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INDONESIA
Bulletin of Network Engineer and Informatics (BUFNETS)
Published by GWEX NET PUBLISHER
ISSN : 29874858     EISSN : 29868017     DOI : https://doi.org/10.59688/bufnets
Core Subject : Science,
Computer Science & IT
The Journal invites original articles and is not simultaneously submitted to another journal or conference. Scopes: Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Computer Graphics, Virtual Reality, Data and Cyber Security. Computer Network: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Fuzzy Sensor and Network, Internet of Things, Data Communication and Networking.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 72 Documents
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A DECISION SUPPORT SYSTEM FOR SELECTION OF SOCIAL WELFARE ASSISTANCE RECIPIENTS IN BENGKAYANG REGENCY USING WEIGHTED PRODUCT ALGORITHM Yuliana Yuliana; Christian Cahyaningtyas; Elvis Pawan
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/732115

Abstract

PPKS is a priority group of recipients of social welfare programs based on Law Number 11 of 2009. The implementation of social services for individuals and groups requires high accuracy in identifying aid recipients according to the standards of the Minister of Social Affairs Regulation Number 16 of 2019. This study aims to build a Decision Support System (DSS) to assist the Bengkayang Regency TKSK team in determining the priority of aid recipients objectively. The method applied is Weighted Product (WP) involving 5 main criteria. The research stages include data collection through observation, basic data design, and system implementation. The results of sample analysis on the data show that the highest final value (V) was obtained by the category of families with social psychological problems (0.271738), followed by the category of neglected age (0.208379), the poor (0.201281), and people with disabilities (0.162531). The highest ranking produced by the system places the alternatives Salamah, Bulhaji, Satem, Keisha, and Sensius as the main priority. The implementation of this data-based system has proven to be able to accurately align manual calculation results with the system, so that it can be an effective consideration instrument for making decisions regarding the selection of social assistance.
PERFORMANCE ANALYSIS OF NAÏVE BAYES CLASSIFIERS BASED ON THE INFORMATION GAIN-BASED FEATURE SELECTION WITH MULTICOLLINEARITY ANALYSIS Luh Putu Risma Noviana Risma; I Gede Aris Gunadi; I Made Gede Sunarya
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/732114

Abstract

This study aims to analyze the performance of the Naïve Bayes Classifier algorithm by comparing several feature selection methods, namely Information Gain-based feature selection, Multicollinearity-based feature selection, and a combination of Information Gain and Multicollinearity. The dataset used in this study consists of 337 toddler stunting cases obtained from Kintamani I and VI Public Health Centers. The experiment was conducted using four testing scenarios: (1) Naïve Bayes Classifier without feature selection, (2) Naïve Bayes Classifier with Information Gain feature selection, (3) Naïve Bayes Classifier with Multicollinearity feature selection, and (4) Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity feature selection. All experiments used a data split of 70% training data and 30% testing data, while model performance was evaluated using a confusion matrix. In the Information Gain feature selection stage, several features achieved the highest gain values, namely BPJS with a gain value of 1.0, immunization with a gain value of 1.0, age with a gain value of 0.842, maternal pregnancy history with a gain value of 0.791, and smoking habits with a gain value of 0.756. These features were retained in the final combined model because they contributed the most to the stunting classification process. In addition to improving predictive performance, the combination of Information Gain and Multicollinearity was also able to reduce feature redundancy, resulting in a more stable classification model. The results showed that the accuracy of the Naïve Bayes Classifier without feature selection was 90.10%, the Naïve Bayes Classifier with Information Gain feature selection achieved 95.05%, the Naïve Bayes Classifier with Multicollinearity feature selection achieved 93.07%, and the Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity achieved the highest accuracy of 96.04%. These findings indicate that the combination of Information Gain and Multicollinearity produced the best performance among all tested methods. In addition, a coefficient of determination (R Square) test was conducted using SPSS, resulting in a value of 0.577, indicating that 57.7% of stunting classification was influenced by independent variables such as age, BPJS, immunization, smoking habits, and maternal pregnancy history, while the remaining 42.3% was influenced by other factors outside the scope of this study. The results also indicate that the Naïve Bayes algorithm combined with Information Gain feature selection and multicollinearity testing can be used as a stable and effective approach for early stunting classification to support decision-making in public health services.
Application of K-Means Clustering Algorithm for Grouping Posyandus Based on Toddler Demographic Density to Optimize Aid Distribution Muhamad Andre Wira Aditya; Melda Agarina
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/736979

