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"Food security is the main problem in food production in Indonesia. One of the reason that the food from corn isis not safe to be consumed caused by contaminated by aflatoxins wich produced by fungi...."

"It has been studied the forecasting of electric power peak load in the Indonesian electric system byusing Artificial Neural Network (ANAU) Back Propagation method with the study period is 2000 - 2025.The long-range forecasting of electric peak load is influenced by economic factors. in this study, it?sselected the economic data which is estimated very influence to forecasting, which in this case becomeinput ofAN1\L i. e.: Gross of Domestic Product (GDP) per-capita, Population, Amount of Households,Electrification Ratio, Amount of CO, Pollution, Crude Oil Price, Coal Price, Usage of Final Energy,Usage Qf Final Energy on Industrial Sector; and Average Electric Charges. Data used for study areactual data, start year 1990 up to 2000. Result of the peak load forecasting in the end of study (2025) byusing ANN is 85,504 MHC meanwhile the load forecasting in the National Electricity General lan(NEGP) is 79,920 MW (the difference of both is about 6. 6%). Based on ANN approach is obtained resultsthat the peak load forecasting in Indonesia in the year 2005, 2010, 2015, 2020 and 2025 are 16,516 MHC24,402 MHC 36, 15 7 MIK 56,060 MW and85,584 MW respectively."

"Road infrastructure includes toll roads developed to support mobility and economic activity. The toll road is part of the road network and is an alternative that can save travelers time and give them better service. The level of service of the toll road is strongly connected to the level of satisfaction of toll road users; therefore, customer satisfaction needs to be included in development models. The purpose of this study was to develop a model approach to customer satisfaction using an artificial neural network (ANN). Two models of customer satisfaction, SERVQUAL and Minimum Service Standards (SPM), have been used to modify the Toll Road Service Quality (TRSQ) model. This study has been able to explain that TRSQ has a value of R2, meaning the result is better than that of the other two models. The TRSQ model itself consists of seven dimensions: information, accessibility, reliability, mobility, security, rest areas, and responsiveness. Reliability is the dimension with the greatest effect on customer satisfaction."

"In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas (LPG), light naphtha, heavy naphtha, kerosene, diesel and unconverted oil (off-test). The input layer of the ANN model consists of the catalyst, feed and recycle flow rates, and bed temperatures, while the output neurons are yields of those products. The results showed that the AAD% (average absolute deviation) of the developed ANN model for training, testing, and validating data are 0.445%, 1.131% and 0.755%, respectively. Then, by considering all operational constraints, the results confirmed that the decision variables (i.e., recycle rate and bed temperatures) generated by the optimization approach can enhance the gross profit of the hydrocracking process to more than $0.81 million annually, which is significant for the economy of the target refinery."

"Transforming seismic data into lateral sonic log properties was carried out successfully using the artificial neural network (ANN). This work is related to a detailed investigation of reservoir properties that requires complete data. The objective of this paper is to build a geological model that has vertical and lateral distribution representing the framework of geological change of sonic log properties. However, detailed well log data analysis only provides information of vertical distribution, therefore effective application of seismic data is required to construct a spatial distribution model that represents the lateral sonic log properties away from a well. This paper presents a strategy for transforming seismic data into pseudo-sonic log data by using ANN approaches rather than a simple approach of empirical relationship. The ANN approach defines a specific function that correlates a series of attributes generated from seismic data, such as amplitude envelope, instantaneous frequency, instantaneous phase, and acoustic impedance by a training mechanism based on the sonic log data as a target parameter. The probabilistic neural network (PNN) as one ANN algorithm is applied to transform seismic attributes into a lateral sonic log. An example of an ANN approach using a real data set from the Indonesian field was presented. The pseudo-sonic log shows a good agreement with the real sonic log data, which is represented with a correlation coefficient of 0.91. Further, the seismic line data was successfully transformed into the pseudo lateral sonic log data that covers the whole seismic line."

