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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Register: Jurnal Ilmiah Teknologi Sistem Informasi BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Teknologi Informasi dan Pendidikan Insyst : Journal of Intelligent System and Computation
Nafi'iyah, Nur
Unknown Affiliation
Arts Computer Science & IT Control & Systems Engineering Education Engineering Languange, Linguistic, Communication & Media

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Clustering Keahlian Mahasiswa Dengan SOM (Studi Khusus: Teknik Informatika Unisla) Nafi'iyah, Nur
Prosiding SNATIKA Vol 3 (2015): Prosiding Snatika (Seminar Nasional Teknologi, Informasi, Komunikasi dan Aplikasinya)
Publisher : LPPM STIKI Malang

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Abstract

Program studi Teknik Informatika merupakan salah satu program studi terfavorite di Universitas Islam Lamongan. Jurusan Teknik Informatika sendiri rencananya akan dibagi menjadi 4 bidang keahlian yaitu Keahlian Informatic atau logika, Software Develop and Enginer, Management Database dan Networking atau Infrastucture. Penelitian akan menerapkan metode Clustering dengan algoritam Clustering Neural Network dalam kasus pengelompokkan keahlian mahasiswa berdasarkan transkip nilai mata kuliah sebagai rekomendasi untuk mengambil bidang keahlian yang sesuai dengan kemampuan mahasiswa. Tujuan dari penelitian ini, yaitu untuk memberikan rekomendasi pemilihan bidang keahlian kepada mahasiswa teknik informatika UNISLA. Peneliti melakukan uji coba training clustering sebanyak 10 kali, dan menunjukkan hasil akurasi rata-rata 82%.
K-NN Klasifikasi Kematangan Buah Mangga Manalagi Menggunakan L*A*B dan Fitur Statistik Nafi'iyah, Nur; Sri Pamungkas, Arif Patriot; Nawafilah, Nur Qomariyah
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 4, No 1 (2019): Jurnal Ilmu Komputer dan Desain Komunikasi Visual
Publisher : Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (526.703 KB)

Abstract

Buah-buahan merupakan bahan pangan sumber vitamin. Buah cepat sekali rusak oleh pengaruh mekanik, kimia dan mikrobiologi sehingga mudah menjadi busuk. Klasifikasi dilakukan pada sekelompok buah mangga yang berbeda-beda jenis kematangannya. Ciri pembeda yang digunakan adalah fitur warna L*A*B. Tujuan penelitian ini yaitu memberikan hasil output klasifikasi kematangan buah mangga manalagi berdasarkan fitur warna menggunakan aplikasi Matlab. Pada penelitian ini akan diusulkan metode GLCM untuk ekstraksi fitur pada buah mangga. Dengan menggunakan K-Nearest Neighboor (KNN) untuk menentukan tingkat kematangan buah mangga. Dataset yang digunakan berjumlah 130 data, terdiri dari 65 data untuk mentah, 15 untuk setengah matang dan 50 untuk matang. Hasil Klasifikasi KNN dengan menggunakan metode GLCM dan L*A*B untuk ekstraksi fitur mendapatkan nilai akurasi sebesar 62.5% pada data uji. Kata kunci: Matlab, Mangga Manalagi, KNN, Lab, GLCM. Fruits are a food source of vitamins. The fruit is quickly damaged by mechanical, chemical and microbiological influences, making it easy to rot. Classification is carried out on a group of mangoes which differ in type of maturity. The distinguishing feature was used is the L*A*B color feature. The purpose of this researchgave the output of the maturity classification ofManalagi mangoes based on color features using the Matlab application. In this research the GLCM method will be proposed for feature extraction in mangoes. By using K-Nearest Neighboor (KNN) to determine the maturity level of the Mango fruit. The dataset used is 130 data, consisting of 65 data for raw, 15 for half-cooked and 50 for mature. The KNN Classification results using the GLCM and L*A*B methods for Feature Extraction get an accuracy value of 62.5% in the test data.Keywords : Matlab, Manalagi Mango, KNN, Lab, GLCM.
Analisis Penghasilan, Pekerjaan, dan Usaha Masyarakat di Masa Pandemi Melalui Penerapan Data Sains Nafi'iyah, Nur; Maghfiroh, Syafaatul
Berdikari: Jurnal Inovasi dan Penerapan Ipteks Vol 9, No 1 (2021): February
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/berdikari.v9i1.9650

