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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) 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 Jurnal Abdimas Kartika Wijayakusuma 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) Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Jurnal Pengabdian Tri Bhakti International Journal of Computer Technology and Science Bulletin of Intelligent Machines and Algorithms
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 Other

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Pengembangan Ekosistem Pariwisata Terpadu Melalui Kolaborasi Multi-Sektor di Pulau Belitung Erpurini, Wala; Leonandri, Dino Gustaf; Alamsyah, Nur; Muhiban, Ayi
Jurnal Abdimas Kartika Wijayakusuma Vol 6 No 2 (2025): Jurnal Abdimas Kartika Wijayakusuma
Publisher : LPPM Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jakw.v6i2.885

Abstract

Pulau Belitung memiliki potensi pariwisata yang sangat besar, baik dari keindahan alam, keunikan budaya, maupun keramahtamahan masyarakatnya. Keberhasilan Belitung menjadi tuan rumah dalam rangkaian kegiatan G20 telah memberikan dampak positif terhadap citra dan pengakuan internasional atas kekuatan sektor pariwisata daerah ini. Namun demikian, untuk menjaga kesinambungan dan daya saing pariwisata Belitung pasca- G20, diperlukanpengembangan ekosistem pariwisata yang terpadu dan berkelanjutan melalui kolaborasi antar sektor. Kegiatan pengabdian masyarakat ini bertujuan untuk memfasilitasi diskusi dan kerja sama antara pemerintah, pelaku industripariwisata, komunitas lokal, pelaku seni budaya, dan sektor pendukung lainnya guna menyusun strategi pengembanganpariwisata secara holistik. Metode yang digunakan meliputi focus group discussion (FGD), pelatihan singkat, dan pemetaan potensi lokal. Hasil kegiatan menunjukkan bahwa sinergi antar sektor sangat diperlukan untuk membentuk ekosistem yang mendukung penguatan tema pariwisata Belitung, peningkatan kualitas layanan, serta pengembangan event-event wisata berbasis kearifan lokal. Penguatan kolaborasi ini diharapkan dapat menjadi fondasi dalam membangun pariwisata Belitung yang kompetitif, inklusif, dan berkelanjutan di masa mendatang.
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.
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.
Sentiment Analysis Model for the Free Lunch Program in Indonesia on Twitter (X) Based on Machine Learning Amelia Tifany Dewi; Nur Alamsyah; Arnold Ropen Sinaga
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i3.18

Abstract

Social media has become a primary platform for the public to voice their opinions on various public policies, including the free lunch program initiated by the Indonesian government. This study aims to analyze public sentiment toward this program through the Twitter (X) platform by utilizing machine learning algorithms. Data collection was conducted from January 2025 to June 2025, with a total of 2,045 comments successfully gathered. Sentiment labeling was performed manually, and only positive and negative sentiments were considered. The data, in the form of relevant comments, were pre-processed and classified into positive and negative sentiments. Three algorithms used in this study are Support Vector Machine (SVM), Naïve Bayes, and Random Forest. Evaluation was performed using data splitting schemes of 70:30 and 80:20, along with 5-fold cross-validation. Unlike previous studies, which primarily focused on sentiment analysis of general social issues or specific topics without emphasizing public policy, this study specifically investigates the public's sentiment regarding a government policy (the free lunch program) and compares the performance of different machine learning models. The results of the study show that the Random Forest model outperformed SVM and Naïve Bayes, achieving an accuracy of 89.41% with a standard deviation of 0.0138. Meanwhile, SVM achieved an accuracy of 88.96% and Naïve Bayes 88.72%. These findings suggest that Random Forest is the most optimal and consistent model for sentiment analysis of public policies on social media.
SHAP-ALE: A Novel Approach to Explainability in Mental Health Prediction using RFR Nur Alamsyah; Budiman Budiman; Wala Erpurini; Hani Fitria Rahmani
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3087

Abstract

The prediction of mental health disorders, such as depression, has become increasingly crucial as global mental health concerns continue to rise. In this study, the predictive task specifically focuses on estimating the prevalence of depressive disorders as the primary target variable, while other mental health conditions such as anxiety and schizophrenia are treated as explanatory features. While machine learning (ML) models, like Random Forest Regressor (RFR), offer high accuracy in predictions, their interpretability remains a challenge. This research introduces SHAP-ALE, an innovative hybrid explainability framework that integrates SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE) to address this gap. SHAP provides both global and local insights into feature contributions, while ALE visualizes feature-target relationships, mitigating bias caused by feature correlations. Using a dataset comprising various mental health disorders and demographic factors, the dataset used in this study was obtained from Kaggle and consists of 6,421 records covering multiple mental health disorder indicators and demographic attributes across different regions and years. RFR model demonstrated robust predictive performance with an R² score of 0.9984 and a Mean Squared Error (MSE) of 0.0016. SHAP analysis revealed that features such as schizophrenia and anxiety disorders significantly influenced predictions, while ALE identified nonlinear relationships between these features and depression prevalence. The combined insights from SHAP and ALE enhance the interpretability of the model, enabling better understanding of the complex factors underlying mental health disorders. This study highlights the contribution of SHAP-ALE as a hybrid explainability framework that integrates local and global interpretability, enabling a more comprehensive understanding of feature interactions and non-linear effects beyond the capabilities of individual methods.
Enhancing Breast Cancer Diagnosis with Ensemble Learning: Leveraging Convolutional Neural Networks and Pretrained Models through Averaged Predictions Elia Setiana; Reni Nursyanti; Nur Alamsyah; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.26

Abstract

Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.
Co-Authors Acep Hendra Aji Gautama Putrada Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Almira Nurchawilah Amelia Tifany Dewi Arnold Ropen Sinaga Arnold Ropen Sinaga Ayi Mohamad Sudrajat Budi Sulistiyo Jati Budiman Danestiara, Venia R Della Octa Irmulvani Devi Silaen Dirham Triyadi Eli Nofita Sari Elia Setiana Erpurini, Wala Fauzan, Mohamad Nurkamal Habibi, Chairul Hani Fitria Rahmani 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 Muhiban, Ayi Mutaqin Akbar Nayla Nurul Azkiya Nur Imam Febrianto Nursyanti, Reni PARAMA YOGA, TITAN R Yadi Rakhman Alamsyah 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 Titan Parama Yoga Tito Sugiharto Tutik Ultsa Rahmatika Valencia Claudia Jennifer Venia Restreva Danestiara Wala Erpurini Wala Erpurini Wala Erpurini Wia Handayani Yeni