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All Journal International Journal of Evaluation and Research in Education (IJERE) ComEngApp : 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 Jurnal Buana Informatika 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 Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Fourier 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 : 31 Documents Claim Missing Document

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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.

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.

PREDIKSI KASUS AKTIF KUMULATIF COVID-19 DI INDONESIA MENGGUNAKAN MODEL REGRESI LINIER BERGANDA Elen Riswana Safila Putri; Fahriza Novianti; Yasirah Rezqita Aisyah Yasmin; Dian Candra Rini Novitasari
TRANSFORMASI Vol 5 No 2 (2021): TRANSFORMASI : Jurnal Pendidikan Matematika dan Matematika
Publisher : Pendidikan Matematika FMIPA Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/tr.v5i2.1231

Regresi linier berganda digunakan untuk mengidentifikasi hubungan antara variabel respons dengan minimal dua variabel prediktor. Variabel respons merupakan variabel yang dipengaruhi, sedangkan variabel prediktor merupakan variabel yang mempengaruhi. Tujuan penelitian ini adalah melakukan prediksi kasus aktif kumulatif dengan variabel prediktor kasus positif kumulatif, kesembuhan kumulatif, dan korban meninggal kumulatif pada kasus COVID-19 di Indonesia sejak 1 Mei 2021 hingga 26 Agustus 2021 menggunakan metode regresi linier berganda. Hasil penelitian ini menghasilkan prediksi dengan MAPE sebesar 2,11%. Prediksi yang dilakukan memiliki akurasi yang sangat baik karena memiliki nilai galat yang sangat kecil. Berdasarkan hasil tersebut disimpulkan bahwa akan terjadi penurunan kasus aktif kumulatif COVID-19 pada 1-5 September 2021 dengan penurunan terbanyak pada 5 September sebesar 17079 orang.

ANALISIS PENDEKATAN STATISTIK DAN FUZZY MAMDANI DALAM PREDIKSI PRODUKTIVITAS PADI Zulfa, Elok Indana; Ferryan, Dhandy Ahmad; Novitasari, Dian Candra Rini
Majalah Ilmiah Matematika dan Statistika Vol 22 No 1 (2022): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v22i1.30304

Indonesia has fertile soil, so it is suitable for agricultural land. However, Indonesia still needs to import rice from abroad because rice productivity in Indonesia is often inconsistent. Therefore, rice productivity in Indonesia needs to be estimated for the future. This study aimed to determine the amount of rice productivity from production factors and harvested area that occurred in Indonesia from September 2021 to June 2022. The data used were rice production and rice harvested from January 2019 to August 2021 sourced from the Central Statistics Agency, Indonesia. Data processing in this study uses the Polynomial Regression method to determine predictions of future rice production and harvest area and the Mamdani Fuzzy Logic method. Data processing in this study uses a prediction method, namely the Polynomial Regression method, to determine future production and harvested area predictions and the Mamdani method of Fuzzy Logic for decision making. The results obtained from the Mamdani polynomial and fuzzy regression methods, predictions of rice productivity in September 2021 to June 2022 have increased in the range of 23.33 to 27.91.Keywords: Fuzzy Mamdani, polynomial regression, prediction, rice productivityMSC2020: 62A86

Detection of potential errors in measurement results of madrasa admission instruments in Indonesia Ahmad Yusuf; Kusaeri Kusaeri; Ahmad Hidayatullah; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar
International Journal of Evaluation and Research in Education (IJERE) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v10i4.21412

