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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Jurnal Teknologi dan Riset Terapan (JATRA)

Nikentari, Nerfita

Universitas Maritim Raja Ali Haji

Author-ID : 805492

Aerospace Engineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Articles

Prediksi Ketinggian Gelombang Laut menggunakan Algoritma Levenberg-Marquardt Nikentari, Nerfita
Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Vol 5 No 2 (2016): Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Gelombang laut adalah merupakan fenomena alam di mana terjadi penaikan dan penurunan air secara periodik, penaikan dan penurunan air ini salah satu aspek penting dalam transportasi dan pembangunan. Mengetahui data masa depan mengenai tinggi gelombang air laut dapat memberi manfaat besar dalam kelancaran transportasi dan pembangunan di wilayah perairan. Data masa depan dapat dihasilkan dengan melakukan prediksi dengan algoritma tertentu. Algoritma Levenberg Marquardt merupakan salah satu jenis algoritma pembelajaran jaringan syaraf tiruan yang dapat digunakan untuk prediksi data. Hasil analisis didapatkan arsitektur jaringan yang ideal adalah dengan 7 node input, 2 hidden layer yang terdiri dari 9 neuron dan 1 output layer dengan mean absolute percentage error (MAPE) adalah 0.175.

Case Based Reasoning untuk Diagnosis Penyakit Gizi Buruk pada Balita Nurfalinda, Nurfalinda; Nikentari, Nerfita
Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Vol 6 No 2 (2017): Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

This research is conducted to build a diagnose system malnutrition among children under five years old. The system was developed with Case Based Reasoning (CBR). CBR is a case based reasoning system, using old knowledge to solve new problems. CBR can provide new solutions to problems by looking at most similarity case to the previous cases that have been stored in the base case. CBR in this research using a bayesian model indexing to find the type of disease malnutrition among children under five years old, the process of indexing is done to speed up the retrieval process. The nearest neighbor methode used in the process to determine the most similar of cases between new cases and the old cases that have been stored in the database as a case base to be used tratment solution.Tests carried out by using 70 case based were recorded in case of data based and 20 case based serve as a new case. Testing is done with five threshold values. The first scenario is to use threshold â¥ 0.95 system able to produce accuracy 20%. The second scenario is to use threshold â¥ 0.90 system able to produce accuracy 45%. The third scenario is to use threshold â¥ 0.85 system able to produce accuracy 60%. The fourt scenario is to use threshold â¥ 0.80 system able to produce accuracy 75%. The fifth scenario is to use threshold â¥ 0.75 system able to produce accuracy 85%.

Prediksi Pasang Surut Air Laut Menggunakan Jaringan Syaraf Tiruan Backpropagation Nikentari, Nerfita; Ritha, Nola; Haryadi, Tri
Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Vol 7 No 1 (2018): Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

The tide of sea water has an effect on the activities carried out in the sea, namely shipping activities, fishing activities, and loading and unloading of ships, because tidal events occur not at the same time therefore the need for tide level prediction. Tide level data for forecast based historical tide data obtained from BMKG Tanjungpinang from January 1 to February 11, 2015, research was done by using Backpropagation. This study using as many as 1000 high tide data with some input parameters such as max iteration, target error, learning rate, number of input, and update learning rate. The accuracy of this forecast is measured by calculating the average error using MSE (Means Square Error). The best modeling result of Backpropagation with 5 hidden layer and learning rate 0,9 produce the smallest MSE 0,0035861.

Penerapan Self Organizing Map (SOM) dan Radial Basis Function (RBF) Untuk Memprediksi Kecepatan Angin Di Perairan Kota Tanjungpinang Julia, Rini Hervianti; Nikentari, Nerfita; Hayaty, Nurul
Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Vol 7 No 2 (2018): Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Wind is very important role in human life, one of them for fishermen, natural conditions play an important role in the fluency of their activities, especially for the people of Tanjungpinang who lived in coastal areas as fishermen. But it will be a problem if the winds move with high intensity that will impact bad weather. To be able to monitor the movement of wind speed, this study made predictions using the method Self Organizing Maps (SOM) and Radial Basis Function (RBF) to predict wind speed. In this study the data used for daily wind speed prediction starts from January 2014 - October 2017. The results of the tests conducted with the 418 of data, the number of clusters obtained 33 from the training process produce 1,51 of RMSE and 28.98% of MAPE and 71,02% of accuracy.

