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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Building of Informatics, Technology and Science Informatika INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Media Informatika Journal of Computer Science and Information Systems (JCoInS)
Nasution, Fitri Aini
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Library & Information Science Other

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Sistem Informasi Penghitungan Pembayaran Kuliner Pada Café Aira Rantauprapat Pohan, Cita; Nasution, Fitri Aini
Journal of Computer Science and Information System(JCoInS) Vol 2, No 4: JCoInS | 2021
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1084.912 KB) | DOI: 10.36987/jcoins.v2i4.2966

Abstract

The development of technology is currently developing very rapidly. and bring a very big change because with technology we can get information, access information quickly. With the development of today we can use computers as a tool to facilitate human work, thereby reducing the risk of errors, in order to be more effective, safe, fast, and accurate.Cafe is a place to relax and chat where visitors can order drinks and food. In general, restaurants or cafes have difficulty calculating the list of food menus and prices to be paid visitors, the difficulty is because the calculation is done by the admin is still manually, often errors in calculations that cause losses in the cafe. This study aims to make cafe operations can run more effectively, safely, quickly, and accurately. With this information system, the admin can control or process the process of payment data in the cafe because it has been computerized, so that the data can be stored properly .
Measurement of Photosynthetic Pigment Content using Convolutional Neural Network Rezeki, Imam Dwi; Nasution, Fitri Aini; Juledi, Angga Putra
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11414

Abstract

Estimation of photosynthetic pigment levels from leaves can be done using conventional methods using laboratory equipment such as spectrophotometers and using digital image processing from leaf images with a computational model. In digital image processing methods, various models are used, such as neural network, CNN, and linear regression. Measurement of photosynthetic pigment levels using image processing methods uses color value data from image data as input to the model used. In this study, we will analyze the effect of various types of color space and inpaint preprocessing settings on the accuracy of the CNN model in measuring leaf photosynthetic pigment levels. The color space types being tested are 4 single color spaces RGB, HSV, LAB, and YCbCr, as well as 6 color combination spaces RGB+HSV, RGB+LAB, RGB+YCbCr, HSV+LAB, HSV+YCbCr, and LAB+YCbCr. The choice of the type of color space takes into account the phenomenon of color constancy and the characteristics of the color space on the lighting elements. In addition, image data is divided into two types, namely through inpaint preprocessing and not, so that in total there are 20 types of input data. After the CNN model training process with various types of color spaces and different preprocessing settings as input data, observations were made on the accuracy values, namely the training MAE and the validation MAE for each model. From 20 types of input data, 3 types of input data are obtained which are recommended as input data that provide the best model accuracy value based on MAE validation with values ​​of 0.08761, 0.09252, and 0.09288. The three recommended input data from the sequence of accuracy values ​​are RGB+LAB without inpaint, RGB with inpaint, and LAB+YCbCr without inpaint.
Design and Build Inventory System using EOQ and ROP Methods (Case Study: CV. Ziefa Karya) Lubis, Rapida Hanim; Nasution, Fitri Aini; Juledi, Angga Putra
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11419

Abstract

As a construction company that manages large quantities of incoming and outgoing goods, CV. Ziefa Karya must carry out an inventory control process, to determine the number of products to be re-supplied in a fast and accurate manner. But what happened was the opposite, with the inventory control process that was still manual, the owner of the company experienced various losses because he did not record goods and went out regularly. Based on these problems, this study tries to provide a solution in order to facilitate the management of inventory. CV. Ziefa Karya must have an inventory system that can manage the supply of goods properly. This study aims to build an inventory system based on information systems on CV. Ziefa Karya by applying the Economic Order Quantity (EOQ) and Reorder Point (ROP) methods. The system development method used is Model Driven Development (MDD) which includes: interviews, observations, literature studies, problem analysis, needs analysis, design, construction, and implementation. From the results of implementation and testing, it is found that the implementation of EOP and ROP in the application has succeeded in making the inventory system function properly. From these results it can be concluded that to build an inventory system on CV. Ziefa Karya uses the EOQ and ROP methods which can provide fast, precise, and accurate information, so the Model Driven Development (MDD) method must be used.
Implementation of the RAD Method to Build Catering Application Android-based Tasyabila, Tasyabila; Sihombing, Volvo; Nasution, Fitri Aini
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11421

