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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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Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score Yunita, Yunita; Fahmi, Muhammad; Salmon, Salmon
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6475

Abstract

Tuition Assistance is money given specifically to students with the aim of alleviating the problem of paying educational costs for less fortunate students so they can continue their education. With the large number of scholarship applicants on a campus, especially Budidarma University, a computerized information system is needed so that the selection of students who receive tuition assistance can run well. One way that can be implemented is by applying data mining with the K-Means algorithm. From the results of applying the data mining method, it can be concluded that there were 10 students who received tuition assistance who were included in cluster 1 and likewise in cluster 2 there were 10 students who did not receive tuition assistance.
Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Algoritma Random Forest Untuk Klasifikasi Data Mining Pada Penyakit Gagal Ginjal Salmon, Salmon; Azahari, Azahari; Ekawati, Hanifah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6476

Abstract

Kidney failure is one of the most common chronic diseases worldwide. This condition occurs when the kidneys lose their ability to filter waste and excess fluid from the blood. Kidney failure is a serious condition that occurs when kidney function decreases significantly or stops altogether. Kidney failure has a wide impact on the physical, mental, and social health of patients. Therefore, early treatment and a holistic approach are needed to minimize its impact. In the health sector, technological advances have enabled more effective processing of medical data through the application of data mining. Data Mining is the process of exploring and analyzing large amounts of data to find patterns, relationships, or valuable information that was previously unknown. Classification in Data Mining is the process of grouping or categorizing data into certain classes or labels based on the attributes or features it has. In the classification itself, there are various algorithms in it such as the K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms are two algorithms that are widely used in classification tasks. Therefore, this study will carry out a comparison process on the performance of the K-Nearest Neighbor algorithm and the Random Forest algorithm. Comparison of data mining algorithm performance to evaluate and determine which algorithm is the most effective and efficient in solving a particular problem based on various evaluation metrics. Overall, the accuracy value obtained is above 90%, but the Random Forest algorithm has better performance. Where the accuracy level results obtained from the Random Forest algorithm are 99.75%. Therefore, the model or pattern produced by the Random Forest algorithm will later be used to assist in the process of diagnosing kidney failure and the Random Forest algorithm is an algorithm that has better performance.
Perbandingan Kinerja Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Darah Tinggi Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6477

Abstract

High blood pressure or hypertension is one of the major health problems in the world. Although this disease can be treated, many individuals are unaware that they have hypertension, because the symptoms are often not visible or felt. Therefore, early detection of high blood pressure is very important to prevent serious complications that can endanger health. In the digital era and advances in information technology, a lot of health data can be used for analysis. One of the rapidly developing approaches to help diagnose disease is by utilizing data mining. Data mining is the process of exploring and analyzing big data to find hidden patterns, information, and knowledge that can be used to support decision making and predictions. One technique in data mining that is often used to predict conditions or diseases is the classification algorithm. However, the comparison of performance between these classification algorithms in the context of hypertension prediction is still limited. This study aims to explore and compare the performance of classification algorithms in predicting hypertension, using a dataset containing medical information about factors that affect a person's blood pressure. The Naive Bayes algorithm is a classification method based on Bayes' theorem and the assumption of independence between features. The C4.5 algorithm is a machine learning algorithm for building decision trees used in data classification. The results of this study are expected to contribute to the development of a data mining-based decision support system that can be used to detect and predict the risk of hypertension. the accuracy value of the Naive Bayes algorithm is 87.01% and the accuracy value of the C4.5 algorithm is 94.72%. From the process that has been carried out, it can be said that the C4.5 algorithm is an algorithm with better performance than the Naive Bayes algorithm. Thus, the model used in the process of diagnosing hypertension is the model of the C4.5 algorithm.
Pengolahan Data Penjualan Pakaian dengan Menerapkan Algoritma Apriori Data Mining Harpad, Bartolomius; Lailiyah, Siti; Yusika, Andi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6478

