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All Journal Informatika Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Journal of Student Development Informatics Management (JoSDIM) Journal of Student Development Information System (JoSDIS)
Syaiful Zuhri Harahap
Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Library & Information Science Public Health Other

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Implementasi E-Commerce CMS Prestashop Sebagai Upaya Strategi dalam Meningkatkan Indeks Penjualan Esteh Indonesia di Jalan Jendral Ahmad Yani Rantauprapat Akbar Madyan; Syaiful Zuhri Harahap; Rizky Maulana
Journal of Student Development Information System (JoSDIS) Vol 6, No 2: JoSDIS | Juli 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/josdis.v6i2.9719

Abstract

This study aims to initiate the implementation of e-commerce using CMS Prestashop as a strategy to increase the sales index of Esteh Indonesia on Jalan Jendral Ahmad Yani, Rantauprapat. Data were obtained through observation and interviews with Esteh Indonesia staff to understand buying and selling activities and customer preferences. Although Prestashop offers easy online transactions, most customers prefer to order through drop shipping services or buy directly at outlets. The main factor influencing this is the level of customer trust in well-known local brands. Prestashop has great potential in expanding the market and increasing operational efficiency, but to achieve success, intensive promotion and socialization strategies are needed. With the right approach, a combination of technology and strengthening customer trust can encourage the implementation of Prestashop, thereby increasing sales and strengthening Esteh Indonesia's position in the local market
Penerapan Sistem Informasi Perpustakaan Berbasis Web Pada Perpustakaan Umum Rantauprapat Reyfo Irfankha; Tiara Syavitri Rambe; Marnis Nasution; Angga Putra Juledi; Syaiful Zuhri Harahap
Journal of Student Development Information System (JoSDIS) Vol 6, No 1: JoSDIS | Januari 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/josdis.v6i1.9022

Abstract

The implementation of a Web-Based Library Information System at the Rantauprapat Public Library aims to increase efficiency and effectiveness in managing library resources. This information system integrates various library functions, such as book cataloging, borrowing, returns, and member management, into a single web-based platform. By using web-based technology, libraries can make access easier for visitors to search for information and borrow online, without being limited by time and place. This research identifies the system's technical and functional requirements and evaluates its impact on library services and visitor satisfaction. The results of implementing this system show an increase in data accuracy, a reduction in service times, and a reduction in administrative errors. Apart from that, this system also makes it easy for visitors to access library information more efficiently. It is hoped that the implementation of this web-based library information system can become a model for other libraries in an effort to improve the quality of library information services in the digital era.
Implementasi Data Mining Untuk Klustering Stunting Gizi Pada Balita Dipuskesmas Sigambal Meggunakan Metode K-Medoids Dan K-Means Melisa Melisa; Syaiful Zuhri Harahap; Masrizal Masrizal
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6159

Abstract

The aim of this study was to identify and understand the different characteristics of toddlers in the context of factors that contribute to nutritional stunting. By using the clustering method, this study aims to group toddlers into several groups based on the similarity of their characteristics, so that more targeted interventions can be designed in dealing with stunting problems. Through this approach, it is hoped that significant patterns and risk factors can be found that distinguish stunted toddlers from toddlers who grow normally, and provide insights that can be used by policy makers and health practitioners to improve the quality of life of children. The method used in this study involves the application of two clustering techniques, namely K-Means and K-Medoids to Group sample data of 116 toddlers. The clustering process is carried out by measuring the distance between the toddler data and the centroid or medoid to determine which group is most suitable. The Data were analyzed to find patterns identifying unique characteristics of each cluster, reflecting differences in nutritional stunting-related risk factors.This process helps in differentiating groups of toddlers who are prone to stunting from those who are not, so that the analysis can be focused on the groups most in need of intervention. The results of clustering analysis showed that as many as 48 toddlers entered the C1 cluster, while the other 68 toddlers entered the C2 cluster. Each cluster describes two groups of toddlers with different characteristics in the context of nutritional stunting risk factors. The findings provide deep insight into the significant differences between the two groups, allowing researchers to identify specific patterns and risk factors. This information is then used to design more specific and effective interventions in addressing nutritional stunting in toddlers, taking into account the unique characteristics of each cluster that has been identified.
Application of Apriori and Fp-Growth Methods in Analyzing Book Lending Patterns Penerapan Metode Apriori dan Fp-Growth dalam Analisis Pola Peminjaman Buku Rahma Faradilah; Syaiful Zuhri Harahap; Irmayanti Irmayanti
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6155

