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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 926 Documents
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Implementasi Metode Fuzzy Sugeno dalam Aplikasi Mobile untuk Analisis Kinerja Sistem Antrian Puskesmas Harahap, Adhelia Febriasari; Aryanti, Aryanti; Anugraha, Nurhajar
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.v6i2.5562

Abstract

The registration process at health centers often faces efficiency issues, causing inconvenience for patients and reducing service productivity. Puskesmas is one such health facility that faces long queue problems almost every day, impacting the quality of service. This study aims to design and develop a health center queuing system based on the Internet of Things (IoT), allowing online registration through a mobile application based on Android and more efficient ticket acquisition. The Fuzzy Sugeno method is applied to evaluate and improve the performance of the queuing system. This application also provides notifications to patients about the time to enter the targeted clinic, thereby reducing waiting times and preventing loss of queue numbers. The implementation of the Fuzzy Sugeno method at Puskesmas has shown significant results in various service aspects. The average patient waiting time decreased from 90 minutes to 40 minutes, reflecting a reduction of 55.6%. Patient satisfaction increased from 3.2 to 4.5 on a scale of 5, or by 40.6%. Registration efficiency improved, with the time required decreasing from 10 minutes to 4 minutes per patient, resulting in a 60% increase in efficiency. Additionally, the service capacity increased, with the number of patients served per day rising from 70 to 100 patients, indicating an increase of 42.8%. The implementation of this technology has succeeded in creating more responsive, effective, and productive health services at Puskesmas, providing a positive impact on the community as a whole.
Pengembangan Algoritma Convolutional Neural Networks (CNN) untuk Klasifikasi Objek dalam Gambar Sampah Putri Vandalis, Yoke Annisa; Soim, Sopian; 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.v6i2.5585

Abstract

Waste is a serious issue facing the world today, with increasing human activity and global economic growth. One important step in waste management is the classification process, which aims to separate types of waste based on their characteristics so they can be recycled, processed, or disposed of properly. Previous research has shown that Convolutional Neural Networks (CNN) are effective algorithms for multi-class classification. Therefore, this study develops an optimized CNN model for automatic waste classification, with a primary focus on improving accuracy through modifications to the CNN architecture. The dataset used consists of 17,366 waste images from various sources, which are then divided into training and testing data after undergoing preprocessing to ensure good data quality before training the model. However, one of the main challenges in developing a CNN model for multi-class classification is the risk of difficulty in learning class features, especially when the model is faced with too many classes. To address this issue, this study implements a strategy by adding convolutional layers to the CNN architecture. This method aims to deepen the network to capture more complex features from the given data, which in turn can improve the model's generalization to new data. Evaluation results show that the modified CNN model achieved a training accuracy of 88% after 40 epochs, with a testing accuracy of around 83%. This research not only contributes to the development of more advanced automatic waste classification technology but also provides a strong foundation for further research in this field. With increased waste management effectiveness, it is hoped to have a positive impact on the environment and public health as a whole..
Penerapan EfficiencyNet Untuk Pembuatan Model CNN Pada Klasifikasi Bahasa Isyarat Putri, Amanda Kanaya; Suroso, Suroso; Handayani, Ade Silvia
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.v6i2.5592

Abstract

This study discusses the application of EfficientNet architecture in developing a Convolutional Neural Network (CNN) model for sign language classification. Sign language is a vital communication method for the deaf community, but automatic recognition remains a challenge in the field of computer vision. One of the primary issues is the limitation in accuracy and efficiency of models in recognizing complex variations of sign language in real-world conditions. EfficientNet, known for its computational efficiency, is used as a backbone to build a CNN model that can classify sign language letter patterns with high accuracy while remaining lightweight. The dataset used in this study is American Sign Language (ASL) with data augmentation techniques to enhance the variety and quality of the dataset. The dataset comprises 14,740 images of sign language letter patterns from various angles and lighting conditions. Experimental results show that the EfficientNet-based model developed achieves training and validation accuracies of 98.40% with a more efficient model size and inference time. This study demonstrates the significant potential of using EfficientNet in developing sign language classification systems that can be applied to devices with limited resources, such as mobile applications and edge computing. These findings are expected to improve accessibility and social inclusion for the deaf and speech-impaired communities. Thus, this research not only contributes to the field of pattern recognition technology but also to efforts to enhance the quality of life for individuals with communication disabilities through the development of effective and efficient assistive tools.
Implementasi IoT dan Sensor Termal untuk Mitigasi Kebakaran Hutan dan Lahan dengan Penentuan Koordinat Br Ginting, Nurul Devani; Handayani, Ade Silvia; Sarjana, Sarjana
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.v6i2.5594

