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Universitas Labuhanbatu
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Penerapan Metode Naïve Bayes Dalam Memprediksi Kepuasan Pelanggan Terhadap Pelayanan (Studi Kasus : Brastagi Supermarket Rantauprapat) Putra, Fasdiansyah; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti; Bangun, Budianto
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7938

Abstract

Supermarkets are crowded shopping centers and have a high potential for violations against consumers, especially because most of the products sold are basic foodstuffs. This study aims to predict the level of customer satisfaction with service in Brastagi supermarket Rantauprapat by applying the Naïve Bayes method of Data Mining algorithm. The primary data collection process is done through the distribution of online questionnaires using Google Form to customers. To ensure the validity of the data, further verification was carried out through direct interviews with customers as well as supermarket managers. The results of this study are expected to provide in-depth analysis and new information for the management of Brastagi Supermarket Rantauprapat regarding customer satisfaction, which can be used as a basis for improving service quality in the future.
Analisis Sentimen Ulasan Produk Suncreen Wardah Pada Marketplace Shopee Menggunakan Metode Naïve Bayes Rambe, Nurhayati; Harahap, Syaiful Zuhri; Ritonga, Ali Akbar; Bangun, Budianto
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7992

Abstract

The development of the digital world and the popularity of online marketplaces such as Shopee have changed the way consumers interact and review products. Reviews of Wardah sunscreen products, which have an important role in skin health, are one of the most widely found. Understanding the sentiment of these reviews is crucial for manufacturers to improve product quality. Therefore, this study aims to analyze and classify consumer sentiment towards Wardah sunscreen products on Shopee. Using the Naïve Bayes classification method, the reviews will be categorized into positive, negative, and neutral sentiments to get an overall picture of the public perception of the product.
Pengembangan Sistem Informasi Berbasis Web Sebagai Sistem Pengelolaan Nilai Sekolah Menengah Siswa SMP Negeri 2 Satap Kualuh Hilir Dengan Menggunakan Metode End User Computing Satisfacation Siregar, Ade Elvi Rizki; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti; Bangun, Budianto
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7879

Abstract

Research is a crucial element in the learning process to measure students ' understanding and evaluate the quality of Education. However, the process of managing student grades is often complicated, especially with dynamic curriculum changes in Indonesia, such as the transition from the 2013 curriculum to The Independent curriculum. Based on the problems in SMP Negeri 2 Satap Kualuh Hilir, this study aims to design and build a web-based student Value Management Information System. The development of this system is expected to be a solution to manage value more quickly, accurately, and efficiently. In addition, this system is designed to maximize the utilization of computer network facilities that are already available in schools, so as to assist teachers and schools in producing assessment reports that are in accordance with the applicable curriculum.
Analisis Sentimen Pelayanan Pembayaran Pajak Menggunakan Metode Algoritma Naïve Bayes Pada Kantor Badan Pendapatan Daerah Labuhanbatu Utara Dengan Menggunakan RapidMiner Purba, Mhd. Rafly; Harahap, Syaiful Zuhri; Nasution, Fitri Aini; Bangun, Budianto
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7959

Abstract

Improving the quality of Public Services is a major need in the era of digitalization, including in the local taxation sector related to the sentiment of services provided in tax payments. The purpose of this study was to analyze public sentiment towards tax payment services in the Office of the regional Revenue Agency (Bapenda) Labuhanbatu Utara by applying Naïve Bayes algorithm using Rapid Miner software. Data analysis through text preprocessing, feature selection, and sentiment classification into positive, negative, and neutral categories. The Data obtained consisted of 225 community comments from the SIMPATDA application and 612 tweets with the hashtag #pajakLabura from Twitter, which reflected people's opinions directly. The analysis process is carried out through the stages of text preprocessing, feature selection, to the classification of sentiments into positive, negative, and neutral categories. The results showed that the Naïve Bayes algorithm is able to classify public opinion with a high degree of accuracy and establish similarities/differences in the aspects of service that are most complained about or appreciated by the public. This study also contributes to the development of data-based evaluation system in the scope of public services.
EFFORTS TO INCREASE COMMUNITY LITERACY Muti'ah, Rahma; Ritonga, Mulkan; Bangun, Budianto; Harimansyah, Harimansyah; Febrianto, Dandi; Sulaiman, Syahrol
Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 1 (2023): MARET
Publisher : LPPM Univ. Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/abdidos.v7i1.1572

