Articles
<![CDATA[Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis ]]>
<![CDATA[Larasati, Ukhti Ikhsani]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.14244
<![CDATA[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%.]]>
<![CDATA[K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor ]]>
<![CDATA[Safri, Yofi Firdan]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v5i1.12057
<![CDATA[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.]]>
<![CDATA[The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas ]]>
<![CDATA[Astuti, Winda Try]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.16648
<![CDATA[The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of landslide data features. It conducted to the neuro fuzzy method in determining landslide prone areas. The distribution of training data and testing data was using 20 k-fold cross validation. The implementation of the neuro fuzzy method on landslide data was obtained an accuracy of 81.9231%. In the implementation of the neuro fuzzy method with information gain was conducted in classification process. The process will stop when the accuracy has decreased. The highest accuracy result was obtained of 88.489% by removing an attribute. So, it can be concluded the accuracy increase of 6.5659% in the implementation of the neuro fuzzy method and information gain in determination of landslide prone areas.]]>
<![CDATA[Expert System Diagnosis of Bowel Disease Using Case Based Reasoning with Nearest Neighbor Algorithm ]]>
<![CDATA[Vedayoko, Lucky Gagah]]>;
<![CDATA[Sugiharti, Endang]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v4i2.11770
<![CDATA[Expert System is a computer system that has been entered the base of knowledge and set of rules to solve problems like an expert. One method in the expert system is Case Based Reasoning. To strengthen the retrieve stage of this method, the Nearest Neighbor algorithm is used. Bowel is one of the digestive organs susceptible to disease. The purpose of this study is to implement expert systems using Case Based Reasoning with Nearest Neighbor algorithm in diagnosing bowel disease and determine the accuracy of the system. Data used in this research are 60 data, obtained from medical record RSUD dr. Soetrasno Rembang. Variables used are general symptoms and types of diseases. The level of system accuracy resulting from scenario are 40 data as source case, and 20 data as target case that is equal to 95%.]]>
<![CDATA[Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier ]]>
<![CDATA[Trihanto, Wandha Budhi]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v4i2.11876
<![CDATA[The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In it’s 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 Naïve 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%.]]>
<![CDATA[Implementasi Logika Fuzzy Mamdani untuk Mendeteksi Kerentanan Daerah Banjir di Semarang Utara ]]>
<![CDATA[Arifin, Saiful]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Sugiman, Sugiman]]>
<![CDATA[ Scientific Journal of Informatics Vol 2, No 2 (2015): November 2015 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v2i2.5086
<![CDATA[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. ]]>
<![CDATA[SISTEM DETEKSI DINI HAMA WERENG BATANG COKLAT MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION ]]>
<![CDATA[Amin, Saiful]]>;
<![CDATA[Alamsyah, Alamsyah]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Unnes Journal of Mathematics Vol 1 No 2 (2012) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v1i2.1066
<![CDATA[Aplikasi penggunaan jaringan syaraf tiruan (JST) melalui pengenalan pola terjadinya sesuatu telah banyak dikaji dalam berbagai bidang ilmu pengetahuan.JST mampu memberikan hasil keputusan berdasarkan data yang dilatihkan. Dengan memanfaatkan JST backpropagation, dibuatlah sebuah sistem deteksi dini hama wereng batang coklat dengan menggunakan software MATLAB. Setelah dilakukan pengujian, sistem menunjukkan bahwa kecepatan dan kelambatan pembelajaran jaringan syaraf tiruan dipengaruhi oleh variasi jumlah neuron hidden layer dan learning rate. Selain itu juga berpengaruh terhadap nilai keakuratan sistem dalam mendeteksi hama wereng batang coklat.]]>
