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Analisis Perbandingan Algoritma C4.5 Dan Naive Bayes Dalam Menilai Kelayakan Bantuan Program Keluarga Harapan Hasibuan, Taufik Molid Hidayat; Harahap, Syaiful Zuhri; Ah, Rahma Muti
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6154

Social assistance is a form of government intervention that aims to help people who are in less fortunate economic conditions. This form of assistance can be in the form of cash assistance, food assistance, or health service assistance. Social assistance programs are often aimed at reducing poverty, addressing hunger, and improving the overall well-being of society. Program Keluarga Harapan (PKH) is a form of conditional social assistance launched by the government of Indonesia to help poor and vulnerable families. The Program aims to improve the quality of life of poor families through the provision of cash assistance accompanied by obligations for recipients to meet certain requirements, such as ensuring their children attend school and regular health checks at health facilities. With the PKH, it is expected to improve the access of poor families to education and health services, which in turn will improve the quality of Indonesian human resources. Thus, the author can evaluate the advantages and disadvantages of each method in the context of the data used. In addition, this comparative analysis also aims to provide more informative recommendations for policy makers. If one of the methods proves to be superior, then it can be adopted to improve the selection process for CCT recipients in the future. However, if both methods have balanced performance, a combination or integration of the two can be the optimal solution. By comparing the performance of Naive Bayes and the C4.5 algorithm, the study not only focused on identifying the right recipients, but also provided valuable insights in choosing the most effective analytical tool for the purpose.

Analisis Minat Masyarakat Menggunakan Media Sosial Menggunakan Algoritma C4.5 dan Metode Naïve Bayes Panjaitan, Nia Putri; Harahap, Syaiful Zuhri; Ah, Rahma Muti
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6156

The analysis of public interest using social media in data mining aims to understand user preferences and interests in various topics or products. By analyzing data from social media platforms, such as posts, comments, and interactions, researchers can identify significant interest patterns and trends, which can be used for more effective marketing strategies or product development that suits the public's desires. Common methods used in this analysis are the C4.5 and Naive Bayes algorithms. The C4.5 algorithm builds a decision tree that makes it easy to visualize and interpret the main factors that influence public interest. Meanwhile, Naive Bayes, with its probabilistic approach, classifies data based on existing features, providing fast and accurate predictions. Both methods are applied to process data from social media and produce in-depth insights into user preferences. The results of the analysis show that the prediction and classification of public interest have good accuracy, with the comparison result values showing very satisfactory performance. Both are able to identify and classify interests accurately, utilizing the advantages of each method to provide a better understanding of what is interesting to the public on social media.

Utilizing FP-Tree and FP-Growth Algorithms for Data Mining on Medicine Sales Transactions at Khanina’s Ardiansyah, Rizaldi; Harahap, Syaiful Zuhri; Ah, Rahma Muti
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.5999

Although Khanina Pharmacy is a growing pharmacy with a lot of processes, the data processing is still done by hand. This study examines the use of the FP-Tree and FP-Growth algorithms to the medication sales transaction system. The FP-Tree and FP-Growth algorithm methods use methods or strategies to choose data in order to identify trends or intriguing details. The FP-Tree and FP-Growth algorithm approaches are two frequently used techniques in data mining. The purpose of this medicine sales transaction data is to identify concurrently purchased products. The FP-Growth Algorithm is used to find item pattern combinations. Use of FP-Tree to identify frequently occurring itemsets from a database in combination with the FP-Growth algorithm. When searching for product attachment patterns for sales tactics in decision-making rules, the Association Rule method is employed. In order to determine which medications are frequently bought by customers, we can create rules using the data in the database. The Rapidminer 5 program was used to conduct the test. This test yielded the following results: the number of itemsets created and rules constructed increased with decreasing support values.

Grouping Student Achievement Data In A Decision Making System Using The Weight Product Method Sari, Adinda Puspita; Masrizal, Masrizal; Ah, Rahma Muti
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6134

Information, modeling, and data manipulation systems are called decision support systems (DSS). When there is uncertainty about the best course of action in semi-structured or unstructured situations, the system is utilized to support decision-making. There are various approaches available for producing decision support systems, one of which is the Weighted Product (WP) Method. With the Weighted Product (WP) approach, attribute ratings are connected by multiplication; however, each attribute's rating must first be increased to the power of the attribute's weight. The normalizing process is same to this one. SPK procedure to choose the winners of the scholarships. Scholarship information from MTS Swasta Alwasliyah Simpang Merbau can be saved in the Decision Support System using this method. This way, in the event that an error arises when entering grades or scholarship information, the wrong information can be fixed without requiring the scholarship information to be re-input. Scholarships are presents to individuals in the form of financial aid intended to be utilized toward their ongoing educational pursuits.

Analisis Prediksi Prestasi Siswa UPTD SD Negeri 30 Aek Batu Dalam Machine Learning Dengan Metode Naive Bayes Ambarita, Mira Nanda; Nasution, Marnis; Ah, Rahma Muti
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6101

Education is one of the efforts made to determine the success of a nation, successful education will continue to produce a good generation as well. Along with the rapid global challenges, the challenges of the world of education are becoming greater, this aspect that encourages learners to achieve the best achievements. Given the presence of teachers in the process of teaching and learning activities is very influential, it should be the quality of teachers must be considered. The problem that often occurs in every school, especially in UPTD SD Negeri 30 AEK Batu, is that there are many students who are lazy to learn, students who lack fun lessons, do not have attention to what has been learned, school assignments are a burden, learning outcomes are only to go to class or graduate from school and school just to meet friends and get pocket money. Therefore, to predict the achievements of different students, the education of UPTD SD Negeri 30 AEK Batu requires accurate data on student achievement so that it can be a reference for education to better know the achievements of students who excel and underachieve. Application of student achievement prediction UPTD SD Negeri 30 AEK Batu in machine learning with naive bayes method can be solved well or not.

