Articles
<![CDATA[MODEL REKOMENDASI BERBASIS FUZZY UNTUK PEMILIHAN SEKOLAH LANJUTAN TINGKAT ATAS ]]>
<![CDATA[Shofwatul ‘Uyun]]>;
<![CDATA[Yuni Madikhatun]]>
<![CDATA[ Jurnal Informatika Vol 5, No 1: January 2011 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/jifo.v5i1.a2794
<![CDATA[Sekolah adalah sebuah lembaga yang dirancang untuk pengajaransiswa dibawah pengawasan guru. Sebagian besar negara memiliki sistempendidikan formal yang umumnya bersifat wajib. Pada setiap tahun, siswaakan mencari sekolah lanjutan yang cocok untuk dirinya. Persoalan munculketika terdapat banyak pilihan sekolah yang memberikan beragam tawarandan pilihan kepada calon siswanya. Hal ini memungkinkan para calon siswamengalami kesulitan dalam menentukan pilihan sekolah yang tepat. Olehkarena itu diperlukan sebuah sistem yang tidak hanya memberikan informasitentang sekolah saja, melainkan juga mampu memberikan rekomendasisekolah berdasarkan kriteria yang diinginkan dari masing-masing calonsiswa. Model rekomendasi yang yang digunakan adalah fuzzy model tahani.Secara umum, logika fuzzy dapat menangani faktor ketidakpastian denganbaik sehingga dapat diimplementasikan untuk rekomendasi pemilihansekolah. Penelitian ini mengambil data sekolah lanjutan tingkat atas (SMAdan MA) di kota Yogyakarta. Sistem yang dibangun selain mampumemberikan rekomendasi juga dapat digunakan sebagai bahan pertimbangandalam memilih sekolah. Sekolah yang direkomendasikan tentunya sekolahyang memenuhi kriteria yang diinginkan oleh para calon siswa.]]>
<![CDATA[MULTI-LAYER INFERENCE FUZZY TSUKAMOTO DETERMINING LAND SUITABILITY CLASS OF COCOA PLANTS ]]>
<![CDATA[Iin Intan Uljanah]]>;
<![CDATA[Shofwatul Uyun]]>
<![CDATA[ JURNAL TEKNIK INFORMATIKA Vol 14, No 1 (2021): JURNAL TEKNIK INFORMATIKA ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah ]]>
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DOI: 10.15408/jti.v14i1.13616
<![CDATA[Determining the land suitability class of plants specifically cocoa (Theobroma cacao) is significant to do because each plant has a different characteristic of growth. This research aims at implementing the algorithm to determine the land suitability class of cocoa plants using the Multi-Layer Inference Fuzzy Tsukamoto (MLIFT). This research uses 18 input variables including 15 non-linguistic variables or crisp and the rest are linguistic ones or fuzzy as the data of growth requirements of cocoa plants. Generally, the algorithm used consists of three main steps those are fuzzification, Tsukamoto inference machine, and defuzzification consisting of three layers. The first layer covers seven inference engines, while each of the second and the third ones only consists of one inference engine. The concept of inference process in Fuzzy Tsukamoto is calculating the weighted average of each result of the nference process. Based on the testing result, it can be concluded that the multi-layer inference Fuzzy Tsukamoto for determining the land suitability class of cocoa plants has an accuracy level amounted 97%.]]>
<![CDATA[SISTEM INFERENSI FUZZY MAMDANI UNTUK PENGHITUNGAN BONUS KARYAWAN PT. ABC ]]>
<![CDATA[Sherly Andini]]>;
<![CDATA[Maria Ulfah Siregar]]>;
<![CDATA[Shofwatul Uyun]]>;
<![CDATA[Nurochman Nurochman]]>
<![CDATA[ JURNAL TEKNIK INFORMATIKA Vol 14, No 2 (2021): JURNAL TEKNIK INFORMATIKA ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah ]]>
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DOI: 10.15408/jti.v14i2.14180
<![CDATA[A bonus in a company is an appreciation of the company for its employees for their dedication to work. Giving the bonus is sometimes prone to subjektives, not relevant to work pertformance, and etc. This is also impelemented in PT. ABC, which rewards employees for their performance. The calculation of employee bonuses at PT. ABC still uses spreadsheet tool so that the results of calculating employee bonuses tend to be subjective and be human error in inputting complex formula. Therefore, to get the suitable employee bonus calculation results, PT. ABC requires a specific computer system for the employee bonus calculation. This research uses Fuzzy Inference System Mamdani method because Mamdani method is often used for fuzzy logic control problems and is accordance with the process of input of human information. In Mamdani method, there are four stages, namely the formation of fuzzy sets, application of