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A Workshop on Basic Statistics With The R Programming Language at SMA Erenos Tangerang Valensius Jimy; Dhela Asafiani Agatha; Ferdinand Nathaniel Widjaya; Bakti Siregar
Jurnal Syntax Transformation Vol 4 No 11 (2023): Jurnal Syntax Transformation
Publisher : CV. Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jst.v4i11.869

The goal of the article is to find out and interpret the effect of giving a workshop on basic statistics with the R Programming Language at SMA ERENOS Tangerang. Researchers used descriptive statistics and basic inferential statistics to analyse student performances. The sample was taken as seventy-two students from SMA Erenos, where the actual number of students was ninety-six for grades X and XI majoring in science and social studies. The score of students was carried out directly from the Quizziz website and it is divided into two sessions, namely the Pre-Test and Post-Test. The results of this study obtained a real influence on learning basic statistics before and after the workshop. Therefore it can be concluded that when students have been given the right method to learn statistics or any other subject, it would be increased the performance of the students.

Analisis Data dalam Supply Chain Management: Klasifikasi dan Pemantauan Data Gudang PT Rajawali Nusantara Indonesia Natalie, Karen; Siregar, Bakti
FARABI: Jurnal Matematika dan Pendidikan Matematika Vol 7 No 1 (2024): FARABI: Jurnal Matematika dan Pendidikan Matematika
Publisher : Program Studi Pendidikan Matematika FKIP UNIVA Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47662/farabi.v7i1.705

This internship report discusses the application of data analysis in supply chain management, with a focus on classification and monitoring of warehouse data at PT Rajawali Nusantara Indonesia. In this report, Excel and R Studio are adopted as data analysis tools to improve efficiency in warehouse management. This research covers two main aspects: first, implementation of a warehouse data classification system using R Studio and Excel to optimize the allocation and placement of goods. Second, using R Studio and Excel for real-time monitoring of warehouse activities, including item selection, delivery, and inventory conditions. Through an experimental approach and historical data analysis, this research evaluates the impact of using Excel and R Studio on operational efficiency and warehouse response speed. The results are expected to provide insight for companies in improving warehouse management.

ANALYZING THE INFLUENCE OF VARIOUS FACTORS ON GENERATION Z'S BEAUTY PRODUCT PURCHASE BEHAVIOR Natalie, Karen; Siregar, Bakti
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.743

In the digital era, Generation Z predominantly relies on digital platforms to select beauty products, both local and international, significantly impacting their purchasing decisions. This study investigates the influence of social media marketing, brand awareness, quality, price, online customer reviews, and discounts on Generation Z’s purchasing behaviours using multiple linear regression and correlation coefficients, analysed with R Studio and SPSS Statistics 25. A quantitative approach was employed, utilizing a questionnaire to collect data from 213 respondents. The results indicate that for international beauty products, social media marketing, brand awareness, product quality, and discounts are significant predictors of purchase intention (R² = 0.832). Conversely, for local beauty products, the key factors influencing purchase decisions are quality, price, online customer reviews, and discounts (R² = 0.790). These findings highlight that the determinants of Generation Z’s purchasing decisions vary based on the product type. While international products are more influenced by marketing and branding, local products are more affected by price and customer feedback. These insights are crucial for beauty brands aiming to tailor their marketing strategies effectively to engage Generation Z.

Analisis Segmentasi Pelanggan Toko Online Marketplace Berdasarkan Recency, Frequency, Monetary, Time, Satisfaction (Rfmts) Menggunakan Algoritma K-Medoids Clustering Windjaya, Putri Angelina; Siregar, Bakti
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 2 (2024): Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.144

The marketplace is an e-commerce website that applies traditional market concepts and implements them online. Due to the rapid development of the times, a business will seek various ways to maintain its business so that it continues to grow and generate a high income. One way is to build and maintain long-term relationships with customers. Therefore, it is necessary to implement a marketing strategy based on customer relationship management, all of which aims to increase turnover and profit while retaining customers. From these problems, this study applies customer segmentation to detect diversity among customers, so those segments represent potential customers to improve marketing strategies. This segmentation needs to consider the value of the customer's recency, frequency, monetary, time, and satisfaction (RFMTS) variables. Recency is the customer's length of time since the last payment. Frequency is how often customers make transactions. Monetary is the number of transactions made by the customer. Time is the time interval between two consecutive purchases by a customer. Satisfaction is the level of customer satisfaction based on the total rating and number of reviews. This study uses the K-Medoids Clustering algorithm to perform segmentation according to customer characteristics. This study uses primary data from a database of one of the online marketplace stores in Indonesia.

