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SVM Algorithm For Predicting Rice Yields Nur Nafi'iyah
Jurnal Teknologi Informasi dan Pendidikan Vol 13 No 2 (2020): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/tip.v13i2.341

Abstract

Agriculture in Indonesia is highly dependent on reservoir irrigation water sources and rain. Because some agricultural land in Indonesia is rainfed. Plants in Indonesia rely on water from rain and irrigation. Weather conditions greatly affect the number of farmers' harvest. Farmers often experience crop failures due to changing weather. From data from the Central Statistics Agency, it is stated that the number of rice yields in 2019 decreased by 7.76% compared to 2018. In order to avoid rice imports and rice food shortages, a breakthrough is needed that can help the government in making policies. One of the breakthroughs is creating a rice yield prediction system. The research process consisted of collecting data via the web: https://www.pertanian.go.id/home/?show=page&act=view&id=61. The data shows the variables of province, year, land area, production. The total number of data is 170 rows, with a division of 130 lines for training, and 40 for testing. Furthermore, the data is processed and processed and normalized. The results of data processing are then trained and predicted with a linear SVM kernel. The results of SVM prediction with original data without normalization of MAPE 6635.53%, and RMSE 1094810.74. The results of SVM prediction with normalized data first, the MAPE value was 9427.714%, and RMSE 0.017.
SEGMENTATION AND COUNTING THE NUMBER OF TEETH PANORAMIC DENTAL IMAGE Nur Nafi'iyah; Endang Setyati
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.181

Abstract

There are many methods for segmentation human teeth or dental radiographs. The most frequently used segmentation method is thresholding. In developing a system of segmentation in human teeth based on panoramic tooth photographs, several very important steps are needed. Segmentation is the separation of teeth from the background and separation of each tooth. The purpose of this study, which is to separate dental image per tooth by segmentation. In addition, the other most important process is feature extraction, which is the process of knowing the most important part of the image of the tooth to be processed. Stages in this study, namely: improving the image with CLAHE, then thresholding, the results of thresholding are segmented using integral projections, the results of segmentation extracted features using tooth centroid. Thresholding or binarization is changing grayscale image into binary forms, the algorithm used is iterative adhaptive thresholding. The accuracy value of the thresholding process or separating teeth from the background is 66.67%. Segmentation is done twice, namely: separating the maxilla and mandible, and separating each tooth. Separates the maxilla and mandible using a horizontal integral projection algorithm. Whereas to separate each tooth using vertical integral projection. In order for the separation process of each tooth to produce the best, the tooth panoramic photo is divided into three parts, namely: the left, right, and center. However, from the process of separation of each tooth the accuracy is 33.33%.
Perbandingan Otsu Dan Iterative Adaptive Thresholding Dalam Binerisasi Gigi Kaninus Foto Panoramik Nur Nafi'iyah; Retno Wardhani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (203.198 KB) | DOI: 10.32815/jitika.v11i1.39

Abstract

Proses binerisasi bertujuan untuk memudahkan pengenalan citra dalam tahap computer vision. Binerisasi merupakan cara mengubah bentuk warna citra ke hitam putih atau biner. Metode otsu merupakan metode konversi citra ke bentuk hitam putih. Metode iterative dan adaptive thresholding merupakan gabungan metode dalam mengubah citra ke biner. Tujuan dari penelitian ini, yaitu: memudahkan dalam tahap ekstraksi citra atau pengambilan informasi terpenting dalam citra. Sehingga proses selanjutnya seperti pengenalan citra atau recognition. Hasil dari penelitian ini berupa citra biner gigi kaninus foto panoramik. Dari perbandingan metode, metode iterative dan adaptive thresholding menghasilkan gambar biner yang lebih baik.
ANALISIS REGRESI LINEAR DAN MOVING AVERAGE DALAM MEMPREDIKSI DATA PENJUALAN SUPERMARKET Nur Nafi'iyah; Eka Rakhmawati
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol 12 No 1 (2021): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (270.279 KB) | DOI: 10.51903/jtikp.v12i1.230