Analisis Data Mining Absensi Siswa SMP Negeri 1 Bilah Barat Dengan Metode Algoritma K-Means Cluestering Sinurat, Shondy Raja Ferdinand; Nasution, Marnis; Ah, Rahma Muti
Jurnal Informatika Vol 12, No 2 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i2.5773

The presence of students in Junior High School has significant implications in the world of Education. The success of education depends not only on the content of the curriculum and the quality of teaching, but is also strongly linked to the extent to which students are regularly present at school. The problem of student absenteeism in junior high school is an issue that should not be ignored, as it can negatively affect academic achievement, the development of social skills, and the overall educational experience of students. A student's absence from school can be caused by a complex of factors. These factors include personal problems such as lack of motivation, family problems such as domestic conflicts, poor physical or mental health, as well as environmental factors such as school accessibility. A deep understanding of the reasons behind student absenteeism is an important first step to addressing this problem. Student attendance Data that has been accumulated over the past 6 months can be a valuable source of information in analyzing student attendance. However, extracting useful information from large and complex attendance data is a challenging task. This is where Data Mining comes in. Data Mining is an approach that enables the identification of patterns, trends, and valuable information in large and complex data.

Penerapan Data mining Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Naïve Bayes Dan Support Vector Machine (Studi Kasus Program Studi Sistem Informasi Universitas Labuhanbatu) Antika, Dewi; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Juledi, Angga Putra
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.v6i3.7917

This study was conducted to classify public satisfaction levels using the Support Vector Machine (SVM) algorithm as the primary data analysis method. The objective of this study was to obtain an accurate and reliable prediction model for determining the Satisfaction and Dissatisfaction categories based on the available data. The theoretical basis used refers to the concept of machine learning, specifically SVM, which works by forming an optimal hyperplane to separate data classes. In addition, model evaluation theories such as the Confusion Matrix were used to objectively measure prediction performance. The research methodology included data collection, pre-processing, dividing the dataset into training and test data, and training the SVM model. Evaluation was conducted using accuracy, sensitivity, and specificity metrics to assess the model's ability to predict data accurately. The results and discussion indicate that the SVM successfully classified the majority of data correctly, with the Satisfaction class having a perfect prediction rate while the Dissatisfaction class still had a small error. Further analysis indicated the need for SVM parameter optimization to improve accuracy in the minority class. The conclusion of this study states that the SVM has good performance in classifying public satisfaction data, although it still requires refinement in recognizing certain class patterns. This finding opens up opportunities for developing more adaptive methods to improve predictive performance.

Kepatuhan Pembayaran Pajak Kendaraan Bermotor Menggunakan Algoritma Decision Tree Dan Random Forest Di Samsat Balige Wijaya, Alief Achmad; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Nasution, Marnis
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.v6i3.7934

This study aims to analyze and predict the total category of Motor Vehicle Tax (PKB) payments based on payment attributes and vehicle types, which is important to improve the effectiveness of tax management and support more appropriate decision making in related agencies; within the theoretical framework, classification models such as Decision Tree and Random Forest are used to predict data categories by utilizing historical patterns in the dataset, because these algorithms are able to capture interactions between attributes and provide logical interpretations of the prediction results; the research methodology is carried out using secondary data of PKB payments for 2024 from Samsat Balige, which is divided into training data and test data for the classification process and its performance is evaluated using accuracy, precision, recall, and F1-Score metrics through the Performance operator in RapidMiner; the results of the study show that Random Forest produces a more balanced prediction distribution with 100% accuracy, while Decision Tree has 96% accuracy but tends to be biased towards the “Low” category, and analysis of important attributes such as Fines, Total Amount, and the number of Jeep and Truck type vehicles shows a significant influence on the PKB payment category; Thus, the research conclusion confirms that Random Forest is proven to be more effective and stable than Decision Tree in predicting the total PKB payment category, is able to capture complex patterns between attributes, and provides accurate predictions on relatively small datasets, making it the optimal choice for PKB data classification.

Model Prediktif Kepuasan Pelanggan Pada Hotel Platinum Menggunakan Motode K-Means Clustering Siregar, Siti Kholijah; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Munthe, Ibnu Rasyid
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.v6i3.7935

Customer satisfaction is a key pillar of success in the competitive hospitality industry, directly impacting loyalty and profitability. Recognizing this, Platinum hotels need the ability to predict guest satisfaction in order to refine their service strategies. This study focuses on the development of predictive models of customer satisfaction at Platinum hotel using the K-Means Clustering method. This method was chosen because of its effectiveness in grouping complex data into homogeneous segments based on common characteristics. Customer Data will be grouped by attributes of their stay to identify different segments of customers with unique levels of satisfaction and preferences. It is hoped that this model can provide deep insights into customer profiles, reveal hidden patterns, and predict future guest expectations. The results of this study will contribute to improving the quality of Service and strategic decision-making at Platinum hotels and can be a reference for the hospitality industry in implementing a data-driven approach.

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