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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.
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Articles 57 Documents
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Search results for , issue "Vol 6 No 1 (2024): June 2024" : 57 Documents clear
Implementasi Library Textblob dan Metode Support Vector Machine Pada Analisis Sentimen Pelanggan Terhadap Jasa Transportasi Online Laia, Yardiana; Berutu, Sunneng Sandino; Sumihar, Yo’el Pieter; Budiati, Haeni
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.v6i1.5090

Abstract

Online transportation services have become an inseparable part of human life today. This research aims to develop an effective sentiment analysis method to measure public opinion about the quality of online transportation services, which has a significant impact on company reputation and public acceptance of these services. In this research, we propose the use of TextBlob library to perform sentiment analysis of public opinion on online transportation services. This library allows to measure the positive, negative and neutral polarity and subjectivity of opinion text collected from Gojek, Maxim and Grab application reviews through Google Play Store. Sentiment analysis steps are carried out starting from data preparation, data pre-processing, data labeling using the Text Blob library. Furthermore, building a sentiment classification model based on the Support Vector Machine (SVM) algorithm through training and testing stages. Model testing results are evaluated with confusion matrix. The results of the analysis with textblob showed that online transportation received the highest positive sentiment of 40.1%, followed by neutral sentiment of 26.7% and negative sentiment of 25.2%. Meanwhile, the model performance measurement results show that the precision obtained the highest value in positive sentiment of 0.93. The recall parameter reaches the highest value in negative sentiment of 0.95 and f1-score in neutral and positive sentiment of 0.92. Thus, this research not only contributes to the development of sentiment analysis classification, but also has a significant practical impact in improving online transportation services and providing useful information to the public, thus encouraging innovation and continuous improvement in online transportation services.
Prediction of Theft with Machine Learning Technology at Police Station Hadmanto, Aditya; Prianggono, Jarot
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.v6i1.5107

Abstract

This study originated from the increase in theft cases in the jurisdiction of Banjarbaru District Police which resulted in material and psychological losses for victims and disturbed the overall sense of security of the community. The research aims to develop a method that can assist the police in preventing and tackling theft crimes more effectively using machine learning algorithms. Research methods include research design, quantitative approach, and data collection and analysis techniques. The data analyzed included various categories of relevant information, such as the victim's gender, age, occupation, location of the incident, as well as details related to the modus operandi and losses suffered by the victim. The main data used is data on victims of theft crimes in the Banjarbaru Police jurisdiction during the 2019-2023 period. Data collection was carried out using primary data available from Min Ops Reskrim Polresta Banjarbaru. using the K-Nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms to process historical data on theft crimes in Banjarbaru. The results reveal the general characteristics of theft cases, including time patterns, locations, and modus operandi, and compare the effectiveness between KNN and NB algorithms in predicting theft crimes. The conclusions emphasize the potential of machine learning in identifying theft patterns and provide recommendations for further development to support better decision-making and planning of crime prevention strategies
Market Basket Analysis to Determine Muslim Clothing Supply in Indonesia Ahead of Eid Al-Fitr Indra Gunawan, Gun Gun; Aji, Tri Wahyu; Juliane, Christina
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.v6i1.5162

Abstract

Enterprise transaction data is a valuable source of insights for companies to increase sales. In preparation for Eid al-Fitr, this study leverages Market Basket Analysis with the FP-Growth algorithm to uncover buying patterns within Indonesia's Muslim clothing market. Market Basket Analysis is one way to explore information through data to find customer buying patterns that are often used as insight into company decision-making. The data processing method uses the FP-Growth algorithm, which generates association rules based on calculating the frequency of occurrence of itemsets. Using the FP-Growth algorithm gives good results in the determination of association rules. From Muslim fashion store transaction data over the last 12 months, it produced 30 item set patterns with a minimum support value of 0.009 and confidence of 0.58. By identifying these in-demand product pairings, businesses can make informed decisions about stock allocation. This ensures they have the right combination of items available to meet customer needs during the surge in demand leading up to Eid al-Fitr. Additionally, these patterns can inform targeted promotional campaigns and strategic bundling initiatives, maximizing sales and customer satisfaction throughout this critical sales period.
Klasifikasi Suara Anjing Menggunakan Pretrained Model Yet Another Mobile Network Berbasis Convolutional Neural Network Djuardi, Rich Deshan; Rochadiani, Theresia Herlina
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.v6i1.5165