Madrasa (Islamic boarding school) in Indonesia have a strategic role in character building. At present madrasa education is still considered second class education. Besides, to improve the quality of madrasas can be started by improving the quality of the student national admission to all madrasas in Indonesia. This study aimed to trace the potential errors in the measurement results of Students National Admission of Madrasah Aliyah Negeri (SNPDB MAN-IC) 2020. Tracing was carried out on two aspects: i) Equality between test sets used based on evidence of test responses; and ii) Further tests on equality between question sets based on evidence of relationship between variables, taking into account the origin of the participating schools (MTs/JHS) and the origin of the participating regions (West, Central and East of Indonesia). This study involved 13,115 participants in 23 MAN-ICs throughout Indonesia in 2020. The materials tested comprised learning potential and academic ability (Mathematics, Natural Sciences, Social Studies, English, Arabic, and Islamic Religious Education). The study used achievement test with mathematics as a sample of test subjects. Based on the test response evidence, it was found that seven of the 15 questions were thought to have an indication of inequality between item sets. The results of tracing the evidence between variables indicated that it was the participants' origin of institutions that influenced the inequality between item sets. On the other hand, regional origin did not affect the inequality between item sets because the majority of participants came from the western region of Indonesia.

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 Sinyal EEG Menggunakan Metode Fuzzy C-Means Clustering (FCM) Dan Adaptive Neighborhood Modified Backpropagation (ANMBP) Dian Candra Rini
Jurnal Matematika MANTIK Vol. 1 No. 1 (2015): Matematika dan Terapan
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (277.307 KB)

Instrumen EEG (electroencephalography) merupakan suatu instrumen yang digunakan sebagai perekam aktivitas otak dengan memperlihatkan gelombang otak. Prinsip kerja EEG adalah dengan mendeteksi perubahan muatan secara tiba-tiba dari sel neuron yang ditandai dengan adanya interictal spike-and-wave pada hasil EEG (electroencephalogram). Terdapat suatu data set sinyal EEG, direkam pada sukarelawan normal dan epilepsi. Pada penelitian ini dengan menggunakan data tersebut akan dilakukan suatu sistem klasifikasi sinyal EEG dengan berdasar pada kondisi normal dan epilepsi. Klasifikasi sinyal EEG menggunakan Metode Adaptive Neighborhood Base Modified Backpropagation (ANMBP). Hasil ekstraksi fitur dari sinyal EEG dengan menggunakan metode Fuzzy C-Means (FCM) Clustering, dimana proses awalnya melalui dekomposisi wavelet menggunakan Discrete Wavelet Transform (DWT) dengan level 2 didapatkan 3 koefisien wavelet kemudian pada masing masing koefisien tersebut di clustering menggunakan FCM dengan 2 cluster sehingga menghasilkan 6 fitur yang akan menjadi vektor fitur. Dari vektor fitur tersebut digunakan sebagai inputan untuk dilakukan proses klasifikasi dengan menggunakan metode ANMBP. Hasil sistem sementara didapatkan recognition rate sebesar 74.37%.

Co-Authors Abdulloh Hamid Adam Fahmi Khariri Adyanti, Deasy Ahmad Hanif Asyhar Ahmad Hidayatullah Ahmad Lubab Ahmad Yusuf Ahmad Zoebad Foeady Alvin Nuralif Ramadanti 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 FAJAR SETIAWAN Faris Mushlihul Amin Ferryan, Dhandy Ahmad Firmansjah, Muhammad Foeady, Ahmad Zoebad Hani Khaulasari Ifadah, Corii Ilmiatul Mardiyah Irkhana Indaka Zulfa Jauharotul Inayah Kusaeri Kusaeri Lutfi Hakim Luthfi Hakim M. Hasan Bisri Mayandah Farmita Moh. Hafiyusholeh Moh. Hafiyusholeh Moh. Hafiyusholeh Mohammad Rizal Abidin Monika Refiana Nurfadila Nanang Widodo Nanang Widodo Nanang Widodo Noviati Maharani Sunariadi Nur Afifah Nur Hidayah Nurissaidah Ulinnuha Putri Wulandari Putroue Keumala Intan Ratnasari, Cristanti Dwi Rifa Atul Hasanah Rozi, Muhammad Fahrur Sari, Ghaluh Indah Permata Setiawan, Fajar Siti Nur Fadilah 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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