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All Journal International Journal of Evaluation and Research in Education (IJERE) Computer Engineering and Applications Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Perfecting a Video Game with Game Metrics Telematika : Jurnal Informatika dan Teknologi Informasi Proceeding of the Electrical Engineering Computer Science and Informatics Scientific Journal of Informatics InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JPSE (Journal of Physical Science and Engineering) Jurnal Informatika INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK BAREKENG: Jurnal Ilmu Matematika dan Terapan JUMANJI (Jurnal Masyarakat Informatika Unjani) Mathvision : Jurnal Matematika Transformasi : Jurnal Pendidikan Matematika dan Matematika Majalah Ilmiah Matematika dan Statistika (MIMS) Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi)

Dian Candra Rini Novitasari

Universitas Islam Negeri Sunan Ampel

Author-ID : 2414869

Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

Published : 28 Documents Claim Missing Document

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Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique Ahmad Zoebad Foeady; Siti Ria Riqmawatin; Dian Candra Rini Novitasari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.18874

The number of people with lung cancer has reached approximately 2.09 million people worldwide. Out of 9.06 million cases of death, 1.76 million people die due to lung cancer. Lung cancer can be automatically identified using a computer-aided diagnosis system (CAD) such as image processing. The steps taken for early detection are pre-processing feature extraction, and classification. Pre-processing is carried out in several stages, namely grayscale images, noise removal, and contrast limited adaptive histogram equalization. This image feature extracted using GLCM and classified using 2 method of neural network which is feed forward neural network (FFNN) dan feed backward neural network (FBNN). This research aims to obtain the best neural network model to classify lung cancer a. Based on training time and accuracy, the best method of FFNN is kernel extreme learning machine (KELM), with a training time of 12 seconds and an accuracy of 93.45%, while the best method of FBNN is Backpropagation with a training time of 18 minutes 04 seconds and an accuracy of 97.5%.

Klasifikasi Kelainan Pada Jantung Melalui Citra Iris Mata Menggunakan Fuzzy C-Means Sebagai Pengambil Fitur Iris Dan Klasifikasi Menggunakan Support Vector Machine novitasari, Dian candra rini; Rozi, Muhammad Fahrur; Veriani, Rafika
INTEGER: Journal of Information Technology Vol 4, No 1: May 2019
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Iridologi merupakan diagnosis sebuah iris mata yang merepresentasikan tanda-tanda seperti warna dan struktur dari iris sehingga didapatkan informasi tentang kesehatan seseorang. Penelitian ini tentang iridologi yang terkomputerisasi oleh sebuah sistem yang digunakan dalam mendeteksi keadaan jantung yang dirancang dengan langkah-langkah seperti pra-proseskonversi citra ari RGB menjadi Grayscale, penghapusan noise menggunakan median filter, pemangkasan, pengelompokan menggunakan Fuzzy C-Means (FCM), deteksi tepi menggunakan metode Canny dan diikuti fitur ekstraksi menggunakan Grey Level Co-occurrence Matrix (GLCM), serta klasifikasi menggunakan Support Vector Machine (SVM). Sampel iris pasien dalam keadaan normal dan tidak normal. Data iris pasien yang memiliki kelainan jantung sebanyak 20 citra. Hasil dari sistem deteksi kelainan Jantung melalui citra iris ini memiliki tingkat akurasi sebesar 75%.

Implementation of Winnowing Algorithm for Document Plagiarism Detection Ulinnuha, Nurissaidah; Thohir, Muhammad; Novitasari, Dian Candra Rini; Asyhar, Ahmad Hanif; Arifin, Ahmad Zaenal
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Plagiarism prevention efforts are being evolved in various sector. Designing and developing plagiarism checker applications is the purpose of this paper. Specifically by knowing the percentage of similarity between the original document and the test document. Winnowing algorithm is used because it can detect plagiarism in documents up to sub-section of the document. In this paper using three validators consisting of computational mathematicians, software engineering experts, and users to test the feasibility of the application. Experiment using several scenarios, the result of the equation using winnowing algorithm is 90.12%.

