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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

Abstract

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 
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

Abstract

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. 
Implementasi Logika Fuzzy Mamdani untuk Mendeteksi Kerentanan Daerah Banjir di Semarang Utara Arifin, Saiful; Muslim, Much Aziz; Sugiman, Sugiman
Scientific Journal of Informatics Vol 2, No 2 (2015): November 2015
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v2i2.5086

Abstract

Kerentanan (Vuinerability) adalah keadaan atau kondisi yang dapat mengurangi kemampuan masyarakat untuk mempersiapkan diri menghadapi bahaya atau ancaman bencana. Logika Fuzzy adalah cara untuk memetakan suatu ke dalam suatu ruang output. Salah satu aplikasi logika Fuzzy adalah untuk menentukan kerentanan daerah banjir di Semarang Utara. Pengujian dilakukan dengan metode Mamdani Fuzzy Inference System. secara manual dan program menggunakan 5 defuzifikasi, yaitu Centroid, SOM (Smallest Of Maximum), LOM (Large Of Maximum), MOM (Mean Of Maximum), Bisector. Dari 2 contoh kasus diperoleh hasil pengujian dengan kesimpulan yang sama. 
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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Abstract

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.
K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor Safri, Yofi Firdan; 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.v5i1.12057

Abstract

Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.14244

Abstract

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%.
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

Abstract

Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semesters. 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.
PENINGKATAN AKURASI PADA ALGORITMA C4.5 MENGGUNAKAN ADABOOST UNTUK MEMINIMALKAN RESIKO KREDIT Nurzahputra, Aldi; Muslim, Much Aziz
Prosiding SNATIF 2017: Prosiding Seminar Nasional Teknologi dan informatika (BUKU 2)
Publisher : Prosiding SNATIF

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Abstract

AbstrakTingkat akurasi dalam penilaian risiko pemohon kredit sangat penting bagi organisasi pemberi pinjaman. Data pemohon kredit yang besar dapat diolah menjadi informasi yang dapat digunakan sebagai pendukung keputusan dalam menentukan permohoanan kredit. Pengolahan data tersebut termasuk dalam bidang data mining.Salah satu metode yang dapat diterapkan dalam permohonan kredit, yaitu klasifikasi. Terdapat beberapa algoritma klasifikasi salah satunya yaitu pohon keputusan atau decision tree. Algoritma decision tree yang terkenal ialah C4.5. Algoritma C4.5 dapat diterapkan dalam mengklasifikasi permohonan kredit. Penelitian ini menggunakan German Credit Card dataset. Adapun tujuan penelitian ini yaitu meningkatkan akurasi dari algoritma C4.5 dengan menerapkan adaboost dalam mengklasifikasi permohonan kredit dengan membandingkan hasil sebelum dan sesudah diterapkan adaboost. Validasi dalam penelitian ini menggunakan 10 fold cross validation. Sedangkan pengukuran akurasi diukur dengan confussion matrix. Hasil percobaan menunjukan terdapat peningkatan akurasi 3.7%. Akurasi penerapan algoritma C4.5 saja mencapai 70.5%. Sedangkan akurasi pnerapan algoritma C4.5 dengan adaboot mencapai 74.2%. Kata Kunci:C4.5, Adaboost, Data Mining, German Credit Card.
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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Abstract

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
Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier Trihanto, Wandha Budhi; 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.v4i2.11876

Abstract

The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In its development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Nave Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%.