Cognitive Artificial Intelligence: Concept and Applications for Humankind

Computation within the human brain is not possible to be emulated 100% in artificial intelligence machines. Human brain has an awesome mechanism when performing computation with new knowledge as the end result. In this chapter, we will show a new approach for emulating the computation that occurs within the human brain to obtain new knowledge as the time passes and makes the knowledge to become newer. Based on this phenomenon, we have built an intelligent system called the Knowledge-Growing System (KGS). This approach is the basis for designing an agent that has ability to think and act rationally like a human, which is called the cognitive agent. Our cognitive modeling approach has resulted in a model of human information processing and a technique called Arwin-Adang-Aciek-Sembiring (A3S). This brain-inspired method opens a new perspective in AI known as cognitive artificial intelligence (CAI). CAI computation can be applied to various applications, namely: (1) knowledge extraction in an integrated information system, (2) probabilistic cognitive robot and coordination among autonomous agent systems, (3) human health detection, and (4) electrical instrument measurement. CAI provides a wide opportunity to yield various technologies and intelligent instrumentations as well as to encourage the development of cognitive science, which then encourages the intelligent systems approach to human intelligence

Design and Implementation of 12-Bit Arithmetic Logic Unit with 8 Operation Codes to Field Programmable Gate Array

Digital system has been a part of human life since the invention of the computer with a microprocessor as the central brain. At the heart of a processor is an Arithmetic Logic Unit (ALU) that handles arithmetic and logic operations. The need for high-speed computation to handle complex computations demands microprocessors with higher performance. The existing 4-opcode 8-bit ALU cannot handle multiplication operations, so a solution is needed. In this research, while raising the appeal of beginners, a 12-bit ALU with eight operation codes (opcode) was designed and implemented in Xilinx’s Field Programmable Gate Array using a schematic diagram approach through logic gates. The designed and implemented ALU provides addition, subtraction, multiplication, square, AND, OR, NAND, and XOR operations. The multiplication operation was tested by performing the computation to provided datasets to obtain the distance travelled by ten military aircraft based on their maximum speed and air travel duration to ensure its performance. The computation performance comparison with an 8-bit ALU with four opcodes was also done. The computation was done for air travel between 10 to 60 minutes with a 10-minute difference. It was found that the 12-bit ALU with eight opcodes outperformed its contender with computation differences between 130.815 ns and 1,468.214 ns. This high performance is supported by the multiply operation that does repeated addition at one time. Based on this finding, the 8-opcode 12-bit ALU is more efficient in the context of computation time, with consistent accuracy. Moreover, the computation time required to calculate military aircraft data with different maximum speeds and air travel duration is only 119.501 ns

PAYMENT INFORMATION SYSTEM FOR INCREASING EMPLOYEE WORKING EFFECTIVENESS IN PR TUNAS MANDIRI IN PACITAN REGENCY

Human capital management information system aims to improve the performance and to reduce errors in the existing business processes at human resources section to facilitate operational activities, especially in the case of employee data management, attendance, and salary calculations. The problem that is faced at this moment is in data management process, attendance, and leave where employee payroll system is the main problem. On the other hand, mobile-based information system will ease the business owner to control his employee’s performance, or the employees can do recaps. The developed information system is based on PIECES analysis that focuses on 6 aspects, namely Performance, Information and Data, Economy, Control and Security, Efficiency, and Service, and used Scrum software development method that includes storyboards based on the developed features, product backlog, and sprints. Based on the result analysis, the developed information system obtained average equation of interest and satisfaction in system usage from 50 respondents who are 86% from senior high school and 16% from bachelor’s degree, obtained the average level of satisfaction 100% for Performance, 95.72% for Information, 96.48% for Economics, 93.29% for Control, 94.56% for Efficiency, and 96.81% for Service. For User Satisfaction Questionnaire Tabulation, it is obtained an average of 4.21 for Performance domain, an average of 4.21 for Information domain, an average of 4.26 for Economics, an average of 4.17 for Control domain, an average of 4.23 for Efficiency domain, and an average of 4.08 for Service domain. The developed information system can improve the accuracy and the effectivity of employee data recording and attendance as well as speed up the employee salary calculation and reduce the error because it is already integrated with the employee attendance system

Multi-agent information-inferencing fusion system for decision support system

In this paper we address the utilization of multi-agent approach to an information-inferencing fusion system.Information-inferencing fusion is an emulation of the way human fuses information delivered by human sensing organs after observing a phenomenon in his/her environment.The fused information is used as the basis for making a decision or an action to face the current situation or anticipate the situation that can probably occur in the future.This human capability is then emulated to Multi-Agent Information-inferencing Fusion System (MAIIFS) based on A3S (ArwinAdang-Aciek-Sembiring). From the results presented in this paper, the A3S method information-inferencing fusion method can deliver comprehensive information in a very quick manner so the decision maker can have situation awareness quicker.Therefore, he can to make the decision in accurate and quick manner

Pemilihan Daging Kelapa Bermutu Berdasarkan Warna dan Tekstur untuk Produksi Wingko Berkualitas Menggunakan Metode Support Vector Machine (SVM) dan Fusi Informasi

