PEMODELAN PROSES BISNIS DAN PENYUSUNAN STANDARD OPERATING PROCEDURE PADA BADAN PENGELOLA PAJAK DAERAH DAN RETRIBUSI DAERAH KOTA BALIKPAPAN

Badan Pengelola Pajak dan Retribusi Daerah (BPPDRD) Kota Balikpapan merupakan salah satu instansi pemerintahan yang bertugas di bidang pengelolaan pajak daerah dan retribusi daerah Kota Balikpapan. BPPDRD Kota Balikpapan bertujuan untuk mewujudkan akuntabilitas pengelolaan pajak daerah dan retribusi daerah, meningkatkan pajak daerah dan retribusi daerah, mewujudkan aparatur yang berkompeten serta mewujudkan pelayanan pajak daerah dan retribusi daerah yang prima. Perubahan Dinas Pendapatan Daerah menjadi BPPDRD pada tahun 2017 menyebabkan perubahan kegiatan yang belum terdokumentasi menjadi SOP. Berdasarkan hal tersebut dilakukan pemodelan proses bisnis dengan menggunakan pendekatan Business process management (BPM) dengan notasi Business Process Modeling and Notation (BPMN) dan penyusunan SOP dengan menggunakan notasi flowchart. Dari penelitian yang dilakukan, dihasilkan 20 model proses bisnis to-be yang telah diperbaiki menggunakan value added analysis dari 20 proses bisnis as-is, serta dihasilkan 20 SOP yang telah disusun dari 20 proses bisnis to-be

Perancangan Arsitektur Sistem dan Teknologi Informasi Menggunakan Togaf ADM (Studi Kasus Dinas Perhubungan Kota Balikpapan)

Utilization of information systems or technology in an organization is needed to improve organizational efficiency. This relates to the rapid development of information systems or technology that affect the ongoing business processes. The effectiveness is in the form of aligning business strategy with information system strategy and business transformation. However, the dilemma faced especially in Government agencies such as the Balikpapan City Department of Transportation is how to align business strategies and information systems strategies that will be used so as to achieve organizational goals.  Responding to the problem, it is necessary to align between the business needs of the organization and the needs of the application to support the vision and mission to be achieved by the Balikpapan City Department of Transportation. This can be achieved by using the framework of The Open Group Architecture Framework (TOGAF) which provides a detailed method of how to build, manage and implement an enterprise architecture known as the Architecture Development Method (ADM), hereafter referred to as TOGAF ADM. This research produces a mapping of business needs and application needs to support the vision and mission to be achieved through the information systems or technology architecture design including business architecture modeling, information system architecture, and technology architecture on the Balikpapan City Department of Transportation

Rule extraction from multi-layer perceptron neural network using decision tree for currency exchange rates forecasting

Neural network can be used in acquiring hidden knowledge in datasets. However, knowledge acquired by neural network was presented in its topology, the weights on the connections and by the activation functions of the hidden and output nodes. These representations are not easily understandable since neural networks act as a black box. The black box problem can be solved by extracting rule from trained neural network. Thus, the aim of this study was to extract valuable information (rule) from trained multi-layer perceptron (MLP) neural networks using decision tree. The main process in extracting rules from MLP using decision tree for currency exchange rate forecasting can be divided into two stages. In the first stage, the MLP network was built based on the parameter that was defined in the previous chapter. We also perform training and testing process experimentally and then the performance was evaluated in order to obtain the network with the best performance. The MLP network which provides the best prediction performance will be extracted by decision tree in the second stage by mapping input-output of the network directly. The forecasting result have shown that MLP network of EUR/USD produced a significant results compared to MLP network of GBP/USD and USD/JPY in term of MSE, RMSE, MAPE, and DS. It is quite evident that as the number of hidden neurons increases, MSE and MAPE decrease. In addition, the number of iterations for each model continues to increase along with the increasing number of hidden neurons. The results on decision tree induction show that C4.5 algorithm induction produced a significant result in term of accuracy 84.07% - 86.34%, precision and recall 93.17% and 81.97% respectively. This study has shown how rule can be extracted from MLP network by decision tree without making any assumptions about the networks activations function or having initial knowledge about the problem domain. The extracted rule can be used to explain the process of the neural network systems and also can be used in other systems like expert systems