implications function, composition of rules and defuzzyfication. Our Mamdani method was designed upon 27 rules which is likely adding complexity and temptation on human. Calculations on a system are tidier and more structureable rather than on spreadsheet tool. The system which is based on web could run almost everywhere as long as there is internet connection. The results of this study indicate that computer calculations result the same as manual calculations by hand. Functionality testing show that the system is functioning 100% and the system access test shows that 60% of respondents strongly agree and 40% of respondents agree with the ease of the system.]]>
<![CDATA[Sistem rekomendasi peminatan peserta didik baru pada kurikulum K-13 menggunakan metode profile matching, simple additive weighting, dan kombinasi keduanya ]]>
<![CDATA[Muhammad Edi Iswanto]]>;
<![CDATA[Maria Ulfah Siregar]]>;
<![CDATA[Shofwatul 'Uyun]]>;
<![CDATA[Muhammad Taufiq Nuruzzaman]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 2, Year 2021 (April 2021) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2021.13902
<![CDATA[Peminatan peserta didik dalam kurikulum 2013 dilakukan sebelum peserta didik memulai belajar di kelas X. Ketepatan dalam penentuannya diperlukan untuk memastikan peserta didik belajar sesuai dengan minat dan bakat yang dimiliki. Penelitian ini menerapkan tiga metode SPK, yaitu profile matching, SAW dan kombinasi keduanya, untuk memberikan rekomendasi peminatan siswa didik ini. Ketiga metode tersebut dikomparasikan menggunakan alternatif dan kriteria yang sama untuk mengetahui metode yang paling dominan. Hasil penelitian ini menunjukkan bahwa penerapan SPK dapat membantu kegiatan PPDB dengan akurasi 79,2 %. Dalam proses penentuan minat bagi peserta didik, metode kombinasi menjadi yang paling dominan dengan persentase sebesar 78 %. Penerapan SPK tidak hanya membantu proses peminatan menjadi lebih cepat, tetapi juga akurat. Hal ini dibuktikan dengan hanya terdapat 6 dari total 122 peserta didik yang memilih peminatan berdasarkan rekomendasi SPK mendapatkan nilai di bawah KKM.]]>
<![CDATA[Analisis Kinerja Algoritma Fuzzy C-Means dan K-Means pada Data Kemiskinan ]]>
<![CDATA[Aniq Noviciatie Ulfah]]>;
<![CDATA[Shofwatul ‘Uyun]]>
<![CDATA[ JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 1 No 2 (2015): JATISI MARET 2015 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP ]]>
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DOI: 10.35957/jatisi.v1i2.30
<![CDATA[One of the local government of Gunungkidul efforts to realize program in order to alleviate poverty is to perform the data collection poverty of its citizens. The local government of Gunungkidul has formulated a collection by weighting against 15 indicators into three groups. The amount of data and indicators to be used will certainly lead to difficulties in implementation, ineffective and less objective. Therefore we need automation in the process of clustering data on poverty. This study aims to analyze the performance of the FCM algorithm and K-means are implemented in the data on poverty in Girijati Purwosari village into 3 clusters. Some of the steps that must be done prior to clustering, first performed pretreatment includes data cleaning and data transformation for clustering is then performed using the second algorithm. The suitability of data between FCM algorithm and the calculation of poverty indicators in the Girijati village is 50 % and for the K - Means algorithm is 83.33 % . Therefore, K- Means algorithm is more appropriately used in data classification of poverty based on the three criteria of poverty, beside FCM algorithm.]]>
<![CDATA[Perbandingan Kinerja Jaringan Syaraf Tiruan Dan Fuzzy Inference System Untuk Prediksi Prestasi Peserta Didik ]]>
<![CDATA[Siti Helmiyah]]>;
<![CDATA[Shofwatul ‘Uyun]]>
<![CDATA[ JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 4 No 1 (2017): JATISI SEPTEMBER 2017 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP ]]>
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DOI: 10.35957/jatisi.v4i1.85
