cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
-
Journal Mail Official
jurnal.bits@gmail.com
Editorial Address
-
Location
Kota medan,
Sumatera utara
INDONESIA
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 926 Documents
 52   53   54   55   56 
Predicting University Graduates Employability Using Support Vector Machine Classification Haikal, Muhamad Fachri; Palupi, Irma
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5655

Abstract

The absorption of graduates into the world of work is a key indicator of higher education institution success, especially amid the tight job market competition due to increasing graduate numbers. Understanding employability and the factors that influence it is crucial for higher education institution to enhance education quality and facilitate graduates' transitions to employment. This research aimed to predict the employability of Telkom University students through their initial job income. Methods involved feature manipulation techniques like Principal Component Analysis, Spearman's rank correlation, and the Chi-square test of independence, followed by SMOTE-ENN to address data imbalance. Modeling was conducted using a Support Vector Machine with Randomized Search hyperparameter tuning, analyzed through Permutation Feature Importance to identify factors affecting employability. The result showed the enhanced SVM model with SMOTE-ENN, Spearman’s rank correlation coefficient as feature selection and randomized search hyperparameter tuning achieved the highest precision, recall, f-score, and accuracy of approximately 0.70, 0.73, 0.71, and 0.73, respectively. Competency features such as ethics, english skills, IT skills, and knowledge were identified as the most influential factors.
Analisis Perbandingan Kinerja Algoritma Klasifikasi Pada Mahasiswa Berpotensi Dropout Tamuntuan, Virginia; Kusrini, Kusrini; Kusnawi, Kusnawi
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5658

Abstract

This research aims to compare the performance levels of two data mining classification algorithms, namely Support Vector Machine and Neural Network Backpropagation, using the K-fold cross-validation method. The data used consists of graduates from 2019 to 2023 at STMIK Multicom Bolaang Mongondow. A total of 80% of the 200 data points were used as training data, while the remaining 20% were used as testing data. K-fold cross-validation was conducted with K set to 5. The results of the study indicate that the Support Vector Machine algorithm achieved an accuracy of 80%, recall of 80%, and precision of 35%, while the Neural Network Backpropagation algorithm achieved an accuracy of 77%, recall of 63%, and precision of 44%.
Implementasi Long Short Term Memory (LSTM) dalam Deteksi Kantuk pada Pengemudi Menggunakan Sensor Detak Jantung Afifah, Inas; Silvia, Ade; Suroso, Suroso
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5664

Abstract

Traffic accidents are often caused by drowsiness or negligent sleep, as well the use of alcohol or drugs. Microsleep, which is drowsiness or falling asleep within a few seconds without the driver realizing it, is a dangerous condition that can lead to death while driving. This research aims to implement the Long Short Term Memory (LSTM) algorithm as an early warning of microlsleep in drivers and develop a drowsiness detection tool using a pulse heart rate sensor. LSTM, with its ability to memory long-range information, has proven to be superior in time series prediction and is applied in real-time driver heart rate data analysis. The results show that the implemented LSTM model has good performance in detecting drowsiness, with MAE values of 6.42 in training data and 6.35 in testing data. RMSE of 8.82 for training and 8.33 for testing. MAPE of 8.87% in training data and 8.97% in testing data, and MSE of 77.80 in training data and 69.47 in testing. Thus, the LSTM algorithm is effective in detecting drowsiness in drivers through heart rate data analysis, which can serve as an early warning system to prevent traffic accidents caused by microsleep.
Comparison of TF-IDF and GloVe Word Embedding for Sentiment Analysis of 2024 Presidential Candidates Abdillah, Tiara Firdausa; Hasmawati, Hasmawati; Bunyamin, Bunyamin
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5668

Abstract

In the ongoing digital era, social media, particularly the social media X, formerly known as Twitter, has become one of the main platforms for sharing public opinions. On the social media, users have the opportunity to express their sentiments or views, including those regarding the presidential election in Indonesia. The main problem in this study is the extent to which public opinion on presidential candidates is reflected in conversations on the social media X. This study involves the combination of Support Vector Machine (SVM) and GloVe Word Embedding algorithms to improve the accuracy of sentiment analysis towards presidential candidates. The performance of the method will be evaluated using a confusion matrix. The results of the study show that while GloVe has the ability to capture global semantic relationships, TF-IDF is more effective in identifying variations and nuances in diverse sentiment data. Therefore, TF-IDF can be a more effective choice for political sentiment analysis in Indonesia, providing more consistent and accurate results. It is seen on the Anies dataset, TF-IDF achieved an accuracy of 0.84 compared to GloVe's 0.82. For the Ganjar dataset, TF-IDF performed better in terms of F1-Score and precision. For the Prabowo dataset, TF-IDF slightly outperformed GloVe in recall, although both techniques had nearly equal high accuracy around 0.93. Keywords: Presidential Candidates; 2024 Elections; SVM; GloVe; Social media X
Air Pollution Classification Prediction Model with Deep Neural Network based on Time-Based Feature Expansion and Temporal Spatial Analysis Muldani, Muhamad Dika; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5675