<![CDATA[ANALISIS MODEL ANTRIAN PADA PERBAIKAN SEPEDA MOTOR DENGAN MNGGUNAKAN PROGRAM VISUAL BASIC ]]>
<![CDATA[Purnawan, Dedy]]>;
<![CDATA[Hendikawati, Putriaji]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Unnes Journal of Mathematics Vol 2 No 1 (2013) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v2i1.1732
<![CDATA[Penelitian dilakukan di Bengkel Yamaha Motor Dewi Sartika Sampangan Semarang, dengan mengambil data primer selama 3 hari pada hari dan waktu sibuk yang dipilih secara random yaitu pada tanggal 12 Juni, 15 Juni, dan 16 Juni 2012. Variabel yang digunakan adalah data waktu kedatangan pelanggan dan data lama pelayanan mekanik. Sistem antrian pada Bengkel Yamaha Motor pada ketiga tanggal tersebut mengikuti model (M/G/5//). Nilai faktor kegunaan pelayanan sebesar 0,8. Waktu tunggu rata-rata dalam antrian yang terlama yaitu sebesar 34 menit 48 detik. Banyaknya pelanggan terbanyak dalam antrian sebesar 4 pelanggan. Banyaknya pelanggan terbanyak dalam sistem sebesar 9 pelanggan. Waktu tunggu rata-rata yang dihabiskan seorang pelanggan terlama di dalam sistem sebesar 93 menit 30 detik. Perhitungan model antrian dapat dilakukan lebih cepat dengan bantuan program visual basic]]>
<![CDATA[SISTEM INFORMASI PENJUALAN BERBASIS WEB PADA STOCKIST CENTRE PT K-LINK INDONESIA CABANG PEKALONGAN ]]>
<![CDATA[Raharjo, Bagus Purbo]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Arini, Florentina Yuni]]>
<![CDATA[ Unnes Journal of Mathematics Vol 3 No 2 (2014) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v3i2.4300
<![CDATA[Sistem informasi penjualan berbasis web merupakan satu cara yang dapat digunakan untuk memonitoring hasil penjualan pada suatu perusahaan. Tujuan dalam tulisan ini adalah menggunakan sistem informasi penjualan berbasis web pada Stockist Centre PT K-Link Indonesia cabang Pekalongan agar membantu pihak perusahaan dan pemilik stockist untuk menyusun laporan penjualan menjadi cepat, tepat, dan lebih efisien, serta memudahkan konsumen dalam melakukan transaksi jual beli produk tanpa harus datang ke Stockist Centre PT Klink di Pekalongan. Metode yang digunakan yaitu studi kasus, perumusan masalah, pemecahan masalah, analisis data dengan menggunakan PHP dan MySQL, dan penarikan kesimpulan. Hasil dari penerapan sistem informasi penjualan berbasis web ini yaitu pemilik perusahaan akan lebih mudah dalam memonitoring transaksi jual beli produk pada Stockist Centre PT K-Link Indonesia cabang Pekalongan. Sistem informasi penjualan ini di buat dengan model pengembangan sistem waterfalls model dan di buat dengan bahasa pemrograman PHP dengan basis data MySQL. Dengan hasil tulisan ini, PT K-Link Indonesia cabang Pekalongan dapat menggunakan website ini dalam melakukan transaksi jual beli produk.]]>
<![CDATA[SISTEM PREDIKSI TAGIHAN LISTRIK USAHA JASA LAUNDRY MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION ]]>
<![CDATA[Maulana, Muhamad Irvan]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Unnes Journal of Mathematics Vol 4 No 1 (2015) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v4i1.7419
<![CDATA[Tujuan dari penelitian ini adalah untuk membuat suatu sistem yang dapat memprediksi besar tagihan listrik bulanan untuk usaha laundry. Perkembangan usaha laundry ini membuat banyak masyarakat ingin membuka usaha ini. Metode yang digunakan pada penelitian ini menggunakan jaringan syaraf tiruan backpropagation dengan algoritma pelatihan Levenberg-Marquardt. Metode Levenberg-Marquardt merupakan salah satu metode optimasi untuk menyelesaikan masalah kuadrat terkecil, sehingga metode ini akan mencapai kekonvergenan yang lebih baik. Dengan memanfaatkan jaringan syaraf tiruan backpropagation algoritma pelatihan Levenberg-Marquardt, dibuatlah sebuah system prediksi tagihan listrik usaha jasa laundry menggunakan software MATLAB untuk memberikan informasi besar tagihan listrik berdasarkan jumlah pakaian yang diproses serta curah hujan bulan yang akan diprediksi. Setelah dilakukan pengujian, sistem menunjukkan bahwa tingkat akurasi pembelajaran jaringan syaraf tiruan dipengaruhi oleh variasi jumlah neuron hidden layer dan learning rate. Berdasarkan pengujian, error sistem dalam memprediksi tagihan listrik berdasarkan Mean Absolute Percentage Error (MAPE) terendah adalah 3,52% atau memiliki tingkat akurasi sebesar 96,48%.]]>