Analisis Klasifikasi Diagnosa Penyakit Diabetes Melitus Berdasarkan Komparasi Algoritma Supervised Learning Siridion, Sherly Taurin; Siregar, Bakti
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 3 (2024): Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.159

Diabetes mellitus is a serious health problem that threatens various age groups, including children, teenagers, and adults. In 2021, the mortality rate due to diabetes mellitus reached alarming levels, making it a global threat, especially in Indonesia, where the number of patients reached 19.5 million. Efforts to address diabetes mellitus include early prediction of the disease's risk in patients, and machine learning approaches have shown potential in this regard. This study employs a quantitative method by utilizing secondary data from the UC Irvine Machine Learning Repository titled "Early Stages Diabetes Risk Prediction". The data was obtained from questionnaires filled out by diabetes patients at Sylhet Diabetes Hospital and validated by healthcare professionals. A total of 520 data samples with 17 attributes were used for analysis. The tested Supervised Learning algorithms include Logistic Regression, K-Nearest Neighbour, Support Vector Machine, Random Forest, Naïve Bayes Classifier, Artificial Neural Network, Decision Tree C4.5, and Gradient Boosting Classifier. The research findings reveal that the Random Forest algorithm achieved the highest accuracy of 98.71% in diagnosing diabetes mellitus. This study significantly contributes to enhancing the understanding of diabetes mellitus and has the potential for further development in finding the best algorithm for early disease prediction. It is hoped that this research will make a significant contribution to the efforts in preventing and managing diabetes mellitus, ultimately improving the quality of life for patients and reducing its impact on the population.

Analisis Segmentasi Pelanggan Toko Online Marketplace Berdasarkan Recency, Frequency, Monetary, Time, Satisfaction (Rfmts) Menggunakan Algoritma K-Medoids Clustering Windjaya, Putri Angelina; Siregar, Bakti
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 2 (2024): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.144

The marketplace is an e-commerce website that applies traditional market concepts and implements them online. Due to the rapid development of the times, a business will seek various ways to maintain its business so that it continues to grow and generate a high income. One way is to build and maintain long-term relationships with customers. Therefore, it is necessary to implement a marketing strategy based on customer relationship management, all of which aims to increase turnover and profit while retaining customers. From these problems, this study applies customer segmentation to detect diversity among customers, so those segments represent potential customers to improve marketing strategies. This segmentation needs to consider the value of the customer's recency, frequency, monetary, time, and satisfaction (RFMTS) variables. Recency is the customer's length of time since the last payment. Frequency is how often customers make transactions. Monetary is the number of transactions made by the customer. Time is the time interval between two consecutive purchases by a customer. Satisfaction is the level of customer satisfaction based on the total rating and number of reviews. This study uses the K-Medoids Clustering algorithm to perform segmentation according to customer characteristics. This study uses primary data from a database of one of the online marketplace stores in Indonesia.

Analisis Klasifikasi Diagnosa Penyakit Diabetes Melitus Berdasarkan Komparasi Algoritma Supervised Learning Siridion, Sherly Taurin; Siregar, Bakti
Mutiara: Multidiciplinary Scientifict Journal Vol. 2 No. 3 (2024): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v2i2.159

Diabetes mellitus is a serious health problem that threatens various age groups, including children, teenagers, and adults. In 2021, the mortality rate due to diabetes mellitus reached alarming levels, making it a global threat, especially in Indonesia, where the number of patients reached 19.5 million. Efforts to address diabetes mellitus include early prediction of the disease's risk in patients, and machine learning approaches have shown potential in this regard. This study employs a quantitative method by utilizing secondary data from the UC Irvine Machine Learning Repository titled "Early Stages Diabetes Risk Prediction". The data was obtained from questionnaires filled out by diabetes patients at Sylhet Diabetes Hospital and validated by healthcare professionals. A total of 520 data samples with 17 attributes were used for analysis. The tested Supervised Learning algorithms include Logistic Regression, K-Nearest Neighbour, Support Vector Machine, Random Forest, Naïve Bayes Classifier, Artificial Neural Network, Decision Tree C4.5, and Gradient Boosting Classifier. The research findings reveal that the Random Forest algorithm achieved the highest accuracy of 98.71% in diagnosing diabetes mellitus. This study significantly contributes to enhancing the understanding of diabetes mellitus and has the potential for further development in finding the best algorithm for early disease prediction. It is hoped that this research will make a significant contribution to the efforts in preventing and managing diabetes mellitus, ultimately improving the quality of life for patients and reducing its impact on the population.