Abstract

With the rise of culinary businesses that sell food and beverages at this time, business actors must be ready to compete to win and retain their customers so that their business can survive. Ara Catering is one of the culinary business actors in Rantauprapat City which focuses on the food and beverage ordering business. The ordering process applied by Ara Catering is still conventional, where customers come directly to the business location or place an order by telephone. The conventional model turned out to cause problems, including errors in ordering and ordering notes that had piled up. Therefore, a solution must be found to overcome this problem. Based on these problems, this study aims to build an android-based catering ordering application. The method applied in this research is the Rapid Application Development (RAD) method in which the process stages include: requirements planning, user design, and implementation. After the design and implementation have been carried out, the results show that the RAD method can be applied in building an Android-based catering application quickly and effectively. The conclusion drawn from this research is that to build an android-based catering application using the RAD method, the phases of the method must be carried out thoroughly. Hopefully the results of this research can make a positive contribution to Ara Catering in developing its business.
Implementation of the Naïve Bayes Method to Determine Student Interest in Gaming Laptops Nasution, Rico Fadly; Dar, Muhammad Halmi; Nasution, Fitri Aini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12562

Abstract

The development of the times resulted in the development of technology to date. With the existence of technology, many people have used technology to help their daily activities. In this study, the author will discuss the technology that is often used by students to help them with their assignments, namely laptops. Laptop is a technology that has been widely used by students, teachers and the public. Having a laptop can make things easier. Until now, each laptop brand continues to develop their laptop production laptops with good specifications. Until now, almost all laptop brands have made gaming laptops that are actually intended for people-people who play games. But with good specifications, gaming laptops can also be used for daily activities. With an attractive design and good specifications, of course you can attract student and public interest in gaming laptops. Therefore the authors made a study of student interest in gaming laptops. With good design and specifications on gaming laptops, the author aims to classify the number of students who are interested and not interested in gaming laptops. The classification will be carried out using the Naïve Bayes method with the number of sample data used as many as 100 student data in data mining. The classification results obtained were 55 students (55% representation) interested in gaming laptops and 45 students (45% representation) had no interest in gaming laptops. The results show that not all students are interested in gaming laptops, even though they have laptops design and great specs.
Analysis of Community Satisfaction Levels using the Neural Network Method in Data Mining Hasibuan, Sabdi Albi; Sihombing, Volvo; Nasution, Fitri Aini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12634

Abstract

Data mining is a process that is carried out to extract data into information. There are several models that can be done in data mining, such as classification, association, clustering, regression. But in this study will be carried out using a classification model. Research conducted on the level of public satisfaction for shopping on the Lazada application. This study aims to determine the level of public satisfaction on the Lazada application. This research was also conducted because the goods sold on the Lazada application are quite cheap and when compared to the original price there is a considerable difference. Therefore, research was conducted on the level of community satisfaction on the Lazada application. This research will be conducted on data mining with a classification model and using the neural network method. The results obtained from the data mining process using 100 community data, the results obtained are 81 community data (representation obtained by 81%) of people who are satisfied shopping on the lazada application and by 19 (representation obtained by 19%) people who are not satisfied shop on the Lazada app. From these results, many people are satisfied with shopping on the Lazada app. So from the results of this classification it can be concluded that the goods sold on the Lazada application are good goods.
ANALYZE THE ADVANTAGES AND COSTS OF PROCESSING DIRTY SWALLOW'S NESTS TO FINISHED PRODUCTS USING THE C5.0 ALGORITHM Pasaribu, Milwan; Irmayani, Decy; Nasution, Fitri Aini
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v10i5.1529

Abstract

Swallow's nest is a very popular health product nowadays. This can happen due to swallow's nest has many health benefits such as antioxidant, anti-inflammatory, antiaging, anticancer, immune-boosting and accelerating wound healing. However, the production of swallow's nest is difficult to do so that the product of swallow's nest becomes expensive. This study aims to analyze the costs and benefits required to produce swallow nests. The method used is the C5.0 algorithm which uses a decision tree. The results showed that the root node was obtained, namely the drying stage of the swallow's nest. If the costs required at this stage were equivalent to the existing capital, the company would get benefit. Conversely, if the required cost exceeds the existing capital, the company will experience a loss. Through the decision tree, it can be determined that the determinant or root node of the swallow's nest production process is the stage of the swallow's nest drying process. If the capital is within the budget (budget) then the company will generate profits. Meanwhile, if the existing capital is insufficient (pass) then the company will suffer losses. Based on the results of this study, it can be suggested to use the C5.0 algorithm method in analyzing the costs and profits generated from a production process that produces finished products for sale.
Penerapan Natural Language Processing dalam Pembuatan Aplikasi Penerjemah Bahasa Melayu Dialek Panai – Bahasa Indonesia Dar, Muhammad Halmi; Hasibuan, Mila Nirmala Sari; Nasution, Fitri Aini
Jurnal Informatika Vol 11, No 3 (2023): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v11i3.5887

