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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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Analisis Efektivitas Studi Independen (MBKM) Pada Mahasiswa Teknik Informatika Menggunakan Algoritma K-Nearest Neighbor Faqihuddin Hanif, Isa; Apoko, Tri Wintolo; Hendriana, Benny; Handayani, Isnaini; Fatayan, Arum; Irdalisa, Irdalisa
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5401

Abstract

The Merdeka Study Program is part of the Kampus Merdeka (MBKM) policy initiated by the Minister of Education and Culture, Nadiem Makarim. Students are given the opportunity to expand their knowledge and skills, both hard and soft, through various off-campus activities. In the Informatics Engineering Study Program at UHAMKA, this program is designed to enrich learning experiences and enhance practical skills relevant to the industry. The program's effectiveness evaluation is conducted through a survey of 41 students to collect data on their experiences and perceptions. The aim is to measure the extent to which the MBKM program positively impacts student learning. The research methodology uses the K-Nearest Neighbor (KNN) algorithm to classify program effectiveness data based on several performance indicators. The analyzed data includes survey results from students who participated in the MBKM program. The research findings show that the KNN classification model has an accuracy of 93.65%, with an average precision of 94.08% and recall of 93.65%, indicating a high level of accuracy in classifying program effectiveness. Most students reported that the MBKM program is very effective in enhancing their skills and knowledge, although some felt neutral or found it less effective. This study concludes that the MBKM program is generally effective in achieving its goals, although improvements and adjustments are still needed to optimize its benefits for all students
Comparative Assessment of Low Job Competitiveness Among University Graduates Using Naïve Bayes and KNN Algorithms Hamonangan, Ricardo; Palupi, Irma; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5406

Abstract

Tracer studies investigate the career outcomes of graduates, encompassing job search experiences, employment conditions, and the application of acquired skills post-graduation. These studies are pivotal for universities and colleges to assess graduate success and shape educational policies. This study aims to elucidate the factors contributing to low job competitiveness through the application of classification models like KNN and Naïve Bayes. It also evaluates how competencies developed during university studies impact this scenario. Key issues addressed include the identification of factors causing low job competitiveness and the assessment of competencies trained during university education. Utilizing a dataset comprising two classes and seven features, the KNN method achieved an accuracy of 71.00%, while Naïve Bayes achieved 70.00%. The data set size is 1853 (around 20% of the survey sample) of unemployed alumni. The results indicate that the lack of specific competencies, particularly those related to practical skills and real-world application, is a major factor contributing to low job competitiveness. The results highlight a specific competency as most crucial in the KNN model, whereas different competencies play significant roles in the Naïve Bayes model. Despite variations in competency importance across models, all features significantly contribute to predictions. This research enhances the classification of workforce competitiveness levels within tracer studies and underscores the potential of KNN and Naïve Bayes algorithms to identify factors influencing low job competitiveness. These findings support informed decision-making in academic and career development initiatives, emphasizing the critical influence of university-trained competencies on job market readiness.
Sentiment and Toxicity Analysis of Tourism-Related Video through Vader, Textblob, and Perspective Model in Communalytic Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5416

Abstract

This study leverages the Tourism and Travel Content Analysis (TTCA) framework to explore user sentiment and behavior in response to digital travel content. Utilizing sentiment analysis models such as VADER and TextBlob, the research analyzed 13,162 posts, revealing that 13.92% were negative, 15.02% neutral, and 71.06% positive, according to VADER. At the same time, TextBlob classified 10.47% as unfavorable, 26.51% as neutral, and 63.02% as positive. Additionally, toxicity scores calculated using Detoxify and Perspective models showed a range from low to high levels of toxic content, highlighting issues like identity attacks, insults, profanity, and threats. The findings underscore the effectiveness of well-crafted narratives in digital content for influencing tourist behavior and visit intentions. However, limitations were noted in the model's ability to fully capture emotional and cultural nuances. Future research should incorporate more advanced analytical tools and diverse datasets to overcome these limitations. Ultimately, the TTCA framework provides valuable insights for enhancing digital marketing strategies and improving user engagement in the tourism secto
Penerapan Deep Learning pada Pengolahan Data Citra dan Klasifikasi Udang Vaname Menggunakan Algoritma Convolutional Neural Network Astiti, Sarah; Nopriadi, Nopriadi; Novrian, Willi; Putra, Yusran Panca
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5418