Abstract

This study investigates the application of the K-Means clustering algorithm to classify Posyandu service areas based on toddler demographic characteristics, with the goal of supporting more efficient planning and targeted distribution of nutritional aid. Using a dataset consisting of 855 toddler records from the Puskesmas Braja Caka region, data preprocessing steps—including one-hot encoding, handling of categorical locality attributes, and Z-score standardization—were performed to ensure consistent feature representation. The Elbow Method indicated that six clusters provided the optimal balance between compactness and interpretability. The resulting cluster distribution comprised 133 toddlers in Cluster 0, 75 in Cluster 1, 151 in Cluster 2, 238 in Cluster 3, 125 in Cluster 4, and 133 in Cluster 5. Further analysis revealed distinct demographic characteristics: Clusters 0 and 2 had higher median ages, Cluster 3 displayed the widest age variability, and Cluster 4 showed the highest proportion of male toddlers. PCA visualization confirmed a clear separation among clusters, while boxplots illustrated meaningful differences in age distribution. These findings demonstrate that K-Means clustering effectively uncovers demographic patterns that can guide policymakers in allocating resources more accurately and prioritizing interventions for communities with higher toddler density or greater nutritional risk. As an actionable recommendation, health authorities are advised to prioritize nutritional supplementation and intensified monitoring in Cluster 3 (highest density, 238 toddlers) and Cluster 4 (male-dominant, youngest age group), while deploying tailored growth-monitoring programs in Clusters 0 and 2 where older toddlers are concentrated. This approach strengthens data-driven decision-making for Posyandu operations.
E-ARCHIVE INFORMATION SYSTEM FOR COOPERATION DOCUMENTS AT THE COMMUNICATION AND INFORMATICS OFFICE OF EAST LAMPUNG REGENCY Fuad Arif Baharudin; Dona Yuliawati
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/736985

Abstract

The development of information technology encourages government agencies to carry out digital transformation in archive management to increase administrative efficiency and effectiveness. The Communication and Information Service of East Lampung Regency still faces obstacles in the management of cooperation documents that are carried out manually, such as difficulties in searching archives and the risk of data loss. This research aims to design and build a web-based Cooperation Document E-Archive Information System as a solution to these problems. The system development method used is the Prototype method, which allows direct interaction between developers and users in the system development process. The system is built using PHP 8.1 with the Model-View-Controller (MVC) architecture and MySQL 8.0 as the database. Black-box testing conducted on 25 test cases achieved a 100% success rate, confirming that all functional requirements were met without errors. User Acceptance Testing (UAT) yielded an acceptance score of 92%, indicating a high level of user satisfaction and system usability. These results demonstrate that the developed system effectively simplifies the document management process—from storage and retrieval to structured archival grouping, thereby improving work efficiency and supporting the implementation of the Electronic-Based Government System (SPBE) in the relevant agencies.
PREDICTION MARKET RISK : A HYBRID LSA-MACHINE LEARNING FRAMEWORK FOR FINANCIAL SENTIMENT CLASSIFICATION Aliffia Putri Dito; Uzer Tarmizi; Supriyanto
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/736997

Abstract

The dynamics of the financial market are heavily influenced by public perception reflected in economic news. Negative sentiment in news is often an early signal of market volatility. However, the high dimensionality and semantic ambiguity of financial text data pose challenges for automatic classification. This research implements a hybrid method of Latent Semantic Analysis (LSA) and Machine Learning for economic news sentiment classification. Using the Financial PhraseBank dataset, the text is processed through pre-processing and TF-IDF feature extraction before undergoing dimensionality reduction using LSA with 300 latent component via Singular Value Decomposition. The experimental result demonstrate that the Support Vector Machine (SVM) algorithm with an RBF kernel provides the best performance with an accuracy of 88.2% and an F1-Score of 85.8%. These findings prove that the integration of latent space in LSA effectively captures the semantic context of economic news, allowing it to be used as a reliable instrument for early market risk mitigation.
THE INFLUENCE OF AI VIDEO ON THE SPREAD OF HOAXES ON SOCIAL MEDIA AMONG GENERATION Z IN INDONESIA WITH THE TECHNOLOGY THREAT AVOIDANCE THEORY (TTAT) Silvia Antana Sukma; Dedy Setiawan
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/738441