"In this research, a layered-recurrent artificial neural network (ANN) using the back-propagation method was developed for simulation of a fixed-bed industrial catalytic reforming unit called Platformer. Ninety-seven data points were gathered from the industrial catalytic naphtha reforming plant during the complete life cycle of the catalytic bed (about 919 days). Ultimately, 80% of them were selected as past horizontal data sets, and the others were selected as future horizontal ones. After training, testing, and validating the model with past horizontal data, the developed network was applied to predict the volume flow rate and research octane number (RON) of the future horizontal data versus days on stream. Results show that the developed ANN was capable of predicting the volume flow rate and RON of the gasoline for the future horizontal data sets with AAD% (average absolute deviation) of 0.238% and 0.813%, respectively. Moreover, the AAD% of the predicted octane barrel levels against the actual values was 1.447%, which shows the excellent capability of the model to simulate the behavior of the target catalytic reforming plant."

"Experience in applying a hybrid artificial neural network (ANN)-genetic algorithm for modeling and optimizing the Hall-Heroult process for aluminum extraction is described in this study. During the stage of modeling, the most important and effective process variables including temperature and cell voltage, metal and bath heights, purity of CaF2 and Al2O3, and bath ratio are chosen as input variables whilst outputs of the model are product purity, ampere efficiency, and product rate. During three years of operation, 19 points were selected for building and training, 7 points for testing, and 7 data points for validating the model. Results show that a feed-forward Artificial Neural Network (ANN) model with 3 neurons in the hidden layer can acceptably simulate the mentioned output variables with the Mean Squared Error (MSE) of 0.002%, 0.108% and 0.407%, respectively. Utilizing the validated model and multi-objective genetic algorithms, aluminum purity and the rate of production are maximized by manipulating decision variables. Results show that setting these decision variables at the optimal values can increase approximately the metal purity, ampere efficiency, and product rate by 0.007%, 0.185%, and 20kg/h, respectively."

"Road infrastructure includes toll roads developed to support mobility and economic activity. The toll road is part of the road network and is an alternative that can save travelers time and give them better service. The level of service of the toll road is strongly connected to the level of satisfaction of toll road users; therefore, customer satisfaction needs to be included in development models. The purpose of this study was to develop a model approach to customer satisfaction using an artificial neural network (ANN). Two models of customer satisfaction, SERVQUAL and Minimum Service Standards (SPM), have been used to modify the Toll Road Service Quality (TRSQ) model. This study has been able to explain that TRSQ has a value of R2, meaning the result is better than that of the other two models. The TRSQ model itself consists of seven dimensions: information, accessibility, reliability, mobility, security, rest areas, and responsiveness. Reliability is the dimension with the greatest effect on customer satisfaction."

"Perencanaan produksi pada sebuah industri, membutuhkan keputusan yang tepat untuk menentukan jumlah produksi agar dapat memenuhi permintaan konsumen tanpa menghasilkan stok berlebih. Peramalan permintaan merupakan salah satu faktor penting dalam perencanaan produksi yang mampu membantu menghasilkan keputusan produksi yang tepat. Pada industri otomotif mobil, peramalan yang akurat sangat dibutuhkan untuk mengatasi permintaan yang tidak menentu, khususnya untuk produk service parts, yang pada kenyataannya memiliki permintaan yang tidak menentu dari konsumen dan seringkali membuat perusahaan mobil yang memproduksinya mengalami kerugian karena backorder atau overstock. Artificial neural network ANN merupakan suatu metode berbasis machine learning dengan cara kerja seperti otak manusia yang juga mampu melakukan peramalan untuk data dengan pola non-linier. Pada penelitian kali ini, dilakukan peramalan dengan objek 10 jenis service parts berbeda dengan menggunakan metode artificial neural network yang kemudian dilakukan perbandingan dengan peramalan metode single exponential smoothing dan croston rsquo;s method untuk dapat membandingkan tingkat akurasi dari peramalan tersebut dan menghasilkan peramalan dengan metode yang paling akurat. Hasil perhitungan pada penelitian ini menunjukkan peramalan metode artifcial neural network mampu menghasilkan peramalan yang lebih akurat dibanding dua metode lain.