Abstract

Forest fires are a serious environmental problem, causing ecosystem damage, air pollution and economic losses. Effective monitoring and mitigation of forest fires requires early detection and tracking of hotspots that have the potential to become fire centers. This research proposes the implementation of Internet of Things(IoT) technology and thermal sensors to detect forest fires through point coordinate mapping using the triangulation method. The method used involves the installation of thermal sensors and NEO-6 GPS modules. The thermal sensor measures the ambient temperature in real-time, while the GPS module provides precise coordinate data in (latitude and longitude). Data from the sensors is transmitted to the ThingSpeak platform via the IoT network, where further analysis is performed to identify hot spots. A triangulation method is then applied to map the position of the hot spot. This research aims to develop a forest fire monitoring system capable of detecting and mapping hotspots in real-time, thus enabling quick and effective mitigation actions. The results obtained from this research show that the system can determine the position of hotspots with coordinate accuracy (latitude and longitude) within a margin of error of less than 5 meters.
Multi-aspect Sentiment Analysis of Shopee Application Reviews using RNN Method and Query Expansion Ranking Novitasari, Ariqoh; Sibaroni, Yuliant; Puspandari, Diyas
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.v6i2.5605

Abstract

Online shopping using e-commerce is a common activity society does in this digital era. Shopee is one of the well-known e-commerce in Indonesia. There are a lot of e-commerce platforms that can easily be accessed through mobile applications like Google Play Store. Users are allowed to review and rate the application they have downloaded. The reviews from the users become an opportunity for e-commerce companies to advance their performances and services. To enhance the understandability of user reviews, a system that can efficiently analyze the sentiment is needed. This study aims to design and establish a system that can perform sentiment analysis on the selected aspects. Sentiment classification is implemented by using the Recurrent Neural Network (RNN) algorithm and Query Expansion Ranking feature selection to classify Shopee application reviews into two classes, which are positive and negative. Feature selection is used to reduce less useful features so that the classification model conducts the classification process optimally and more efficiently. In conclusion, the evaluation results based on an 80:20 data split ratio indicate that the RNN achieves the highest accuracy of 95% in the delivery cost aspect, 93% in the delivery speed aspect, and 86% in the application access aspect.
Analisis Sentimen Ulasan Aplikasi Instagram di Google Play Store: Pendekatan Multinomial Naive Bayes dan Berbasis Leksikon Wijaya, Novresia; Panjaitan, Erwin Setiawan
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.v6i2.5615

Abstract

Social media platforms like Instagram play a significant role in the daily lives of many individuals. To understand user experiences with social media, we can read reviews and ratings provided by users. However, these ratings often may not accurately reflect the content of their reviews. Therefore, it is important to analyze these responses to understand the common complaints users have. This study aims to develop an accurate sentiment analysis method for Instagram user reviews by combining Naïve Bayes with a lexicon-based approach to address the discrepancy between star ratings and the content of reviews. The main issue addressed is how to accurately analyze Instagram user sentiment, given the potential discrepancies. To tackle this problem, the study employs a Naïve Bayes method combined with a lexicon-based approach to determine positive and negative sentiments towards user reviews. The testing results show an accuracy of 92%, with a precision of 84%, recall of 91%, and an F1-Score of 87%.
Perbandingan Kinerja Pre-Trained Word Embedding Terhadap Performa Klasifikasi Sentimen Ulasan Produk Tokopedia Dengan Long Short-Term Memory(LSTM) Dirfas, Naufal Angling; Nastiti, Vinna Rahmayanti Setyaning
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.v6i2.5634

Abstract

The product review dataset is a rapidly growing and interesting source of data to explore. The increase in the number of internet users and customer shopping habits through online stores has a significant impact on the growth of product review data, especially for online stores in Indonesia, such as Tokopedia. The sample data used amounted to 1079. This research aims to evaluate the performance of three types of pre-trained word embeddings, namely FastText, GloVe, and Word2Vec, in the Long Short-Term Memory (LSTM) model for sentiment classification of product reviews on Tokopedia. An automated sentiment classification system is needed to process many product reviews, making it easier for sellers to know what consumers think of their products. This research contributes by evaluating the impact of various pre-trained word embeddings on the performance of LSTM models in sentiment classification tasks. In addition, this research also aims to measure the effectiveness of LSTM models combined with multiple pre-trained word embeddings. By implementing a deep learning architecture, computers can learn and recognize contextual data stored in review sentences. The research was conducted in three stages: model selection, layer setup, and hyperparameter optimization, to feature in-depth testing of the deep learning architecture used and the appropriate combination of layers and parameters to obtain high sentiment classification performance. The experimental results show that FastText with LSTM provides the best performance with 85.08% accuracy, Word2Vec with 84.62% accuracy, and GloVe with 83.04% accuracy. The main contribution of this research is to present an in-depth test of the product review dataset and provide a deep learning architecture along with a combination of layers and parameters that has the best performance in recognizing sentiment on the product review dataset. This architecture achieves higher performance than the BERT method with CNN and BiLSTM layers.
Implementasi Data Mining Dalam Klasifikasi Tingkat Kesenjangan Kompetensi PNS Menggunakan Metode Naive Bayes Kurniawan, Putra; Wasilah, Wasilah; Sutedi, Sutedi; Nugroho, Handoyo Widi
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.v6i2.5641

