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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Mantik Informatika Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer Networks, Architecture and High Performance Computing International Journal of Business, Technology, and Organizational Behavior (IJBTOB) Majalah Ilmiah UPI YPTK Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Journal of Student Development Information System (JoSDIS) Jurnal Pendidikan Indonesia (Japendi)
Syaiful Zuhri Harahap
Universitas Labuhanbatu
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Analisa Data Penjualan Pada Apotek Ritonga Farma Menggunakan Data Mining Apriori Lestari, Putri Anggraini; Nasution, Marnis; Harahap, Syaiful Zuhri
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.v12i2.5651

Abstract

A pharmacy is a place or business that is specifically dedicated to providing medicines and other health products to the public. This place is also known as a drugstore or drug store in some countries.  Pharmacies provide medicines both prescribed by doctors and over-the-counter (over-the-counter), to help patients cope with health problems they are experiencing.Some pharmacies also offer additional services such as blood pressure checks, vaccinations, simple health checks, and health counseling to the public.  In applying a priori methods to pharmacy, a deep understanding of data structure and proper product classification is needed to overcome this problem. By knowing the pattern of frequent purchases, pharmacies can place items that are often purchased together close together on shelves or strategic locations. This can increase the convenience of buyers and speed up the purchase process. A priori methods are techniques in data mining that are used to find hidden patterns or associations in large datasets.  A priori methods look for relationships between items in a dataset that often appear together.  The main principle of the a priori method is that if an item-set appears frequently together, then it is likely that the item-set will also appear frequently together in other transactions.
Implementasi Metode Naive Bayes dan Neural Network Untuk Menentukan Minat Masyarakat Pada Handphone Samsung M, Nelvi Nurrizqi; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
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.v12i3.6163

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.
Analisis Sistem Informasi Pengelolaan Data Alumni MAN Labuhanbatu Berbasis Codeigniter PHP Framework Hasibuan, Muhammad Adlin; Harahap, Syaiful Zuhri; Nasution, Marnis
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.v12i3.6157

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.
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

Abstract

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.
Analisis K-Means dan Naive Bayes Untuk Pengelompokan Rawan Bencana di Daerah Kabupaten Labuhanbatu Lubis, Nadira Jannah Adeni; Harahap, Syaiful Zuhri; Ritonga, Irmayanti
Jurnal Informatika Vol 12, No 1 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i1.5492

Abstract

A natural disaster is an event that arises from the state of nature and has a significant impact on humans. Natural disasters can include events such as floods, volcanic eruptions, earthquakes, tsunamis, landslides, blizzards, droughts, hail, heat waves, hurricanes, tropical storms, typhoons, tornadoes, wildfires, and the spread of disease.Natural disasters that hit Labuhanbatu Regency include various types, such as floods, fires, tornadoes, and landslides. Each region within the district has specific characteristics associated with a particular type of natural disaster. In order to understand the level of vulnerability to disasters in Labuhanbatu District, K-Means and Naive Bayes methods are implemented to classify the level of vulnerability to frequent disasters.The results of this analysis will improve understanding of the level of vulnerability to disasters in Labuhan Batu Regency, enabling interested parties to identify areas that require increased attention in disaster mitigation and management efforts. In addition, the use of a combination of K-Means and Naive Bayes methods can serve as a solid basis for the development of more effective early warning systems in the future.
Analisis Pola Pembelian Melalui Ponsel Menggunakan Algoritma Apriori dan Fp–Growth Pada Millenium Ponsel Andriani, Nur Putri; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
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.v12i3.6158

Abstract

The purpose of this research is to understand the main factors that influence consumer decisions in purchasing the device. By exploring information about consumer preferences, needs, and behavior, this study seeks to identify purchasing trends and understand how aspects such as mobile phone features, price, and brand influence consumer choices. The main objective of this study is to provide in- depth insights to technology industry players so that they can develop more effective and relevant marketing and product strategies to meet dynamic market needs. To achieve this goal, this study uses the Apriori and FP-Growth methods, which are data mining algorithms that are effective in finding associations and patterns in transaction data. The Apriori method focuses on identifying the frequency of occurrence of itemsets and forming association rules based on support and confidence values, while FP- Growth uses a tree approach to store and extract frequently occurring patterns more efficiently. Both methods allow for in-depth analysis of mobile phone purchase data, so that complex patterns can be revealed more accurately and quickly. The results of this study indicate that there is a very clear mobile phone purchasing pattern among consumers, with confidence values reaching 90% for some association rules. For example, consumers who purchase phones with AMOLED displays tend to also choose large battery capacities from certain brands. These patterns indicate strong and consistent preferences across consumer groups, providing manufacturers with opportunities to target specific market segments with tailored product offerings. These findings not only provide valuable insights into consumer behavior but also help companies optimize their marketing strategies and increase their competitiveness in the technology industry.
Rancang Bangun Sistem Informasi Persediaan Barang Pecah Belah Berbasis Web (Studi Kasus Toko Podomoro) Dengan Metode Fifo Dan Lifo Patriya, Angga Prayoga; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Jurnal Informatika Vol 12, No 1 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i1.5493