Determination of Mount Eruption Insurance Premiums in Indonesia Based on Collective Risk and Level of Risk Spread Kalfin, Kalfin; Sukono, Sukono; Januaviani, Trisha Magdalena Adelheid; Siregar, Bakti
International Journal of Business, Economics, and Social Development Vol. 5 No. 1 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i1.576

Volcano eruption insurance is an insurance product that provides financial protection to policy holders who experience losses due to volcanic eruptions. This insurance product is still rarely developed in Indonesia, even though this country is very vulnerable to natural disasters. Therefore, this research aims to carry out a simulation of determining volcanic eruption disaster insurance premiums based on collective risk and the level of risk distribution. The data used in this research is the frequency of events and economic losses due to volcanic eruptions. Event frequency and loss data are analyzed using a collective risk model. Apart from that, determining insurance premiums also takes into account loading factors and the level of risk distribution from volcanic eruption disaster data. From the results of the analysis, it was found that natural disaster insurance premiums had increased, along with an increase in the loading factor provided. In addition, insurance premium expenses are influenced by the collective risks faced by customers. The greater the collective risk faced, the greater the insurance premium that customers must bear. Based on the results of the estimates carried out, it is hoped that this research can provide an overview to the Indonesian government in estimating the Mount Meletus insurance scheme. Meanwhile, insurance companies can get an idea of determining insurance premiums according to conditions in the field.

Implementation of K-means Clustering Algorithm for the Indonesian Stock Exchange Siregar, Bakti; Yosia, Yosia
JURNAL SISFOTEK GLOBAL Vol 14, No 1 (2024): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v14i1.10860

In the dynamic field of financial markets, effective analysis and understanding of stock market behavior are very crucial for investors, analysts, and policymakers. This study investigates the implementation of the K-means algorithm for clustering stocks listed on the Indonesian Stock Exchange (IDX). The main objectives of this research include exploring IDX's clustering patterns, identifying groups based on their trading characteristics, and evaluating algorithm performance. Some challenging parts have been addressed, such as data quality, feature selection, determining the optimal number of clusters, scalability, interpretability, and evaluation. Precise data preprocessing, feature engineering, and algorithm optimization provide insight into the clustering structure of the Indonesian stock market, helping investors in portfolio diversification, risk management, and strategic decision-making. The results show the potential of the K-means algorithm in thoroughly uncovering important patterns on the IDX, thereby contributing to the advancement of market analysis methodologies adapted to the Indonesian financial environment.

KLASIFIKASI HARGA MOBIL MENGGUNAKAN METODE DECISION TREE ALGORITMA C4.5 Anggraiwan, Yonathan; Siregar, Bakti
Computatio : Journal of Computer Science and Information Systems Vol. 6 No. 2 (2022): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v6i2.19994

Kendaraan bermotor seperti mobil sudah menjadi kebutuhan sehari-hari bagi banyak orang, khususnya di perkotaan. Kendaraan tersebut memiliki berbagai bagian yang menentukan harga, seperti mesin, jumlah silinder, hemat borosnya penggunaan bahan bakar, dan sebagainya. Sehingga sangat penting bagi para pengguna untuk membeli mobil yang sesuai dengan kebutuhan dan kapasitas keuangan mereka. Selain itu, penentuan harga mobil ini juga menjadi hal yang harus dipertimbangkan agar konsumen dapat menjangkaunya. Oleh karena itu, pada penelitian ini dilakukan pemodelan klasifikasi harga mobil, menggunakan metode decision tree algoritma C4.5 dengan aplikasi RapidMiner, dimana harga dari mobil tersebut dipengaruhi oleh beberapa faktor, seperti besar tenaga kuda pada mesin mobil, jenis transmisi, jumlah silinder, dan faktor lainya. Hasil penelitian ini diperoleh tingkat akurasi model klasifikasi sebesar 82.1% dan R-square sebesar 98.5%, yang berarti variabel independent sangat mempengaruhi variabel dependent pada model ini. Jika ditinjau dari model regresinya dan melihat nilai Root Mean Square Error (RMSE) diperoleh 6.95%, artinya adalah model ini dapat memprediksi harga mobil dengan ketepatan sebesar 93%.

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