Mutu daging kelapa adalah faktor utama yang menentukan kualitas produksi wingko baik yang berasal dari kelapa muda atau kelapa tua dari varietas genjah. Dalam upaya menjaga kualitas produksi wingko kelapa, diperlukan teknik dalam memilih daging kelapa yang bermutu tinggi secara konsisten dengan bantuan teknologi. Dalam penelitian ini telah dibangun sebuah sistem pencitraan digital berbasis Kecerdasan Artifisial untuk pemilihan daging kelapa bermutu. Pemilihan tersebut didasarkan pada warna dan tekstur dengan memanfaatkan Support Vector Machine (SVM) sebagai pengklasifikasi, dan fusi informasi. Pengolahan citra digital menggunakan kombinasi metode Hue, Saturation, Value (HSV) dan metode Gray-Level Co-Occurrence Matrix (GLCM) sebagai pengekstraksi fitur warna dan fitur energi. Kedua macam fiur tersebut difusikan menjadi fitur tunggal guna mempercepat klasifikasi oleh SVM sebagai landasan pemilihan daging kelapa. Dengan menggunakan sistem ini, pemilihan daging kelapa bermutu berhasil mencapai akurasi sebesar 50%. Dalam penelitian ini juga ditemukan bahwa ketidak tepatan pelabelan memberi dampak signifikan pada akurasi pemilihan daging kelapa.AbstractThe quality of coconut meat is a primary factor which determines the quality of wingko production whether that comes from young coconut or old one from Genjah variety. In the effort of maintaining the quality of coconut wingko production, a technique for selecting high quality of coconut meat in consistent way with the aid of technology is needed. In this research, an Artificial Intelligence-based digital imaging system for selecting quality coconut meat has been developed. The selection is based on color and texture by utilizing Support Vector Machine (SVM) as classifier and information fusion. The digital image processing uses the combination of Hue, Saturation, Value (HSV) and Gray-Level Co-Occurrence Matrix (GLCM) methods as color and energy feature extractors. Both features are fused to obtain single feature to accelerate SVM classification as the basis for selection the coconut meat. By using this system, the selection of quality coconut meat is successful to achieve the accuracy as much as 50%. In this research it was also found that incorrectly labeling gives significant impact to the accuracy of coconut meat selection

Pengenalan Jenis Tanaman Mangga Berdasarkan Bentuk dan Tekstur Daun Menggunakan Kecerdasan Artifisial K-NearestNeighbor (KNN) dan Fusi Informasi

Memilih tanaman mangga yang sesuai dengan yang diinginkan menjadi sebuah tantangan dihadapkan pada tanaman marga Mangifera yang ada saat ini. Kesalahan pemilihan jenis tanaman mangga dapat menyebabkan kekecewaan pada pembeli dan menurunkan kepercayaan kepada penjual tanaman mangga karena dapat dianggap memberikan jenis tanaman yang salah. Permasalahannya adalah jenis tanaman mangga dapat diketahui setelah tanaman tersebut berbuah. Oleh karena itu, dalam upaya mereduksi kesalahan dalam pemilihan sebelum melakukan pembelian tanaman mangga, maka dirancang  dan dibangun sebuah sistem pencitraan digital untuk pengenalan jenis tanaman mangga berdasarkan bentuk dan tekstur daun menggunakan metode Kecerdasan Artifisial K-Nearest Neighbor (KNN) yang digabungkan dengan Fusi Informasi guna memperoleh hasil klasifikasi dengan akurasi yang lebih baik. Data citra daun empat macam daun tanaman mangga yakni jenis Gadung, Lalijiwo, Golek dan Irwin, diproses menggunakan metode Local Binary Pattern (LBP) dan Entropy untuk ekstraksi fitur tekstur, dan metode Rectangularity untuk ekstraksi fitur bentuk. Kedua macam fitur tersebut difusikan menjadi masukan bagi pengklasifikasi KNN. Berdasarkan dari hasil-hasil pengujian, K-NN berhasil mengenali keempat jenis tanaman mangga tersebut dengan akurasi tertinggi sebesar 70% pada nilai K = 5, K = 9, K = 10 dan K = 11. Dari hasil pengujian juga diperoleh hasil bahwa fusi informasi mampu mempercepat sistem mengenali jenis tanaman mangga sebesar 0,11 detik. AbstractChoosing the right desired Mango plant is a challenge faced with various types of the existing Mangifera clan plants. The wrong choice of Mango plant species can end up with buyer disappointment and reduce the trust to the seller because it can be considered as providing the wrong type of plant. This happened because the type of Mango plant can only be identified after it bears fruit. In the effort to reduce such error, a digital imaging system was designed and built for recognizing the  types of Mango plants based on the leaf shape and texture using Artificial Intelligence’s K-Nearest Neighbor (KNN) combined with Information Fusion to accelerate the classification with a consistent classification results. The image data consists of four kinds of Mango plant leaves, namely Gadung, Lalijiwo, Golek and Irwin. The leaf texture feature was extracted using the Local Binary Pattern (LBP) and Entropy methods, while the leaf shape feature was extracted using the Rectangularity method. The two features are fused as the input for the KNN classifier. Based on the test results, KNN was able to identify the four types of the Mango plant with the highest accuracy of 70% at values of K = 5, K = 9, K = 10, and K = 11. Besides that, it is also obtained a result that, the information fusion is able to speed up the recognition the types of Mango by 0.11 seconds