Abstract

Toko Podomoro is a glassware store that requires a fast and accurate data processing information system to facilitate the work. In modern times like today, Podomoro stores still use recording on books that have not been computerized. Purchase of goods and expenditure of goods is still manual by recording the data of incoming goods and goods out of the warehouse, hence the frequent occurrence of errors in the processing of incoming and outgoing goods data. While making a report that will be made is also still using expenditure records manually by writing in the book, not to mention the record of the purchase of lost goods, which is very important because of the evidence of the reports made. Therefore, it is necessary to have a system of goods investment in Podomoro stores to make it easier for store owners to input goods and stock reports, purchase and expenditure reports.
Application Of Data Mining In Selecting Superior Products Using The K-Means And K-Medoids Algorithm Methods Hermika, Eva; Harahap, Syaiful Zuhri; Ritonga, Irmayanti
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.v12i3.5968

Abstract

As a supermarket, we are committed to always improving everything, including selecting the greatest goods. To evaluate which items are more superior or popular and which are less popular, you will want a sizable amount of information sources. To select products and identify those that belong in the superior product cluster, researchers employed the clustering method. The clustering strategy uses two forms of cluster analysis, k-means and k-medoids, which have related techniques. The research results show that the k-means algorithm's Davies Bouldin value is -0.430, whereas the k-medoids algorithm's Davies Bouldin value is -1.392. This suggests that the Davies Bouldin value of the k-medoids approach is the lowest, showing that the grouping findings of the k-means method are  a better method to apply to the issue of choosing better products.
Implementasi Data Mining Untuk Klustering Stunting Gizi Pada Balita Dipuskesmas Sigambal Meggunakan Metode K-Medoids Dan K-Means Melisa, Melisa; Harahap, Syaiful Zuhri; Masrizal, Masrizal
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.v12i3.6159

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.
Peningkatan Efisiensi dan Penjualan Toko Fashion Outlet Rantauprapat di Jalan Sisingamangaraja Melalui Implementasi E-Commerce PrestaShop Nasution, Intan Baiduri; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 5, No 4: JCoInS | 2024
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v5i4.6805

Abstract

The purpose of this study is to assess the impact of implementing the PrestaShop e-commerce platform on the efficiency and sales of Fashion Outlet Rantauprapat on Sisingamangaraja. Using a case study approach using quantitative and qualitative data, this study analyzes purchase data before to and after PrestaShop implementation, as well as conducts research with store owners. The results of the study show that PrestaShop significantly improves operational efficiency, reduces market volatility, and increases sales. However, challenges such as a lack of technical knowledge and skills must be addressed. This study found that PrestaShop has a large potential to become an effective tool for Fashion Outlet Rantauprapat in terms of increasing sales in the digital era.
Co-Authors Ah, Rahma Muti Aini, Putri Aisyah Hayati Ali Akbar Ritonga Amin, Mhd. Andini, Novira Dwi Andriani, Nur Putri ANTIKA, DEWI Aprilianto, Muhammad Ardiansyah, Rizaldi Bangun, Budianto Cahya, Susilo Tiadi Christoval, Peter Dalimunthe, Annisa Putri Faradilah, Rahma Fatma, Nurul Febriyanti, Ade Eka Hanif, Khairil Hansyah, Praida Harahap, Vivi Nadenia Hasibuan, Mila Nirmala Sari Hasibuan, Muhammad Adlin Hasibuan, Taufik Molid Hidayat Hermika, Eva Ibnu Rasyid Munthe Irmayani, Deci Irmayanti Irmayanti Irmayanti, Irmayanti Irmayati, Irmayati Iwan Purnama Iwan Purnama JP, Gafar Ilyaz Juledi, Angga Putra Juwita Juwita, Juwita Laila Sari Lestari, Putri Anggraini Lianah Lianah, Lianah Listia, Bella Ayu Lubis, Nadira Jannah Adeni M, Nelvi Nurrizqi Marnis Nasution Masrizal Megawati Pasaribu Meidy Putra Panusunan Siregar Melisa Melisa Melyani, Sri Mira Handayani Siregar Mth, Sri Rezky Aprilawati Br Muhammad Halmi Dar Munthe, Ibnu Rasyid Mushtafa Haris Munandar Muti’ah, Rahma Naibaho, Restu Fauzy Nasution, Fahri Emil Afandi Nasution, Fitri Aini Nasution, Intan Baiduri Nasution, Khodijah Nasution, Marnis Novita, Rini Pane, Dinda Nurinayah Panjaitan, Nia Putri Pasaribu, Nova Tresia Patriya, Angga Prayoga Pransiska, Apprillia Yudha Priyanti Priyanti Purba, Mhd. Rafly Putra, Fasdiansyah Putri Lestari, Putri Rafika, Mulya Rahma Muti’ah Ramadan, Ahmad Ramadhani Ramadhani Rambe, Aida Zahrah Hasanati Br Rambe, Nurhayati Rambey, Khiarul Akhyar Ritonga, Akbar Pramuja Ritonga, Ali Akbar Ritonga, Irmayanti SANDI ARDIANSYAH Sari, Kurnia Tika Sigit Prasetyo Nugroho Sihotang, Diko Pradana Sirait, Roby Gusmawan Siregar, Ade Elvi Rizki Siregar, Siti Kholijah Siregar, Siti Wahdina Sitepu, Melda Betaria Sitompul, Muhammad Sofyan Surbakti, M. Aufa Nayaka Fathan Suryadi, Sudi Suryadi, Sudi Syavitri, Tiara Wardani, Syafira Eka Wijaya, Alief Achmad Yeni Syahfutri S Yenni Syahfutri Sipahutar ZURAIDAH ZURAIDAH