Abstract

− Air pollution is one of the most significant global challenges, with serious impacts on the health of living beings. In Indonesia, particularly in major cities such as Jakarta and Surabaya, the increase in the Air Quality Index (AQI) over the past few years indicates worsening air quality conditions. This decline in air quality is caused by increased industrial activities, motor vehicle emissions, and deforestation. Rising AQI levels pose severe health risks, including respiratory and cardiovascular diseases, and present major challenges for urban planning, public health management and environmental policy. Addressing this issue requires concerted efforts to implement sustainable practices, reduce emissions, and improve air quality management. The increasing air pollution level indicate the need for a more effective approach to identify and classify air quality index results with relevant success rates without using relatively expensive air quality index detection tools. This research aims to classify the air quality index using a Deep Neural Network model based on time-based feature expansion and spatial-temporal analysis. The Deep Neural Network model is used to extract complex patterns and hidden features in the data and help generate more accurate air pollution classifications. Meanwhile, time-based feature expansion is useful for extending the time representation in the data. The results of this research are expected to make a significant contribution in improving the global understanding of air pollution. By providing a cost-effective and efficient method for air quality monitoring, this study can lead to better pollution control measures. Furthermore, the insight gained from this research can help policymakers develop strategies to mitigate the adverse effects of air pollution on public health and the environment.
Microcontroller-Based Automatic Liquid Soap Refill System Process with Circular Economy Integration Salsabila, Helmi; Alijoyo, Antonius; A, Muhammad Syiarul
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5677

Abstract

This research aims to develop a microcontroller-based automatic liquid soap refill system integrated with the concept of a circular economy. The main problem addressed is the high use of single-use soap bottles, which negatively impacts the environment. As a solution, the designed system uses sensors to detect refill needs and perform automatic refills. A microcontroller controls this process, ensuring efficiency and user convenience. Testing was conducted to assess the system's performance under various operational and environmental conditions. The test results showed that the system functions well. Additionally, using this system reduces the use of single-use soap bottles, supports environmental sustainability, and reduces the cost of purchasing new bottles. This research concludes that microcontroller-based automation technology can be integrated with the circular economy concept to produce efficient, environmentally friendly, and economical solutions. This automatic liquid soap refill system not only enhances user convenience but also contributes positively to environmental and economic sustainability. It is hoped that the research results can serve as a reference for developing other automation technologies that support the circular economy concept.
Penerapan Algoritma Yolov3 pada Sistem Cerdas Pendeteksi dan Pengendali Hama Bawang Merah Berbasis IoT As'ad, Avif; Suroso, Suroso; Ciksadan, Ciksadan; Hawayanti, Erni
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5697

Abstract

Technological advancements play a crucial role in enhancing the efficiency of modern agriculture, particularly in addressing pest management challenges. This study focuses on the development of an automatic pest detection system for shallot crops using a combination of Arduino Uno microcontroller, ESP32-CAM camera module, and YOLOv3 object detection model. The system is designed to detect pests in real-time through images captured by ESP32-CAM and analyzed using YOLOv3, then provide an automatic response by spraying pesticides only in areas where pests are detected. The study began with the development of hardware and software for the automatic pest detection system. Arduino Uno is used as the main microcontroller to control the entire system, while ESP32-CAM is responsible for capturing images and detecting pests. The YOLOv3 model is trained using the COCO dataset, supplemented with sample images of pests on shallot crops to improve detection accuracy. The training process is conducted using a GPU to speed up model learning. Field tests on shallot crops infested with various types of pests show that this system has a high accuracy rate in detecting pests and effectively provides automatic pesticide spraying responses. The spraying system's effectiveness reaches 93%, ensuring pesticides are sprayed only in areas where pests are detected, thus optimizing pesticide use and reducing negative environmental impacts. This system offers an efficient and environmentally friendly solution for pest control and has significant potential for application in various agricultural scenarios. This research contributes to the improvement of agricultural productivity and the welfare of farmers in Indonesia.
Analisis Deteksi Mata Kantuk Di Wajah Pengemudi Menggunakan Support Vector Machine (SVM) Berbasis Citra Real-Time Maharani, Ullya Dwi; Handayani, Ade Silvia; Lindawati, Lindawati
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5701

