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All Journal Techno.Com: Jurnal Teknologi Informasi JEECS (Journal of Electrical Engineering and Computer Sciences)
Wiwiet Herulambang
Universitas Bhayangkara Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Kidney Disease Diagnosis in Human Using the Backward Chaining Method Devi Ayu Ariyanto; Wiwiet Herulambang; Rani Purbaningtyas
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 4 No. 1 (2019): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v4i1.119

Abstract

Kidney disease is a disease that must be avoided by everyone. Because this disease is difficult to detect and often threatens a person's life. The problem that often occurs is that the kidneys are not functioning properly, so the body does not try to reject enough water, salt, and other materials that can cause death unless the treatment is analyzed. An accurate analytical ability is needed in determining the diagnosis of a person's kidney condition. But with the convenience of expert doctors, sometimes there are also disadvantages such as limited working hours (training) and the number of patients so they have to wait in line. The backward chaining method is an inference method that performs the search process starting from the goal, which is the conclusion of the solution to the problem at hand. Stages that must be done is to find a set of data or facts, from the facts sought conclusions that are the solution of the problem at hand. The system can be run if the user chooses a disease and chooses the symptoms that are felt and then gets a diagnosis and a solution, and medication.
Banking Price Forecasting Application Using Neural Network Time Series Method Wiwiet Herulambang; Fardanto Setyatama; Diana Nur Arofah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 4 No. 1 (2019): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v4i1.125

Abstract

In the capital market is a meeting place for investors to make an offer with demand for securities as a means of business funding or as a means for companies to get funds. One of the assets to invest in the capital market is stocks. In terms of business aspects, stock investment has good growth but this does not apply to all stock sectors. Because in fact the development of capital markets in Indonesia turned out to be ups and downs. It can cause changes in demand and supply that will affect investor psychology in predicting stock prices. This stock price forecasting system will be created using the Neural Network Time Series method. Using historical data as a reference in the neural network training process can be used as a basis for predicting bank stock prices the next day. In the tests that have been carried out using the application forecasting stock prices of state banks using the neural network time series method with the backpropagation algorithm, the average accuracy rate of the State Savings Bank (BTN) is 97.32%, Bank Negara Indonesia (BNI) 98.25%, Bank Mandiri 97.68% %, and at Bank Rakyat Indonesia (BRI) 98.59%.
Realtime Portable Music's Genre Classificator with The Kohonen (SOM) Methods Using Raspberry PI Wiwiet Herulambang; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 3 No. 2 (2018): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v3i2.131

Abstract

Music genre is one of the digital music data that is determined to classify music based on all the characterequations of each type. The characteristics in question are usually seen from the frequency of music, rhythmicstructure, instrumentation structure, and harmony content that the music has. Classification of music genres inrealtime (automatic / not manual), giving effect to the classification is no longer relative / subjective, because itis done based on predetermined parameters. In this study Raspberry Pi microcomputer is used, which is quiteconcisely used as a portable media and is quite powerful for realtime data processing. Raspberry Pi is used as asound processing unit, music genre identifier, and information on the results of the introduction of the musicgenre. This system input is in the form of music sound (realtime), while the system output is information (text)about the music genre. Whereas for the process of recognizing the music genre, the Self Organizing Maps (SOM)type Neural Neural Network (JOM) method is also used, or also known as the Kohonen ANN Network. Thefeature extraction stage uses the Music Genre Recognition by Analysis of Text (MUGRAT) method, with ninefeatures related to the spectral surface of music, and six features related to beat / rhythm of music. MelFrequency Cepstral Coefficients (MFCCs) feature extraction process was carried out as input from theclassification process using the Self Organizing Map (SOM) method. The classification results using the SOMmethod give an accuracy value of 74.75%. Accuracy of classification results using training data as many as 400pieces which are divided into 4 musical genres amounting to 74.75%.
Optimization of Raw Materials Stock Eggtray PT. Era Light Box Gresik Using Genetic Algorithm Dwiki Ashari Kusumawardhana; Wiwiet Herulambang
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 3 No. 2 (2018): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v3i2.133

