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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 82 Documents
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Search results for , issue "Vol 7 No 1 (2025): June (2025)" : 82 Documents clear
Kombinasi Metode Rank Order Centroid dan Additive Ratio Assessment Untuk Pemilihan Aplikasi Manajemen Inventaris Tanniewa, Adam M; Sah, Andrian; Kurniawan, Robi; Prayogo, M Ari
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.6347

Abstract

Selecting an appropriate inventory management application is a challenge for business actors, especially SMEs, due to the variety of features, costs, and complexities offered. Manual selection is often carried out without a clear systematic approach and tends to be influenced by bias, resulting in suboptimal decisions. This study aims to integrate the Rank Order Centroid (ROC) and Additive Ratio Assessment (ARAS) approaches in developing a Decision Support System (DSS) to determine the best inventory management application. ROC is used to assign proportional weights to criteria based on priority ranking, while ARAS evaluates alternatives using these weights and relative utility values against the ideal solution. The developed system includes key features such as data management for criteria, alternatives, and values, as well as the ability to generate recommendations through alternative ranking. Based on a case study, the best alternative identified is Sortly: Inventory Simplified, with the highest utility score of 0.8627, followed by Housebook - Home Inventory (0.8528), inFlow Inventory (0.8336), and Inventory Stock Tracker (0.7056). Usability testing showed an average user acceptance rate of 91%, categorized as "Excellent". The main contribution of this research is the implementation of a practical and efficient combination of ROC and ARAS for selecting inventory management applications. The findings can be adopted by businesses to support more accurate and efficient decision-making.
Comparative Analysis of CNN and SVM Algorithms for Pneumonia Classification from Chest X-Ray Images Bela, Ar Ainun; Lhaksmana, Kemas M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7335

Abstract

Pneumonia significantly threatens human health, especially in children and the elderly. Diagnosing pneumonia using chest radiographs is time consuming and requires expert interpretation. This study proposes a comparative analysis of two algorithm models, namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN), in CNN algorithm, it specifically uses DenseNet121 and InterceptionV3 architectures for the classification of chest X-ray images in pneumonia and normal categories. The methods used include data preprocessing with normalization and augmentation. The dataset is split into training and testing subsets, and implementation of SVM and CNN algorithms for classification. Kaggle provided the dataset for this study, comprising 5,863 chest X-ray images. Metrics such as accuracy, precision, recall, and F1-score calculated from the confusion matrix were used to evaluate the model’s effectiveness. The test findings show that the DenseNet121 model has the best performance among the three models, with an accuracy, recall, and F1-score of 94%. The InceptionV3 model achieved 89% in accuracy, recall, and F1-score, which is higher than DenseNet121. Meanwhile, the SVM model showed the lowest performance with an accuracy of 81%, precision of 85%, recall of 81%, and F1-score of 79%. These outcomes signifies that Convolutional Neural Network (CNN) architectures, particularly DenseNet121, have superior capabilities in extracting complex features from chest X-ray images and show great potential to be applied in automatic and accurate pneumonia detection systems.
Perbandingan Algoritma LSTM, BI-LSTM, dan CNN untuk Klasifikasi Komentar Masyarakat: Pembangkitan Serigala Direwolf pada Media X Novriyandi, Agung; Hendrasuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7398

Abstract

The resurrection of the Direwolf by Colossal Biosciences, a biotechnology company based in Dallas, Texas, USA, through cloning and gene editing technology has sparked widespread debate and discussion in society. Cloning is the process of creating a genetically identical copy of an organism, and in this context, it is used to bring back the Direwolf, a species that has been extinct for around 12,500 years. This study aims to compare the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (BI-LSTM), and Convolutional Neural Network (CNN) algorithms in classifying public comments related to the resurrection of the Direwolf on Media X. Using a dataset of 3400 comments, after undergoing cleaning and preprocessing to eliminate noise and improve data quality, 1424 valid comments were obtained, consisting of 869 negative, 270 positive, and 285 neutral comments. This study will evaluate the performance of the three algorithms based on metrics such as accuracy, precision, and recall. The evaluation results show that the LSTM model has the highest accuracy at 73%, followed by BI-LSTM at 70%, and CNN at 66%. Based on these results, the LSTM approach can be considered a better approach in classifying public comments related to the topic of Direwolf resurrection. The results of this study are expected to provide useful information for the development of sentiment analysis systems and understanding public opinion related to cloning and gene editing technology.
Landscape of AHP Integration in Decision Support Systems: A Bibliometric Analysis of Scopus Publications Saputra, Imam; Mesran, Mesran; Utomo, Dito Putro; Siregar, Annisa Fadillah
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7451

