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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 Simetris Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Intiqad: Jurnal Agama dan Pendidikan Islam Telematika : Jurnal Informatika dan Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Fourier InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Proceeding of the Electrical Engineering Computer Science and Informatics JPSE (Journal of Physical Science and Engineering) Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science Jurnal Informatika INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK JURNAL MEDIA INFORMATIKA BUDIDARMA Desimal: Jurnal Matematika JTERA (Jurnal Teknologi Rekayasa) BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Informatika Universitas Pamulang JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Telematika Mathvision : Jurnal Matematika Building of Informatics, Technology and Science Transformasi : Jurnal Pendidikan Matematika dan Matematika Jurnal Mnemonic Majalah Ilmiah Matematika dan Statistika (MIMS) Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Papanda Journal of Mathematics and Sciences Research Sains Data Jurnal Studi Matematika dan Teknologi Journal Serambi Engineering (JSE)

Sari, Dian Candra Rini Novita

Universitas Islam Negeri Sunan Ampel Surabaya

Author-ID : 1971240

Religion Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Identification of Stunting Disease using Anthropometry Data and Long Short-Term Memory (LSTM) Model Faris Mushlihul Amin; Dian Candra Rini Novitasari
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Children with unbalanced nutrition are currently crucial health issues and under the spotlight around the world. One of the terms for malnourished children is stunting. Stunting is a disease of malnutrition found in children aged under 5 years; as many as 70% of stunting sufferers are children aged 0-23 months. There are several ways to diagnose stunting, one of which is using stunting anthropometry. Stunting anthropometry can measure the physique of children so that some of the features that characterize the presence of stunting can be identified. Features resulted from the stunting anthropometry cover age, height, weight, gender, upper arm circumference, head size, chest circumference, and hip fat measurement. The process of identifying stunting can be simplified using an intelligent system called the Computer-Aided Diagnosis (CAD) system. CAD system contains 2 main processes, namely preprocessing and classification. Preprocessing includes normalization and augmentation of data using the SMOTE method. The classification process in this study uses the LSTM method. LSTM is a modification of the Recurrent Neural Network (RNN) method by adding a memory cell so that it can store memory data for a long time and in large quantities. The results of this study compare between the results of models that apply preprocessing and the one without preprocessing. The model that only uses LSTM has the best accuracy of 78.35%; the model with normalization produces an accuracy of 81.53%; the model that uses SMOTE produces an accuracy of 81.66%; and the model that uses normalization and SMOTE produces the best accuracy of 85.79%.

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.

Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm Unix Izyah Arfianti; Dian Candra Rini Novitasari; Nanang Widodo; Moh. Hafiyusholeh; Wika Dianita Utami
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.63676

Sunspot is an area on photosphere layer which is dark-colored. Sunspot is very important to be researched because sunspot is affected by sunspot numbers, which present the level of solar activity. This research was conducted to make prediction on sunspot numbers using Gated Recurrent Unit (GRU) algorithm. The work principle of GRU is similar to Long short-term Memory (LSTM) method: the information from the previous memory is processed through two gates, that is update gate and reset gate, then the output generated will be input for the next process. The purpose of predicting sunspot numbers was to find out the information of sunspot numbers in the future, so that if there is a significant increase in sunspot numbers, it can inform other physical consequences that may be caused. The data used was the data of monthly sunspot numbers obtained from SILSO website. The data division and parameters used were based on the results of the trials resulted in the smallest MAPE value. The smallest MAPE value obtained from the prediction was 7.171% with 70% training data, 30% testing data, 150 hidden layer, 32 batch size, 100 learning rate drop.

Implementation of Winnowing Algorithm for Document Plagiarism Detection Nurissaidah Ulinnuha; Muhammad Thohir; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar; Ahmad Zaenal Arifin
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 Deasy Adyanti; Dian Candra Rini Novitasar; Ahmad Hanif Asyhar; Fajar Setiawan
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 Ahmad Zoebad Foeady; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar; Muhammad Firmansjah
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.