Abstract

Sales in supermarkets during the Covid 19 pandemic will change normally. Sales in normal times may always be crowded and a lot. It is different during a pandemic because several layers of society experience economic difficulties. Supermarkets that cannot solve this problem may have a loss or may have to close temporarily. From this background, we propose a predictive analysis of sales of electronic and health goods in supermarkets using Linear Regression and Moving Average methods. The data used in this research were taken from the Kaggle dataset website. The data consists of several types of goods, but we use the electronics and health categories. The purpose of this research to analyze the two algorithms in predicting electronic sales and health data. The tool used to help analyze is python. How to analyze the two algorithms with MSE (Mean Square Error) and RMSE. The results of the analysis show that the Moving Average method has the best performance as evidenced by the MSE and RMSE values โ€‹โ€‹of electronic data which are 57,603, 7.59, health data are 50,489, 7,106. While the linear regression method prediction results on electronic data and health, the MSE and RMSE values โ€‹โ€‹are 114.79, 10.71, 59.965, 7,744.
Analisis Peramalan Stok Barang dengan Metode Weight Moving Average dan Double Exponential Smoothing pada Jovita Ms Glow Lamongan Nur Nafi'iyah
Intelligent System and Computation Vol 1 No 1 (2019): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v1i1.23

Abstract

Jovita MS Glow Lamongan merupakan agen yang menjual produk kecantikan dari brand MS Glow, produk yang dijual di antaranya perawatan wajah, tubuh, kosmetik dengan perkembangan penjualan dari bulan ke bulan semakin meningkat maka dibutuhkan perhitungan perkiraan jumlah barang yang akan dibeli untuk meramalkan persediaan barang bulan berikutnya. Persediaan barang yang tidak tepat dapat menimbulkan kerugian maka perlu adanya sistem peramalan. Oleh karena itu penelitian menggunakan metode Weight Moving Average dan Double Exponential Smoothing untuk menentukan nilai error yang lebih kecil. Data yang digunakan pada penelitian ini mulai bulan Januari 2015 sampai bulan Desember 2016. Metode Weight Moving Average yaitu metode yang memberikan bobot yang berbeda untuk setiap historis sedangkan Metode Double Exponential Smoothing merupakan metode yang memiliki nilai pemulusan dua kali pada waktu sebelum data sebenarnya. Hasil peramalan kedua metode ini menghasilkan nilai error Weight Moving Average yaitu 698.7180 dan Double Exponential Smoothing yaitu 1.429.1015, sehingga Weight Moving Average adalah metode yang tepat digunakan untuk meramalkan persediaan barang karena memiliki nilai error yang lebih kecil.
KNN FOR CLASSIFICATION OF FRUIT TYPES BASED ON FRUIT FEATURES Moh. Afis Azman; Nur Nafi'iyah
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 7 No. 1 (2022)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v7i1.100

Abstract

Research related to the recognition of fruit types has been done previously. Research related to the recognition of many types of fruit applies computer vision and artificial intelligence. The purpose of this research is to apply artificial intelligence science with the KNN method to identify the type of fruit. The KNN method has a good performance in previous studies. We tried to use KNN by determining the most optimal K value. There are five types of fruit images used in this study, namely Apples, Grapes, Oranges, Mangoes, and Strawberries. The fruit image is extracted with colour, texture, and shape features with a total of 15 features, namely the average value of R, the average value of G, the average value of B, the value of skewness R, the value of skewness G, the value of skewness B, the value of grayscale entropy. , grayscale contrast value, grayscale energy value, grayscale correlation value, grayscale homogeneity value, binary area value, binary circumference value, binary major axis value, and binary minor axis value. The dataset used in this study was taken from Kaggle, with a dataset of 2750 images, each type of fruit contained 550 images, 2500 training images were used and 250 images were used for testing. The experimental results show that the KNN method with K=1 has the highest accuracy, which is 99.6%. The KNN method can be used optimally in classifying fruit types based on colour, texture, and shape features.
SISTEM PENENTUAN KUALITAS PISANG MENGGUNAKAN METODE FUZZY TSUKAMOTO Rofika Arista; Miftahus Sholihin; Nur Nafi'iyah
Journal of Information System Management (JOISM) Vol. 1 No. 1 (2019): Juli
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (676.057 KB) | DOI: 10.24076/joism.2019v1i1.22