Abstract

This study employs bibliometric analysis to provide a comprehensive overview of the research landscape concerning the integration of the Analytic Hierarchy Process (AHP) and Decision Support Systems (DSS). Utilizing 1770 documents retrieved from the Scopus database (1985-2025) and employing Biblioshiny for analysis, this research examines publication trends, citation patterns, keyword co-occurrence, collaboration networks, and thematic evolution within the field. The findings reveal a significant growth in publications, particularly after 2015, highlighting the increasing scholarly interest. Citation analysis identifies influential works and key contributing countries. Keyword analysis underscores "decision support systems," "analytic hierarchy process," and "decision making" as central themes, with emerging interest in areas like "artificial intelligence." Collaboration network analysis illustrates significant co-authorship patterns and international collaborations. Thematic mapping further categorizes research themes, identifying well-established "Motor Themes" (e.g., "decision support system," "GIS") and fundamental "Basic Themes" (e.g., "decision making," "analytic hierarchy process"). This study provides valuable insights into the intellectual structure, evolutionary trends, and collaborative dynamics of the AHP-DSS integration research field, highlighting its robust nature and potential future directions.
Implementasi Metode MAUT dalam Analisis Penentuan Tenaga Pengajar Non ASN Terbaik Maulana, Imam; Irmayani, Deci; Suryadi, Sudi
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7460

Abstract

The need for quality teaching staff is becoming increasingly important along with the development of technology and globalization, including in educational institutions such as SDN 115467 Kanopan Ulu. In addition to teaching staff from ASN, this school also relies on non-ASN staff who play a significant role in supporting the quality of education. However, the process of determining the best non-ASN teaching staff is often faced with the challenges of subjectivity and differences in assessment standards. To overcome this, this study proposes the implementation of a Decision Support System (DSS) based on the Multi Attribute Utility Theory (MAUT) method. The MAUT method allows for more objective, transparent, and fair decision-making by considering various assessment criteria, such as competence, experience, and contribution of teaching staff. In this study, non-ASN teaching staff data were analyzed using the Microsoft Excel application and DSS software during the research period in October 2024. Based on the application of this method, Tuti Alawiyah (A15) was ranked first with the highest score, namely 0.731. These results indicate that Tuti Alawiyah has the best performance according to the criteria used in the MAUT method, reflecting her superiority over other candidates. The results of the study indicate that the MAUT method is able to provide accurate and consistent evaluation results, thus supporting a more rational and in-depth decision-making process. This study not only provides theoretical contributions to the development of the DSS system, but also provides practical benefits for educational institutions to improve the motivation of non-ASN teaching staff and, overall, the quality of education. This topic is relevant to the needs of modern education in Indonesia, especially in efforts to improve the transparency and accuracy of teaching staff assessments.
Data Mining Dalam Clusterisasi Risiko Tinggi Obesitas Menggunakan Metode K-Means Clustering Hasby, Anzila; Bangun, Budianto; Masrizal, Masrizal
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7462

Abstract

Obesity is a condition of excess body fat due to an imbalance between calorie intake and expenditure. This problem has become a global epidemic, including in Indonesia, with serious impacts on physical, mental, and social health. Women are more susceptible to obesity due to biological factors and lifestyle choices, as evidenced by data from a community health centre where 76.6% of central obesity patients were women. This study developed an obesity risk segmentation model for women using the K-Means Clustering algorithm based on secondary data from Kaggle (n=898), incorporating variables such as age, family history, dietary patterns, physical activity levels, and mode of transportation used. The results of preprocessing and StandardScaler normalisation showed two optimal clusters (Silhouette Score: 0.267), where Cluster 1 (young age 24.53 years, family history of obesity 1.91, fast food consumption 1.84, low physical activity 2.71) has a higher risk compared to Cluster 0 (age 41.41 years with a healthier lifestyle), revealing a significant interaction between genetic factors and lifestyle as the main triggers. These findings provide a scientific basis for group-based interventions, such as targeted nutrition education programmes for the young population, while demonstrating the effectiveness of data mining approaches in public health for classifying the risk of non-communicable diseases.
Decision Support System for Aircraft Takeoff and Landing Using Mamdani Fuzzy Logic Based on Weather Parameters Armansyah, Armansyah; Irianto, Suhendro Yusuf
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7464

