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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 969 Documents
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Sistem Kontrol dan Monitoring Suhu Air Kolam Ikan Lele Fardu Farm Menggunakan PLC Outseal Mega V.3 dengan Sensor BGT-D718-PH Berbasis IoT Irvan, Mhd Abdinul; Zarory, Hilman; Ullah, Aulia; Faizal, 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.v6i1.5375

Abstract

In cultivating catfish, there are several things that must be considered, namely the water temperature of the catfish pond. High temperatures have a stressful impact on fish, if low temperatures inhibit fish growth. Currently, most catfish farmers check the water temperature manually, by going to the pond to check with a manual measuring instrument, this has shortcomings in terms of accuracy and is time consuming. To overcome this problem, this research discusses controlling and monitoring the temperature of catfish ponds using the Outseal Mega V.3 PLC and Internet of Things (IoT) based BGT-D718-PH Sensor. With Outseal Mega V.3 PLC control which regulates the on and off logic status of devices connected to the PLC, one of which is the Bgt-D718-Ph temperature sensor. This sensor is used as an input to read the pool temperature value which replaces manual measuring instruments, so the system checks the pool water temperature automatically. Then the temperature sensor data is collected and displayed on the Blynk application in real time using Esp8266 via the internet network. The main aim of this research is to increase efficiency in raising catfish by controlling and monitoring the water temperature of catfish ponds. Through the integration of IoT and PLC technology, the system can monitor water temperature in real-time, control the temperature of the pond water with upper and lower limits if the temperature is high by using an aerator as water circulation to stabilize the water temperature of the catfish pond and provide buzzer sound notifications and Blynk notifications. if the pool water temperature is low and does not match the desired range. Research methods include hardware and software design, as well as sensor testing and system testing. The results of research on an IoT-based system are able to control the water temperature of the catfish pond so that it is at a normal temperature and monitor the temperature of the pond water which is displayed on the Blynk platform which is connected directly to the internet. The Bgt-D718-Ph temperature sensor functions well with average reading errors of 1.71% and 1.74%. In this test, research was carried out for 14 days by comparing temperature sensors and thermometer measuring instruments by taking morning and afternoon data. The temperature that occurred during the research was 2 conditions, namely high temperature and low temperature. It was found that there was a decrease in temperature during the day due to the lowest rainy weather on day 4 where the temperature reached 24.6 ℃. Then there was an increase in temperature on the 7th day reaching 35.1℃, which means the pool temperature was exposed to hot sunlight. The average sensor error value was found to be 1.41% in the morning and 1.44% in the afternoon.
Sentiment Analysis of the 2024 Indonesian Presidential Dispute Trial Election using SVM and Naïve Bayes on Platform X Maharani, Zahra Nabila; Luthfiarta, Ardytha; Farsya, Nabila Zibriza
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.v6i1.5380

Abstract

Indonesian presidential dispute trial election are crucial activities in the democratic process where open exchanges of views and opinions occur. Sentiment analysis can help understand public opinion regarding these sessions. This study aims to conduct sentiment analysis of the 2024 Indonesian presidential dispute trial election using the Support Vector Machine (SVM) and Gausian Naïve Bayes (GNB) with Nazief Adriani and Sastrawi stemming methods on Platform X. The research addresses the challenge of uncertainty in interpreting public sentiment towards Indonesian presidential dispute trial election. SVM and GNB was chosen for its ability to classify large and complex data sets. The Nazief Andriani and Sastrawi stemming techniques were employed to reduce words to their base forms, thereby enhancing the quality of text analysis. The study was conducted on Platform X, which provides access to text data from various sources including social media and news platforms. The data used covered specific periods before, during, and after Indonesian presidential dispute trial election. The keywords used for the crawling process are “sidang sengketa pilpress”, “sidang sengketa pemilu”, and “sidang pilpres”. The classification technique is carried out by classifying it into two classes, namely positive and negative. In applying sentiment analysis using machine learning methods, there are several methods that are often used. Based on the results comparation of tests carried out on 2,443 tweets using SVM with Sastrawi stemming method produce the best accuracy of 91.1%, precision 90%, recall 91%., and F1-Score 91%.
Klasifikasi Kematangan Buah Mangga Menggunakan Pendekatan Deep Learning Dengan Arsitektur DenseNet-121 dan Augmentasi Data Permata, Rizkiya Indah; Yanto, Febi; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
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.v6i1.5381