Abstract

The spread of Artificial Intelligence (AI)-generated hoax videos on social media is increasing and potentially influences Generation Z behavior in disseminating false information in Indonesia. This study aims to analyze the effects of perceived threat, safeguard effectiveness, safeguard cost, self-efficacy, and digital literacy on avoidance motivation and their impact on hoax dissemination behavior using the Technology Threat Avoidance Theory (TTAT) approach. This study employed a quantitative method with purposive sampling involving 250 Generation Z respondents in Indonesia. The sample size was determined based on Hair Jr.’s rule of thumb. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that perceived threat, safeguard cost, and self-efficacy have a positive and significant effect on avoidance motivation, while safeguard effectiveness has no significant effect. Avoidance motivation significantly affects hoax dissemination behavior. In addition, digital literacy significantly influences avoidance motivation and hoax dissemination behavior and also has an indirect effect through avoidance motivation as a mediating variable. This study demonstrates that TTAT factors and digital literacy play an important role in shaping Generation Z’s behavior in responding to AI-based hoaxes on social media. The practical implication highlights the importance of improving digital literacy and awareness of AI-generated hoaxes to reduce the spread of false information.
CLASSIFICATION OF BALI SONGKET USING A CROSS-MODAL RETRIEVAL METHOD Marcellino Immanuel Ndoki; I Gede Iwan Sudipa; Ketut Laksmi Maswari; Made Suci Ariantini; Ni Wayan Jeri Kusuma Dewi
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/738143

Abstract

The development of information technology can be utilized to support the preservation of traditional cultural heritage, including Balinese songket, which possesses diverse motifs and high cultural value. However, the influence of modernization has reduced younger generations’ understanding of Balinese songket motifs. Based on a survey involving 21 respondents aged 20–29 years, 47.6% showed low understanding of Balinese songket motifs, 23.8% had moderate understanding, and 23.8% did not understand the motifs at all. This condition indicates the existence of a cultural knowledge gap that requires preservation efforts through digital technology. Therefore, this study aims to develop a Cross-Modal Retrieval system for Balinese songket motif recognition using a Deep Learning approach.The proposed system utilizes a combination of Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) to model the semantic relationship between visual and textual data. The dataset consisted of 108 image-caption pairs divided into 35 Balinese songket motifs. In this study, ResNet was employed as a visual feature extractor to capture motif patterns, textures, and color characteristics from images, while BERT was used to generate contextual textual embeddings from caption descriptions. Both visual and textual embeddings were projected into a shared embedding space to enable text-to-image and image-to-text retrieval through similarity matching.Prior to training, image preprocessing was performed through cropping, resizing, and image augmentation techniques such as flipping and rotation to improve data variability. Text preprocessing included lowercasing and tokenization to standardize textual input. The model was trained for 300 epochs using a batch size of 16, learning rate of 3e-5, and AdamW optimizer. Experimental results obtained a train loss of 0.4165, validation loss of 2.0716, and Recall@K of 91.67%. These results indicate that the proposed model successfully generated discriminative embedding representations and achieved high retrieval accuracy in matching relevant image-text pairs.Although the model achieved strong retrieval performance, the gap between train loss and validation loss indicates limitations in generalizing to unseen data. This issue is influenced by the high visual similarity among several Balinese songket motifs, particularly in geometric patterns, woven textures, and color compositions, which reduce inter-class embedding separation. Nevertheless, the experimental results demonstrate that the Cross-Modal Retrieval approach effectively integrates visual and textual information for Balinese songket classification and retrieval tasks.In conclusion, the proposed system shows strong potential as an interactive digital medium for preserving and introducing Balinese songket motifs, especially for younger generations. Future work can focus on expanding the dataset, improving semantic caption quality, applying more diverse augmentation strategies, and optimizing fine-tuning, feature fusion, and attention mechanisms to enhance embedding quality and reduce the semantic gap between image and text modalities.
COMPARISON OF CNN, FASTER R-CNN, AND MASK R-CNN FOR BRAIN TUMOR DETECTION USING MRI IMAGES Jose Julian Hidayat
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/737266

