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<![CDATA[Perancangan Sistem Informasi Perpustakaan Berbasis Website di SMP Negeri 1 Anjatan ]]>
<![CDATA[Ashila, Annisa Nur]]>;
<![CDATA[Sari, Dian Pemata]]>;
<![CDATA[Widodo, Suprih]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.5964
<![CDATA[SMP Negeri 1 Anjatan is a junior high school located in Anjatan Indramayu Regency. To make it easier for Library Officers to manage libraries, a library information system is needed. the Information System created includes: attendance visits, management of borrowing and returning books, and the process of managing book collections.An information system is a collection of elements interconnected with each other that form a single whole for integrating data, processing and storing and distributing information. Library Information System is a system that exists in an organization that meets the needs of daily data processing, supporting activities in data storage, and providing certain outside parties with the necessary reports. The method used in the development of this library information system using SDLC with RAD model. Stages in the RAD method are requirement planning, user design, constructions, and cutover. Making this information system using PHP Programming Language, HTML, CSS. Using Visual Studio Code as note editor, in the implementation of data base using local server from MySQL.Questionnaires that will be used for prototype testers at the user design stage and the results of contructions are SUS (System Usability Scale) and blackbox testing. Where SUS to measure the Usability of Information Systems and use Black box testing to measure functional suitability.The results of SUS prototype testing included in the good category and acceptability ranges included in the acceptable category. The results of black box testing, all questions on the questionnaire were successful, which means the score of the black box testing is 100%. Based on testing, the Library Information System can be said to be feasible to use.]]>
<![CDATA[Analisis Prediksi Prestasi Siswa UPTD SD Negeri 30 Aek Batu Dalam Machine Learning Dengan Metode Naive Bayes ]]>
<![CDATA[Ambarita, Mira Nanda]]>;
<![CDATA[Nasution, Marnis]]>;
<![CDATA[Ah, Rahma Muti]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6101
<![CDATA[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.]]>
<![CDATA[Analisis Pola Pembelian Melalui Ponsel Menggunakan Algoritma Apriori dan Fp–Growth Pada Millenium Ponsel ]]>
<![CDATA[Andriani, Nur Putri]]>;
<![CDATA[Harahap, Syaiful Zuhri]]>;
<![CDATA[Irmayanti, Irmayanti]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6158
<![CDATA[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.]]>
<![CDATA[Perancangan UI/UX Aplikasi Booking Online Pada Elaine Studio Dengan Metode Design Thinking ]]>
<![CDATA[Kasih, Andaristy Mutiara]]>;
<![CDATA[Ismail, Ismail]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6014
<![CDATA[Nail art is currently experiencing a significant increase in popularity, driven by beauty trends and growing consumer preferences. The nail art salon industry is undergoing growth and transformation driven by new trends and technological advancements. This makes it an exciting time for salon owners looking for solutions by adopting technology to help businesses attract more customers and also increase customer satisfaction. The process of booking appointments and providing information on Elaine Studio is still done manually, namely booking through social media such as Instagram, Whatsapp or directly face to face to come to the studio and is also still recorded manually to book an appointment. This study aims to design and develop an effective user interface (UI) and User Experience (UX) for the nail art online booking mobile application using the Design Thinking method. The main focus of the study is to create an intuitive and user-friendly design, which is able to increase ease of Use and user satisfaction. After going through a series of design and testing iterations, the application is tested using the system Usability Scale (SUS) method to evaluate its usability level.]]>
<![CDATA[Penerapan Metode KNN untuk Menentukan Minat Calon Mahasiswa ]]>
<![CDATA[Riyanto, Tiara]]>;
<![CDATA[Yanris, Gomal Juni]]>;
<![CDATA[Hasibuan, Mila Nirmala Sari]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6153
<![CDATA[This study focuses on the implementation of data mining to determine the interests of prospective male and female students in the Informatics Management Department using the K-Nearest Neighbors (KNN) method. The analysis process is carried out through the Knowledge Discovery in Databases (KDD) stages, which include data selection, pre-processing, transformation, data mining, and pattern evaluation. The KDD stage ensures that the data used has been prepared and processed properly to produce an accurate and relevant model. The KNN method is used to classify sample data consisting of 82 prospective male and female students. The results of this study indicate that 63 out of 82 prospective students are interested in the Informatics Management Department, while 19 other prospective students are not interested. This classification process shows that the KNN method is able to identify the interests of prospective students with a high level of accuracy, providing useful information for universities in understanding the preferences of their prospective students. Evaluation of the research results using two evaluation tools, namely Test and Score and Confusion Matrix, showed perfect results with an accuracy of 100%. Both of these evaluation tools are consistent in assessing the performance of the KNN model, confirming that this model works very well in classifying prospective student interests. In conclusion, the KNN method is proven to be effective and reliable in determining prospective students' interest in the Informatics Management Department, providing a strong foundation for similar applications in the future.]]>
<![CDATA[Application Of Data Mining In Selecting Superior Products Using The K-Means And K-Medoids Algorithm Methods ]]>