Abstract

Mango is a seasonal fruit in Indonesia. In lowland areas and hot climates, this mango plant can grow abundantly. People who use mangoes generally focus more on the characteristics of the fruit which require a more precise classification to be more certain. Traditional classifications sometimes fail to properly articulate maturity criteria. This research classifies mango ripeness using a deep learning approach with densenet-121 architecture, parameters, learning rate, dropout, and data augmentation. Augmentation is the process of changing or modifying an image in such a way that the computer will detect that the image has been changed is the same picture. The original dataset was 895 data, after being augmented it became 1790 data consisting of three classes, namely ripe mango, young mango, and rotten mango. The test compares the original data and the original data added with augmentation. Accuracy using original data is 95.95%. Meanwhile, using original data combined with augmentation gets an accuracy of 99.73%
Sistem Monitoring Ph Air Kolam Ikan Lele Menggunakan Sensor Bgt-D718-Ph dan PLC Outseal Berbasis Internet of Things Mubarak, Rifqi; Zarory, Hilman; Ullah, Aulia; Faizal, 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.v6i1.5387

Abstract

Catfish farming is one of the choices for communities to meet the need for animal protein and also contributes to the development of the fisheries sector. High demand from consumers in various regions in Indonesia makes the opportunity for catfish farming very promising. Water quality plays an important role in the success of catfish farming, especially pH fluctuations that can affect the health and growth of fish. In an effort to monitor and regulate pH values effectively, the development of a system using Internet of Things (IoT) technology has been carried out. This system allows real-time monitoring of water pH in catfish ponds remotely through the Blynk application. Additionally, this research also includes the design and development of a measuring device using IoT technology based on ESP8266 to monitor catfish pond pH in real-time without physical presence at the location. This device not only provides real-time data display for decision-making considerations but also offers efficiency in time usage. The aim of this research is to develop a real-time water pH monitoring system for catfish ponds at Fardu Farm Pekanbaru, which utilizes the BGT-D718-PH RS-485 sensor and PLC Outseal mega v3 for data processing, as well as communication with the Blynk application through ESP 8266 as the IoT-based HMI. In this study, testing was conducted over a period of 14 days by comparing the readings of the measuring instrument with those of the sensor, resulting in an error of 0.04. Throughout the testing period, the pH levels in the catfish pond fluctuated due to varying weather conditions, ranging from rainy to hot weather. The testing was conducted under two conditions: normal pH and problematic pH. From these tests, it can be concluded that the water pH monitoring system has been successfully developed. Consequently, this system can serve as an effective solution for catfish farmers to monitor pH conditions without the need to be physically present at the location
Perbandingan Algoritma Apriori dan Fp-Growth dalam Pengaplikasian Market Basket Analysis untuk Strategi Bisnis Retail Amelia, Rizky; Darmansyah, Darmansyah; Rismadin, Ahmad Maulana
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.v6i1.5388