Abstract

Activities in some sectors experience a decrease in revenue due to the Covid-19 outbreak, for example, the business of traders who experience a decline turnover. From some respondents who work in the field of trade, entrepreneurship, self-employment experience the impact of the Covid-19 pandemic. This can be seen from the results of questionnaire distributed through google form https://bit.ly/2DiRYzL. Despite the pandemic, work and learning activities still have to run online, for example, conducting online Student Community Service (KKN), with socialization activities to fill out questionnaires related to the impact of the Covid-19 pandemic. In addition, the author also held online KKN activities by promoting tourist attractions in Sekaran village through https://www.youtube.com/watch?v=Ed3bdRIxlpY. The purposes of distributing questionnaire are to know the impact of the pandemic and in order to obtain solutions. This questionnaire was distributed to KKN places, namely Sekaran, Babat, Kebonsari, Turi, Kentong, Tunggul, Priyoso villages in Lamongan Regency, and Bayureno,  Bojonegoro, and Banyulegi Mojokerto. The questionnaires that have been completed were then analyzed using basic statistics such as average value, maximum value, and minimum value. The results of the distribution of questionnaires obtained 990 respondents, with an analysis of 77% of respondents experienced changes in income during the pandemic. Jobs affected by Covid-19 are traders, entrepreneurs, self-employed, farmers and online hired motorcycle drivers. Their incomes decreased by 50% to 68%. There were 43 layoffs out of 990 respondents.
Modification CNN Transfer Learning for Classification MRI Brain Tumor Wardhani, Retno; Nafi'iyah, Nur
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2272

Abstract

Identification, or detecting the infected part of a brain tumor on an MRI image, requires precision and takes a long time. MRI (Magnetic Resonance Imaging) is a magnetic resonance imaging technique to examine and take pictures of organs, tissues, and skeletal systems. The brain is essential because it is the center of the nervous system, which controls all human activities. Therefore, 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 due to noise, and the bone and tumor (lumps of flesh) have the same appearance. AI (artificial intelligence), or digital image processing and computer vision, can analyze MRI images to detect or identify tumors correctly. This study proposes changes to the last layer of CNN (Convolution Neural Network) transfer learning (VGG16, InceptionV3, and ResNet-50) to identify brain tumor disease on MRI. Data were taken from Kaggle with types of glioma, meningioma, no tumor, and pituitary, with a total of 5712 training images and 1311 testing images. The proposed changes include a flattening layer and a pooling layer. The result is that replacing the flatten layer further improves accuracy, and the accuracy of the transfer learning CNNs (VGG16, InceptionV3, and ResNet-50) is 0.918, 0.762, and 0.934, respectively.
U-Net Analysis Architecture For MRI Brain Tumor Segmentation Wardani, Retno; Nafi'iyah, Nur; M., Kemal Farouq
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.
Identifying Types of Corn Leaf Diseases with Deep Learning Firmansyah, Rahul; Nafi'iyah, Nur
Intelligent System and Computation Vol 6 No 1 (2024): 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.v6i1.347

Abstract

The government is trying to increase corn yields to meet the Indonesian population's food needs and for export abroad. Some farmers have yet to gain experience with the types of diseases in corn, so they need tools or systems to guide and provide information to new farmers. Many previous studies have developed automatic systems to identify corn leaf diseases, with the goal of increasing corn crop production by early recognition and control. We propose a system for identifying types of corn leaf diseases using the CNN (Convolutional Neural Network) method to be more precise in recognizing corn diseases early on. The methods used in previous research mostly used deep learning with high accuracy results above 90%. CNN is one of the deep learning methods, so we use it to identify types of leaf diseases. Our data comes from Kaggle; we process it first. The Kaggle dataset has corn plants similar to those in Indonesia, so we use this data with identification classes (Blight, Common rust, Gray leaf spot, and Healthy). The training data is 2000 images with 500 images for each class, and the testing data is 120 images with 30 images for each class. The evaluation results show that the classification process using the CNN method has an accuracy of 84.5%. The results we produced for identifying types of corn leaf disease still lack accuracy in their prediction, indicating the need to improve the CNN architecture model.
Comparison of Machine Learning for Mental Health Identification (The DASS-21 Questionnaire) Wardhani, Retno; Nafi'iyah, Nur
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.860

Abstract

Stress appears at almost any age. Stress can disrupt mental and physical balance, and even students can experience it. Early detection of an individual's emotions is crucial. Researchers hope that by taking such actions, an individual can maintain self-control and prevent the stress they are experiencing from worsening. Bodily characteristics such as speech, body language, eye contact, and facial expressions indicate stress, depression, and anxiety. Psychological activities and human life are associated with physiological emotions. The three categories of negative thoughts or sad emotions are stress, anxiety, and depression. This research assesses or finds students who experience anxiety, depression, and stress. This study compares methods for determining mental health through the distribution of DASS-21 scale questionnaires. The researcher classified the research results using Naive Bayes, Decision Tree, k-NN, SVM, and Logistic Regression methods. According to experiments, SVM is effective for identifying mental health anxiety, depression, and stress with accuracy, recall, and precision of 0.86, 0.90, and 0.80. At Universitas Islam Lamongan, 138 engineering faculty students answered the DASS-21 questionnaire.
Recognizing the Types of Beans Using Artificial Intelligence Nafi'iyah, Nur; Setyati, Endang; Kristian, Yosi; Wardhani, Retno
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.
Analisis Peramalan Stok Barang dengan Metode Weight Moving Average dan Double Exponential Smoothing pada Jovita Ms Glow Lamongan Nafi'iyah, Nur
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.
Co-Authors Endang Setyati Firmansyah, Rahul M., Kemal Farouq Maghfiroh, Syafaatul Nawafilah, Nur Qomariyah Retno Wardhani Sri Pamungkas, Arif Patriot Wardani, Retno Yosi Kristian