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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 1,045 Documents
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Deteksi Dini Stunting pada Balita Menggunakan 1D Convolutional Neural Network (1D-CNN) pada Data Antropometri Numerik Vidry Anggelia Siregar; Rusliyawati Rusliyawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9384

Abstract

Stunting remains a major public health challenge in Indonesia, with a national prevalence of 21.6%. Its impact extends beyond impaired physical growth to affect cognitive development and long-term productivity. Early detection is typically performed through manual anthropometric measurements and Z-score calculations, which are relatively impractical and prone to computational errors, especially in resource limited settings. This study proposes a one-dimensional convolutional neural network (1D-CNN) based approach to detect stunting in children under five using numerical anthropometric data of age, sex, and height without manual feature engineering. The model was evaluated on 120,999 samples and achieved a recall of 99.3%, with only 4 out of 552 stunting cases going undetected, demonstrating strong ability to minimize false negatives in the context of public health screening. In comparison, the Random Forest model achieved 99.9% accuracy and an F1-score of 98.2%, demonstrating excellent overall classification performance. Nevertheless, 1D-CNN offers architectural advantages through automatic representation learning based on one-dimensional signal structures, making it more adaptable to the inclusion of sequential variables, the integration of longitudinal growth sensor data, and the development of future IoT based monitoring systems. Therefore, the proposed approach is not only competitive in detection performance but also provides greater scalability and flexibility for the continued development of digital screening systems at the primary healthcare level.
Comparison of Clustering Algorithms for Analyzing the Impact of Conflict on Poverty and Inflation M Raykah Alam Ramadan; Dhio Pratama Wiransyah; Satria Ramadhani; Rayya Ramadhan Simangunsong; Ken Dhita Tania; Alsella Meiriza; Ahmad Rifai
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9512

Abstract

Armed conflict can have significant impacts on the social and economic conditions of a region, particularly on poverty levels and inflation. This study aims to analyze the impact of conflict on key economic indicators using a Knowledge Management System (KMS) approach and to compare the performance of clustering algorithms in identifying underlying data patterns. The research applies clustering analysis by comparing K-Means, DBSCAN, and Hierarchical Clustering algorithms to group data based on similarities in economic characteristics. The dataset used in this study consists of several indicators, including poverty levels before and during conflict, extreme poverty rates, inflation rates, GDP changes, and currency devaluation. Data preprocessing techniques such as normalization are applied to ensure comparability among variables. The evaluation of clustering performance is conducted using Silhouette Score and Davies–Bouldin Index to determine the most effective algorithm. The results show that clustering methods are able to identify distinct grouping patterns of regions based on the level of conflict impact on economic conditions. Among the evaluated algorithms, DBSCAN demonstrates superior performance in handling complex and uneven data distributions. The analysis also indicates a consistent tendency for poverty and inflation to increase during periods of conflict, highlighting the economic vulnerability of affected regions. Furthermore, the integration of clustering results into a Knowledge Management System enables the transformation of analytical outputs into structured knowledge that can support data-driven decision making. These findings are expected to contribute to the development of more effective economic policies and analytical frameworks in conflict-affected areas.
Perbandingan Model LSTM dan Temporal Fusion Transformer untuk Prediksi Harga Emas Nilasari Nilasari; Rujianto Eko Saputro; Giat Karyono
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8204

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Temporal Fusion Transformer (TFT) in forecasting daily gold prices using multivariate data. The dataset was obtained from Kaggle (2005–2024) and includes ten key economic variables, such as stock indices, the US Dollar Index, crude oil prices, silver prices, and 10-year Treasury yields. The research stages consisted of data preprocessing through missing value interpolation, Z-score-based outlier clipping, normalization with MinMaxScaler on the training set, and data transformation tailored to each model architecture. Model performance was evaluated using four regression metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R², and Mean Absolute Percentage Error (MAPE). Results indicate that TFT outperforms LSTM across all metrics, achieving RMSE of 19.35, MAE of 14.51, R² of 0.9906, and MAPE of 0.74%. The Diebold–Mariano (DM) test yielded a p-value of 0.02, confirming that the performance difference between the two models is statistically significant. These findings highlight the importance of the attention mechanism and variable selection network in TFT for enhancing multivariate predictive accuracy. However, this study is limited by the exclusion of non-economic external variables such as market sentiment and geopolitical factors. Future research should incorporate additional variables and explore hybrid approaches to achieve more robust gold price forecasting.
Deteksi Manipulasi Citra Medis MRI Menggunakan Watermarking Least Significant Bit dengan Autentikasi SHA-256 dan ECDSA Y Noven Dhimas Nugroho; Wildanil Ghozi
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.8967

