Articles
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.
Security Login System on Mobile Application with Implementation of Advanced Encryption Standard (AES) using 3 Keys Variation 128-bit, 192-bit, and 256-bit
Utami, Hamdan Dian Jaya Rozi Hyang;
Arifudin, Riza;
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 6, No 1 (2019): Mei 2019
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v6i1.17589
The development of mobile applications is unbalanced with the level of its security which is vulnerable to hacker attacks. Some important things that need to be considered in the security of mobile applications are login and database system. A login system that used the database as user authentication and passwords are very vulnerable to be hacking. In securing data, various ways had been developed including cryptography. Cryptographic algorithms used in securing passwords usually used MD5 encryption. However, MD5 as a broader encryption technique is very risky. Therefore, the level of login system security in an android application is needed to embed the Advanced Encryption Standard (AES) algorithm in its process. The AES algorithm was applied using variations of 3 keys 128-bit, 192-bit, and 256-bit. Security level testing was also conducted by using 40 SQL Injection samples which the system logins without security obtained 27.5% that be able to enter the system compared to the result of login systems that use AES algorithm 128-bit, 192-bit or 256-bit was obtained 100% that cannot enter into the system. The estimation of the average encryption process of AES 128, 192 and 256 bits are 5.8 seconds, 7.74 seconds, and 9.46 seconds.
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): Mei 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%.
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.
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.
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
Application of Fuzzy Algorithms and Analytical Hierarchy Process Modification in Decision Support Systems for Lazis Scholarship UNNES
Permadi, Dimas Bayu Satria;
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.v7i1.21820
Lazis scholarship is a scholarship given to underprivileged students and does not yet have a system that supports the decisions to be taken. AHP is one of the most popular decision making methods in solving problems. But, AHP has several weaknesses. So that it will be modified based on previous research and the addition of fuzzy algorithms to get a better decision support system method. The results of this research were A009 students with the final result priority index value of 0.004176516 getting the first position. And the addition and modification in in this research is better than the standard decision support system. Fuzzy c-means produce scores that are more variable than manual grouping. Using sorting and ranking will produce a pairwise comparison matrix that is definitely consistent and has an average faster processing time is 0.028396 seconds, whereas with the standard method is 0.284415 seconds. Modification of alternative priorities also have a relatively faster average implementation time of 0.3165 seconds than the standard calculation with 2.6003 seconds. And modifications to the FPIV, if  taking the top 25 ranking in the standard FPIV produces 3 the same value while in the modified FPIV there is 1 same value.
Decision Making System to Determine Childbirth Process with Naïve Bayes and Forward Chaining Methods
Kumalasari, Putri Laksita;
Arifudin, Riza;
Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v7i2.25352
Childbirth is the last stage before the infant comes into the world. There may be incidents that could cause death in the process of childbirth for mothers and infants. Lack of knowledge and attention to the labor process can increase maternal mortality rate. Maternal mortality rate in Indonesia was recorded at 190 per 100,000 live births on 2015. The figure is still far from the fifth Millennium Development Goals target of 102 per 100,000. The increasing development of technology in health informatics to provide health care more effective can be used to help overcome the problems of pregnant women. To reduce maternal mortality rates, a web-based expert system is perfect one for use. Naïve Bayes method is a simple, fast and high accuracy method. Forward Chaining method is a inference method that performs a fact or statement that starts from the condition (IF) then to the conclusion (THEN). Based on analysis of the method obtained with 233 patients data on childbirth process using expert system, the Naïve Bayes method has accuracy in diagnosing by 90,987124463519% while Forward Chaining method accuracy is 86.69527897%.
Prediction of COVID-19 Using Recurrent Neural Network Model
Alamsyah, Alamsyah;
Prasetiyo, Budi;
Hakim, M. Faris Al;
Pradana, Fadli Dony
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/sji.v8i1.30070
The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN). In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. The research results show the percentage of accuracy is 88.
Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library
Yulianto, Muhamad Maulana;
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.v5i1.14148
Nowadays internet technology provide more convenience for searching information on a daily. Users are allowed to find and publish their resources on the internet using search engine. Search engine is a computer program designed to facilitate a user to find the information or data that they need. Search engines generally find the data based on keywords they entered, therefore a lot of case when the user can’t find the data that they need because there are an error while entering a keyword. Thats why a search engine with the ability to detect the entered words is required so the error can be avoided while we search the data. The feature that used to provide the text suggestion is autocomplete and spell checking using Levenshtein distance algorithm. The purpose of this research is to apply the autocomplete feature and spell checking with Levenshtein distance algorithm to get text suggestion in an error data searching in library and determine the level of accuracy on data search trials. This research using 1155 data obtained from UNNES Library. The variables are the input process and the classification of books. The accuracy of Levenshtein algorithm is 86% based on 1055 source case and 100 target case.