Abstract

Deep learning-based shrimp image processing has become a rapidly growing research field in recent years. This technology aims to increase efficiency and accuracy in various applications related to the fishing and aquaculture industry, such as monitoring shrimp health, disease detection, species classification, and assessing the quality and quantity of harvested crops. Based on observations to date, fish sellers and buyers in the market have difficulty distinguishing vaname shrimp cultivated in tarpaulin ponds and earthen ponds. This research aims to apply deep learning techniques to determine the classification of Litopenaeus vannamei shrimp cultivation results in earthen ponds and tarpaulin ponds. To facilitate this research, the author uses a classification method by applying two Convolutional Neural Network (CNN) architectures, namely Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50). The dataset used in this research is 2,080 images per class of vannamei shrimp from two types of shrimp ponds. The results of this research are learning rates of 0.001 and 0.0001 on the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizer to evaluate their effectiveness in model training. The VGG-16 and ResNet-50 models were trained with a learning rate parameter of 0.0001, taking advantage of the flexibility and reasonable control provided by the SGD optimizer. Lower learning rate values ​​were chosen to prevent overfitting and increase training stability. Model evaluation showed promising results, with both architectures achieving 100% accuracy in classifying vannamei shrimp from ground and tarpaulin ponds. The conclusion of this research is to highlight the superiority of using SGD with a learning rate of 0.0001 versus 0.001 on both architectures, then the significant impact of optimizer selection and learning rate on the effectiveness of model training in image classification tasks
Klasifikasi Penyakit Jantung Tipe Kardiovaskular Menggunakan Adaptive Synthetic Sampling dan Algoritma Extreme Gradient Boosting Permana, Acep Handika; Umbara, Fajri Rakhmat; Kasyidi, Fatan
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5421

Abstract

Cardiovascular diseases are conditions that commonly affect the cardiovascular system, such as heart disease and stroke. According to data from the World Health Organization (WHO), 17.9 million deaths worldwide in 2019 were attributable to cardiovascular disease. Early detection is crucial, but diagnosing heart disease is complex in developing countries due to the limited availability of diagnostic tools and medical personnel. This study uses the Heart Disease Dataset from Kaggle, consisting of 15 attributes and 4238 records, to develop a heart disease classification model using XGBoost. The research stages include data imputation, data transformation using LabelEncoder, data balancing using ADASYN, data splitting (80% training data, 20% testing data), and hyperparameter tuning with Bayesian Optimization. The results show that the XGBoost model with ADASYN performs better, with a ROC-AUC of 0.971 and an accuracy of 0.916, compared to the model without ADASYN, which has a ROC-AUC of 0.698 and an accuracy of 0.841. Based on the research results, ADASYN has proven effective in improving model performance on imbalanced datasets. Additionally, Bayesian Optimization plays an important role in finding the optimal parameter combination, which can further enhance model performance. With this research, the impact is quite significant in the development of early detection methods for cardiovascular heart disease, particularly through the application of the XGBoost classification algorithm
Klasifikasi Sentimen Untuk Mengetahui Kecenderungan Politik Pengguna X Pada Calon Presiden Indonesia 2024 Menggunakan Metode IndoBert Oktariansyah, Indro Abri; Umbara, Fajri Rakhmat; Kasyidi, Fatan
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.v6i2.5435

Abstract

X has evolved into one of the most popular social media platforms in the world. In Indonesia, the use of X is quite widespread, especially in discussions about the presidential election, which is currently a hot topic. Everyone has different views on the candidates, both positive and negative. With a large amount of tweet data from users, this information can serve as a data source for processing and analysis. Various methods can be used to analyze and classify sentiment from this data, one of which is using BERT. This research conducts sentiment classification using BERT with the IndoBert model. The research aims to classify sentiments towards tweets related to the 2024 Indonesian presidential election to understand the political inclinations of X users, evaluate the performance of the IndoBert model in sentiment classification, and assess the extent to which back translation augmentation and synonym augmentation techniques can enhance the model's performance. Data was collected using crawling techniques for seven days leading up to the election and manually labeled by annotators. Synonym augmentation and back translation techniques were used to balance data in minority classes. The data was divided into 80% training data, 10% test data, and 10% validation data. The classification process was conducted using the IndoBert model that had been fine-tuned. The research results show that IndoBert with synonym augmentation achieved the highest accuracy, which was 82% in the first experiment and 81% in the second experiment. On the other hand, back translation only reached an accuracy of 78% in the first experiment and 74% in the second experiment. This indicates that synonym augmentation proved to be more effective in increasing data variation and model performance on the dataset used in this research.
Pemantauan dan Pengendalian Kekeruhan Air Kolam Pembibitan Ikan Lele Dengan PLC Outseal Berbasis IoT, Di Fardu Farm Pekanbaru Akbar, Muhammad Razzaq; Zarory, Hilman; Mursyitah, Dian; Faizal, Ahmad
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.v6i2.5452