Optimal ANFIS Model for Forecasting System Using Different FIS Adyanti, Deasy; Rini Novitasar, Dian Candra; Asyhar, Ahmad Hanif; Setiawan, Fajar
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Adaptive Network Based Fuzzy Inference System (ANFIS) using time series analize is one of intelligent systems that can be used to predict with good accuracy in all fields like in meteorology. However, some research about forecasting has less emphasis on the structure of the FIS ANFIS. Thus, in this paper, the optimization of the ANFIS model for predicting maritime weather is carried out by analyzing the appropriate initialization determinations of the three fuzzy Inference structures ANFIS which includes FIS structure 1 (grid partition), FIS structure 2 (subtractive clustering) and FIS structure 3 (fuzzy c-means clustering). In this paper, the variable input used are two hours (t-2) and one hour (t-1) before, and data at that time (t), and the output of this system is the prediction of next hour, six hours, twelve hours and next day of variable ocean currents velocity (cm/s) and wave height (m) using the three FIS ANFIS approaches. Based on the smallest goal error (RMSE and MSE) of the three FIS ANFIS approaches used to predict the ocean currents speed (velocity) and wave height, the model is best generated by subtractive clustering. It can be seen that subtractive clustering produces the smallest RMSE and MSE error values of other FIS structure.

Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier Foeady, Ahmad Zoebad; Rini Novitasari, Dian Candra; Asyhar, Ahmad Hanif; Firmansjah, Muhammad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Diabetic Retinopathy (DR) is the cause of blindness. Early identification needed for prevent the DR. However, High hospital cost for eye examination makes many patients allow the DR to spread and lead to blindness. This study identifies DR patients by using color fundus image with SVM classification method. The purpose of this study is to minimize the funds spent or can also be a breakthrough for people with DR who lack the funds for diagnosis in the hospital. Pre-processing process have a several steps such as green channel extraction, histogram equalization, filtering, optic disk removal with structuring elements on morphological operation, and contrast enhancement. Feature extraction of preprocessing result using GLCM and the data taken consists of contrast, correlation, energy, and homogeneity. The detected components in this study are blood vessels, microaneurysms, and hemorrhages. This study results what the accuracy of classification using SVM and feature from GLCM method is 82.35% for normal eye and DR, 100% for NPDR and PDR. So, this program can be used for diagnosing DR accurately.

PENGKLASTERAN LAHAN SAWAH DI INDONESIA SEBAGAI EVALUASI KETERSEDIAAN PRODUKSI PANGAN MENGGUNAKAN FUZZY C-MEANS Nur Afifah; Dian Candra Rini Novitasari; Ahmad Lubab
Jurnal Matematika MANTIK Vol. 2 No. 1 (2016): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (180.695 KB)

The number of rice field in Indonesia is decreasing due to development of residential areas and buildings. Consequently, it reduces foodstuff availability and government should import it from other. Increasing food production and minimizing imported food can be started by clustering fields as an evaluation. This clustering is approached by Fuzzy C-Means. Training and Testing data are implemented on Matlab and yield three categories, wide, medium and narrow field. Moreover, the most potential field is East Java, Central Java, and West Java

Prediction of Sea Surface Current Velocity and Direction Using LSTM Irkhana Indaka Zulfa; Dian Candra Rini Novitasari; Fajar Setiawan; Aris Fanani; Moh. Hafiyusholeh
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 1 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.63669

Labuan Bajo is considered to have an important role as a transportation route for traders and tourists. Therefore, it is necessary to have a further understanding of the condition of the waters in Labuan Bajo, one of them is sea currents. The purpose of this research is to predict sea surface flow velocity and direction using LSTM. There are many prediction methods, one of them is Long short-term memory (LSTM). The fundamental of LSTM is to process information from the previous memory by going through three gates, that is forget gate, input gate, and output gate so the output will be the input in the next process. Based on trials with several parameters namely Hidden Layer, Learning Rate, Batch Size, and Learning rate drop period, it achieved the smallest MAPE values of U and V components of 14.15% and 8.43% with 50 hidden layers, 32 Batch size and 150 Learn rate drop.

PENGELOMPOKKAN SUNSPOT PADA CITRA MATAHARI DENGAN MENGGUNAKAN K-MEANS CLUSTERING Rifa Atul Hasanah; Dian Candra Rini Novitasari; Nanang Widodo; Ahmad Hanif Asyhar
MathVisioN Vol 1 No 02 (2019): September 2019
Publisher : Prodi Matematika FMIPA Unirow Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.616 KB)

