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All Journal Nuansa Informatika Telematika JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization 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 Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom International Journal of Advances in Data and Information Systems EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Nusantara Journal of Dinda : Data Science, Information Technology, and Data Analytics Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika AJAD : Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Business Analytics (IJBA) Formosa Journal of Applied Sciences (FJAS) Paradigma Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Pengabdian Tri Bhakti International Journal of Computer Technology and Science
Nur Alamsyah
Fakultas Ilmu Komputer PASIM
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Kaunang, Valencia Claudia Jennifer; Alamsyah, Nur; Nursyanti, Reni; Budiman, Budiman; Danestiara, Venia R; Setiana, Elia
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6647

Abstract

Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems. 
Multi-Task Learning for Traffic Sign Recognition using Multi-Scale Convolutional Neural Networks Akbar, Mutaqin; Susilawati, Indah; Jati, Budi Sulistiyo; Alamsyah, Nur
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1406

Abstract

Traffic signs are an essential component of road infrastructure. According to the Department of Transportation, Indonesia has over 300 distinct traffic signs, categorized based on their functions and purposes. TSR systems have been widely integrated into various intelligent transportation technologies, such as Driver Assistance Systems (DAS), Advanced Driver Assistance Systems (ADAS), and Autonomous Driving Systems (ADS). The output generated by TSR serves as a critical input for DAS, ADAS, ADS, and other intelligent systems. This article presents a CNN-based classification for traffic sign recognition using multi-task learning (MTL), focusing on traffic signs in Indonesia. The dataset was collected from direct capture with the help of a cellphone camera, indirect capture by utilizing screenshots on a digital map application, and they are captured from several different angles, during the day and at night. The proposed CNN architecture incorporates multi-scale within an MTL framework. The use of a multi-scale approach will hopefully enhance the model’s ability to recognize traffic signs in varied and complex environments. And the integration of MTL will enable the model to handle multiple related tasks concurrently, sharing learned features across tasks. During the training stage, the MS-CNN outperformed a standard CNN model by demonstrating lower initial loss, higher starting accuracy, and achieving 100% accuracy by the 8th epoch with a minimal error rate of just 0.003. In the testing stage, the model achieved exceptional results, as shown by the confusion matrix, it successfully classified all traffic sign types (10 classes) and accurately categorized each sign into one of two categories—warning or prohibition. All performance metrics, including precision, recall, and F1-score, reached 100% for both output tasks, confirming the robustness and reliability of the model.
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.
Etika Digital Dan Penyebaran Hoaks Sinaga, Arnold Ropen; Alamsyah, Nur; Hermawan, Arief Karditya
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edition Oktober)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i2.150

Abstract

The development of digital technology has driven significant transformation in the agribusiness sector, creating new opportunities while also presenting challenges. The background of this study stems from the urgent need to enhance the efficiency and competitiveness of the agricultural sector through the utilization of digital technology. The aim of this research is to identify the challenges and explore the opportunities in implementing digital agribusiness in Indonesia. A descriptive qualitative approach was employed, with data collected through literature review and interviews with agribusiness practitioners and technology experts. The findings indicate that digitalization opens up opportunities in terms of market access, increased productivity, and distribution efficiency. However, challenges such as limited infrastructure, low digital literacy among farmers, and unequal access to technology remain major obstacles. In conclusion, to optimize the potential of digital agribusiness, a collaborative strategy involving the government, private sector, and educational institutions is needed to create an inclusive and sustainable ecosystem.
Pengembangan Ekowisata Berbasis Bambu dan Teknologi Informasi untuk Mendukung Restorasi Lahan Perbukitan Melalui Partisipasi Masyarakat Lokal Erpurini, Wala; Leonandri, Dino Gustaf; Alamsyah, Nur
Jurnal Pengabdian Tri Bhakti Vol 7 No 2 (2025): Jurnal Pengabdian Tri Bhakti
Publisher : Lembaga Pengabdian kepada Masyarakat Universitas Langlangbuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70825/jptb.v7i2.2379

Abstract

Eco-tourism development in hilly areas often encounters environmental degradation and limited community capacity in tourism management and digital promotion. This program aims to enhance bamboo-based eco-tourism by empowering local communities through information technology to support land rehabilitation. The methods included participatory approaches, bamboo planting for land restoration, construction of bamboo-based tourism facilities, and digital literacy training covering online marketing, tourism information systems, and QR-code-based educational media. The results indicate that bamboo cultivation strengthens soil stability, reduces erosion risk, and improves the visual appeal of the tourism area. Furthermore, improved digital skills enabled communities to expand destination promotion and increase tourist interest through digital platforms. These findings suggest that integrating ecological conservation with information technology can enhance the competitiveness of local eco-tourism, promote community economic independence, and support sustainable, community-based tourism management.
Co-Authors Acep Hendra Aji Gautama Putrada Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Arnold Ropen Sinaga Budi Sulistiyo Jati Budiman Danestiara, Venia R Della Octa Irmulvani Devi Silaen Dirham Triyadi Eli Nofita Sari Erpurini, Wala Fauzan, Mohamad Nurkamal Habibi, Chairul Hani Fitria Rahmani Hasan Nuraripin Hermawan, Arief Karditya Ikke Dian Oktaviani Indah Susilawati Ismi Nur Muhamad Jennifer Kaunang, Valencia Claudia Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Leonandri, Dino Gustaf Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Andriansyah Nurcahya Muhamad Nurkamal Fauzan Muhammad Yusup Iskandar Mutaqin Akbar Nur Imam Febrianto PARAMA YOGA, TITAN R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Rijwan Rijwan Rimbun Kencana RIO ANDRIYAT KRISDIAWAN Setiana, Elia Sidik Prabowo Silvana Anggraeni, Zulmeida Syafrial Fachri Pane Tiara Nurhasanah Titan Parama Titan Parama Yoga Tito Sugiharto Tutik Ultsa Rahmatika Valencia Claudia Jennifer Venia Restreva Danestiara Wala Erpurini Wala Erpurini Wia Handayani Yeni