Abstract

Clustering of book borrowing patterns in the University of Labuhanbatu library aims to identify and understand student preferences and habits in borrowing books. With this analysis, the library can be more effective in managing book collections, ensuring the availability of frequently borrowed books, and improving the quality of service according to student needs. Using clustering techniques also helps in designing a more targeted book procurement strategy, so that existing resources can be optimally utilized to support the teaching and learning process. In this study, the methods used are Kf-Growth and Apriori to identify book borrowing patterns. Kf-Growth is used to find frequent itemsets or collections of books that are often borrowed together, while Apriori is used to generate association rules that reveal the relationships between borrowed books. Both of these methods allow for a more in-depth and comprehensive analysis of book borrowing patterns in the library, with the ability to handle large amounts of data and identify significant relationships between items. This process involves several stages, including data preprocessing, algorithm application, and evaluation of the results to ensure the validity and accuracy of the resulting clustering. The results of the clustering analysis show a very good confidence value, with many male and female students borrowing the book "Pengantar Akuntansi" consistently. This borrowing pattern shows that books related to economics and accounting have a high level of demand. The Kf-Growth and Apriori methods have proven to be very effective in clustering, providing accurate and reliable results. With these results, the Labuhanbatu University library can take more informative and strategic steps in managing book collections, ensuring that frequently borrowed books are always available, and improving the borrowing experience for students.
Simulasi Kinerja Karyawan di Kantor Pertanahan Labuhanbatu Menggunakan Algoritma C4.5 Khodijah Nasution; Masrizal Masrizal; Syaiful Zuhri Harahap
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6160

Abstract

Employee performance analysis using the C4.5 algorithm in data mining aims to identify and classify employees based on their performance. The analysis process includes several stages, namely data analysis, preprocessing, model design in data mining, and method evaluation. From 47 sample data analyzed, the results show that 40 employees have good characters, while 7 employees have bad characters. Good employee characters are characterized by punctuality and high discipline in carrying out their duties. Conversely, bad employee characters are characterized by unpunctuality and low discipline, which have a negative impact on productivity and efficiency in the workplace. The results of this classification help identify areas that require more attention and intervention to improve overall employee performance. Model evaluation is carried out using two widgets, namely Test and Score and Confusion Matrix. The evaluation results of these two widgets show perfect accuracy of 100%. Meanwhile, the Confusion Matrix widget shows that all predictions are in accordance with the actual data without any errors in classification. These results confirm that the C4.5 algorithm is very effective and accurate in classifying employee performance. The perfection of the evaluation results shows that the C4.5 algorithm is very suitable for use as a classification model in employee performance analysis. The 100% accuracy of both widgets indicates that this algorithm is not only able to predict correctly but also consistently in various evaluation tools.
Analisis Minat Masyarakat Menggunakan Media Sosial Menggunakan Algoritma C4.5 dan Metode Naïve Bayes Nia Putri Panjaitan; Syaiful Zuhri Harahap; Rahma Muti Ah
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

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

Abstract

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.
Penerapan Data Mining Untuk Evaluasi Data Penjualan Menggunakan Metode Clustering Dan Agoritma Hirarki Divisive Studi Kasus Toko Sembako Pujo Ade Eka Febriyanti; Syaiful Zuhri Harahap; Masrizal Masrizal
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6161

Abstract

The larger a company, the longer the company stands, the more companies have branches, of course, the greater the data owned. These data can be consumer data, purchase data, sales data, payroll data, and many other data. All data will usually be stored in a database. But many companies, even the Information Technology (IT) division, do not realize how valuable the pile of old data generated by the company in transactions and activities. Data mining is the study of methods for generating knowledge or finding patterns for processing data. So it's not just information, it's knowledge. Data Mining has several methods including clustering. Clustering is a well-known and widely used method in data mining. The main purpose of this clustering method is to Group a number of data/objects into clusters (groups) so that the cluster will contain the same data as each group. In this study, Divisive hierarchy algorithm is used to form clusters. From the pattern obtained is expected to provide knowledge for the company Media World Pekanbaru as a supporting tool to take policy.
Implementasi Data Mining Menggunakan Metode Algoritma FP-Growth Dan Algoritma Apriori Pada Toko IBR Jaya Untuk Meningkatkan Penjualan Restu Fauzy Naibaho; Syaiful Zuhri Harahap; Angga Putra Juledi
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6128