<![CDATA[Achievement is a result of someone who excels in any field. In the educational world, achievement is often associated with academic value that serve as a reference for learners say in academic achievement. Manual data processing takes long time. It is necessary to use the achievements of predictive computing system that can helpful for the prediction process. Data taken from MAN Model Palangkaraya form of eleven subjects of UAS when MTs value and the average value of one semester report cards when the Supreme Court. For the neural network backpropagation data is normalized with small intervals are [0.1, 0.9] and for data fuzzy inference system is the original data is multiplied 10. Then do the testing using neural networks and fuzzy inference system which will compare the results obtained. Based on data have been tested, the percentage of learners' achievements prediction on back propagation neural network in the training and validation process to produce a percentage of 100% with one hidden layer architecture, the optimal parameters MSE = 0.0001, learning rate = 0 , 9, momentum = 0.4. As for the prediction of learners' achievements in the fuzzy inference system mamdani method by using S curve and bell curve (PI curve) to produce a percentage of 83.8%.]]>
<![CDATA[PENGENALAN WAJAH DUA DIMENSI MENGGUNAKAN MULTI-LAYER PERCEPTRON BERDASARKAN NILAI PCA DAN LDA ]]>
<![CDATA[Shofwatul Uyun]]>;
<![CDATA[Muhammad Fadzlur Rahman]]>
<![CDATA[ Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 2 No 1 (2013): Komputa : Jurnal Ilmiah Komputer dan Informatika ]]>
Publisher : <![CDATA[Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM) ]]>
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DOI: 10.34010/komputa.v2i1.77
<![CDATA[Manusia memiliki kecerdasan multi intelligence yang sangat kompleks sehingga secara otomatis mampu mengenali seseorang yang pernah ditemui. Saat ini banyak sekali sistem pengenalan wajah yang sedang dikembangkan baik secara supervised maupun unsupervised. Jaringan Syaraf Tiruan (JST) merupakan salah satu metode supervised, dimana salah satu metode pembelajarannya disebut dengan Multi-Layer Perceptron (MLP). Penentuan banyaknya node pada hidden layer secara tepat mempengaruhi kinerja dari MLP pada sistem pengenalan wajah. Penelitian ini menggunakan 12 citra wajah sebagai data latih yang diekstraksi menjadi covarian matriks lalu diambil nilai eigen dari setiap data citra menggunakan metode principal component analysis (PCA) dan linear discriminant analysis (LDA). Setiap data menghasilkan 4 nilai eigen yang menjadi masukan pada algoritma pelatihan MLP yang menghasilkan nilai bobot optimal yang menjadi acuan untuk mengenali citra wajah. Berdasarkan hasil pengujian dan perbandingan variasi nilai parameter yang digunakan untuk mengenali citra wajah telah didapatkan nilai akurasi optimal sebesar 77,77%. Aristektur dari MLP tersebut terdiri atas : 4 node di input layer, 8 node di hidden layer dan 3 node di output layer dengan nilai epoch pelatihan sebesar 60x104.]]>
<![CDATA[Recommendation System for Giving Scholarships to New Students Using TOPSIS Method ]]>
<![CDATA[Nuri Guntur Perdana]]>;
<![CDATA[Muhammad Akid Musyafa]]>;
<![CDATA[Elvanisa Ayu Muhsina]]>;
<![CDATA[Shofwatul Uyun]]>
<![CDATA[ IJID (International Journal on Informatics for Development) Vol. 4 No. 2 (2015): IJID December ]]>
Publisher : <![CDATA[Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/ijid.2015.04204
<![CDATA[Yayasan Pondok Pesantren Wahid Hasyim (YPPWH) is one of the bases of modern Islamic education in Daerah Istimewa Yogyakarta (Special Region of Yogyakarta). As one of devotion to education and society, formal education institutions, namely Madrasah Ibtidaiyah (Religion Elementary School), Madrasah Tsanawiyah (Religion Junior High School) and Madrasah Aliyah (Religion Senior High School) Wahid Hasyim currently have scholarship opportunities for students who excel and underprivileged, especially those from the local population. With the increasing number of scholarship applicants, making a challenge for the manager of the agency to be able to give a correct decision, effective and efficient in data management Fellow truly eligible to receive a scholarship. This study uses a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The criteria used in these systems vary according to the scholarships provided by the Foundation. Based on the results of case selection calculations show that the results using the same system with manual calculations. The system is able to provide scholarships recommendation. This system has been passed fit for use for 100% testing of system functionality using the alpha test, the test results and accessing interface produces 65.15% of the respondents strongly agreed with the system interface, as well as the results of the test content for the page admin, counseling teachers and students each yield 88.89%, 83.33% and 100% of respondents stated strongly agree with making this system.]]>