Abstract

Obstacles that occur today in the PT. Era ray Box still use manual calculation for the stock of rawmaterials, so the impact on the delay in the department information and management company itself. In additionthe company also often lose money because the number of ordering goods is increasing and are still usingmanual calculation so that the risk of causing an invalid calculation and not achieving the target onproduction.Genetic algorithms are search techniques in computer science to find a settlement forecasts foroptimization and search problems. The results of the optimization experiments that have been carried out, theresults obtained are different by comparing the 10 iterations, 100 iterations and 1000 iterations. From theexperiments performed by the user with a genetic algorithm system in the output of raw materials Dregs S.awal2438333333 kg, Dregs Exit 3002885622 kg, 342 kg S.awal spindles, spindles Exit 1069444444 kg, 988 kgS.awal Carton, Cardboard Exit 9358 kg, obtained the best fitness value is 28679.2.
Forecasting Sales Prices Appartment Using Fuzzy Tsukamoto (case Study My Tower Apartment) Ayu Purnamasari; Wiwiet Herulambang; R Dimas Adityo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 3 No. 1 (2018): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v3i1.141

Abstract

Currently apartment buildings are not foreign anymore we see let alone in the middle of the city. The price is still fairly cheap or under the price of housing makes a lot of interest to have an apartment either for personal or investment. Each developer has a strategy to attract buyers whether it increases or decreases the price of the apartment in each period or every year. The research was conducted to help the company change the price of the apartment by using method Fuzzy Tsukamoto based on input-an Inflation Value in the year to be predicted, View apartment, and Sales Price before. The results here show that for the output of the program is different from the original data, that is more expensive or the price is higher than the original data. There is an ratio of error 0.7609184% to -0.334665% or difference between Rp.200.000.000 to Rp.400,000.
3D Simulation of Plant Growth Modeling Using Neuro-Fuzzy, Lindenmayer System, and Turtle Geometry Wiwiet Herulambang; Retantyo Wardoyo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 2 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v1i2.169

Abstract

Applications that are able to predict plants growth patterns as a function of the nutrients obtained from fertilization pattern, is very useful in agriculture. The purpose of this study was to design and build a system of plants growth simulation models with Neuro-fuzzy method, then visualized by methods Lindenmayer system represented by three-dimensional use of Turtle Geometry. As the object of research is Soybean (Glycine max (L.) Merrill). Modeling parameters is long growth trunk / branches (L), a wide cross section of the leaf (W), and branch growth (B), as a function of changes in the fertilizing elements Nitrogen (N), Phosphate (P) and potassium (K). Modeling done on the vegetative phase of the soybean crop.First step is the modeling output L-W-B as a function of changes in the values of NPK using neurofuzzy (ANFIS). The final step is to combine plant growth pattern parameters (L-W-B) and L-system strings into the visualization process plant structure using Turtle Geometry.The test results on the system to grow plants pattern proves that ANFIS method is quite adaptive to variation of NPK value changes, and able to predict the output value L, W, and B. The final result of string-set of L-system and also it's visualization by Turtle Geometry, has proven to be influenced by variations in the composition of NPK values. Overall, the system has been running as expected.
Modeling the Effect of Fertilization on Growth Pattern of Brassica Rapa Using Backpropagation Neural Network Wiwiet Herulambang
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 1 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v1i1.182

Abstract

Application that able to predict plant growth patterns as function of nutrients obtained from fertilization pattern is very useful in agriculture, especially for research .It can be realized with support of biological sciences, mathematics, and computer technology, which popularly called by bioinformatics.The purpose of this research was to design and build a simulation system of fertilization effect on plants growth patterns with Backpropagation Neural Network. As the object of research is green mustard (Brassica Rapa). The parameters of growth modeling arethe number of seedling leaves and the length of leaves as function of changes in fertilizing elements (micro and macro) which are applied. First, green mustard are planted in the test field and then some fertilizing variations are applied for each plant. Fertilizing variations marked by variations of micro and macro nutrients in the applied fertilizer. The growth of each plant is monitored and recorded, from germination until the plant is ready for harvest. Observational data of plant growth then processed by Backpropagation Neural Network into a model of green mustard growth. From the model, software system of green mustard growth simulation as the function of fertilizing variations is built. The system testing is done using data obtained from direct observations at the plant field. Fertilization effects on green mustard growth patterns is evident in the increasing number of seedling leaves and length of leaves which indicates a reproductive improvement of the plant. Using Backpropagation Neural Network with five neuron in its hidden layer, the minimal error of the system achieved when the minimal epoch is 1000. Through experiment on several data variation of green mustard growth, the average obtained precision for NL (number of leaves) and LL (length of leaves) are 83% and 85%, respectively, which indicate that this system has achieved the expected target.
Prediction of Skincare Sales Turnover Using the Support Vector Method at the Widya Msglow Sidoarjo Company Oktaviana Isbirotin; Wiwiet Herulambang; Rahmawati Febrifyaning Tias; Rangsang Purnama; Ahmadi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 2 (2023): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v8i2.10

