m2 and building_land_ratio as new features recommended for improvement in accuracy. The bottleneck method is derived from the data collection area of the Rumah123.com website, encompassing data preprocessing and data exploration. The following metrics will be used to evaluate each model: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Based on our study, we conclude that both Random Forest Regression and XGBoost Regression achieve the highest accuracy, with R² values of 0.9941 and 0.9955, respectively, after adjustment. Conversely, Linear Regression and KNN Regression have the lowest accuracy, with KNN Regression being the least accurate. The primary contribution of this study is the development of a more accurate house price prediction model that can be applied in cities with similar market characteristics. These findings provide practical insights for property developers and buyers when making investment decisions.
Comparative Analysis of Machine Learning Algorithms for Indonesian Twitter Sentiment Classification on the Jakarta–Bandung High-Speed Rail Project Muhammad Noerhadi; Budiman; Sardjono
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.3

Abstract

The rapid growth of social media in Indonesia has opened up new opportunities to gauge public opinion on major national initiatives. One of the most controversial projects is the Jakarta–Bandung High-Speed Railway (KCJB), which has sparked mixed responses due to its financial, environmental, and socio-political implications. To meet the need for systematic analysis, this study applies sentiment analysis to Indonesian Twitter data to evaluate public perspectives on the KCJB project. This research uses a step-by-step methodology, including data collection via the Twitter API, text preprocessing, manual tagging into positive and negative sentiments, and feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Four machine learning algorithms—Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest—were trained and verified on stratified data splits, with performance evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that SVM consistently outperforms other models, achieving up to 73% accuracy with balanced precision and recall, as well as the highest AUC value. These findings confirm the robustness of SVM in handling high-dimensional Indonesian text. In addition to its academic contribution to sentiment analysis in languages with limited resources, this research offers practical implications by providing data-driven insights for policymakers and businesses for real-time monitoring, strategic communication, and informed decision-making.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Ahmad Fauzi Ramadhan Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Alif Januantara Prima Amos Duan Nugroho Anto Widianto Ari Rizki Fauzi Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Danestiara, Venia R Dani Rizky Zaelani Darsiti . Dirham Triyadi Dirham Triyadi Erpurini, Wala Fahmi Abdullah Fauzi Ramadhan, Ahmad Fikri Rizqillah Hasani Gelar, Trisna Gunthur Bayu Wibisono Habibi, Chairul Hamzah, Encep Hani Fitria Rahmani Hasan Nuraripin Hernawan, Kartika Nursyabanita Ilham Ramadhan Ismi Nur Muhamad Jennifer Kaunang, Valencia Claudia Karlina, Nichi Hana Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Muhammad Noerhadi Muhammad Rizki Ramadhan Nasution, Vani Maharani Niqotaini, Zatin Nur Alamsyah NUR ALAMSYAH Nur Alamsyah Nur Alamsyah, Nur Nursyanti, Reni PARAMA YOGA, TITAN R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah R. Yadi Rakhman Alamsyah Rakhman Alamsyah, Rd. Yadi Rd. Yadi Rakhman Alamsyah Rd. Zidni Rizan Al-Zhahir Yanuar Reni Nursyanti Reni Nursyanti Reni Nursyanti Reynaldy Gimnastiar Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Setiana, Elia Silvana Anggraeni, Zulmeida Sophian Ramadhan Suci Fitriani Setiawan Tarsinah Sumarni Tiara Permata Hati Titan Parama Titan Parama Yoga Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Venia Restreva Danestiara Wulandari Wulandari Yoga Rizki Rahmawan Zein Suna Arfigan Said