Abstract

The advancement of information technology drives businesses to strategize to remain competitive. Retail businesses face challenges from changing consumer behaviors that prioritize convenience and speed in shopping, potentially reducing revenue if businesses fail to adapt quickly. An effective approach involves reanalyzing sales transaction data to identify consumer purchasing patterns, providing guidance for strategic decision-making. One effective technique is data mining using Market Basket Analysis models to analyze shopping baskets and identify correlations between items purchased. This model utilizes algorithms like Apriori and FP-Growth to generate association rules. Preprocessing the dataset to derive frequent itemsets, followed by applying association rules, helps identify significant correlations or patterns within the dataset. The Apriori and FP-Growth algorithms are applied with predefined minimum support and minimum confidence levels. Interpretation involves testing and verifying the discovered patterns against previous facts or hypotheses. The application of these algorithms shows their impact on the dataset size, as the chosen minimum support and confidence levels affect the number of association rules generated. Experiments with FP-Growth and Apriori algorithms indicate that using a minimum support of 0.02 requires longer execution time compared to 0.06. Using a minimum support of 0.02 and minimum confidence of 0.1 yields similar rules across different data divisions (30%, 40%, 50%, and 55% splits) of datasets containing 1200 and 2364 records out of 100% data. Both algorithms achieve 100% accuracy, demonstrating reliability and validity in discovering significant patterns within the same dataset
Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Logistic Regression dan K-Nearest Neighbor Setiawan, Bagus; Baihaqi, Kiki Ahmad; Nurlaelasari, Euis; Handayani, Hanny Hikmayanti
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.v6i1.5389

Abstract

The government has launched the latest innovation in data collection in the realm of population data which relies on digital technology through mobile applications using photos or QR codes which aims to reduce the use of physical prints of identity cards and the availability of blank KTPs with the aim of simplifying the administrative process and no longer requiring population documents. printing or saving in physical format such as an KTP file. In implementing the population identity application, some people feel anxious due to limited internet access, lack of knowledge about the application, as well as concerns about the security and privacy of identity data in digital format. This research aims to conduct sentiment analysis on reviews of digital population identity applications by comparing logistic regression and k-nearest neighbor algorithms. The dataset was taken using the Google Play Scraper library in Python which got 1700 raw data taken from 12-February to 26 March 2024 and then pre-processed and got 1108 clean data. The results of this research show that the comparison between the logistic regression algorithm and k-nearest neighbor algorithm shows that the k-nearest neighbor algorithm is better than the logistic regression algorithm with an accuracy result of 80.43%, a difference of 3.60% compared to k-nearest neighbor. So it can be concluded that the digital population identity application is still considered poor in its use because it has a negative sentiment of 73.9% and it can be seen in this research that the comparison results of the k-nearest neighbor algorithm prove that its performance is better than logistic regression
Optimalisasi Penggunaan Image Stitching dan Seam Carving dalam Pengembangan Tur Virtual Responsif Buana, Pratama Angga; Putri, Astrid Novita; Adinugroho, Suryo
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.v6i1.5392

Abstract

Nglurah Village, Tawangmangu, still faces challenges in effective tourism promotion due to the use of conventional promotional methods. This study employs a mixed-method approach to identify the issues and expectations of tourism managers. The results indicate that 50% of managers struggle with tourism promotion, and 70% believe that the implementation of technology is crucial to promote the village's tourism potential in an attractive and interactive way. This research aims to combine image stitching and seam carving methods to enhance the quality and diversity of responsive virtual tour applications. Image stitching is the process of merging multiple 360° panoramic images into a single comprehensive image that accurately represents the actual scene. Meanwhile, seam carving is a technique for carefully cropping an image to remove less important parts while preserving important areas when the image is resized. The collaboration of these two methods is expected to produce virtual tour applications that can adapt to the shape and size of gadgets without losing important content. This virtual tour application is expected to enhance the user experience in virtual tourism and open up opportunities for technology-based tourism development in Tawangmangu Village. Additionally, this research can increase the attractiveness of Tawangmangu Village tourism and provide a foundation for further research in virtual tour development.
Implementasi Convolutional Neural Network untuk Klasifikasi Tingkat Kematangan Buah Nanas Menggunakan YOLOv8 Prasetya, Syadina A.; Mihuandayani, Mihuandayani; Abast, Yansen; Mangole, Michael; Rahman, Jonathan
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.v6i1.5396