Abstract

In its input, so far the use of sales transaction data has only been stored as an archive. In fact, the data can be utilized and processed into useful information to increase product sales or product innovation. In this case, sales data analysis needs to be carried out. With information about sales patterns, it can be seen what consumers buy most often. So from consumer purchasing patterns, decision making can also be done by the store related to the products to be sold. The data mining process in analyzing sales data, the Apriori algorithm can be utilized in the sales data process, namely by providing a relationship between sales transaction data. The data in question is sales data on clothes or pants that are ordered so that consumer purchasing patterns are obtained. Thus, the store can use the data to take suitable business actions. In this case, data can be used as a consideration to ensure the next sales strategy. The existence of information about sales patterns can find out what consumers buy most often. The products that are most often purchased by consumers are Hightwais Jeans Snow, Neda Tunik Full Buttons with 100% support for each product. by knowing the products that are most often purchased by consumers, the company can develop a strategy in determining the purchase of clothes and pants to maintain the availability of clothes and pants needed by consumers.
Rancang Bangun Kontrol Air Conditioner Otomatis Menggunakan Algoritma Propotional Integral Derivative (PID) Berbasis Internet of Things Sinaga, Putri; Enri, Jon; Handayani, Ade Silvia
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5690

Abstract

An efficient and responsive air conditioning control system is essential for maintaining thermal comfort as well as energy efficiency in a room. This research develops an AC control system based on the number of people entering or leaving the room using the PID (Proportional-Integral-Derivative) algorithm integrated with Internet of Things technology through the Blynk platform. Infrared proximity and temperature sensors are used to detect the number of people in the room and measure the room temperature. This data is then processed by the ESP32 microcontroller which runs the PID algorithm to generate a control signal that will adjust the AC temperature towards the setpoint. This system allows the AC temperature to be dynamically adjusted based on changes in the number of people in the room, which is expected to optimize energy consumption. In addition, integration with Blynk allows real-time monitoring and control of the system through a smartphone app, providing users with greater flexibility and control. The trial results show that the system is able to respond to changes in the number of people and maintain the room temperature within comfortable limits (setpoint). As such, it offers an effective solution for smarter room temperature management.
Pemanfaatan Transformer untuk Peringkasan Teks: Studi Kasus pada Transkripsi Video Pembelajaran Fadlilah, Muhammad Furqon; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6342

Abstract

Abstract−In the digital era, learning videos are increasingly being used, however, they often contain irrelevant information, making it difficult to comprehend the content. This study proposes an approach based on the Whisper and T5 models to generate text summaries from YouTube educational video transcripts. Whisper is used for speech-to-text transcription, focusing on model variants that offer a low Word Error Rate (WER) and time efficiency. Subsequently, the T5 model is fine-tuned to produce accurate text summaries, with a strategy of segmenting the transcript to address input length limitations. Text preprocessing is not applied as it resulted in better evaluation quality. The results show that the combination of Whisper Turbo and the optimized T5 model provides the best performance, with F1-Scores on the ROUGE metrics of 39.23 (ROUGE-1), 13.17 (ROUGE-2), and 23.84 (ROUGE-L). This approach successfully generates more relevant and comprehensive text summaries, enhancing the effectiveness of video-based learning. Therefore, this research makes a significant contribution to the development of text summarization technology for learning videos.
Prediksi Produksi Kelapa Sawit Menggunakan Algoritma Support Vector Regression dan Recurrent Neural Network Alfakhri, Rezky; Permana, Inggih; Novita, Rice; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6441

