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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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.

DECISION SUPPORT SYSTEM BASED ON BENEFIT COST RATIO METHOD FOR PROJECT TENDER Rukmana, Siti Hardiyanti; Muslim, Much Aziz
APTIKOM Journal on Computer Science and Information Technologies Vol 2 No 1 (2017): APTIKOM Journal on Computer Science and Information Technologies (CSIT)
Publisher : APTIKOM Publisher

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The procurement process became one of the important aspects for PT. PLN (Persero) to operate thecompany. One way to meet these needs is through the project tender. The tender process aims to get high-gradematerials with the lowest prices that meet the criteria of efficiency PT. PLN (Persero). In order to simplify thebidding process required a decision support system. The method used in this system is Benefit Cost Ratio (BCR).Input in this application are the documents and the tender offer price from bidders with complete tender documentsthat have been validated by prospective bidders and then selected by the tender committee to make an assessment andvalidation winner. The output of this process is the winner of the tender project based on calculations Benefit CostRatio (BCR). Therefore, the method Benefit Cost Ratio (BCR) can be used as a decision support system to determinethe winner of the project tender.

PENERAPAN ADABOOST UNTUK KLASIFIKASI SUPPORT VECTOR MACHINE GUNA MENINGKATKAN AKURASI PADA DIAGNOSA CHRONIC KIDNEY DISEASE Listiana, Eka; Muslim, Much Aziz
Prosiding SNATIF 2017: Prosiding Seminar Nasional Teknologi dan informatika (BUKU 3)
Publisher : Prosiding SNATIF

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AbstrakDatabase masa kini berkembang dengan sangat pesat khususnya dalam bidang kesehatan. Data tersebut apabila tidak diolah dengan baik maka akan menjadi sebuah tumpukan data yang tidak bermanfaat, sehingga perlu adanya proses untuk mengolah data tersebut menjadi sebuah informasi yang bermanfaat. Proses tersebut biasa disebut dengan data mining yang merupakan suatu bidang ilmu penelitian yang mampu mengolah database menjadi pengetahuan yang dapat dimanfaatkan khusunya dalam penelitian ini akan digunakan untuk mendiagnosa penyakit, diantaranya chronic kidney disease. Salah satu metode data mining yang digunakan untuk memprediksi sebuah keputusan dalam suatu hal adalah klasifikasi, di mana dalam metode klasifikasi ada algoritma support vector machine yang bisa digunakan untuk mendiagnosa chronic kidney disease. Dalam penelitian ini untuk meningkatkan akurasi algoritma support vector machine dalam mendiagnosa chronic kidney disease menggunakan adaptive boosting (adaboost) sebagai ensemble learning dengan pemilihan kernel, nilai parameter C, dan iterasi yang sesuai. Dari hasil percobaan, menerapkan adaboost, dengan kernel linier dan pemilihan nilai parameter C pada algoritma support vector machine dalam mendiagnosa chronic kidney disease menunjukkan bahwa tingkat akurasi mempunyai peningkatan sebesar 37% dengan pemaparan hasil seperti berikut, 62,5% (SVM); 97,75% (SVM+linier kernel); 99,5% (SVM+linier kernel +adaboost). Kata Kunci: adaboost, data mining, SVM, Adaptive boosting, chronic kidney disease

Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs Perbawawati, Anna Adi; Sugiharti, Endang; Muslim, Much Aziz
Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.14149

The development of technology capable to imitating the process of human thinkingÂ and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs. Â Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy

Implementasi Cloud Computing Menggunakan Metode Pengembangan Sistem Agile Muslim, Much Aziz; Retno, Nur Astri
Scientific Journal of Informatics Vol 1, No 1 (2014): May 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i1.3639

Cloud computing merupakan sebuah teknologi yang menyediakan layanan terhadap sumber daya komputasi melalui sebuah jaringan. Sumber daya yang di sediakan di dalam cloud computing meliputi mesin, media penyimpanan data, sistem operasi dan program aplikasi. Fitur dari cloud computing dipercaya akan jauh lebih hemat dan memuaskan. Masalah yang muncul adalah bagaimana mengimplementasi Cloud Computing dengan menggunakan Windows Azure Pack dan bagaimana provisioning Windows Azure Pack SQL Database. Fokus pada penelitian ini adalah pada proses deploying dan provisioning SQL Database Server. Pengimplementasian cloud computing menggunakan metode pengembangan sistem agile dengan langkah-langkah meliputi perencanaan, implementasi, pengujian (test), dokumentasi, deployment dan pemeliharaan. Untuk menjalankan proses tersebut kebutuhan perangkat yang dipersiapkan meliputi perangkat keras seperti PC Server Cisco UCS C240 M3S2, Hardisk 8753 GB, 256 GB RAM, bandwith minimal 1 Mbps dan kebutuhan perangkat lunak meliputi Windows Server 2012 R2, VMM, Windows Azure Pack, IIS, SQL Server 2012 dan Web Patform Installer. Hasil dari implementasi cloud computing menggunakan metode pengembangan sistem agile adalah terbentuknya sebuah sistem cloud hosting provider dengan menggunakan Windows Azure Pack dan SQL Server 2012 sebagai sistem utama dan pengelolaan database menggunakan Microsoft SQL Server Management

Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO) Priliani, Erlin Mega; Putra, Anggyi Trisnawan; Muslim, Much Aziz
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.14613

Data mining is the process of finding patterns or interesting information in selected data by using a particular technique or method. Utilization of data mining one of which is forecasting. Various forecasting methods have progressed along with technological developments. Support Vector Regression (SVR) is one of the forecasting methods that can be used to predict inflation. The level of accuracy of forecasting is determined by the precision of parameter selection for SVR. Determination of these parameters can be done by optimization, to obtain optimal forecasting of SVR method. The optimization technique used is Weight Attribute Particle Swarm Optimization (WAPSO). The use of WAPSO can find optimal SVR parameters, so as to improve the accuracy of forecasting. The purpose of this research is to implement SVR and SVR-WAPSO to predict the inflation rate based on Consumer Price Index (CPI) and to know the level of accuracy. The data used in this study is CPI Semarang City period January 2010-February 2018. Implementation experiments using Netbeans 8.2 gives results, SVR method has an accuracy of 94.654%. SVR-WAPSO method has an accuracy of 97.459%. Thus, the SVR-WAPSO method can increase the accuracy of 2,805% of a single SVR method for inflation rate forecasting. This research can be used as a reference for the next researcher can make improvements in determining the range of SVR parameters to get the value of each parameter more effective and efficient to get more optimal accuracy.

Implementation of Decision Tree and Dempster Shafer on Expert System for Lung Disease Diagnosis Alfatah, Abdul Muis; Arifudin, Riza; Muslim, Much Aziz
Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i1.13440

The expert system is a computer system that contains set of rules to solve problems like an expert. The lungs are one of the vulnerable respiratory organs. The purpose of this research is to implement decision tree and dempster shafer method on lung disease diagnosis and measure the accuracy of the system. The symptom was searched using forward chaining decision tree and the diagnosis was calculated using dempster shafer method. Dempster Shafer method calculates the possibility of a lung disease based on the density of probability value that possessed by each symptom. This research used 65 data obtained from medical record of Puskesmas Tegowanu Grobogan Regency. General symptoms and types of disease are used as a variable. Based on the results of the study, it can be concluded that the results of the diagnosis using dempster shafer method has an 83.08% accuracy.

Implementation of Analytic Network Process Method on Decision Support System of Determination of Scholarship Recipient at House of Lazis Charity UNNES Rahmanda, Primana Oky; Arifudin, Riza; Muslim, Much Aziz
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.11852

The scholarship is one of the forms of giving/ rewarding funds to individuals or students to use for sustainability during their education. Scholarships are awarded as government or institutional efforts to ease the burden of students in meeting the need for increasingly expensive education costs. The mechanism for selecting scholarship recipients, the selection team of UNNES Charity House of Lazis still use the scoring of the scholarship scores manually based on the total sum of criteria assessment without considering the priority weighted value of each criterion. So that cause the disbursement of scholarship funds that are not on target. To solve the problem, it is necessary to apply a decision support system to help provide consideration of the award of the scholarship recipient. Decision support system used requires data as a guidance assessment in the form of data criteria and alternative data by implementing Analytic Network Process method. The ANP method is used to determine the criteria and alternate priority weight values and the results are rankings. The purpose of this research is to build and implement ANP method in decision support system of awarding of scholarship recipients. The criteria used include the work of parents, parent income, the amount/ grade of Single Tuition, grade point average cumulative. The results of this study indicate that the use of ANP method implementation can determine the scholarship recipients who declared feasible or not to receive the scholarship based on the ranking results of the priority weight of the alternative.

Penyajian Data Pelanggan pada Lima Area PT. Telekomunikasi Indonesia, Tbk. Kandatel Pekalongan Menggunakan Google Earth Muslim, Much Aziz; Pramesti, Atikah Ari
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i2.4026

Prosedur sistem penyajian data pelanggan di PT. Telekomunikasi Indonesia, Tbk. Kandatel Pekalongan khususnya bidang Divisi Business Services masih menggunakan cara manual, hanya menggunakan media Micorsoft Excel. Dalam hal ini peneliti ingin menerapkannya dalam bentuk aplikasi Google Earth untuk membuat penyajian data pelanggan, karena Google Earth dapat memetakan bumi dari superimposisi gambar yang dikumpulkan dari pemetaan satelit, fotografi udara dan globe GIS tiga dimensi sehingga akan menghasilkan data yang akurat. Penyajian data dengan menggunakan Google Earth dilakukan dengan memanfaatkan bahasa markup HTML. Dengan cara ini, Divisi Business Service akan menjadi lebih mudah ketika menyajikan data-data para pelanggan Telkom yang mencakup lima area yaitu Batang, Pekalongan, Pemalang, Tegal dan Brebes.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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.

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