Abstract

Pisang adalah buah yang memiliki rasa sangat manis dan aroma yang harum. Buah ini menjadi favorit banyak orang karena harganya yang terjangkau dan memiliki banyak manfaat dan nutrisi untuk tubuh. Penentuan kualitas pisang telah ditetapkan berdasarkan SNI 7422-2009 pisang. Tetapi kurangnya pengetahuan perusahaan ataupun konsumen dalam menentukan kualitas pisang mengakibatkan kesehatan menurun karena kesalahan mengkonsumsi buah pisang. Proses menentukan kualitas pisang berdasarkan 3 variabel kriteria, yaitu: ukuran, kecacatan dan kesegaran. Di mana hasil kualitas pisang terdapat kategori kelas C, B, A dan Super. Metode Tsukamoto dalam menentukan kualitas pisang menggunakan defuzzifikasi nilai rata-rata, dan menggunakan kriteria kualitas pisang yang terdiri dari tiga variable, yaitu ukuran, kecacatan, dan kesegaran. Hasil penelitian ini didapatkan kelas pisang berdasarkan kriteria yang diinputkan dan memiliki nilai akurasi sebesar 78%.
LINEAR REGRESSION FOR DISCOUNTING PRESENTATION RECOMMENDATIONS (Kaggle Dataset) Nur Nafi'iyah; Nur Fahmi Maulidi
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol 13 No 2 (2022): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v13i2.326

Abstract

In the business of selling goods, there must be goods that do not sell well and sell well. How to make unsold items sell well by giving customers a discount or discount strategy. The goal is to provide discounted prices to attract customers' attention and increase sales turnover. Prediction to give the right discount presentation is needed in the discount strategy. How to determine discount prediction using linear regression method, looking for line equations by training data taken from Kaggle.com. The data were trained to find the constants and coefficients of the independent variables. The resulting line equation will be tested to determine the discount prediction that will be given and calculate the error value. The data used is painting sales data taken from Kaggle.com, a total dataset of 1525, and a prediction calculation is made with 225 lines of test data. The input variable used is the price of the painting, and the output variable is the discount presentation. The process of evaluating the prediction results by calculating the MAE value, the difference between the actual data and the predicted data, the MAE value from the trial is 0.108.
U-Net Analysis Architecture For MRI Brain Tumor Segmentation Nur Nafi'iyah
Jurnal Teknologi Informasi dan Pendidikan Vol 16 No 2 (2023): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v16i2.713

Abstract

Identification, segmentation and detection of brain tumor-infected parts on MRI images require precision and a long time. MRI of the brain has an important role, one of which is used for analysis or consideration before performing surgery. However, MRI images cannot provide optimal results when analyzed because of the presence of noise and the bone and tumor (clots of flesh) have the same appearance. Many studies related to brain tumor segmentation have been carried out before, and some of the good methods are CNN U-Net. We segmented brain tumors on MRI with U-Net. The purpose of this study was to analyze the results of changes in the number of neurons in the convolution layer of the U-Net architecture in segmenting brain tumors. We use two scenarios of changing the number of neurons at the U-Net convolution layer. The first scenario is the number of neurons successively at each level of the U-Net architecture [32,64,128,256,512], and the second scenario is [16,32,64,128,256]. And the results of scenario two can segment brain tumors on MRI images that resemble ground truth. The results of brain tumor segmentation in MRI images with the U-Net second scenarios have an average Dice value of 0.768.
Recognizing the Types of Beans Using Artificial Intelligence Nur Nafi'iyah; Endang Setyati; Yosi Kristian; Retno Wardhani
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3054

Abstract

Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, we analyzed 69 features and explored their correlations within various image types, including RGB, L*a*b, HSV, grayscale, and binary images. The primary objective of this study is to pinpoint the features most strongly correlated with the recognition of bean leaf types, specifically green bean, soybeans, long beans, and peanuts. Our dataset, sourced from farmers' fields and verified by experienced senior farmers, consists of 456 images. The most highly correlated feature within the bean leaf image category is STD b in the L*a*b image. Furthermore, the most effective method for leaf type recognition is Neural Network Backpropagation, achieving an accuracy rate of 82.28% when applied to HSV images.