Abstract

Oil palm is one of the important plantation crops and a leading commodity in Indonesia. PT. XYZ is a company engaged in receiving Fresh Fruit Bunches (FFB) to be processed into Crude Palm Oil (CPO) and Palm Kernel (PK). So far, the company has conducted statistical analysis with a correction value of 5% - 12% on the production results each month in targeting production results. However, this method is still lacking, because it uses manual calculations and considers estimates from personal experience. Therefore, this research proposes a data mining technique with Support Vector Regression (SVR) and Recurrent Neural Network (RNN) algorithms to predict production output precisely. In this study, testing was carried out on SVR hyperparameters, namely Kernel, C, Gamma, and Epsilon. While in RNN, testing is carried out on the optimizer, and the learning rate. In addition, the window size is also determined through a series of experiments, namely 3, 5, and 7. The comparison results show that the RNN model outperforms SVR with an RMSE value of 0.0928, MAPE of 14.32%, and R2 of 0.6164. The RNN model was then implemented to predict the next 3-month period. The prediction results show that there will be a significant increase in production in the first month, then a slight decrease in the second month, and an increase again in the third month.
Implementation of IndoBERT in Sarcasm Detection using Random Forest Towards Sentiment Analysis Sibarani, Sabrina Adela Br; Purba, Ronsen; Limbong, Ricky Paian
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.5801

Abstract

Sarcasm, a subtle form of irony, often introduces a discrepancy between the literal meaning of words and the intended message, making it a significant challenge for sentiment analysis systems. Misinterpreting sarcasm in social media comments can lead to inaccurate sentiment classification, hindering decision-making processes in areas like customer feedback analysis and social opinion mining. This study addresses this issue by evaluating the effectiveness of sarcasm detection in Indonesian text using a Random Forest Classifier (RFC) integrated with IndoBERT. The research employs 10-fold cross-validation to measure performance. Without IndoBERT, the RFC model achieved average accuracy, precision, recall, and F1-score of 78.83%, 78.83%, 79.01%, and 78.83%, respectively. Incorporating IndoBERT significantly improved performance, with all metrics exceeding 84%. Furthermore, 5-fold cross-validation achieved the highest performance, with all metrics reaching 97.24%. This research contributes to developing more robust natural language processing models tailored to Indonesian linguistic contexts, specifically for sarcasm detection.
Klasifikasi Penerima Bantuan Program Indonesia Pintar (PIP) Pada Siswa SMK Menggunakan Algoritma KNN, NBC dan C4.5 Putra, Tandra Adiyatma; Permana, Inggih; Zarnelly, Zarnelly; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6395

Abstract

The Indonesia Smart Program (PIP) is a government initiative aimed at providing educational assistance to students from underprivileged families. This research was conducted at SMKN 4 Pekanbaru to enhance the accuracy of distributing PIP aid using data mining methods. Three classification algorithms were used to identify students eligible for assistance: K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), and C4.5. The data used in this study included attributes such as parental occupation, income, and the type of transportation used. The data processing involved cleaning, normalization, and splitting into test and training sets. The results showed that the KNN algorithm performed best with an accuracy of 84.20%, precision of 89.83%, and recall of 99.18%. The C4.5 algorithm excelled in model simplicity, while NBC showed less optimal results compared to KNN.
Enhancing Student Sentiment Classification on AI in Education using SMOTE and Naive Bayes Saekhu, Ahmad; Berlilana, Berlilana; Saputra, Dhanar Intan Surya
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6469

Abstract

This study investigates student sentiment regarding the use of artificial intelligence (AI) in education, employing the Naive Bayes model enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues. Class imbalance, a common challenge in sentiment classification, often skews model performance toward majority classes, reducing its effectiveness in recognizing minority classes. To mitigate this, SMOTE was applied to generate synthetic samples for minority classes, achieving a more balanced class distribution. The results demonstrate that incorporating SMOTE improved the Naive Bayes model's accuracy from 65% to 78.87% and significantly increased sensitivity to minority classes. Evaluation metrics, including precision, recall, and F1-score, showed satisfactory performance for certain classes, notably classes 2 and 4. However, challenges remained with class 1, where classification accuracy was lower, indicating inherent complexities in its data patterns. While SMOTE successfully enhanced model performance, it also introduced a potential risk of overfitting, particularly with limited original datasets, highlighting the importance of data quality and size. This research offers actionable insights for educators, developers, and policymakers, emphasizing the need for AI systems in education that are adaptive and responsive to student perceptions. The study concludes that Naive Bayes combined with SMOTE is an effective approach for sentiment analysis in imbalanced datasets. Future research should explore more sophisticated models and larger datasets to achieve more comprehensive and representative outcomes.

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