Abstract

Every entrepreneur will certainly follow technological developments in the business world. MsGlow is one of theskincare businesses. The skincare business is one of the businesses that must compete with rapid and complexchanges, and this very competitive makes business people have to think of strategies for business continuity in order to compete and also survive. One way that can be done is to utilize existing sales data. The importance of fast andprecise operational data processing, information system facilities can be an alternative to solving problems in dataprocessing, minimizing errors and accelerating the data processing process. As the number of sales transactionsincreases, there will be a buildup of data that has not been processed optimally. With the above problems, aforecasting system was created that can forecast skincare sales turnover using the Support Vector Machine (SVM)method. In this study, turnover in several areas will be forecasted. The kernel function variations used in SupportVector Machine (SVM) are RBF, Linear, and, Polynomial Degree 2. The results obtained from this research trialshow that the overall forecasting model is good. The accuracy of the three areas obtained with the RBF kernel has arelatively good MAPE. In the accuracy test to predict skincare sales turnover, the three areas got a fairly goodaccuracy value of 94.46%. In the Sidoarjo area, it is predicted that there will be a lot of decrease in turnover in 2023-2024.
Battery Health Monitoring System Lithium-Ion Based on Fuzzy Logic Yogo Bekti Firmanto; Ahmadi Ahmadi; Rangsang Purnama; Wiwiet Herulambang
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 1 (2024): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i1.3

Abstract

Batteries that can store electrical energy and are easy to carry make them the most practical technology choice as an electricity source. Even so, lithium-ion batteries are not free from the risk of damage when used. Therefore, a Lithium-ion battery health monitoring system was created. This system uses the INA219 sensor as a current and voltage detector and the DS18B20 sensor as a temperature detector. Arduino as a data process. The test results show that all components function well. In battery capacity testing, the highest error was 1.8%. For the DS18B20 sensor as a temperature sensor, an error of 2.4% was obtained. Testing capacity against temperature on the battery when the temperature was 25 C, the current was 485mAh; when the temperature was 44.8 C, the current was 550mAh, there was a difference of 65mAh or 11%. This difference corresponds to the difference in battery capacity. Testing using the Fuzzy Logic method was carried out on 3 batteries with different capacities to obtain the State of Health (SOH) value for each battery. Testing is carried out in real-time, as well as Matlab simulation. In battery test 1, with a capacity of 2200mAh and the highest temperature of 32.1 oC, the device's State of Health (SoH) was 90%, and Fuzzy Matlab was 87.6%. Battery 2, 1500mAh capacity with the highest temperature of 33.4oC obtained State of Health (SOH) of 60%, Fuzzy Matlab 60%. Battery 3 Capacity 2200mAh, Highest temperature 32.2 oC, State of Health (SOH) of 90%, Fuzzy Matlab 87.6%. The test results show that the overall error is still below 5%. A properly functioning Internet of Things (IoT) system can display information on lithium-ion batteries' State of Health (SoH) on devices and smartphones.
Co-Authors Agnes Fridawati Ahmadi Ahmadi Ahmadi Arif Arizal Ayu Purnamasari Azziyati Nur Budi Santoso Devi Ayu Ariyanto Diana Nur Arofah Dwiki Ashari Kusumawardhana Eko Prasetyo Erwin Dwi Riyanto Farah Dibba Fardanto Setyatama Irine Ratih Paramitha M Mahaputra Putra Muhammad Choirul Nurrohim Muhammad Zulfikkih Oktaviana Isbirotin R Dimas Adityo Rahmawati Febrifyaning Tias Rahmawati Febriyaning Tias Rangsang Purnama Rani Pubaningtyas Rani Purbaningtyas Rarasmaya Indraswari Reni Vivit Ayu Mawarti Retantyo Wardoyo Rifki Fahrial Zainal Rika Rokhana Syariful Alim Syariful Alim Weka Sadya Wasana Yogo Bekti Firmanto