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Production planning in an industry, required precise decisions to made in order to determine the amount of product that will be produced to fulfill the customer's demand without produce excess stock. Demand forecasting is one of the most important factor in production planning process that able to generate precise production decision.

The automotive industry like car manufacturer, always need an accurate demand forecast serve the uncertain demand of their products, especially the service parts product, that in fact always has uncertainity in it's demand and frequently causing the manufacturer company lose their profit due to tha backorder and overstock occurence. Artificial neural network is a machine learning computation method that could work similarly like human brain that also can forecast a non linier data.

In this research, the data is gained from the demand of 10 car's service parts in a car manufacturer and forecasted with artificial neural network and also two other methods, single exponential smoothing and croston's method to generate a forecasting with the most accurate method. The result of the calculation in this research shows that forecasting with artificial neural networks produce the most accurate forecast for the car's service parts demand."

"PT.X telah memiliki pengalaman operasional dan reputasi yang balk selama berpuluh tahun dalam menggeluti bidang bisnis Percetakan Security khususnya uang kertas yang sangat vital dan memiliki pasar yang captive.PT.X secara teoritis memiliki kapasitas produksi diatas permintaan, namun masih memiliki permasalahan dalam hal ketepatan penyerahan produknya. Perrnasalahan ini diakibatkan terjadinya penyimpangan performa standar dari unit Flokulasi ( pengolah limbah tinta ) yang berdampak pada menurunnya kinerja permesinan di unit cetak Intaglio. Disisi lain PT.X berupaya untuk meningkatkan kemampulabaan dan kemamputumbuhan , oleh karena itu upaya yang diambil PT.X adalah memperbaiki sekaligus meningkatkan kinerja sistim produksinya.Untuk mengatasi permasalahan tersebut, maka dikembangkan suatu bentuk Strategi Manufaktur yang diimplementasikan melalui langkah - langkah perbaikan pada sistim pengendalian proses Flokulasi. Adapun langkah pertama yang dilakukan adalah untuk mengetahui kondisi aktual perusahaan termasuk kinerja dari lini permesinannya, dimana metode yang dipakai adalah analisa kuantitative atas laporan keuangan dan analisa kinerja bisnis yaitu analisa SWOT.Sedangkan kondisi kinerja Manufaktur dari unit Produksi diukur dengan memakai rasio MCE (Manufacturing Cycle Effectiveness) , rasio Machine Effectiveness dan sebagai pembanding dilakukan analisa Benchmarking atas dua Industri sejenis.Metode yang dipakai untuk meningkatkan mutu sistim pengendalian proses Flokulasi adalah dengan mengembangkan suatu bentuk teknologi berberbasis Artificial Neural Network, yang memiliki kemampuan untuk memprediksi hasil akhir/output dari proses Flokulasi yang sedang berlangsung.Semua ini akan menunjang usaha peningkatan kemampulabaan ,kemamputumbuhan terutama dari segi mutu produk dan ketepatan waktu penyerahan produk sesuai dengan tuntutan konsumen.

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PT.X has many years of operational experience and a good reputation in the business of Security Printing especially paper money and has a Captive market.Theoretically PT.X has a production capacity exceeding the demand , but has problem in the delivery time. This in turn lowers the machinery performance in the Intaglio printing section, which is basically due to the deviation of the performance standard of the Flocculation unit from the water treatment plant.PT.X expect to increase profitability and growth by enhancing the performance of the production system.To solve this problem by developing a form of Manufacturing Strategy implemented through remedial steps taken in the process control system of the Flocculation unit. The first step is to know the actual condition of the company including the production line machinery . This is done through the quantitative analysis from the financial reports and qualitative analysis of business performances using a SWOT analysis. The next step is to measures the manufacturing performance from the capability of production facility by Manufacturing Cycle Effectiveness ratio, Machine Effectiveness ratio and Benchmarking analysis .The method used in order to enhance quality of the Flocculation process is through the development of technology based on Artificial Neural Network, which is to predict the output of Flocullation process.We concluded that new system will be useful to help improvement effort for the company to increase profitability and growth, such as product quality and delivery time in accordance with the requirement."