<![CDATA[Comparison of Edge Detection Method in Case of Blood Pattern Recognition Using Backpropagation Algorithm ]]>
<![CDATA[Agung Nur Hidayat]]>;
<![CDATA[Ahmad Subhan Yazid]]>;
<![CDATA[Shofwatul Uyun]]>
<![CDATA[ IJID (International Journal on Informatics for Development) Vol. 3 No. 1 (2014): IJID May ]]>
Publisher : <![CDATA[Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/ijid.2014.%x
<![CDATA[There are 4 types of blood: A, B, O, and AB. So far, the process of checking blood type depends on the officer’s work accuracy. To keep the validity of the results, a system is needed to help humans to recognize the blood types. This recognition can be done by computers by applying the method of blood pattern recognition through an image. The data domain of this study is a scan of blood type checks obtained from PMI Yogyakarta City. A total of 54 images were used in the training and recognition process. The image used in .bmp extension with a size of 400 x 200 pixels. Before the recognition process, first execute the preprocessing image, that is convert the image to grayscale image. The next process is edge detection with a Sobel operator or Prewitt operator. The use of these two operators aim to determine the optimal operator for recognition of blood type case. After the edge detection process, the image is converted to binary so it can be processed by feature extraction. The last step is the implementation of artificial neural network backpropagation algorithm with bipolar sigmoid activation function for hidden layer and linear activation for output. As a result, the optimal neural network architecture is three hidden layers with each hidden layer having three nodes. The optimal value for the mean squared error parameter is 1e-1 or 0.1, epoch 1000 and learning rate 0.01. In this study, Sobel operator was better than Prewitt operator in introducing blood type types. When viewed from the difference in processing time, the Prewitt operator is slightly faster than the Sobel operator with a difference of 0.000052 seconds. From 39 training data and 14 test data, the percentage of success in the recognition of blood type was 92.86%.]]>
<![CDATA[Patient Data Clustering using Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC) ]]>
<![CDATA[Rosalia Susilowati]]>;
<![CDATA[Ahmad Subhan Yazid]]>;
<![CDATA[Shofwatul Uyun]]>
<![CDATA[ IJID (International Journal on Informatics for Development) Vol. 3 No. 1 (2014): IJID May ]]>
Publisher : <![CDATA[Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/ijid.2014.%x
<![CDATA[Generally, the current system development only include the input, view, and reports. At Jogja Hospital, a system with a patient database can only provide information about the percentage of male and female patients. Its unable to extract more specific information, even though medical record data has a lot of information. The complete information should be used as a reference for the authorities to make a decision. This information can be obtained by analyzing and processing the medical record data. One way to extract information from this data is clustering. The domain of this study is patient data. Before the data is clustered, preprocessing is needed through name standardization, numeration, and data normalization. During the clustering process, the algorithms used are Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC). Two algorithms are implemented to determine which algorithm is the most appropriate and fast to handle the processing of patient data. The results of the study show that the processing time required to do clustering with FCM algorithm is relatively faster than AHC algorithm. For data with small volumes, the iteration of FCM algorithm is more than AHC algorithm, however, the results of the clustering using FCM algorithm are easier to interpret than AHC algorithm. From the visualization of clustering results, found that the cluster pattern with FCM algorithm is better based on the three variables used as references. So the most suitable algorithm to use is Fuzzy C-Means (FCM) for processing patient data.]]>
