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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 64 Documents
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Search results for , issue "Vol 6 No 2 (2024): September 2024" : 64 Documents clear
Penerapan Algoritma K-Medoids dan FP-Growth dengan Model RFM untuk Kombinasi Produk Pertiwi, Tata Ayunita; Afdal, M.; Novita, Rice; Mustakim, Mustakim
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.v6i2.5268

Abstract

Competition in the business world has increased, resulting in companies having to optimize sales and retain their customers. Customers are an important company asset that must be well looked after. The aim of customer segmentation is to understand customer purchasing behavior so that companies can implement appropriate marketing strategies. Aurel Mini Mart is a retail business that does not yet consider the recency, frequency and monetary value of customer shopping. So far, promotions have been carried out only based on estimates, without taking into account accurate data and information. This research combines the RFM model with data mining techniques to segment customers. Based on the 5 clusters formed from the clustering process, gold customers are in cluster 1 which has high loyalty with low recency value, high frequency and high monetary value. This shows that customers in this segment often make purchases for quite large amounts of money. Meanwhile, customers in clusters 2, 3, 4, and 5 are dormant customers who rarely make transactions and the amount of money spent is also small. After the customer segmentation process is complete, the next step is to use the FP-Growth Algorithm to associate the products purchased by customers. This aims to obtain a better product combination, so that the sales strategy can be more effective and the company can make a profit.
Sentimen Analisis Social CRM Pada Media Sosial Instagram Menggunakan Machine Learning Untuk Mengukur Retensi Pelanggan F. Safiesza, Qhairani Frilla; Afdal, M; Novita, Rice; Mustakim, Mustakim
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.v6i2.5269

Abstract

To create and maintain a superior competitive advantage in a knowledge-based economy, businesses must be able to utilize data and manage customer relationships through the implementation of Customer Relationship Management (CRM), particularly Social CRM. Social CRM is a renewal of business strategy that is created to engage customers in a collaborative conversation and create mutually beneficial value in a trusted and transparent business environment. Seeing this development as one of the successful culinary companies in the Souvenir sector in Pekanbaru, the company must be able to process all the information obtained. Currently, the company has never analyzed comments on social media, especially the Instagram account. These comments are useful for evaluation material and can be a parameter of customer satisfaction and to see the potential for customer retention. To assess positive and negative comments on the Instagram account, sentiment analysis can be carried out using machine learning, namely 3 classification algorithms, namely Naive Bayes Classifier (NBC), Support Vector Machine (SVM) and Random Forest (RF). The sentiment results show that the SVM and NBC algorithms obtain the best accuracy of 74.26% compared to RF, and the results of the social CRM analysis show that customers are more satisfied with the company in terms of products, services, and actions taken by the company, so that the company is considered capable of retaining its customers.
Analisis Sentimen Masyarakat Terhadap Pinjaman Online di Twitter Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor Afandi, Rival; Afdal, M; Novita, Rice; Mustakim, Mustakim
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.v6i2.5300

Abstract

The very rapid development of technology has had a big impact on humans. The influence of technological developments that we can feel is in the financial sector. One thing that is quite popular lately is online loans. Pinjol or online loan is a fast and easy online money lending service via an application or website, with fast approval and disbursement, but often has high interest and short tenors. On Twitter, review comments and information used are stored in text form. One of the processes for retrieving text mining information in the text category is Sentiment Analysis to see whether a sentiment or opinion tends to be Positive, Negative or Neutral in the reviews of Pinjol application user comments. In the data collection results there were 600 initial data, namely 122 Positive reviews, 432 Negative reviews and 43 Neutral reviews. Then the sentiment classification process using the Naive Bayes and K-NN algorithms produces accuracy, precision and recall of 68%; 83% and recall 74% on the Naive Bayes algorithm, while the results of accuracy, precision and recall on K-NN are 72%; 74% and recall 96% with experiments using 80% training data and 20% test data
Implementasi Algoritma Random Forest Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online Digoogle Play Store Wibisono, Yudistira Arya; Afdal, M.; Mustakim, Mustakim; Novita, Rice
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.v6i2.5368

Abstract

Online lending programs are examples of financial service platforms offered directly by commercial fintech players. However, there are rampant cases of fraud and unethical actions by some online lenders such as threatening and harassing billing methods due to late payments. This research aims to classify sentiment from user reviews of online loan applications on the Google Play Store into positive, negative, or neutral categories. This research conducts sentiment analysis of user reviews of online loan applications such as AdaKami, AdaModal, Cairin, FinPlus and UangMe using a text mining approach. This approach can perform sentiment classification on user reviews quickly. Data was collected using the scrapping technique on the Google Play Store and obtained a total of 200 data on each online loan application. The modeling used in this research is the division of training data and test data as much as 80:20. The highest accuracy results using the Random Forest algorithm are Cairin and UangMe applications with 85% accuracy. While the application that gets the lowest accuracy result is the AdaModal application with 75% accuracy. A visualization analysis using word clouds was also conducted to understand the context of user reviews of the pinjol apps. The results show that users almost always discuss loan limits in every sentiment across the five apps.
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%.
Deteksi Potensi Depresi dari Unggahan Media Sosial X Menggunakan IndoBERT Situmorang, Gilbert Fernando; Purba, Ronsen
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.v6i2.5496

Abstract

Over the past few decades, mental disorders such as depression have increased and become a serious public health issue. Many affected individuals choose not to seek professional support due to social stigma. Social media platforms like X provide opportunities to study mental health on a large scale because users often share their personal experiences and emotions. However, there are challenges in understanding language patterns and context in posts, necessitating appropriate techniques and models to effectively detect potential depressions. Utilizing Natural Language Processing (NLP) techniques, this study analyzes 37,554 texts from social media posts to detect potential depressions. This study employs the IndoBERT model, an adaptation of BERT trained on Indonesian text data, to identify potential depression from social media texts. Data were collected through scrapping using negatively and positively connotated keywords, which were consulted with psychiatrists. The text pre-processing includes case folding, text cleaning, spell normalization, stopword removal and stemming. The data were then labeled using the IndoBERT emotion classification model, categorizing negative emotions as depression and positive emotions as normal. The model was trained and evaluated using accuracy, precision, recall, and F1-score metrics, with the best results showing an accuracy of 94.91%, precision of 94.91%, recall of 94.91%, and an F1-score of 94.91%. The results indicate that the IndoBERT model is effective in detecting potential depression from social media texts. However, there are limitations due to the reliance on social media posts, which may not fully reflect the users’ emotional conditions.

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