Abstract

Detecting brain tumours from MRI images remains a difficult challenge due to differences in tumour appearance and the necessity for high diagnostic precision. This study looks at three deep learning algorithms with varying levels of complexity: CNN as a baseline classification model, Faster R-CNN as a region-based detection method, and Mask R-CNN, which combines detection with segmentation. The dataset is divided into four categories: glioma, meningioma, pituitary, and non-tumor. The experimental results show that more advanced structures tend to perform better. The CNN model achieves an accuracy of 0.8900000000 with an F1-score of 0.8871239227, although it has problems in capturing specific tumour characteristics. Faster R-CNN enhances detection capability, with an F1-score of 0.9053533622 and an accuracy of 0.9068750000, especially when recognising tumour locations more precisely. Mask R-CNN achieves the best performance, with an accuracy of 0.9300000000 and an F1-score of 0.9288687706, indicating more consistent results across all classes. Mask R-CNN has the advantage of capturing both object position and structural features via segmentation, hence minimising misclassification. These results imply that integrating detection and segmentation is critical for improving medical image analysis. As a result, Mask R-CNN provides a more reliable method for detecting brain tumours using MRI data.
Comparative Analysis of Machine Learning and Deep Learning Approaches for Renewable Energy Output Forecasting: An Evaluation of XGBoost, LightGBM, and LSTM Models Ayang Kinasih; Iqbal May Aryanto; Tomy Pratama Zuhelmi; Syaiful Mansur; Ayu Sintianingrum; Eko Hari Tiarto
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/737434

Abstract

The increasing integration of renewable energy into smart grids requires accurate forecasting to maintain grid stability and optimize energy management. This study compares the performance of three forecasting models—XGBoost, LightGBM, and Long Short-Term Memory (LSTM)—for predicting solar photovoltaic (PV) output, wind power output, and total renewable energy generation. To improve forecasting capability, the study applied feature engineering techniques, including time-based variables, rolling averages, and lag features to capture temporal dependencies within the dataset. An 80/20 chronological train-test split was used to maintain time-series integrity. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). Results show that gradient boosting models significantly outperformed the LSTM model across all forecasting targets. LightGBM achieved the best performance for solar PV forecasting (RMSE: 3.27, R²: 0.987) and total renewable energy prediction (RMSE: 4.66, R²: 0.988), while XGBoost produced the highest accuracy for wind power forecasting (RMSE: 3.26, R²: 0.987). In contrast, the LSTM model generated negative R² values, indicating poor predictive performance. These findings demonstrate that gradient boosting methods are highly effective for structured renewable energy forecasting datasets and offer strong potential for intelligent smart grid applications. The forecasting framework is designed to support IoT-enabled smart grid systems, where renewable energy data are continuously collected through distributed sensors and transmitted via intelligent communication networks. The proposed models can assist real-time energy monitoring, edge-based prediction, and intelligent network management for modern smart grid infrastructures.
IOT-BASED SMART HELMET PROTOTYPE FOR FIELD SECURITY SAFETY IN OIL PALM PLANTATIONS Rasyid Abdullah Habib Syaban; Andi Prayogi; Muhammad Akbar
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/737748

Abstract

Oil palm plantation security officers face serious risks including occupational accidents, physical assaults during Fresh Fruit Bunch (FFB) theft incidents, and communication limitations in vast and remote field areas. Existing surveillance systems remain manual, lacking real-time position tracking, anomaly detection, and visual verification capabilities. This research develops an IoT-Based Smart Helmet Prototype integrating a GPS NEO-M8N module for real-time location tracking, an IMU MPU6050 sensor for four-level impact classification (NONE/LOW/MEDIUM/HIGH), and dual OV3660 cameras on two ESP32-S3 CAM WROOM N16R8 microcontrollers for simultaneous front and rear RTSP video streaming. All data is transmitted over a 4G modem to a VPS running Mosquitto MQTT broker, MediaMTX media server, Redis cache, Node.js REST API, and a Next.js web dashboard with interactive Leaflet mapping. Functional testing at the Institut Teknologi Sawit Indonesia (ITSI) Experimental Plantation demonstrated a GPS coordinate error of less than 0.0015%, IMU impact classification consistency of 98% across 50 trials, streaming latency of 3–5 seconds via HLS, MQTT average latency of 28 ms, and panic button response under 1 second. The system provides a viable IoT solution for improving field security monitoring in oil palm plantations.

Page 7 of 8 | Total Record : 72

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