Abstract

Civil Servants (Aparatur Sipil Negara or ASN) play crucial roles as implementers of public policy, community service providers, and national unifiers. The government's primary focus is on enhancing the quality and efficiency of public services. In the Provincial Government of Lampung, planning for the enhancement of the competencies of Civil Servants (Aparatur Sipil Negara or ASN) has become a current priority activity. This emphasis is due to the absence of reference data for determining competency development for each ASN. The Assessment Center is one method for determining the competency level of Civil Servants (ASN). However, its implementation faces several challenges such as budget constraints, time limitations, and a shortage of assessors. Based on the results of the 2023 Merit System Index assessment by the Civil Service Commission (KASN), it was recommended that mapping and evaluating employee competency gaps can be carried out through the Human Capital Development Plan (HCDP). In its implementation, a self-assessment method using a questionnaire based on the competency dictionary from the Regulation of the Minister of Administrative and Bureaucratic Reform No. 38 of 2017 is used to address the constraints of the assessment center. The questionnaire is specifically targeted at technical civil servants (PNS) in the Lampung Provincial Government. The analysis of this questionnaire data produces a classification of civil servants based on the level of competency gaps (none, low, medium, high). In this study, the classification results are tested using one of the data mining classification techniques, namely the Naïve Bayes method. The objective of this research is to evaluate the performance of the Naïve Bayes algorithm in classifying the levels of competency gaps among civil servants. Based on the research findings, it can be concluded that the classification system for competency gap levels among civil servants in the Lampung Province Government can be modeled. The testing of the model, which implemented the Naïve Bayes classification method using RapidMiner tools on the research dataset, achieved an accuracy rate of 98.02%. The conclusion is that the Naïve Bayes algorithm performs well in classifying the competency gap levels among civil servants. With the achieved accuracy level, the resulting classifications can be utilized by the Lampung Provincial Government in planning the development needs of civil servant competencies
Decision Tree Algorithm for Predicting Alumni Job Competitiveness Through Waiting Time Working Panuluh, Bagus; Palupi, Irma; Gunawan, Putu Harry
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.v6i2.5647

Abstract

The absorption of alumni from universities into the world of work is an essential indicator that universities must pay attention to. One-way universities can pay attention to their alums is through tracer studies, where they can evaluate their curriculum's relevance to what is needed in today's world of work. One aspect that can be seen from the tracer study to assess the competitiveness of alums is the waiting time for alums to get their first job. This is because the sooner alums get jobs, the better the curriculum the university provides to students. This research aims to apply machine learning to predict the waiting time for alums from Telkom University to get their first job and find out what factors influence the waiting time for work. The algorithm used in the research is the Decision Tree with hyperparameter tuning using Grid Search and feature selection application. There are 3 methods of feature selection used for comparison: Spearman's Rank Correlation, Chi-square, and Principal Component Analysis. This research produces the best prediction model in applying Chi-square and hyperparameter tuning with an accuracy of 0.79, recall of 0.79, precision of 0.80, and F1-Score 0.75. Several features, such as the number of companies registered, how to find and get work, internship and practicum experience, ethical competency, discussion, and IT skills, have the biggest effects on the model.
Implementasi Metode Resampling Dalam Menangani Data Imbalance Pada Klasifikasi Multiclass Penyakit Thyroid Nugraha, Najmi Cahaya; Hikmayanti, Hanny; Indra, Jamaludin; Juwita, Ayu Ratna
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.v6i2.5652

Abstract

It is estimated that at least 17 million Indonesians suffer from thyroid disorders. Interestingly, nearly 60% of those living with a thyroid disorder do not receive a diagnosis. Thus, it is necessary to carry out research that applies methods to predict thyroid disease. Before applying prediction methods, it is crucial to implement classification methods to obtain an accurate prediction model. However, to achieve optimal classification results and to avoid inaccuracies, a balance in the used data is required. Data imbalance is a condition where the ratio between classes in the data is uneven, which can result in the generated model becoming biased. The main objective of the research is to present a solution that can improve the accuracy of early detection of thyroid diseases through addressing data imbalance and implementing appropriate classification algorithms. The research methodology began with the collection and analysis of a dataset consisting of 9172 data points. Preprocessing was then performed, resulting in 5321 training data points and 1331 test data points. The testing phase employed 7 different classification algorithms with 7 different resampling methods and evaluation using a confusion matrix. This research achieved the highest accuracy rate of 98%, obtained from the combination of the Random Forest Algorithm and the Random Over Sampling method. It can be concluded that the combination of the Random Forest Algorithm with the Random Over Sampling resampling method can improve early detection accuracy for thyroid diseases.

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