Abstract

Medical image security is a crucial aspect of maintaining the integrity and authenticity of diagnostic data, particularly during digital transmission and storage processes that are vulnerable to manipulation. Minor modifications to pixels can lead to misdiagnosis; thus, protection methods are required to verify integrity without compromising visual quality. However, previous studies still face a trade-off between system complexity, computational efficiency, and tamper detection capabilities. This research aims to develop a medical image watermarking method capable of efficiently detecting changes in diagnostic areas with minimal distortion. The proposed method integrates automated Region of Interest (ROI) segmentation based on Otsu thresholding, 1-LSB watermark embedding in the Region of Non-Interest (RONI), and authentication based on SHA-256 and ECDSA digital signatures. The primary contribution of this study is an integrated framework that combines automated segmentation and cryptographic authentication to maintain image integrity without sacrificing clinical information. Experimental results demonstrate that the method maintains high image quality, with an average PSNR of 75.04 dB, low MSE, and the highest SSIM of 0.9999975. This performance is achieved through a small payload (99 bytes) that modifies only 1.21% of pixels in the RONI. In terms of efficiency, the method exhibits relatively fast computational performance with average embedding and extraction times of 0.14 seconds and 0.095 seconds, respectively, on 256×256 pixel images using an AMD Ryzen 5 5600H and 16 GB RAM. The system is capable of detecting ROI manipulation, identifying global payload damage, and remains valid under RONI changes, although it remains limited against large-scale manipulation due to the fragile nature of the LSB technique.
Analisis Perbandingan Metode Edas Dan Aras Dalam Pemilihan Platform Freelance Terbaik Untuk Pekerja Jarak Jauh (Remote Worker) Rexlicky Verdhika Sagatha; Zaenal Abidin
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9554

Abstract

The trend of remote workers has increased significantly, driving the high adoption of global freelance platforms. However, the diversity of policies regarding service fees, withdrawal limits, and levels of competition across platforms often makes it difficult for beginner remote workers to determine the most optimal choice. This study aims to analyze and compare the recommendation results of a Decision Support System (DSS) using the Evaluation based on Distance from Average Solution (EDAS) method and the Additive Ratio Assessment (ARAS) method in selecting freelance platforms. The study evaluates five platform alternatives (Upwork, Fiverr, Fastwork, Freelancer, and Projects.co.id) using a mixed-methods approach that combines factual platform policy data (Administrative Fee Deduction and Minimum Withdrawal) with user perception data (UI/UX, Security, and Level of Competition). The analysis results show a high level of consistency between the two methods for the best alternative, where Upwork (A1) ranks first with an Appraisal Score (AS) of 0.965 in EDAS and a Utility Degree (Ki) of 0.958 in ARAS. However, the comparative analysis reveals differences in rankings at the 4th and 5th positions, caused by the extreme value (outlier) sensitivity of the EDAS algorithm on cost attributes and the more tolerant stability of the ARAS algorithm in providing proportional value compensation. This study concludes that a comparative method not only provides validated recommendations but also reveals the characteristics of each algorithm in handling anomalies in cost attribute data. The main contribution of this study is to provide a valid comparative decision-making framework for remote workers in optimizing platform selection, while also enriching the academic literature regarding the disclosure of algorithmic sensitivity in the ARAS and EDAS methods when handling cost data anomalies.
Perbandingan Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Google Maps Pusat Perbelanjaan Eliza Cahyaningrum; Astrid Novita Putri
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9558

Abstract

The rapid growth of user reviews on Google Maps is not always accompanied by ease in understanding the sentiment contained within them, causing tourists and the general public to face difficulties in determining shopping centers with good reputation and service quality. The lack of information regarding visitor satisfaction levels, along with various facility-related issues such as crowd density, limited parking space, and the comfort of public facilities, combined with the large number of subjective and unstructured reviews, makes manual sentiment analysis ineffective and potentially leads to less accurate conclusions. This investigation aims to analyze sentiment from Google Maps reviews of shopping centers in the city of Semarang utilizing the Support Vector Machine (SVM) and Naïve Bayes methods. The data were collected from five shopping centers with the highest number of reviews in Semarang, namely Paragon Mall, Mall Ciputra, Java Mall, DP Mall, and Queen City Mall. The investigation method includes text preprocessing, TF-IDF weighting, and sentiment classification into three classes: negative, neutral, and positive. The dataset was divided into training and testing data with a ratio of 80:20. The outcomes reveal that the Naïve Bayes method achieved an accuracy of 85.56%, while the Support Vector Machine (SVM) method achieved an accuracy of 89.20%. Considering the outcomes, the SVM method performs better in classifying sentiment from Google Maps reviews of shopping centers in Semarang.
Optuna-Driven Hyperparameter Optimization in Tsukamoto Fuzzy Logic for House Price Estimation Annisa Aurelia Fitriani; Nabilah Putri Wijaya; Susanto Susanto; Nur Wakhidah
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9573