Abstract

Improving literacy, especially reading and writing literacy in society, must be one of the centers of attention of various parties. Low literacy skills will encourage people to respond incorrectly to information that has the potential to take actions that are not in accordance with existing rules and can even hinder the growth of the global human development index as the government hopes, and can even harm themselves. This service activity is carried out in an effort to improve literacy in village communities. Activities are carried out by means of outreach, discussions and training that directly involve the community. The implementation of this community service activity proves that the discussion, socialization and training methods can improve the literacy skills of the village community.
Optimasi Prediksi Harga Sawit Menggunakan Teknik Stacking Algoritma Machine Learning dan Deep Learning dengan SMOTE Karim, Abdul; Bangun, Budianto; Prayetno, Sugeng; Afrendi, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7239

Abstract

The prediction of palm oil prices plays a strategic role in decision-making within the agribusiness sector, particularly in addressing market volatility and imbalanced historical data distribution. This study aims to optimize the accuracy of palm oil price prediction by applying a stacking approach that combines machine learning and deep learning algorithms, while integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance issues. Three main models were employed in this study: Random Forest, Long Short-Term Memory (LSTM), and a model enhanced with SMOTE. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics, supported by confusion matrix analysis. The results indicate that the model integrated with SMOTE outperforms the others, achieving an accuracy of 0.5447, precision of 0.5512, recall of 0.5447, and F1-score of 0.5462. This model also demonstrates a more balanced classification performance compared to the LSTM and Random Forest models. These findings confirm that the application of oversampling techniques such as SMOTE, when combined with appropriate algorithms, can significantly enhance predictive performance in imbalanced datasets. The study contributes to the development of predictive models for commodity prices based on historical data and opens opportunities for further exploration of more adaptive hybrid methods in future research.
Analisis Seleksi Penerimaan Karyawan Bank Menggunakan Metode Simple Additive Weighting (SAW) dan Pembobotan Rank Order Centroid (ROC) Naibaho, Sri Fitriani; Bangun, Budianto; Masrizal, Masrizal
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7360

Abstract

This study aims to improve the efficiency and effectiveness of the employee recruitment selection process at Bank BRI Aek Kanopan. The main problem faced is the lack of transparency and systematicity in decision making, so an objective approach is needed to determine the best candidate. As a solution, this study uses the Simple Additive Weighting (SAW) method, which allows structured weighting of criteria, such as last education, technical skills, work experience, communication skills, and age. The SAW method is applied to normalize data and compare candidates based on preference values ​​(Vi). This study involved 25 alternative candidates as evaluation objects, with the normalization and weighting process carried out based on predetermined criteria. The normalized data are presented in a table, with preference values ​​ranging from 0.667 to 0.793, which indicates the level of suitability of candidates to the selection criteria. The results of the study indicate that the SAW method is effective in determining the best candidates, increasing transparency, and facilitating decision making by management. Based on these findings, Bank BRI Aek Kanopan is advised to adopt the SAW method routinely, train staff involved in the selection process, and conduct periodic evaluations to maintain the effectiveness of this method. This research provides an important contribution in the development of human resource management, especially in improving the efficiency and accuracy of the employee recruitment selection process.
Analisis Faktor-Faktor yang Mempengaruhi Tingkat Kelulusan Siswa Menggunakan Algoritma KNN Sianipar, Vitasari; Irmayani, Deci; Bangun, Budianto
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7386

Abstract

Student graduation rates are influenced by various academic and non-academic factors, making it necessary to develop analytical methods to classify students based on their likelihood of graduation. This study applies the K-Nearest Neighbors (KNN) algorithm to analyze the factors affecting student graduation at SD Negeri 112269 Padang Lais. The KNN algorithm works by calculating the Euclidean distance between the tested student data and other student data, then determining the graduation status based on the majority of the K nearest neighbors. The results indicate that using K=5 produces highly accurate classifications with an accuracy rate of 100%, where students with the smallest distance to those who have graduated are more likely to pass. The contribution of this study is to demonstrate that the KNN method can serve as a decision-support tool for predicting student graduation and provide insights into the use of classification algorithms in educational decision-making. Future research can enhance the model by incorporating more diverse variables and testing it on larger datasets to improve prediction generalization.
Klasifikasi Jenis Bunga Iris Berdasarkan Fitur Morfologi Menggunakan Algoritma Naive Bayes Sari, Ely Novita; Irmayani, Deci; Bangun, Budianto
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7401

Abstract

This study aims to classify the types of Iris flowers based on morphological features using the Naive Bayes algorithm. Iris flowers consist of three types, namely Iris-Setosa, Iris-Versicolor, and Iris-Virginica, which can be distinguished based on the length and width of the petals as well as the length and width of the sepals. The dataset used in this research is the Iris dataset, which contains information on four morphological features from these three types of flowers. The Naive Bayes algorithm was chosen because of its advantages in performing probability-based classification in a simple, fast, and effective manner, especially for data with independent features. The research stages include data collection, feature extraction, splitting the data into training and testing sets, training the model using the Naive Bayes algorithm, and testing the model to evaluate classification accuracy. The results of the study show that the Naive Bayes model is able to classify the test data accurately, with the highest probability value obtained in the Iris-Versicolor class, with a value of P(Versicolor│X)=1. This indicates that the test data has the highest similarity to that species compared to the other two species. Thus, the Naive Bayes algorithm proves effective for classifying types of Iris flowers based on their morphological features.
Penentuan Pola Pada Dataset Penjualan Dalam Data Mining Menggunakan Metode Apriori Utami, Ulfa; Irmayani, Deci; Bangun, Budianto
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7498