Abstract

Traffic accidents in Indonesia are a serious issue with a high number of fatalities, and one of the main causes is microsleep, which is a brief moment of sleep while driving. To address this problem, this research has developed a sleePIness detection system based on the Internet of Things (IoT) using a Raspberry PI and a webcam, utilizing the Support Vector Machine (SVM) algorithm. The system is designed to detect the driver’s eye condition and provide a warning through a buzzer if the eyes are closed for more than 3 seconds. The research results indicate that the SVM model with a polynomial kernel has a training accuracy of 85.04%, demonstrating its ability to classify eye data into "opened" and "closed" categories. Evaluation with various SVM kernels, including linear, radial basis function (RBF), and polynomial, shows that the polynomial kernel performs the best with an accuracy of 85%, precision of 86%, and recall of 85% in detecting closed eyes. Although the system is effective in real-time detection of driver sleePIness, challenges remain with lighting conditions and camera positioning. Further testing is needed to improve the reliability and accuracy of the system in various situations. By providing early warnings to drivers, this system has significant potential to enhance road safety and prevent accidents caused by drowsiness.
Enhancing Sentiment Analysis Effectiveness with LSTM Variants, and Stratified K-Fold on Imbalanced Dataset Andriyanto, Rifki; Kusrini, Kusrini
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5721

Abstract

Sentiment analysis on hotel reviews often faces the challenge of class imbalance, where positive reviews significantly outnumber negative or neutral ones. This study aims to improve the effectiveness of sentiment analysis models on imbalanced hotel reviews by examining combinations of word embedding methods (FastText, Word2Vec, Doc2Vec) and model architectures (LSTM, BiLSTM, BiLSTM-Attention). Class imbalance is addressed using SMOTE, and model evaluation is conducted using Stratified K Fold cross-validation. Results show that Doc2Vec consistently outperforms FastText and Word2Vec as a word embedding method, especially when combined with the BiLSTM-Attention architecture. The use of SMOTE and Stratified K Fold also proves effective in improving model performance on imbalanced datasets. This study concludes that the selection of appropriate word embedding methods and model architectures, along with the implementation of class imbalance techniques, is crucial in developing effective and robust sentiment analysis models for hotel reviews.
Analisis Kinerja Algoritma Decision Tree Dan Random Forest Dalam Klasifikasi Penyakit Kardiovaskular Utami, Nisa; Baihaqi, Kiki Ahmad; Awal, Elsa Elvira; Waiddin, Deden
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5722

Abstract

Cardiovascular disease is a disease with a fairly high number of deaths. In Indonesia, the term cardiovascular is more popular with heart disease, which is a condition that can cause narrowing and blockage of blood vessels. Cardiovascular disease has two risks, the first is a risk that can be changed, such as stress, increased blood pressure, unhealthy diet, increased glucose levels, abnormal cholesterol and lack of physical activity. Meanwhile, risks that cannot be changed include family disease, gender, age and obesity. In this research, we can examine and analyze the performance of the two best classification algorithms, namely the decision tree algorithm and the random forest algorithm, in classifying cardiovascular disease based on the cause of the disease. The aspects studied are the performance results of each algorithm and evaluated using Area Under the Curve (AUC), classification report, k-Fold Cross Validation and Confusion matrix. The dataset used was taken from the Kaggle website with the data used being Cardiovascular Disease data which consists of 68.205 rows (patient data) and 17 attributes. . Based on the evaluation results using the Area Under The Curve (AUC) value, the highest result was obtained at 0.761 by the Random Forest algorithm with balanced data conditions with Random oversampling. Meanwhile, the lowest AUC value was obtained by the Decision Tree algorithm with unbalanced data of 0.592. Based on these results, it is known that the Random Forest algorithm with a balanced data scheme is a better algorithm, with a balanced data scenario using SMOTE and Random Oversampling techniques.

Page 54 of 93 | Total Record : 926

 52   53   54   55   56 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol 7 No 3 (2025): December 2025 Vol 7 No 2 (2025): September 2025 Vol 7 No 1 (2025): June (2025) Vol 6 No 4 (2025): March 2025 Vol 6 No 3 (2024): December 2024 Vol 6 No 2 (2024): September 2024 Vol 6 No 1 (2024): June 2024 Vol 5 No 4 (2024): March 2024 Vol 5 No 3 (2023): December 2023 Vol 5 No 2 (2023): September 2023 Vol 5 No 1 (2023): June 2023 Vol 4 No 4 (2023): March 2023 Vol 4 No 3 (2022): December 2022 Vol 4 No 2 (2022): September 2022 Vol 4 No 1 (2022): June 2022 Vol 3 No 4 (2022): March 2022 Vol 3 No 3 (2021): December 2021 Vol 3 No 2 (2021): September 2021 Vol 3 No 1 (2021): June 2021 Vol 2 No 2 (2020): December 2020 Vol 2 No 1 (2020): June 2020 Vol 1 No 2 (2019): December 2019 Vol 1 No 1 (2019): June 2019 Vol 1 No 1 (2019): Edisi Juni More Issue