Pada lapisan fotosfer nampak sunspot atau bintik matahari yang mana sunspot tersebut dapat menimbulkan ledakan-ledakan, seperti ledakan dahsyat (flare) dan pelontaran massa korona (Coronal Mass Ejection/CME). Ledakan-ledakan ini dapat mengganggu komunikasi radio frekuensi tinggi dan kebisingan radio yang mengganggu komunikasi dan sistem radar. Untuk mengetahui tingkat kompleksitas grup sunspot dan aktivitasnya digunakan klasifikasi metode Zurich, yang berisi tentang klasifikasi jenis grup sunspot. Informasi ini sangat penting untuk mengetahui seberapa besar gangguan yang didapatkan dari jenis grup sunspot tersebut. Tujuan dari penulisan yaitu untuk meneliti bagaimana mengelompokkan sunspot pada citra matahari dengan menggunakan K-Means Clustering. Pengolompokan sunspot menggunakan data citra matahari. Citra matahari diproses untuk diambil posisi x,y. Pengambilan posisi x, y sesuai dengan piksel sunspot yang digunakan untuk proses clustering. Hasil penelitian yaitu cluster piksel sunspot yang menunjukkan grup sunspot, hasil clustering telah menunjukkan hasil yang baik dengan nilai Silhouette Coefficient sebesar 0.9381, yang berarti bahwa struktur dari cluster termasuk kuat.

ANALISIS PREDIKSI JUMLAH PENDUDUK DI KOTA PASURUAN MENGGUNAKAN METODE ARIMA Ilmiatul Mardiyah; Wika Dianita Utami; Dian Candra Rini Novitasari; Moh. Hafiyusholeh; Dewi Sulistiyawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 3 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Laju pertumbuhan penduduk di Kota Pasuruan pada tahun 2019 sebesar 0.68% dengan jumlah penduduk 200.422 jiwa. Tingginya pertumbuhan penduduk dapat mempengaruhi kepadatan penduduk. Penelitian ini bertujuan untuk memprediksi pertumbuhan penduduk Kota Pasuruan menggunakan metode ARIMA (Autoregressive Integrated Moving Average). Metode ARIMA adalah cara prediksi data deret waktu yang memiliki tiga model, yaitu AR (Autoregressive), MA (Moving Average), ARMA (Autoregressive Moving Average). Metode ini memiliki parameter (p,d,q) dapat diketahuidari plot ACF dan PACF untuk memastikan model yang akan digunakan untuk prediksi. Dalam penelitian ini data yang digunakan merupakan data penduduk Kota Pasuruan tahun 1983 sampai tahun 2019 sejumlah 37 data. Dari data tersebut didapatkan ARIMA model (1,1,1) dengan jumlah penduduk Kota Pasuruan pada tahun 2020 adalah 203.221 jiwa, didapatkan nilai MSE 10542507.06 dan MAPE 1.52%.

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method Utami, Tri Mar'ati Nur; Novitasari, Dian Candra Rini; Setiawan, Fajar; Ulinnuha, Nurissaidah; Farida, Yuniar; Sari, Ghaluh Indah Permata
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.28906

Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions. Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error. Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.

Co-Authors Abdulloh Hamid Adam Fahmi Khariri Adyanti, Deasy Ahmad Hanif Asyhar Ahmad Hidayatullah Ahmad Lubab Ahmad Yusuf Ahmad Zoebad Foeady Arifin, Ahmad Zaenal Aris Fanani Chalawatul Ais Dewi Sulistiyawati Dina Zatusiva Haq Diva Ayu Safitri Nur Maghfiroh Elen Riswana Safila Putri Evi Septya Putri Fahriza Novianti FAJAR SETIAWAN Fajar Setiawan Faris Mushlihul Amin Ferryan, Dhandy Ahmad Firmansjah, Muhammad Foeady, Ahmad Zoebad Ifadah, Corii Ilmiatul Mardiyah Irkhana Indaka Zulfa Jauharotul Inayah Kusaeri Kusaeri Luthfi Hakim M. Hasan Bisri Mayandah Farmita Moh. Hafiyusholeh Moh. Hafiyusholeh Moh. Hafiyusholeh Monika Refiana Nurfadila Nanang Widodo Nanang Widodo Nanang Widodo Noviati Maharani Sunariadi Nur Afifah Nurissaidah Ulinnuha Putri Wulandari Putroue Keumala Intan Ratnasari, Cristanti Dwi Rifa Atul Hasanah Rozi, Muhammad Fahrur Sari, Ghaluh Indah Permata Setiawan, Fajar Siti Ria Riqmawatin Suwanto Suwanto Suwanto Suwanto Tasya Auliya Ulul Azmi Thohir, Muhammad Ulinnuha, Nurissaidah Unix Izyah Arfianti Utami, Tri Mar'ati Nur Veriani, Rafika Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Dianita Utami Yasirah Rezqita Aisyah Yasmin Yuni Hariningsih Yuniar Farida, Yuniar Yuyun Monita Zulfa, Elok Indana

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