Prediksi Kecepatan Angin Menggunakan Adaptive Neuro Fuzzy (ANFIS) dan Radial Basis Function Neural Network (RBFNN) Nikentari, Nerfita; Bettiza, Martaleli; Sastypratiwi, Helen
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 4, No 1 (2018): Volume 4 No 1
Publisher : Program Studi Informatika

Angin sebagai salah satu fenomena alam yang mempengaruhi berbagai aspek dalam kehidupan manusia baik pengaruh positif maupun negatif. Aspek ini berperan besar dalam ekonomi, pariwisata, pembangunan, transportasi maupun perdagangan masyarakat. Data angin dalam hal ini kecepatan angin belum dapat diketahui secara pasti nilainya oleh karena itu perlu adanya prediksi. Adaptive Neuro Fuzzy Inference System (ANFIS) dan Radial Basis Function Neural Networkc(RBFNN) adalah algoritma yang dapat digunakan untuk prediksi data. Penelitian iniÂ menggunakan ANFIS dan RBFNN untuk memprediksi kecepatan angin. Data prediksi yang digunakan dalam penelitian ini adalah data time series. Data kecepatan angin diperoleh dari BMKG (Badan Meteorologi Klimatogi dan Geofisika) Tanjungpinang, Kepualuan Riau. Hasil prediksi dengan kedua metode ini dibandingan dengan data asli untuk mengetahui metode mana yang lebih akurat dalam prediksi data. Hasil pengujian menggunakan kedua algoritma memperlihatkan akurasi terbaik (paling mendekati data asli/target) diperoleh oleh RBFNN yaitu dengan nilai RMSE adalah 0,1766 dan hasil RMSE ANFIS adalah 1,1456.

Open API untuk Warung Makan Usaha Kecil dan Industri Rumahan Tekad Matulatan; Nerfita Nikentari; Martaleli Bettiza; Hendra Kurniawan; Nola Ritha
Jurnal Teknologi dan Riset Terapan (JATRA) Vol 2 No 1 (2020): Jurnal Teknologi dan Riset Terapan (JATRA) - June 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jatra.v2i1.1800

Many food stalls are small businesses or home industries with a capital under 10 million rupiah and are generally located in the yard of the stall owner, and do not have branches. The most common obstacle was the lack of customers caused by the location of the stall which was not strategic and the information about the stall service was not widespread. OPEN API Warung Makan is the implementation of Community Service activities funded from an internal grant 2019 Raja Ali Haji Maritime University. This API is intended to be open to any application developer to take advantage of this free service to be aimed at food stalls that fall into the category of small businesses or home industries. OPEN API Warung Makan provides two parts of service, namely for customers and stall owners. OPEN API Warung Makan uses Raja Ali Haji Maritime University's cloud infrastructure and does not require Authentication Tokens or the like.

Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting Nikentari, Nerfita; Wei, Hua-Liang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp960-970

Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.

Optimasi Jaringan Syaraf Tiruan Backpropagation Dengan Particle Swarm Optimization Untuk Prediksi Pasang Surut Air Laut Nikentari, Nerfita; Kurniawan, Hendra; Ritha, Nola; Kurniawan, Denny
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 5: Oktober 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Indonesia merupakan negara maritim yang lebih dari 70 % wilayahnya adalah lautan. Lautan memiliki banyak fenomena alam yang mempengaruhi kehidupan sehari-hari masyarakat bahari atau masyarakat yang hidup tergantung pada laut. Salah satu fenomena alam dari laut adalah pasang surut. Pasang surut air laut dalam hal ini tinggi memegang peranan penting pada masyarakat diberbagai aspek seperti transportasi, pariwisata dan ekonomi. Prediksi tinggi pasang surut air dapat bermanfaat untuk memudahkan segala aktifitas masyarakat bahari. Penelitian ini menggunakan metote Particle Swarm Optimization (PSO) dan Jaringan Syaraf Tiruan (JST) untuk prediksi tinggi pasang surut air laut. Metode Particle Swarm Optimization dan Neural Network memiliki beberapa parameter inputan seperti, jumlah neuron input, learning rate, swarm, c1,c2 inertia min, inertia max. Data yang digunakan sebanyak 1000 yang terbagi menjadi 700 data training dan 300 data testing. Hasil pengujian menunjukkan akurasi prediksi adalah 91.56 % dengan menggunakan 90 swarm, learning rate 0,9 dan iterasi sebanyak 20 kali. AbstractIndonesia is a maritime country where 70% of its territory is the ocean. Oceans have many natural phenomena that affect the daily lives of maritime communities or people who live dependent on the sea. One of the natural phenomena of the sea is tide level. Tide level plays an important role in the community both directly and indirectly such as transportation, tourism and the economy. Predictions of tide level can be useful to facilitate all marine activities. This study uses Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN) to forecast tide level. PSO is used to optimize the minimum error value on the network in order to get the ideal ANN network. The Particle Swarm Optimization and Neural Network methods have several input parameters such as number of input neurons, learning rate, swarm, c1, c2 inertia min, inertia max. The number of data being used in this reseach is 1000 which divided into 700 training data and 300 testing data. The test results shows the prediction accuracy level is 0. 078373 using 90 swarms, learning rate is 0.9 and iteration is 20 times.

Co-Authors Denny Kurniawan Haryadi, Tri Hayaty, Nurul Helen Sastypratiwi, Helen Hendra Kurniawan Hendra Kurniawan Julia, Rini Hervianti Martaleli Bettiza Matulatan, Tekad Nola Ritha Nurfalinda, Nurfalinda Ritha, Nola Wei, Hua-Liang

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