APLIKASI NATIVE ANDROID SEBAGAI FRONT END PEMESANAN MENU MAKANAN DI RESTORAN MENGGUNAKAN WEB SERVICE

Restoran adalah suatu tempat atau bangunan yang diorganisasi secara komersil, yang menyelenggarakan pelayanan dengan baik kepada semua tamu baik berupa makan maupun minum. Proses pemesanan makanan pada suatu restoran merupakan salah satu hal yang penting dalam bisnis restoran. Pencatatan pesanan pelanggan secara langsung di lokasi restoran biasanya dapat dilakukan dengan menggunakan media alat tulis. Penggunaan  media  alat  tulis  dan  kertas  tersebut mempunyai  kendala, beberapa  kendala  yang  dapat muncul  pada saat proses pemesanan menu  adalah  penyampaian  pesanan konsumen  ke  bagian  dapur dapat  memakan  waktu lama  dikarenakan  jarak,   tidak terbacanya  tulisan  tangan  pencatat  pesanan, terselipnya  kertas  catatan  pesanan  yang  dapat mempengaruhi urutan pemrosesan pesanan, adanya pemesanan  yang  rangkap,  dan  adanya  pemesanan yang terlupa. Masalah tersebut dapat diatasi dengan aplikasi native front end guna untuk membantu mempermudah pelayan dalam pemesanan menu.Metodologi yang digunakan dalam penelitian ini yaitu melakukan pengumpulan data menggunakan metode kuisioner, wawancara dan studi pustaka. Kemudian dianalisis untuk menentukan kebutuhan user dan kebutuhan sistem. Implementasi aplikasi menggunakan bahasa JAVA dan web service sebagai media pertukaran data. Pengujian sistem dilakukan dengan 2 metode, yaitu Blackbox Test dan Pengujian Post-Study SUS (Software Usability Scale).Hasil dari penelitian ini adalah native front-end aplikasi pemesanan menu  direstoran guna untuk mempermudah pelayan dalam pemesanan menu.. Pengujian sistem dengan metode Black box dan Pengujian Post Study SUS (Software Usability Scale) untuk mengetahui aplikasi  telah berjalan sesuai mana mestinya.Kata kunci : Pemesanan menu, native front-en

Applying Artificial Neural Networks in Forecasting US Dollars-Indonesian Rupiah Exchange

This paper investigates artificial neural networks prediction modeling of foreign currency rates using Levenberg Marquardt (LM) learning algorithms. The models were trained from historical data using US Dollar (USD) currency rates against Indonesian Rupiah (IDR). The forecasting performance of the models was evaluated using a number of statistical measurements and compared. The results show that significant close prediction result can be made using simple architecture forecasting model. LM1 and LM6 model achieves closer prediction of the actual value than that other model.Both forecasting models attain significantly high rate of predicting correct directional change (above 80%). The effect of network architecture on the performance of the forecasting model is also presented

Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting

As the largest financial market in the world, foreign exchange (Forex) is becoming a very profitable market with a daily transaction of more than 3.0 trillion U.S. dollars. Therefore, predicting about it has been a challenge for many years. Artificial Neural Network (ANN) provides better performance of forecasting but it tends to get stuck in local minima and there is no optimal way to determine the best classifier on it. Meanwhile, Decision Tree (DT) is able to generate classifier in the form of a tree. This paper proposes a hybrid prediction model by combining both ANN and DTalgorithm to predict exchange rates. The models are constructed by using the better of parameters and architectures based on related work such as filtering mechanism, number of hidden layers, number of hidden neurons, training algorithm, and error measurement, with the assumption that if the hybrid model is constructed by the better parameters and architectures, then the output of the model also produces better resul

Pendidikan Karakter dalam Pembiasaan Ibadah Shalat Siswa SDN Tanjung Pura III dan MDTU Darul Fatwa

Character can be defined as how to apply or engrave the value of goodness in the form of actions or behavior, so that people who are dishonest, cruel, greedy, and behave badly are said to have bad character. On the other hand, people whose behavior is in accordance with moral rules are called people of noble character. In cultivating character education, it is necessary to have awareness from various parties to start and become habitual. In this regard, this study aims to reveal the values of character education that were developed on the values of character education in increasing prayer. The approach used in this research is a descriptive qualitative approach. The data of this research is the application of character education in improving prayer worship. The process of collecting data using the method of observation, interviews and documentation. After the data is collected, it is then analyzed using verification analysis techniques. From the analysis that has been carried out, it is then presented with the reduction method. From the analysis that has been carried out, it is then presented using the selection method. The presentation of the results of data analysis was carried out by observation, interviews and documentation. The results showed that the values of character education that were revealed in the application of character education in improving students' prayer worship the values found were: (1) religious, (2) honest, (3) tolerance, (4) hard work, (5) curiosity, (6) friendly/communicative, (7) responsibility and the value of findings, namely the value of good habits for praying. Based on the results of this study, the authors suggest to principals and teachers, in order to teach the values of character education in improving prayer, students are formed who have character and always perform prayers

The Use of Artificial Neural Network for Forecasting of FTSE Bursa Malaysia KLCI Stock Price Index

Stock forecasting has become an issue of interest in financial market. There are many prediction techniques have been reported in stock prediction. Artificial Neural Networks are viewed as one of the more suitable technique for prediction model. In this paper, an experiment on the forecasting of the FTSE Bursa Malaysia Stock Index was conducted to investigate the influence of neural network’s architecture on prediction performance by using multilayer perceptron with Levenberg Marquardt training algorithm. The result show FTSE8 and FTSE9 model achieves closer prediction of the actual value than other model

Knowledge of extraction from trained neural network by using decision tree

Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. As the number of hidden neurons increase, mean squared error and mean absolute percentage error decrease, and more time they need to deal with the dataset, that is result of investigation from neural network architectures. Decision tree induction generally performs better in knowledge extraction result with accuracy and precision level from 84.07 to 93.17 percent. The extracted rule can be used to explaining the process of the neural network systems and also can be applied in other systems like expert systems

Foreign Exchange Forecasting by using Artificial Neural Networks: A Survey of Literature

Foreign exchange (Forex) is the global scale trading of currency and the most liquid financial market. Therefore, predicting Forex has been challenged for many years. On the other hand, Artificial Neural Network (ANN) was widely used by researchers as a prediction technique since it can provide the best prediction result. This paper surveys recent literature in the domain of ANN which used to forecast foreign exchange. This paper classifies the literature based on forecasting model, input data type, forecasting intervals, and evaluation method. This paper reveals progressive applications in addition to existing gap and less considered area and determines the future work of researchers