Forecasting Solar Activities based on Sunspot Number Using Support Vector Regression (SVR) Suwanto Suwanto; Dian Candra Rini Novitasari
JPSE (Journal of Physical Science and Engineering) Vol 5, No 1 (2020): JPSE (Journal of Physical Science and Engineering)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Progress on the 4.0 industrial revolution was the most significant is the entire computer and robot is connected to the internet connection. Satellites as one of the internet network transmitters have the threat of destruction if a solar storm occurs. The size of the activity that is on the sun can be known by observing sunspots. Solar activity in the future is known by forecasting sunspot numbers. This research will forecast sunspot numbers using support vector regression (SVR), its aimed to minimized adverse effects on the earth as an outcome of solar storms. The best SVR results on forecast sunspot numbers are on annual sunspot number obtained using RBF kernel. Measurement results from MSE, RMSE, and MAAPE respectively by 35.32, 5.94, and 0.12. Forecasting concluded accurately based on MAAPE value, on 2020 and 2021 indicated potentially flare because the result of forecasting sunspot numbers is more than twenty. DOI: http://dx.doi.org/10.17977/um024v5i12020p006

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM Hanimatim Mu'jizah; Dian Candra Rini Novitasari
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 3, Year 2021 (July 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14104

Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

Pengolahan Citra Digital untuk Identifikasi Kanker Otak Menggunakan Metode Deep Belief Network (DBN) Muhammad Syaifulloh Fattah; Dina Zatusiva Haq; Dian Candra Rini Novitasari
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.13089

The brain tumor is a dangerous disease for humans that can interfere with the functioning of the human brain. Brain tumors can develop into malignant brain tumors or brain cancer and cause death, so early detection is necessary to diagnose brain tumor disease. One way of early detection is to use the anatomy of an MRI scan of health images. The MRI scan results can diagnose patients, but it takes longer time. Therefore digital image processing is needed to facilitate an analysis so that it can be seen in the brain image there are tumor cells or not. In addition to digital image processing, a system that analyzes and detects data is also needed. The Deep Belief Network (DBN) method is used to identify data. This study conducted trials on the learning rate and network architecture. The results of the identification of brain cancer using the DBN method obtained a sensitivity (TP rate) value of 90.9%, a specificity (TN rate) of 100%, an accuracy of 95%, and a precision of 100% with a learning rate of 0.1 and using a 4-12-10-1 network architecture.

ANALISIS PERAMALAN BEBAN LISTRIK JANGKA PENDEK MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Dian Candra Rini; Uswatun Khasanah; Wika Dianita Utami; Putroue Keumala Intan
MathVisioN Vol 1 No 1 (2019): Maret 2019
Publisher : Prodi Matematika FMIPA Unirow Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.087 KB)

Prediksi atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang akan diproduksi guna mencegah terjadinya beban berlebih yang dapat menyebabkan kerusakan pada sistem trafo atau kekurangan beban listrik yang mengakibatkan krisis energi listrik pada konsumen. Oleh karena itu, dibutuhkan suatu metode alternatif untuk memprediksi beban listrik jangka pendek, salah satunya yaitu metode Adaptive Neuro Fuzzy Inference System pada penelitian ini. Data diperoleh dari PT. PLN (Persero) APD Jawa Timur yang berisi data beban listrik per setengah jam dari bulan februari sampai september tahun 2018 yang digunakan sebagai data aktual. Data kemudian dilatih dengan menggunakan metode ANFIS dan didapatkan hasil peralaman bulan september 2018. Data uji coba tanggal 1 s/d 28 september tergolong memiliki tingkat kesalahan yang rendah yaitu MAPE menunjukkan hasil 7,926%. Nilai error terbesar terjadi pada tanggal 18 september yaitu sebesar 20,51% sedangkan nilai error terkecil terjadi pada tanggal 16 september yaitu sebesar 1,117%.