Abstract

Aviation safety is highly influenced by weather conditions, particularly during take-off and landing, necessitating an accurate feasibility assessment. Traditional manual methods rely on subjective judgment, making them prone to inconsistencies and errors. This study proposes a decision support system utilizing Mamdani fuzzy logic to process real-time meteorological data from the Radin Inten II station and assess take-off and landing feasibility. The system evaluates key weather parameters, including wind speed, wind direction, visibility, precipitation, and cloud height. Testing 31 data samples from BMKG, the system achieved an accuracy of 96.77%, with 30 out of 31 outputs matching standard aviation criteria. These results indicate that the system significantly improves decision-making reliability. The Mamdani fuzzy logic approach proves effective in interpreting complex weather data and generating consistent, data-driven recommendations to support safe aircraft operations.
Density-Based Spatial Clustering, K-Means and Frequent Pattern Growth for Clustering and Association of Malay Cultural Text Data in Indonesia Mustakim, Mustakim; Salisah, Febi Nur
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7512

Abstract

Several studies state the need to develop information technology to disseminate information related to culture in Indonesia. There are many similar studies but they still have weaknesses, one of which is that they do not use machine learning and intelligent computing. This research answers the challenges of previous researchers, namely developing machine learning-based learning applications using the Density-Based Spatial Clustering of Application Noise (DBSCAN) and Frequent Pattern Growth (FP-Growth) algorithms. The results of the modeling of the two algorithms are deemed to still require improvement in the future, as it is proven that DBSCAN does not yet have optimal validity. So in this research, one of the comparison algorithms is used, namely K-Means Clustering, with a better evaluation than DBSCAN. The modeling results were implemented into mobile programming as a cultural learning application in Indonesia, especially Riau Malay Culture, the black box testing results had an accuracy of 100% and the User Acceptance Test (UAT) was 86%. Thus, it is concluded that this application can be used effectively and efficiently for general users.
Modifikasi Algoritma Sattolo Shuffle Untuk Mengacak Soal Pada Aplikasi Ujian Online Nasution, Surya Darma; Mesran, Mesran
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7580

Abstract

The use of Computer-Based Testing (CBT) systems has become a popular evaluation method due to its efficiency and ability to accelerate the assessment process. However, challenges such as cheating and the similarity of question sequences among participants still frequently occur. This study aims to design and implement a modified Sattolo Shuffle algorithm with the addition of a Linear Congruential Generator (LCG) as a source of random numbers in the exam question randomization process. The Sattolo Shuffle algorithm was chosen because it produces a single cyclic permutation that ensures each question element is repositioned, reducing the potential for recurring patterns. The LCG is used to generate random indices deterministically but variably, based on specific parameters and an initial value (seed) derived from the participant’s serial number. The implementation was carried out in a web-based CBT system consisting of 50 questions in each exam session. Testing on three participants showed that the generated question sequences were completely different, with no identical orders found. Each participant received a unique combination of questions with an even distribution of question positions. Initial results demonstrate the algorithm's effectiveness in increasing question variation and preventing duplication, making it a potential solution to enhance security and fairness in CBT administration. This research is expected to contribute significantly to the development of more randomized, fair, and cheat-resistant online exam systems.
A Comparative Analysis of LSTM and GRU Models for AQI Forecasting in Tourist Destinations Ardianto, Luluk; Astuti, Yani Parti
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.6633

Abstract

The Air Quality Index (AQI) is a critical metric for assessing air quality and its impact on human health, particularly in densely populated and tourist-heavy areas such as Malioboro, Yogyakarta. As one of Indonesia's most popular tourist destinations, the region experiences significant air quality fluctuations influenced by human activities, including transportation and tourism. This study evaluates the performance of two advanced deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting AQI and key pollutant parameters, PM10 and PM2.5, using two years of air quality data collected between January 2022 and December 2023. The results demonstrate that the LSTM model consistently outperforms GRU in predicting AQI (MSE: 163.757, RMSE: 12.797, MAE: 7.432, MAPE: 0.133) and PM2.5 (MSE: 32.001, RMSE: 5.657, MAE: 3.005, MAPE: 0.139), indicating its capability to model complex temporal patterns effectively. Conversely, the GRU model achieves better accuracy for PM10 predictions (MSE: 58.592, RMSE: 7.655, MAE: 4.168, MAPE: 0.180), showcasing its computational efficiency with competitive performance. These findings underscore the suitability of LSTM for applications prioritizing accuracy, while GRU provides a viable option for scenarios requiring faster computations. This research highlights the potential of leveraging deep learning models to tackle air quality challenges in urban and tourist areas, paving the way for informed decision-making and sustainable development initiatives

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