Abstract

Classifying fruit ripeness is a crucial stage in agriculture and food processing, ensuring optimal product quality standards. Specifically for pineapples, assessing ripeness manually requires considerable time and experience. According to data from the Bolaang Mongondow Department of Trade in 2019, around 8.75% of pineapples in Lobong Village—the largest pineapple-producing village in Bolaang Mongondow Raya—were spoiled or wasted, indicating that the harvest timing for pineapples was often inaccurate. In response to this challenge, research was conducted to introduce a deep learning methodology utilizing the Convolutional Neural Network (CNN) with YOLOv8 (You Only Look Once) version 8 to autonomously classify the ripeness stages of pineapples. By developing a pineapple ripeness classification system using the YOLO algorithm, it is expected to address these issues and assist farmers in determining the ripeness level of pineapples for sales and processing purposes. The use of CNN with YOLOv8 is chosen due to its ability to quickly and accurately detect objects based on images in real time. Additionally, the trained YOLOv8 model can classify the ripeness levels of pineapples, thereby helping farmers sort the fruit according to its ripeness stage. Tests conducted to measure the performance of the YOLOv8 algorithm in detecting and classifying pineapple ripeness showed promising results with an mAP value of 81%, Precision of 70.5%, and Recall of 75.9%, producing a satisfactory level of accuracy across various levels. This research can optimize the process of sorting pineapple ripeness stages, thereby improving product quality and enhancing farmers' competitiveness in both domestic and export markets
Travel Content Evaluation through Sentiment and Toxicity Analysis using CRISP-DM 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.v6i1.5397

Abstract

This research, framed by the CRISP-DM methodology, offers a comprehensive analysis of sentiment and toxicity in digital content, focusing on tourism-related videos. Utilizing advanced machine learning models like VADER and TextBlob for sentiment analysis, as well as APIs such as Detoxify and Perspective for toxicity assessment, the study analyzed 25,361 posts, with 23,292 processed for sentiment and 24,171 for toxicity. Various algorithms, including k-NN, DT, NBC, and SVM, were applied with SMOTE to address data imbalance. The SVM algorithm achieved the highest performance with an accuracy of 54.80% and an F-measure of 66.01%, while others showed lower efficacy. The deployment phase integrated these models for real-time analysis, providing actionable insights into user engagement. Findings emphasize the significant impact of sentiments on brand perception and the necessity of managing toxic behavior for a healthier online environment. Despite limitations such as dataset imbalance and model dependency, the study offers valuable recommendations for content creators, advocating for robust moderation and sentiment-based strategies to enhance user interaction. Future research should include diverse datasets and advanced tools to improve the findings' robustness and applicability. This research contributes to understanding digital content dynamics and provides strategic insights for optimizing content creation and user engagement.
Penerapan Metode Naïve Bayes untuk Analisis Sentimen pada Ulasan Pengguna Aplikasi ChatGPT di Google Play Store Hermawan, Tri Ramadhani Putra; Dzikrillah, Akhmad Rizal
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.v6i1.5400

Abstract

ChatGPT is a chatbot application developed by OpenAI. It has attracted a large number of users in a short period of time. User comments are categorized into positive and negative, indicating their sentiments on using this app. Although ChatGPT provides convenience from its various features, it also has its downside if misused. Some people think that people will depend on the information provided by this chatbot and reduce the desire to find out the information themselves, because the information from ChatGPT still uses the old generation model. From this concern, a deeper research on sentiment analysis of people who have used the ChatGPT application is made. It is hoped that this research will be able to collect data on public responses to ChatGPT, both pros and cons. Research data will be taken from reviews of ChatGPT application users in the Play Store. Google Collaboratory with Google Play Scraper will be used during the data collection process. The data that has been obtained will go through a preprocessing stage to be cleaned. After the data is successfully cleaned, the data will go through the process of labeling positive and negative data, and will be classified through the Naïve Bayes method. The study results show that the application of the Naïve Bayes method is able to classify user sentiment using Confusion Matrix with a percentage accuracy value of 94.05%, a percentage precision value of 95% for positive and 81.25% for negative. Then the percentage of recall value for positive is 98.84%, and for negative is 48%.

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