Jurnal Informatika: Jurnal Pengembangan IT
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Rancang Bangun Aplikasi Diet untuk Ibu Menyusui Pasca Persalinan dengan Algoritma Mifflin-St Jeor
No full textPregnancy is a significant and transformative period for women, both physically and emotionally. During this time, it is crucial for expectant mothers to prioritize their own health and well-being to create a healthy environment for their growing baby. One of the physical changes that many breastfeeding mothers experience after childbirth is weight gain. Factors contributing to this include increased caloric needs, lack of sleep, reduced physical activity, and feelings of stress and fatigue due to caring for a newborn. Maintaining a healthy weight is vital to reduce the risk of various health issues and ensure the quality of breast milk for the baby. However, it is important to note that mothers should not engage in strict dieting during the postpartum period, or the puerperium, which lasts up to 40 days after delivery. During this time, mothers should gradually resume normal activities and movement. To support breastfeeding mothers in maintaining their health after childbirth, a structured and monitored approach that provides tailored information according to each stage of development is necessary. The Laav application, available for iOS, is designed to calculate and record the caloric intake of breastfeeding mothers, helping them achieve proper nutrition while maintaining an ideal weight. The application is built using the User-Centered Design (UCD) methodology and uses the Mifflin-St Jeor algorithm to calculate calories. The application is programmed in SwiftUI, a language optimized for the iOS platfor
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Jenis Tanah Berbasis Android
No full textBawen District is one of the sub-districts in Semarang Regency, Central Java. This region has an area of land used for agriculture around 63.29%. In this area the population still uses soil as a planting medium. Soil is one of the planting media which plays an important role for the survival of plants. With so many types of soil that have different properties and characteristics, the treatment of these soils is also different. So it is necessary to have a soil classification to know how to manage the soil properly. To facilitate the classification of soil types, Deep Learning technology can be utilized with images as input which are then processed using the Convolutional Neural Network (CNN) algorithm. In order to get a model that has a high level of accuracy, an experiment was carried out on several influential parameters and an evaluation of the model was carried out using a confusion matrix. The confusion matrix has several values such as accuracy, precision, recall, and f1-score. Tests have been carried out and the results of this study are models that have a training accuracy of 97% with a loss value of 0.0880 and a testing accuracy of 95% with a loss value of 0.1513
Pengembangan Sistem Klasifikasi Karakteristik Siswa Berbasis Website dengan menggunakan Algoritma C4.5
No full textStudent characteristics are one of the attributes of knowing a student's thinking skills and academic abilities. In the process of teaching and learning, appropriate learning strategies must be applied to students. The Hippocrates-Galenus typology categorizes personality types into four different categories, namely sanguine, choleric, melancholic and phlegmatic. Classification of characteristics that use an approach to students based only on experience or intuition can produce inaccurate results and take a lot of time to process. A system with the ability to predict student characteristics is needed in order to be able to assess students more quickly. In this study, the C4.5 algorithm was implemented into a system that aims to carry out the process of classifying the characteristics of students. From the results of the tests carried out, the C4.5 algorithm obtains an accuracy of 90.08%. This shows it is able to classify student characteristics well by using the C4.5 algorith
Tabel Partisi Pada STARS: Konsep Dan Evaluasi (Studi Kasus STARS UKSW)
No full textDatabase performance is one of the main components in supporting the sustainability of a system, in this case, STARS. In the system context, data will usually be collected into a database. Tied to the data collection process, this really affects the performance of a system as a whole, in this case when executing a query to get a return of the execution results, because the performance of the database itself will be affected by the amount of data available. One way to improve the performance of the database is to use the partition table concept. Thus, in this research a design and evaluation of the partition table will be carried out which will then be applied to the SWCU STARS database. This research focuses more on the use of vertical partitions and list partitions by utilizing PostgreSQL version 14. The stages used in this study. These stages include data collection, partition design, technical partition, testing and implementation. The results of this study indicate that partition tables have better performance than non-partition tables. Judging from some of the sql syntax, namely update, delete and select, while insert has poor performance for partition tables compared to non-partition table
Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor
No full textAbstract − E-commerce's rapid growth has resulted in an increase in online transactions and shifts in consumer behavior. In Indonesia, the use of e-commerce has grown rapidly, with many online platforms emerging. Understanding public sentiment towards e-commerce in Indonesia is crucial for businesses to improve their services and maintain customer satisfaction. In this review, study propose a methodology for feeling investigation of popular assessment on the utilization of web-based business in Indonesia, utilizing directed learning calculations. The study involved collecting data from the website Google Play Store. The study performed data preprocessing, including removing stop words, tokenization, and stemming, before applying the K-Nearest Neighbor (K-NN) algorithm to classify sentiments into positive or negative. The evaluation was conducted using confusion matrix and classification report. The results showed that the proposed approach was effective in analyzing public sentiment towards e-commerce in Indonesia, with an accuracy rate of 82%. The study concluded that the proposed strategy could help businesses enhance their services and better satisfy customers' requirements and expectations.Keywords – Sentiment Analysis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN. Abstrak - Perkembangan e-commerce yang pesat telah menyebabkan peningkatan transaksi online dan perubahan perilaku konsumen. Di Indonesia, penggunaan e-commerce tumbuh pesat dengan banyak platform online bermunculan. Memahami sentimen masyarakat terhadap e-commerce di Indonesia sangat penting bagi bisnis untuk meningkatkan layanan dan menjaga kepuasan pelanggan. Oleh karena itu, dalam penelitian ini peneliti mengusulkan sebuah pendekatan untuk melakukan analisis sentimen opini publik mengenai penggunaan salah satu e-commerce di Indonesia dengan menggunakan algoritma K-Nearest Neighbor. Pengumpulan data dilakukan dari website Google Play Store dengan tujuan untuk memperoleh pandangan dan pengalaman masyarakat terkait penggunaan salah satu e-commerce di Indonesia. Setelah data terkumpul, dilakukan proses preprocessing untuk membersihkan data, termasuk menghilangkan stopwords, tokenisasi, dan stemming. Setelah itu, algoritma K-Nearest Neighbor (K-NN) digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Evaluasi dilakukan dengan menggunakan confusion matrix dan classification report untuk menilai keakuratan algoritma. Hasil penelitian menunjukan bahwa pendekatan yang diusulkan efektif dalam menganalisis sentimen masyarakat terhadap e-commerce di Indonesia, dengan tingkat akurasi 82%. Penelitian ini memiliki implikasi penting bagi bisnis e-commerce di Indonesia dalam meningkatkan layanan dan memenuhi kebutuhan serta harapan pelanggan secara lebih baik.Kata Kunci - Sentimen Analisis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN
Sistem Pakar Diagnosis Penyakit Pada Ikan Bawal Bintang dengan Pendekatan Naive bayes
No full text The star pomfret is a type of cultivated fish that has high economic prospects. The focus of the main problem in this study is the disease that attacks the star pomfret fish commodity. If this is allowed to continue, it will cause crop failure and cause the fishermen to lose money. Through this research, an expert system is one solution that can overcome these problems. The expert system built will apply the Naive Bayes method with the stages of entering the dataset into the database which will be used as training data, then the user inputs testing data to be processed into the Bayes method, in the final result the probability value of each disease will be displayed which will then be given recommendations on how to control it disease. From the symptoms selected by the user, namely: white or pale spots on the surface of the body, bleeding on the surface of the body, protruding eyes, the fish looks difficult to breathe, mucus production increases until the body runs out of mucus / roughness, fish lose their appetite, slow movement and slow growth get disease results Cryptocaryon with a value of 93.4. The results of tests carried out on 17 data obtained an accuracy value of 94% so that the expert system is suitable for use as a tool for diagnosing disease in pomfre
Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV
No full textLow Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance
Pengenalan Alfabet SIBI Menggunakan Convolutional Neural Network sebagai Media Pembelajaran Bagi Masyarakat Umum
No full textSIBI is one of the Sign Languages used in Indonesia and has been widely used in the community, especially the school (SLB). Communication limitations of the deaf and speech community cause limited communication with the general public, especially many general public who do not know Sign Language or SIBI. For this reason, this research was conducted in order to become a learning media for the general public in recognizing the SIBI alphabet so that it can support communication with the deaf and speech community. This research was conducted to become a medium that can be used as a learning medium in the introduction of the SIBI alphabet. The method used in this research is CNN. CNN is used because it is a deep learning method that has the most significant results in image recognition. The data used is 2,600 images which are divided into 80% training data and 20% validation data. Training was done ten times by comparing the parameters that produce the best accuracy. The parameters used are batch size and epoch. From ten trials, the best accuracy is obtained using batch size 8 and epoch 50. The best accuracy produced is 85% training accuracy and 87% validation accuracy
Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN
No full textShallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other method
Pengembangan Aplikasi Kamus Bahasa Bima-Inggris-Indonesia Menggunakan Rapid Application Development
No full textThe dictionary is one of the solutions for learning about vocabulary and translating it. The use of dictionaries in the form of books is less effective and efficient, so it is necessary to develop electronic dictionaries in the form of dictionary applications available on smartphones. This study aims to develop an Android-based three-language dictionary application, namely Bima, Indonesian, and English, which includes a speech to text feature. The software development method uses the Rapid Application Development (RAD) method. This method has four main stages: requirements planning, design, construction, and cutover. Data collection methods in this research include observation, interviews, documentation, and literature study. The data analysis technique used is qualitative data analysis of data resulting from observations, interviews, and literature studies. The application was built using the Flutter and Codeigniter frameworks. In the final stage of dictionary application development, testing was carried out on the application's functionality using the black box method. The results of the test show that the application runs very well; all buttons and features work as they should after fixing bugs and problems found in the final test before launch