Abstract

In everyday life, pets such as dogs often become an inseparable part of human life. Motivations for keeping a pet can vary from individual to individual, ranging from the need for a loyal companion to the responsibility of caring for another living creature. Among the various choices of pets, dogs are often considered the most loyal and loyal friends towards humans. This uniqueness makes many people choose to keep dogs as part of their family. Often, dog owners may not understand the message that the sounds produced by their beloved pets are trying to convey. These dog sounds have a special purpose that can reflect various emotions, such as joy, sadness, or anger. A dog's voice can also be an indicator of their health that owners need to pay attention to. The main focus of this research is to develop dog voice classification technology to help owners understand and communicate with their pet dogs. In this research, a pre-trained YAMNet model is used as a basis for classifying various audio events. The model training process uses the CNN algorithm contained in the YAMNet architecture. The total data used was 373 data which were classified into 4 classes, namely, bark, howling, growling, whimper. The results of this research model achieved 97.8% accuracy with precision, recall and f1-scores for each class >= 95%.
Analisis Sentimen Terhadap Publisher Rights Dalam Mengunggah Konten Digital Menggunakan Ensemble Learning Putri, Anisa; Mustakim, Mustakim; Novita, Rice; Afdal, M
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.v6i1.5179

Abstract

Digital content encompasses various forms of information, ranging from informative text to interactive videos. YouTube, as one of the most popular social media platforms, is widely used in Indonesia. However, the proposed Publisher Rights Bill or the Draft Presidential Regulation on the Responsibility of Digital Platforms for Quality Journalism has sparked debate. In the context of YouTube, this regulation has the potential to threaten content creators. Negative reactions from various parties highlight concerns about the impact of this regulation. Therefore, this study aims to analyze sentiment towards Publisher Rights in the uploading of digital content using an ensemble learning approach. The analysis found that 60% of the sentiment was negative, reflecting concerns about copyright, royalties, or ethical issues. A total of 32% of the sentiment was neutral, indicating uncertainty or a lack of information, and only 8% of the sentiment was positive, supporting the policy of protecting publisher rights and recognizing their value and contributions. This study employed ensemble techniques based on Bagging (Random Forest) and Boosting (Adaboost), where the accuracy of Random Forest was higher at 83% compared to Adaboost's accuracy of 68%.
Perbandingan Algoritma Linear Regression, Support Vector Regression, dan Artificial Neural Network untuk Prediksi Data Obat Putri, Suci Maharani; Novita, Rice; Mustakim, Mustakim; Afdal, M
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.v6i1.5184

Abstract

Regression is a crucial focus in various fields aiming to forecast future values to aid decision-making and strategic planning. Different regression algorithms have their advantages and disadvantages, and their performance can vary depending on the data characteristics. Therefore, further analysis is needed to identify the appropriate algorithm that provides the best solution for the problem at hand. This study compares three popular regression algorithms: Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) to predict drug data at a pharmacy in Riau province. Currently, the pharmacy lacks an accurate method for estimating monthly drug needs, relying instead on rough estimates. This often results in either shortages or overstock, leading to losses, especially if the drugs expire. Three types of drugs, namely Amoxicillin, Antacids, and Paracetamol were selected to test the proposed algorithms. The analysis and comparison show that the SVR algorithm outperforms the others on all three drug types when focusing on the RMSE metric. However, when the focus is on the MAPE metric, the ANN algorithm proves to be superior. Although LR does not excel in any metric, all three algorithms (LR, SVR, and ANN) have MAPE values below 10%, indicating highly accurate predictions. This accuracy is evidenced by the prediction results of all proposed models, which effectively follow the patterns and trends in the actual data
Analisis Potensi Bencana Tanah Longsor di Kabupaten Banjarnegara Menggunakan Interpolasi Inverse Distance Weigthed (IDW) Supit, Christanti Ekkelsia; Prasetyo, Sri Yulianto Joko
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.v6i1.5187

Abstract

Landslides are a common disaster in Indonesia, especially in Banjarnegara Regency, caused by geomorphology and tropical climate. This is triggered by several factors, namely high rainfall and slope steepness, impacting communities and resulting in losses and even fatalities. According to data obtained from the BNPB website for the period of 2018-2023, there were 51 landslide disasters. Based on this background, the research problem formulates an analysis of landslide-prone areas using rainfall data, classification, and overlay techniques. The research objective is to produce mapping of areas potentially prone to landslides. The study discusses the analysis of rainfall data and slope classification, followed by overlay techniques to produce mapping. The research is supported by the Inverse Distance Weighted (IDW) method and overlay technique. The results obtained from the study include rainfall maps, slope maps, and landslide-prone maps from overlay results. Thus, based on the research findings, the conclusion is drawn that out of a total of 20 districts, there are 7 districts with a very high potential for experiencing landslides, namely Susukan, Mandarija, Madukara, Pagedongan, Sigalu, Pandanarum, and Pagetan
Applying Data Mining Techniques to Investigate the Impact of Smoking Prevalence on Life Expectancy in Indonesia: Insights from Random Forest Models Dalimunthe, Abdul Hakim; Samsir, Samsir; Subagio, Selamat; Siagian, Taufiqqurrahman Nur; Watrianthos, Ronal
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.v6i1.5201