Abstract

In catfish hatchery farming, monitoring and controlling water quality is essential to ensure optimal conditions for fish growth. One of the key parameters that need to be monitored is water turbidity, which can affect fish health and the quality of the water environment in the hatchery pond. Monitoring the turbidity of catfish hatchery ponds has generally been done manually by farmers, which has several significant drawbacks. This manual process is inefficient as it requires physical presence at the pond site and must be performed periodically, consuming time and labor. In this study, the researchers propose an innovative solution using an automated system based on the PLC Outseal Mega V3 and the BGT-D718-TDS sensor, connected with the Internet of Things (IoT) to monitor and control the turbidity of catfish hatchery ponds. This research integrates IoT technology into the automatic control of pond water turbidity, aiming to ease operational burdens in catfish farming. The results of a 14-day experiment showed that the system effectively controlled pond water turbidity, with significant reductions observed on day 12 by 4.4 ppm and on day 13 by 3.46 ppm. Thus, implementing IoT technology in water quality monitoring and control can be an important step in enhancing operational efficiency and sustainability in aquaculture.
Penerapan Metode Analytical Hierarchy Process dan Additive Ratio Assessment Dalam Menentukan Target Promosi Universitas Mahendra, Rifqi Gusnar; Trenady, Revangga Alif; Pungkasanti, Prind Triajeng
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.v6i2.5469

Abstract

The Semarang University New Student Admissions (PMB) Department has set a target for the number of new students every year of 4,500 students. To meet this target, the PMB section carries out promotions to several high schools and vocational high schools, however, the PMB section has not yet chosen the school promotion site. apply decision support methods with certain methods that can help select schools for university promotion. This research applies a decision support method with the Analytical Hierarchy Process (AHP) to calculate the weight of each criterion and will continue with ranking calculations using the Additive Ratio Assessment (ARAS) method. The results of applying the AHP method for weighting criteria are: number of school alumni students with a weight of 0.36; the number of active school students was 0.26; the distance between school and university is 0.12; and school-university collaboration of 0.26. From the ARAS ranking method, it was found that SMKN 8 Semarang had the highest score of 0.911; in second place at SMKN 4 Semarang with a score of 0.864; and in third place at SMA Institut Indonesia with a score of 0.823. The aim of this research is to provide better alternative decisions so that it can help the Semarang University PMB section in deciding school promotion targets.
Penerapan Metode Entropy Dan Metode EDAS Dalam Penerimaan Karyawan Baru Sebagai Pendukung Keputusan Trenady, Revangga Alif; Mahendra, Rifqi Gusnar; Pungkasanti, Prind Triajeng
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.v6i2.5473

Abstract

Selection of new employees is an important thing in a company, because of the variety of criteria that applicants have, so the right method is needed to determine the desired employees. So far, decisions have been made only from a Human Resource Development (HRD) perspective, which sometimes does not match the criteria expected by the company. Therefore, a system is needed to determine the results of new employee selection so that employees are found who meet the criteria expected by the company. This research applies the Entropy and Evaluation methods based on Distance from Average Solution (EDAS) as a decision support system. This research aims to assist companies in making decisions regarding the selection of new employees. The Entropy method is used to determine the weight of each selection criterion, while the EDAS method is applied to evaluate prospective employees. This research was conducted at a spring bed manufacturing company in Semarang by analyzing applicant data which included various criteria such as education, work experience, technical skills, test scores and expected salary. The applicant data sample taken from the company was 15 applicants. From the weighting calculations using the Entropy method and the EDAS method, the result was that Yulius Bagus Caesar had the highest suitability value, namely 1. The research results showed that the combination of Entropy weighting and the EDAS method could simplify the employee selection process based on the expected criteria. Thus, using a combination of Entropy weighting and the EDAS method can be a solution for companies to facilitate the selection of new employees
Implementasi Algoritma Gaussian Naïve Bayes Dalam Klasifikasi Status Gizi Pada Balita Kurniawan, Hery; Rahim, Abdul; Siswa, Taghfirul Azhima Yoga
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.v6i2.5493

Abstract

Nutritional status is a condition related to nutrition that can be measured and results from the balance between the body's nutritional needs and nutrient intake from food. In Indonesia, nutritional problems such as malnutrition and other nutritional issues are still prevalent. In this context, the use of machine learning (ML) and data mining (DM) techniques and tools can be very helpful in tackling challenges in the manufacturing sector. Therefore, this study will use the Naïve Bayes Classifier algorithm with a Gaussian model. The data used is the nutritional status data of toddlers from January to July 2023 in Samarinda City. The attributes in this study include Gender, Birth Weight, Birth Height, Age at Measurement, Body Weight, Body Height, ZS BW/A, BW/A, ZS BH/A, and BH/A. The determination of toddlers' nutritional status in this study is based on the BW/BH index, which consists of 6 classes: severe malnutrition, undernutrition, good nutrition, risk of overnutrition, overnutrition, and obesity. From the study conducted, it was found that the Naïve Bayes Classifier algorithm with the Gaussian model can accurately classify toddlers' nutritional status. From the data processing performed, it was found that the accuracy value of the Gaussian model is 81.85%.

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