Abstract

Dian trading business is one of the grocery stores engaged in buying and selling the main household needs of nine basic ingredients which have been doing a lot of grocery sales transactions. This transaction Data continues to grow every day and in the IBR Jaya store sales transaction data is only presented as an archive or report and it is not mentioned what the benefits of these data are. Nah, the problem at the IBR Jaya store is the improvement of improvements due to the shortage of basic food stocks that are often purchased by consumers are not available which results in improvements and usability improvements then the FP-Growth algorithm is used to analyze patterns of improvement and a priori algorithms for comparison through archived transaction data goods that will be purchased later as a reference to increase food stocks so as to increase sales at the IBR Jaya Food Store in the hope that this increase can help this is one of many ways to make money online. Association rules are a process in Data Mining to establish all associative policies that meet the minimum requirements for support (minsup) and trust (minconf) in a database . In association rules, there are 2 methods that can be used, namely a priori method and FP-Growth method. In this study the method used is FP-Growth algorithm and a priori algorithm, FP-Growth algorithm and a priori method is a method to find the most frequently appearing data set (frequent itemset) without using candidate generation that is suitable to analyze a data transaction.
Analisis Sistem Informasi Pengelolaan Data Alumni MAN Labuhanbatu Berbasis Codeigniter PHP Framework Muhammad Adlin Hasibuan; Syaiful Zuhri Harahap; Marnis Nasution
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6157

Abstract

Alumni data management information system is a platform designed to manage and maintain alumni data effectively and efficiently. This system makes it easy for educational institutions to collect, store, and manage information about their graduates, such as personal data, education history, career, and their contribution to the alumni community. Using database technology and specialized software, the system enables fast data retrieval, organized data storage, and real-time monitoring of alumni activities. The existence of this information system also helps in maintaining good relations between institutions and alumni, as well as supporting alumni programs such as reunion events, networking, and career development. In addition, the alumni data Management Information System serves as a strategic tool in improving the quality and reputation of educational institutions. Research on alumni data management information system analysis using the CodeIgniter PHP framework as a programming language is interesting to do. CodeIgniter PHP Framework is known as one of the lightweight and efficient frameworks in web application development, so it can provide advantages in managing complex alumni data.
Implementasi Metode Naive Bayes dan Neural Network Untuk Menentukan Minat Masyarakat Pada Handphone Samsung Nelvi Nurrizqi M; Syaiful Zuhri Harahap; Irmayanti Irmayanti
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6163

Abstract

Naive Bayes and Neural Network methods are used in analyzing people's interest in Samsung mobile phones to gain a better understanding of consumer preferences. Naive Bayes is a simple but very effective probability-based classification method. This method generates possible consumer interests by analyzing features such as price, specifications, and brands, and calculating the probability of different categories. Naive Bayes is very useful in situations where data has independent features, and can provide accurate results at high speed. By identifying patterns of people's preferences, this method can help Samsung adjust marketing and product strategies that are more in line with consumer needs. On the other hand, Neural Network offers more complex analytical capabilities by imitating the way the human brain works through a network of neurons. This method is used to process larger and more complex data in understanding consumer interest patterns in Samsung mobile phones. Neural Network can identify deeper relationships between various factors, such as the interaction between camera features and user needs, using deep learning processes. The purpose of using Neural Network is to capture nuances and trends that cannot be identified with simple methods, thereby providing a more comprehensive view of what drives consumer interest. The use of these two methods of analysis in public interest in Samsung mobile phones has provided very satisfactory results. The calculation values obtained from both methods show a high level of accuracy in the classification of consumer interest. The results of this analysis provide valuable insights for Samsung in understanding consumer preferences and needs, as well as helping the company in designing more effective products and marketing strategies. Thus, the combination of the use of Naive Bayes and Neural Networks not only provides stron g results, but also provides a more holistic approach to consumer data analysis.
Co-Authors Ade Eka Febriyanti Ade Parlaungan Nasution Akbar Madyan Angga Putra Juledi Bhakti Helvi Rambe Budianto Bangun Christine Herawati Limbong Dedi Naingolan Elfi Zahra Yuni Fasdiansyah Putra Fauziah Hanum Gafar Ilyaz JP Ibnu Rasyid Munthe Iin Kurniawati Dewi Indry Rizza Inny Rahayu Rambe Irmayanti Irmayanti Juli Hati Fitri Khairil Hanif Khodijah Nasution Lili Marliani Lisa Ariani Marnis Nasution Masitoh Ritonga Masrizal Masrizal Mei Lia Dani Harahap Melisa Melisa Mhd. Amin Muhammad Adlin Hasibuan Muhammad Irwansyah Hasibuan Mulya Rafika Nelvi Nurrizqi M Nia Edi Putri Nia Putri Panjaitan Nur Ainun Gulo Nur Putri Andriani Nurwahyunita Nurwahyunita Praida Hansyah Rahma Faradilah Rahma Muti Ah Ratna Juliani Siregar Restu Fauzy Naibaho Reyfo Irfankha Rinda Puspita Rista Andini Ritonga Rizky Maulana Sudi Suryadi Taufik Molid Hidayat Hasibuan Tiara Syavitri Rambe Zaitun Ritonga