Abstract

This study investigates the relationship between smoking prevalence and life expectancy in Indonesian provinces using data mining techniques, specifically focusing on the application of random forests. The primary objective is to quantify the potential impact of reducing smoking prevalence on population health outcomes. Data were sourced from the Indonesian Central Bureau of Statistics, which included life expectancy and smoking prevalence data from 2021 to 2023. The methodology involved aggregating life expectancy data from the district to the province level, followed by the application of a random forest model to predict life expectancy based on smoking prevalence and other socioeconomic indicators. Key findings indicate a weak to moderate negative correlation between smoking prevalence and life expectancy, with higher smoking rates associated with lower life expectancies. Predictive modeling suggests that a reduction in smoking prevalence could lead to significant improvements in life expectancy. For example, a 5% reduction in smoking rates could increase the average life expectancy by approximately 0.3 years, while a 15% reduction could result in an increase of about 0.9 years by 2025. These results underscore the detrimental impact of smoking on population health and highlight the importance of effective tobacco control measures. The predictive models developed in this study provide valuable information for policymakers, enabling targeted public health strategies and resource allocation. This research contributes to the field by demonstrating the utility of data mining techniques in public health and offering a comprehensive analysis of the relationship between smoking and life expectancy in Indonesia. The findings advocate for the urgent implementation of smoking cessation programs to enhance life expectancy and improve public health outcomes
Implementation of Data Mining for Interpretation of KSE Scholarship Applicant Number Data using Naive Bayes Algorithm Purnama, Riyan Hidayah; Ikhwan, Ali
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.v6i1.5203

Abstract

The purpose of this study is to interpret the large number of KSE scholarship applicant data, which is expected to provide a positive contribution in developing the KSE scholarship branding strategy, optimizing resource allocation and increasing the attractiveness of companies to allocate their CSR funds to the Karya Salemba Empat Foundation using data analysis techniques. The problem currently experienced is that the Karya Salemba Empat Foundation has been selecting KSE scholarship recipients manually, which results in the decision-making process not being able to be carried out quickly, accurately and efficiently. As one way to improve data accuracy, a method or computational model is needed in the form of a machine learning algorithm using the Naive Bayes method. With this Naive Bayes method, it is very appropriate to use to produce Knowledge. This study shows how the Karya Salemba Empat Foundation can utilize data to increase its value. From the results of the test carried out using 4,492 rows of data and 6 data variables and the pattern accuracy of 92% with an error margin of 8%, it shows that the naïve bayes method is almost perfect in processing its data. The results of this study are expected to provide in-depth insight into how the application of data science can help the Karya Salemba Empat Foundation increase its appeal and strategy
Perbandingan Kinerja Klasifikasi Penyakit Ginjal Menggunakan Algoritma Support Vector Machine (SVM) dan Decision Tree (DT) Madani, Puja Milenia Sriwildan; Rohana, Tatang; Baihaqi, Kiki Ahmad; Fauzi, Ahmad
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.v6i1.5206

Abstract

Chronic Kidney Disease is one of the deadliest diseases. In the early stages, the disease may go undetected, so patients tend to take it lightly, however, the disease can progress little by little and become serious without being detected. This can lead to complications of other diseases and can cause permanent damage to the kidney organs. Therefore, this study aims to classify individuals who are at risk of having Chronic Kidney Disease which can help medical personnel in an effort to reduce the number of people with the disease. This study uses Chronic Kidney Disease data obtained from the UCI Repository web. The data has 25 attributes with 400 rows. This research compares the Support Vector Machine and Decision Tree algorithms and uses the Confusion Matrix evaluation method. The results showed that the Support Vector Machine algorithm has superior accuracy, precision, recall, and f1-score results compared to the Decision Tree algorithm. The accuracy of the Support Vector Machine algorithm is 97.5, precision is 0.98, recall is 0.96, and f1-score is 0.97. While the Decision Tree algorithm obtained accuracy of 92.5, precision of 0.92, recall of 0.90, and f1-score of 0.91. with these results, this research can be continued into an application that can classify individuals at risk of Chronic Kidney Disease

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