Articles
Penerapan Algoritma Greedy Pada Mesin Penjual Otomatis (Vending Machine)
-, Alamsyah;
Putri, Indriani Tiara
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v1i2.4608
Dalam memasarkan produk minuman dan makanan ringan, Indonesia masih banyak menggunakan tenaga manusia untuk menyalurkan produk tersebut dari pabrik sampai ke konsumen akhir. Jika setiap toko membeli produk dalam jumlah banyak, maka didapatkan keuntungan yang besar dalam penjualan produk tersebut. Hal ini menyebabkan harga penjualan produk yang sampai ke konsumen akhir lebih mahal daripada harga asli yang diberikan oleh pabrik. Dengan permasalahan tersebut, penulis mencoba membuat aplikasi mesin penjual otomatis (vending machine). Pada umumnya vending machine tidak memberikan uang kembalian. Disini penulis mencoba membuat vending machine dengan menerapkan algoritma Greedy agar dapat memberikan uang kembalian sehingga harga penjualan produk sesuai dengan harga asli pabrik. Algoritma Greedy diterapkan untuk menentukan pecahan berapa saja yang muncul dalam proses pengembalian uang dengan meminimalkan jumlah uang logamnya. Penulis menggunakan aplikasi Visual Basic untuk menerapkan program vending machine.Â
PENERAPAN ALGORITMA GREEDY PADA MESIN PENJUAL OTOMATIS (VENDING MACHINE)
-, Alamsyah;
Putri, Indriani Tiara
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v1i2.4608
Dalam memasarkan produk minuman dan makanan ringan, Indonesia masih banyak menggunakan tenaga manusia untuk menyalurkan produk tersebut dari pabrik sampai ke konsumen akhir. Jika setiap toko membeli produk dalam jumlah banyak, maka didapatkan keuntungan yang besar dalam penjualan produk tersebut. Hal ini menyebabkan harga penjualan produk yang sampai ke konsumen akhir lebih mahal daripada harga asli yang diberikan oleh pabrik. Dengan permasalahan tersebut, penulis mencoba membuat aplikasi mesin penjual otomatis (vending machine). Pada umumnya vending machine tidak memberikan uang kembalian. Disini penulis mencoba membuat vending machine dengan menerapkan algoritma Greedy agar dapat memberikan uang kembalian sehingga harga penjualan produk sesuai dengan harga asli pabrik. Algoritma Greedy diterapkan untuk menentukan pecahan berapa saja yang muncul dalam proses pengembalian uang dengan meminimalkan jumlah uang logamnya. Penulis menggunakan aplikasi Visual Basic untuk menerapkan program vending machine.
Analisis Sistem Pendaftaran pada Web Forum Ilmiah Matematika Unnes 2014
Alamsyah, Alamsyah;
Arus, Afrilian Ardi
Scientific Journal of Informatics Vol 1, No 1 (2014): May 2014
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v1i1.3645
Penelitian ini bertujuan untuk menganalisis efektivitas dan efisiensi penerapan sistem pendaftaran pada Forum Ilmiah Matematika (FIM) Unnes 2014. Berdasarkan observasi yang dilakukan, penulis menemukan bahwa pendaftaran berbasis web lebih efektif karena mempermudah sistem pendaftaran, terutama di luar wilayah Semarang. Kelemahan dari sistem yang digunakan selama ini yaitu pendaftaran yang digunakan dinilai kurang efisien karena pendaftar dapat melakukan lebih dari satu kali input data dengan atribut yang sama. Oleh karena itu, dirancang sebuah sistem pendaftaran berbasis web berupa database yang hanya dapat memuat satu data dengan atribut seperti nama, NISN, asal sekolah dan lain-lain.
Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time
Hidayat, Kukuh Triyuliarno;
Arifudin, Riza;
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v5i1.12720
The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.
Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing
Ashari, Imam Ahmad;
Muslim, Much Aziz;
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v3i2.7911
Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.
Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods
Ilham Insani, Muhammad;
Alamsyah, Alamsyah;
Putra, Anggyi Trisnawan
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v5i2.16143
Expert Systems is a computer systems that has been entered the base knowledge and a set of rules used to solve problems like an expert. Methods that can be used in the expert systems which is Naïve Bayes and Certainty Factor. Naïve Bayes method can handle quantitative calculations and discreate data and only requires a little research data to estimate the parameters needed in the clasification and Certainty Factor which is suitable for measuring something whether it is certain or not in diagnosing. Diabetes is one of the most frequent diseases suffered in Indonesia. The purpose of this research is implementation expert systems used Naïve Bayes and Certainty Factor in diagnosing diabetes and knowing the level of accuracyof the systems. Data that is used by researchers as much 100 data medical record, obtained from the medical record RSUD Bendan Kota Pekalongan. The variabels used in this research is age, gender, the symptoms of the desease diabetes and result diagnose desease from expert. The accuracy rate of this system derived from the scenario distribution data 70 training data and 30 testing data that is equal to 100% according to the doctor's diagnosis.
Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis
Larasati, Ukhti Ikhsani;
Muslim, Much Aziz;
Arifudin, Riza;
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v6i1.14244
Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.
Scheduling Optimization of Sugarcane Harvest Using Simulated Annealing Algorithm
Afifah, Eka Nur;
Alamsyah, Alamsyah;
Sugiharti, Endang
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v5i2.14421
Scheduling is one of the important part in production planning process. One of the factor that influence the smooth production process is raw material supply. Sugarcane supply as the main raw material in the making of sugar is the most important componen. The algorithm that used in this study was Simulated Annealing (SA) algorithm. SA apability to accept the bad or no better solution within certain time distinguist it from another local search algorithm. Aim of this study was to implement the SA algorithm in scheduling the sugarcane harvest process so that the amount of sugarcane harvest not so differ from mill capacity of the factory. Data used in this study were 60 data from sugarcane farms that ready to cut and mill capacity 1660 tons. Sugarcane harvest process in 19 days producing 33043,76 tons used SA algorithm and 27089,47 tons from factory actual result. Based on few experiments, obtained sugarcane harvest average by SA algorithm was 1651,63 tons per day and factory actual result was 1354,47 tons. Result of harvest scheduling used SA algorithm showed not so differ average from mill capacity of factory. Truck uses scheduling by SA algorithm showed average 119 trucks per day while from factory actual result was 156 trucks. With the same harvest time, SA algorithm result was greater and the amount of used truck less than actual result of factory. Thus, can be concluded SA algorithm can make the scheduling of sugarcane harvest become more optimall compared to other methods applied by the factory nowdays.
A Novel Construction of Perfect Strict Avalanche Criterion S-box using Simple Irreducible Polynomials
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v7i1.24006
An irreducible polynomial is one of the main components in building an S-box with an algebraic technique approach. The selection of the precise irreducible polynomial will determine the quality of the S-box produced. One method for determining good S-box quality is strict avalanche criterion will be perfect if it has a value of 0.5. Unfortunately, in previous studies, the strict avalanche criterion value of the S-box produced still did not reach perfect value. In this paper, we will discuss S-box construction using selected irreducible polynomials. This selection is based on the number of elements of the least amount of irreducible polynomials that make it easier to construct S-box construction. There are 17 irreducible polynomials that meet these criteria. The strict avalanche criterion test results show that the irreducible polynomial p17(x) =x8 + x7 + x6 + x + 1 is the best with a perfect SAC value of 0.5. One indicator that a robust S-box is an ideal strict avalanche criterion value of 0.5
Comparison of Dynamic Programming Algorithm and Greedy Algorithm on Integer Knapsack Problem in Freight Transportation
Sampurno, Global Ilham;
Sugiharti, Endang;
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v5i1.13360
At this time the delivery of goods to be familiar because the use of delivery of goods services greatly facilitate customers. PT Post Indonesia is one of the delivery of goods. On the delivery of goods, we often encounter the selection of goods which entered first into the transportation and  held from the delivery. At the time of the selection, there are Knapsack problems that require optimal selection of solutions. Knapsack is a place used as a means of storing or inserting an object. The purpose of this research is to know how to get optimal solution result in solving Integer Knapsack problem on freight transportation by using Dynamic Programming Algorithm and Greedy Algorithm at PT Post Indonesia Semarang. This also knowing the results of the implementation of Greedy Algorithm with Dynamic Programming Algorithm on Integer Knapsack problems on the selection of goods transport in PT Post Indonesia Semarang by applying on the mobile application. The results of this research are made from the results obtained by the Dynamic Programming Algorithm with total weight 5022 kg in 7 days. While the calculation result obtained by Greedy Algorithm, that is total weight of delivery equal to 4496 kg in 7 days. It can be concluded that the calculation results obtained by Dynamic Programming Algorithm in 7 days has a total weight of 526 kg is greater when compared with Greedy Algorithm.