<![CDATA[Hermika, Eva]]>;
<![CDATA[Harahap, Syaiful Zuhri]]>;
<![CDATA[Ritonga, Irmayanti]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.5968
<![CDATA[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.]]>
<![CDATA[Sistem Informasi Jual Beli Kelapa Sawit Berbasis Web pada Peron Reskianto ]]>
<![CDATA[Yasdomi, Kiki]]>;
<![CDATA[Utami, Urfi]]>;
<![CDATA[Maradona, Hendri]]>;
<![CDATA[Dona, Dona]]>;
<![CDATA[Rahayu, Susi]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6117
<![CDATA[In Surau Gading village, the majority of the population is employed as oil palm farmers. Every two weeks, farmers harvest oil palm fruit. After harvesting the oil palm fruit, the farmers sell it to Peron Resdianto, who is a young entrepreneur in the village of Surau Gading. Almost every day, the platform owner purchases agricultural products made from palm oil. Because a large number of customers sell their agricultural products, palm oil platform owners have to make sales to the factory once every two days. Once the palm oil mill receives the sales proceeds, the platform owner disburses payments to the palm oil farmers who sell their agricultural products. As information technology advances, it has now spread to almost all fields and is developing at a rapid pace. Information technology's application is evolving, as evidenced by the constant advancements in the field. The author wants to design a web-based information system for selling and buying palm oil on the Resdianto platform using the PHP and My SQL programming languages. We designed this system using system modeling tools, personal home page (PHP), MYSQL, HTML, data flow diagrams (DFD), Xampp, flowcharts, and entity relationship diagrams (ERD). Peron Reskianto can improve performance by creating a web-based information system for buying and selling palm oil, which will assist platform owners in collecting data on palm oil purchases and sales. This information system guarantees security and reduces errors.]]>
<![CDATA[Implementasi Deep Learning Untuk Menentukan Harga Buah Sawit ]]>
<![CDATA[Manurung, Romtika]]>;
<![CDATA[Sihombing, Volvo]]>;
<![CDATA[Hasibuan, Mila Nirmala Sari]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6029
<![CDATA[This study aims to analyze the price of palm oil using Convolutional Neural Network (CNN) method in deep learning. CNN was chosen for its ability to process complex data and recognize patterns from diverse data. The stages of research include data analysis, data pre-processing, predictive model design for CNN method, CNN classification model prediction results, CNN method evaluation, and CNN method evaluation results. This study aims to produce a model that can predict the price of oil palm with high accuracy, based on data covering a variety of characteristics of farmers and the quality of oil palm crops. Prediction results were conducted using data from 50 oil palm farmers. From the prediction, as many as 23 data farmers get a price of IDR 2,300, 13 other farmers get a price of IDR 2,000, and the remaining 14 data farmers get a price of IDR 1,800. The results of this prediction are based on data from farmers and the quality of oil palm crops they grow and produce. By utilizing the CNN method, the model can capture various factors that affect the price of palm oil, including the quality of palm fruit and agricultural conditions. Evaluation of the CNN method showed very good results, with almost perfect accuracy. This method managed to predict palm oil prices very precisely, showing that CNN can be an effective tool in the analysis of palm oil prices. The results of this evaluation confirmed that the CNN method can be relied upon to provide accurate predictions, helping farmers and palm oil industry players in determining prices that are in accordance with the quality and condition of the crop.]]>
<![CDATA[Implementasi Data Mining Untuk Klustering Stunting Gizi Pada Balita Dipuskesmas Sigambal Meggunakan Metode K-Medoids Dan K-Means ]]>
<![CDATA[Melisa, Melisa]]>;
<![CDATA[Harahap, Syaiful Zuhri]]>;
<![CDATA[Masrizal, Masrizal]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6159
<![CDATA[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.]]>
<![CDATA[Sistem Pendukung Keputusan Penghuni Asrama Mahasiswa Kalimantan Tengah di Yogyakarta Menggunakan Metode Simple Additive Weighting (SAW) ]]>
<![CDATA[Mahmudie, Bagus]]>;
<![CDATA[Witanti, Arita]]>
<![CDATA[ Jurnal Informatika Vol 12, No 3: INFORMATIKA ]]>
Publisher : <![CDATA[Fakultas Sains & Teknologi, Universitas Labuhanbatu ]]>
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DOI: 10.36987/informatika.v12i3.6824
<![CDATA[The Central Kalimantan Student Dormitory, located at Jalan Pakuningratan No.61 Yogyakarta, is one of the provincial dormitories established and funded by the regional government to serve as housing for students from outside the region who stay for a certain period. It is specifically intended for students currently studying, with a capacity of 24 rooms. Due to the limited number of rooms and the growing number of students from Central Kalimantan, which exceeds 24 individuals, the dormitory faces challenges in managing its residents. To assist dormitory managers in selecting suitable candidates for residency, the author conducted research on a web-based decision support system designed to streamline the selection process for Central Kalimantan students seeking accommodation at the dormitory in Yogyakarta. This system evaluates students based on criteria that reflect their eligibility to occupy a dormitory. By utilizing web-based decision support technology and employing the Simple Additive Weighting (SAW) method, the system processes various criteria, including data on prospective residents, academic readiness, adaptability, need for social support, and commitment to dormitory rules. With an accuracy rate of 93.3%, as determined by comparing original data, this system ensures a faster, more accurate, transparent, and efficient selection process based on objective data.]]>
