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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) 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 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 Journal of Computing Innovations and Emerging Technologies Bulletin of Intelligent Machines and Algorithms Jurnal Informatika TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
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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

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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.
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.
Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection Budiman Budiman; Reni Nursyanti; R Yadi Rakhman Alamsyah; Imannudin Akbar
International Journal of Quantitative Research and Modeling Vol. 1 No. 3 (2020): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v1i3.72

Abstract

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
Information System Security Audit SIMKA(Sistem Informasi Kearsipan) at Badan Pendapatan Daerah Jawa Barat Kota Bandung III Using COBIT 5 Framework and Standard IS0/IEC 27002 Suci Fitriani Setiawan; Titan Parama Yoga; Budiman Budiman
International Journal of Quantitative Research and Modeling Vol. 4 No. 3 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i3.499

Abstract

One of the main problems for an agency or a company is the security of information systems. High security is needed to maintain the confidentiality and misuse of information within the organization. To improve the security of business operations and the quality of information technology resources, it is necessary to evaluate to op Badan Pendapatan Daerah Jawa Barat Kota Bandung III, namely SIMKA BAPENDA whose function is to collect data on PKB (pajak kendaraan bermotor) and BBNKB (bea balik nama kendaraan bermotor) which manage the data computerized. The purpose of this study is to carry out a security audit of SIMKA BAPENDA at the Badan Pendapatan Daerah Jawa Barat Kota Bandung III using the COBIT 5 framework and ISO/IEC 27002 to document audit findings of the information system audit of the Badan Pendapatan Daerah Jawa Barat Kota Bandung III to make a report on the audit results. Based on the results of research that has been done through interviews and questionnaires using framework and using the APO13 and DSS05 sub domains, the results show that the Capability Existing is at level 1 while Capability Level is level 3 so the Capability Gap is 2.
Co-Authors Acep Hendra Aditya Gunaldhi Aggi Panigoro Sarifiyono Ahmad Fauzi Ramadhan Aidha Salsa Billa Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Alif Januantara Prima Almira Nurchawilah Amos Duan Nugroho Anto Widianto Anugrah Adiwilaga Arafat Febriandirza Ari Rizki Fauzi Artika Arista Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Chery Cardinawati Sitohang Danestiara, Venia R Dani Rizky Zaelani Darsiti Darsiti Deandy Zahran Vierdansyah Deden Pradeka Devi Aprianti Rimadhani Agustini Dhika Wdiyanto Dirham Triyadi Dirham Triyadi Esa Prakasa Fachrizal Ardiansyah Fahmi Abdullah Fahreja Ramadhan Fauzi Ramadhan, Ahmad Fikri Rizqillah Hasani Fitri Kinkin Gelar, Trisna Gunthur Bayu Wibisono Habibi, Chairul Hamzah, Encep Hani Fitria Rahmani Hasan Nuraripin Henki Bayu Seta Hernawan, Kartika Nursyabanita Ihsan AlFauzi 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 Raka Deny Abdi Putra Rakhman Alamsyah, Rd. Yadi Rd. Yadi Rakhman Alamsyah Rd. Zidni Rizan Al-Zhahir Yanuar Reni Nursyanti Reni Nursyanti Reni Nursyanti Reynaldy Gimnastiar Rezza Novian Noor Rochmat Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Setiana, Elia Silvana Anggraeni, Zulmeida Sophian Ramadhan Suci Fitriani Setiawan Syifa Aqila Humaira Tarsinah Sumarni Tiara Permata Hati Titan Parama Titan Parama Yoga Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Valencia Claudia Jennifer Kaunang Venia R Danestiara Venia Restreva Danestiara Wala Erpurini Wala Erpurini Wulandari Wulandari Yoga Rizki Rahmawan Zein Suna Arfigan Said