Abstract

The Panai dialect of Malay is the mother tongue used by the speaking community in four sub-districts in Labuhanbatu Regency. The reduced number of native speakers who are skilled in the Panai Malay dialect can threaten the sustainability of this language. Efforts to preserve the Panai Malay dialect must be made to avoid extinction. One way that can be done is to document vocabulary in the form of a translator application. This study aims to create an application translator for the Panai-Indonesian dialect of Malay by applying natural language processing. As for the potential users of this application, they are the people of Labuhanbatu in general, especially those in the four sub-districts previously described. The stages of the research method used were: requirement analysis, design, implementation, testing, and maintenance. This research focuses on technology for improving information and communication technology content in the context of local wisdom (culture and language) in Indonesia. The focus of this research is in line with the Strategic Plan (RENSTRA) of Labuhanbatu University, which covers the fields of information and communication technology and cultural arts. From the results of this study, it is hoped that local wisdom in Labuhanbatu Regency will be maintained as social capital for the resilience of the Indonesian nation.
Analisis Sentimen Ulasan Pengguna Aplikasi pada Google Play Store Menggunakan Algoritma Support Vector Machine Lubis, Sanny Khairani; Dar, Muhammad Halmi; Nasution, Fitri Aini
Jurnal Informatika Vol 11, No 2 (2023): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v11i2.5860

Abstract

One of the most popular e-commerce sites in Indonesia is Shopee. As the largest marketplace application in Indonesia, Shopee provides product and service review features to users on the Google Play Store. The review feature is very helpful to find out whether user reviews are positive or negative. Having user reviews will help Shopee improve its services. To identify a very large number of user reviews, it is not possible to do it manually by reading them one by one. This process will take a very long time and is not effective. Therefore, we need a method that is able to identify reviews from users more effectively and efficiently. This research aims to conduct sentiment analysis of user reviews of the Shopee application on the Google Play Store by applying the Support Vector Machine algorithm. The research stages carried out started with dataset collection, dataset labeling, preprocessing, TF-IDF weighting, classification, and evaluation. From the research results, accuracy was 70.88%, precision was 49.49%, recall was 52.55%, and F1-score was 49.84%. From these results, it can be concluded that the performance of the support vector machine algorithm in classifying the sentiment of user reviews of the Shopee application on the Google Play Store is quite good.
Analisis Perbandingan Naïve Bayes dan Neural Network dalam Klasifikasi Minat Masyarakat pada Kursus Komputer Fitria, Nabila Syah; Suryadi, Sudi; Nasution, Fitri Aini
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6999

Abstract

In the digital era, the use of technology in education is growing, especially in improving people's digital literacy through computer courses. To analyze people's interest in courses, a data mining-based approach is needed that can process large amounts of data and identify certain patterns. Naïve Bayes and Neural Network are two widely used classification methods, where Naïve Bayes works based on independent probabilities between features, while Neural Network uses artificial neural networks to capture more complex patterns. This study aims to compare the two methods in classifying people's interest in LKP Ibay Komputer and evaluate the accuracy of each model. The classification results show that both methods produce the same predictions, namely 53 data are categorized as interested and 20 data as not interested. The model accuracy reaches 100%, indicating very high classification performance. Although these results seem ideal, perfect accuracy like this often raises questions regarding the validity and robustness of the model in real-world scenarios. Factors such as relatively small dataset sizes, overly structured data patterns, or lack of variation in training data can cause results that appear too good. Therefore, it is important to conduct additional evaluations such as cross-validation or testing on different datasets to ensure that the model does not experience overfitting and remains reliable in broader predictions. With these results, it can be concluded that both Naïve Bayes and Neural Networks have optimal performance in classifying people's interest in computer courses, but the choice of method can be adjusted according to needs, where Naïve Bayes excels in computational efficiency, while Neural Networks are more adaptive to more complex data.
Co-Authors Ardian, Aldi Bangun, Budianto Fitria, Nabila Syah Harahap, Ismalya Wahyuni Hasibuan, Mila Nirmala Sari Hasibuan, Sabdi Albi Irmayani, Deci Irmayani, Decy Juledi, Angga Putra Lashiyanti, Allbila Rahajeng Lubis, Rapida Hanim Lubis, Sanny Khairani Muhammad Halmi Dar Munthe, Ibnu Rasyid Nasution, Rico Fadly Pasaribu, Milwan Pohan, Cita Priyanti Priyanti Purba, Mhd. Rafly Rezeki, Imam Dwi Ritonga, Ali Akbar Sahbana, Ahmad Sihombing, Volvo Simanjuntak, Ebrika Nadia Suryadi, Sudi Syaiful Zuhri Harahap Tasyabila, Tasyabila Yanris, Gomal Juni