Abstract

The property sector faces challenges in determining accurate house selling prices due to subjectivity and market uncertainty. The relationship between physical attributes, such as land area and building area, and price is not always linear, making conventional methods often less precise in estimation. This study aims to design a decision support system to objectively estimate house prices in the Plamongan area, Semarang. The method used is Fuzzy Tsukamoto Logic. This preliminary study explores the integration of the Tree-structured Parzen Estimator (TPE) algorithm through the Optuna framework to automatically optimize membership function limits, replacing manual trial and error methods. The dataset was collected via scraping techniques, providing a pilot dataset of 26 data points. Final model performance evaluation showed a Mean Absolute Percentage Error (MAPE) value of 11.39%, which falls into the 'Good Forecast' category. However, given the highly limited sample size, these findings primarily serve as a proof-of-concept that requires further validation with larger, multi-variable datasets. These results prove that integrating the Fuzzy Tsukamoto method with hyperparameter optimization is effective in reducing subjectivity and providing reliable property price estimates. The primary contribution of this research is providing a mathematical proof-of-concept for an automated, objective property valuation system that eliminates human bias in fuzzy parameter configuration, offering a practical baseline tool for localized real estate markets.
Evaluasi Validitas Model Machine learning pada Klasifikasi Stunting Berbasis Data Antropometri dan Hubungan Deterministik Turwan Aldi Putra; Nirwana Hendrastuty
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9584

Abstract

Stunting is a chronic nutritional problem among infants and toddlers that affects children’s growth and development. Various studies have utilized machine learning for nutritional status classification based on anthropometric data; however, the validity of the resulting models has rarely been examined. This study aims to evaluate the validity of machine learning models in classifying stunting status using the XGBoost, Random Forest, and Naïve Bayes algorithms. The dataset consists of 120,999 anthropometric records of infants, with age, gender, and height as features, and nutritional status as the target variable. The research process included preprocessing, data transformation, and model evaluation using the k-fold cross-validation method with accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest and XGBoost achieved very high accuracy, at 99.91% and 99.08%, respectively, while Naïve Bayes reached only 55%. This stark difference in performance indicates that ensemble-based models are capable of capturing very strong patterns in the data, while Naïve Bayes struggles due to the interdependence among features. Furthermore, the high accuracy of certain models suggests a deterministic relationship between features and labels, which could potentially make the models less robust against data containing measurement errors or noise.
Analisis Sentimen X Terhadap Isu Industri Sawit Prabowo Subianto Menggunakan TF-IDF dan Machine Learning Ibrahim Akbar Arga Dewangga; Rama Aria Megantara
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9596

Abstract

This study aims to analyze public sentiment on the X platform regarding the palm oil industry issue associated with Prabowo Subianto and to compare the performance of Decision Tree, Support Vector Machine (SVM), and Random Forest algorithms. The dataset consisted of 3,785 tweets collected through a crawling process. The data were then processed through cleaning, case folding, text normalization, tokenizing, stopword removal, and stemming. Sentiment labeling was conducted using a lexicon-based approach, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) and traintest data splitting. The labeling results show that public opinion was dominated by positive sentiment with 3,018 tweets (79.7%), while negative sentiment accounted for 767 tweets (20.3%). The experimental results indicate that SVM achieved the best performance with an accuracy of 0.90, followed by Random Forest with 0.86 and Decision Tree with 0.84. SVM also demonstrated more stable performance based on precision, recall, and F1-score across both sentiment classes. These findings indicate that SVM is the most effective model for Indonesian-language sentiment classification on palm oil policy issues and has strong potential to support public policy evaluation based on social media data.
Classification of School Students Lifestyle Risks Based on Smoking Behavior Using Naïve Bayes Oktaria Dwi Cahyani; Deltari Balka; Dinni Rezky Amelia; Rainda Cintari Aulya; Ken Ditha Tania; Allsela Meiriza; Zaqqi Yamani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9668

Abstract

This study aims to classify students' lifestyle risks based on smoking behavior using the Naïve Bayes algorithm within a knowledge management framework. The research was conducted on students at a vocational high school within the coverage area of a local community health center. The dataset consisted of 277 valid records after undergoing data selection, cleaning, and transformation stages. The modeling process was carried out using RapidMiner software with an 80:20 data split for training (221 students) and testing (56 students). The evaluation metrics used included accuracy, precision, recall, and confusion matrix. The experimental results demonstrate that the Naïve Bayes model achieved an accuracy of 85.92%, precision of 86.12%, and recall of 92.86% for the unhealthy class. Furthermore, the classification results were integrated into a knowledge management framework to support decision-making processes in schools and community health centers. This study contributes to the application of predictive data mining in adolescent health and demonstrates how classification models can serve as effective tools for early detection, preventive interventions, and evidence-based policy formulation in educational and health settings.

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