Abstract

In everyday life and the business world, buying and selling activities play a central role. For companies, daily transaction data is not just a record, but an important asset that holds the potential to increase sales through analysis. The volume of sales data generated daily is enormous, making manual processing inefficient and prone to errors. The complexity of the number of products sold also makes it difficult to gain a comprehensive understanding of purchasing patterns. Dynamic changes in consumer preferences further complicate demand forecasting and may lead to inventory issues. This study aims to address these issues by analysing sales data to identify products that are frequently purchased together. This information will be utilised in designing more effective marketing strategies, such as cross-promotions or product bundling. Additionally, this data is useful for demand forecasting and optimising inventory management. The ultimate goal is to provide relevant product recommendations to customers and enhance their satisfaction. To achieve this objective, this study applies data mining techniques, specifically the Apriori Association method. Data from 15 types of items in 28 weekly transactions at TOKO BANGUNAN MAJU BERSAMA will be analysed as an initial sample to identify the most frequently purchased combinations of construction tools. The Apriori method will associate each item based on a minimum support value of 0.25 and a minimum confidence value of 0.80. The application of this method resulted in 4 rules from 3-item patterns with confidence values ranging from 0.88 to 0.89.
Co-Authors Abdillah, Ihsan Abdul Karim Abdul Karim Abdul Karim Afrendi, Mohammad Agus Susanto Ahmad Habibullah, Imam Akbar Ritonga, Ali Ali Akbar Ritonga Alpiansyah, Fredy Anjar, Agus Ardian, Aldi Aritonang, Putri Armaini, Indah Azwar Try Afandi Barasa, Kristina Budi Febriani Dahrul Aman Harahap Dandi Febrian Dandi Febrian Dandi Febrianto Deci Irmayani Delima Harahap, Risma Dwi Fitriani Dwi Fitriani Elysa Rohayani Hasibuan Febriani, Lisa Febrianto, Dandi Fitri Aini Nasution Guna Dharma, Aditya Halawa, Prianus Hanggi Kurniawan Harahap, Risma Delima Hari Mansah Harimansyah Harimansyah Harimansyah Harimansyah, Harimansyah Hary Syahputra Hasby, Anzila Hasibuan, Dilla Puspita Hutagalung, Charles Efendy Ibnu Rasyid Munthe Ibnu Rasyid Munthe Inez Chania Panjaitan Irmayani, Deci Irmayanti Irmayanti Irmayanti Irmayanti, Irmayanti Iwan Purnama Iwan Purnama Jati, Dewi Sekar Juwita Juwita, Juwita Karim, Abdul Karim Khailizah Khailizah Lubis, Rizky Ramadhan Hasan Masrizal Masrizal mawarni, Putri Sigit Mesran, Mesran Mila Nirmala Sari Hasibuan Muhammad Halmi Dar Muti'ah, Rahma Muwanti, Apri Naibaho, Sri Fitriani Nasution, Fitri Aini Nasution, Marnis Nona Oktari Novilda Elisabeth Mustamu Nurjannah Nurjannah Prayetno, Sugeng Prayetno, Sugeng Prayetno Purba, Mhd. Rafly Putra, Fasdiansyah Rahma Muti’ah Rahmadani Pane Rakhmi Khalida Ramadani Pane Rambe, Nurhayati Ridho Kurniawan Riskawati Riskawati Ritonga, Ali Akbar Ritonga, Mulkan Rohani Rohani Rohanita Hasibuan, Lily Safitri, Nina Sakinah Sakinah Sari, Ely Novita Sianipar, Vitasari Sihombing, Volvo Siregar, Ade Elvi Rizki Siregar, Sakinah Ubudiyah Sitorus, Sahat Parulian Sulaiman, Syahrol Suryadi, Sudi Sutrisno Dwi Raharjo Syafitri, Risma Syahputra, Rapian Syahrol Sulaiman Syahrol Sulaiman Ritonga Syahrol Sulaiman Ritonga Syaiful Zuhri Harahap Syaiful Zuhri Harahap Tegar, Tegar Tria Wulandari Utami, Ulfa Windo Windo Tan Yusmaidar Sepriani Zakaria Pratama