Co-Authors Ababil, Achmad Fachril Yusuf Abdulloh Hamid Abdulloh Hamid Achmad Teguh Wibowo Adam Fahmi Khariri Adyanti, Deasy Afandi, Achsan Ahmad Hanif Asyhar Ahmad Hidayatullah Ahmad Zoebad Foeady Ahmad Zoebad Foeady Aisyah, Nora Alvin Nuralif Ramadanti Amin, Faris Mushlihul Ananda Nur Izza Arifin, Ahmad Zaenal Aris Fanani Ariyanto Wijaya, Indra Ariyanto, Dimas Azmi, Tasya Auliya Ulul Cahyani, Nabila Rahma Chalawatul Ais Damayanti, Adelia Deasy Adyanti Desy Nur Fitriani Dewi Sukmawati, Chandra Dewi, Chandra Dewi, Ratna Cintya Dian Krisnawati Dianita Utami, Wika Dilla Dwi Kartika Diva Ayu Safitri Nur Maghfiroh Elen Riswana Safila Putri Fahriza Novianti Fajar Setiawan FAJAR SETIAWAN Fajar Setiawan Fajar Setiawan Fajar Setiawan Fanani, Aris Faris Mushlihul Amin Farmita, Mayandah Ferryan, Dhandy Ahmad Firmansjah, Muhammad Fitria, Nur Annisa Foeady, Ahmad Zoebad Galuh Andriani Ganeshar B.D. Prasanda Gita Purnamasari R Hani Khaulasari Hanimatim Mu'jizah Haq, Dina Zatusiva Ifadah, Corii Indriyani, Jiphie Gilia Irkhana Indaka Zulfa Jauharotul Inayah Khudin, Nasroh Kurniawan, Mohammad Lail Kusaeri Kusaeri Lubab, Ahmad Luluk Mahfiroh Lutfi Hakim Lutfi Hakim Lutfi Hakim Luthfi Hakim Luthfi Hakim M. Hasan Bisri Maliki, Naufal Ridho Mardiyah, Ilmiatul Masruroh Kusman, Umi Maulana, Aashif Amiruddin Maulana, Achmad Resnu Maulana, Jeneiro Mergianti, Wahyu Ningtiyas Moh. Hafiyusholeh Mohammad Rizal Abidin Mohd Fauzi, Shukor Sanim Monika Refiana Nurfadila Muhammad Azhar MUHAMMAD FAHRUR ROZI Muhammad Fahrur Rozi Muhammad Syaifulloh Fattah Muhammad Thohir Musfiroh Musfiroh, Musfiroh Nahdloh, Airu Nanang Widodo Nanang Widodo Nanang Widodo Nanang Widodo Nisa Trianifa Novia Adibatus Shofah Noviati Maharani Sunariadi Noviati Maharani Sunariadi Nur Afifah Nur Hidayah Nurissaidah Ulinnuha Pramesti, Diah Devi Pramono, Wahyu Joko Puspitasari, Wahyu Tri Putri Wulandari Putri, Evi Septya Putroue Keumala Intan Rafika Veriani Ramadanti, Alvin Nuralif Ratnasari, Cristanti Dwi Rifa Atul Hasanah RIFA ATUL HASANAH Rozi, Muhammad Fahrur Rozzy, Fahrul Safira, Icha Dwi Sani, Puteri Permata Sari, Firda Yunita Sari, Ghaluh Indah Permata Sari, Yana Vita Setiawan, Fajar Setyawati, Maunah Siti Nur Aisah Siti Nur Fadilah Siti Nur Fadilah Siti Ria Riqmawatin Sukarni, Adinda Ika Sulistiya Nengse Sulistiyawati, Dewi Suwanto Suwanto Suwanto Suwanto Swindiarto, Victory T. Pambudi Tasya Auliya Ulul Azmi Thalia Anindya Ardine Unix Izyah Arfianti USWATUN KHASANAH Utami, Tri Mar'ati Nur Utami, Wika Dianita Utami Dianita Veriani, Rafika Vina Fitriyana Wanda N.P. Sunaryo Wijaya, Indra Ariyanto Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wisnawa, Gede Gangga Yasirah Rezqita Aisyah Yasmin Yuliati, Dian Yuliawanti, Felia Dria Yuni Hariningsih Yuniar Farida Yusuf, Ahmad Yuyun Monita Yuyun Monita Zahroh, Khofifah Auliyatuz Zulfa, Elok Indana

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