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<![CDATA[PENGGUNAAN FITUR ABSTRAKSI DAN CATATAN PUBLIKASI PENULIS UNTUK KLASIFIKASI ARTIKEL ILMIAH DENGAN METADATA YANG TERBATAS ]]>
<![CDATA[Sa'dyah, Halimatus]]>;
<![CDATA[Ulinnuha, Nurissaidah]]>
<![CDATA[ JUTI: Jurnal Ilmiah Teknologi Informasi Vol 11, No 1, Januari 2013 ]]>
Publisher : <![CDATA[Department of Informatics, Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/j24068535.v11i1.a18
<![CDATA[Bertumbuhnya jumlah artikel ilmiah membuka ranah penelitian baru di bidang optimasi klasifikasi dokumen berbasis metadata. Dalam ranah ini, persoalan pokok yang harus dijawab adalah bagaimana cara memanfaatkan fitur metadata yang terbatas untuk menghasilkan nilai presisi dan recall yang tinggi dalam proses klasifikasi artikel ilmiah. Dalam makalah ini diusulkan sebuah metode klasifikasi artikel ilmiah dengan menggunakan atribut abstraksi dan catatan publikasi penulis pada metada data sebagai fitur. Hasil uji coba menunjukkan bahwa sistem klasifikasi yang menggunakan abstraksi dan catatan publikasi penulis sebagai fitur menghasilkan nilai presisi tertinggi sebesar 0.87 dan recall 0.59 sedangkan sistem klasifikasi yang menggunakan abstraksi sebagai fitur menghasilkan nilai presisi 0.75 dan recall 0.51. Hasil uji coba juga menunjukkan bahwa nilai presisi dan recall dari sistem klasifikasi stabil ketika nilai.]]>
<![CDATA[Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method ]]>
<![CDATA[Utami, Tri Mar'ati Nur]]>;
<![CDATA[Novitasari, Dian Candra Rini]]>;
<![CDATA[Setiawan, Fajar]]>;
<![CDATA[Ulinnuha, Nurissaidah]]>;
<![CDATA[Farida, Yuniar]]>;
<![CDATA[Sari, Ghaluh Indah Permata]]>
<![CDATA[ Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v8i2.28906
<![CDATA[Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions. Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error. Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.]]>
<![CDATA[Comparative Study of Bankruptcy Prediction Models ]]>
<![CDATA[Isye Arieshanti]]>;
<![CDATA[Yudhi Purwananto]]>;
<![CDATA[Ariestia Ramadhani]]>;
<![CDATA[Mohamat Ulin Nuha]]>;
<![CDATA[Nurissaidah Ulinnuha]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 3: September 2013 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v11i3.1143
<![CDATA[ Early indication of Bankruptcy is important for a company. If companies aware of potency of their Bankruptcy, they can take a preventive action to anticipate the Bankruptcy. In order to detect the potency of a Bankruptcy, a company can utilize a model of Bankruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for Bankruptcy prediction. It is expected that the comparison result will provide insight about the robust method for further research. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP), Hybrid of MLP + Multiple Linear Regression), it can be concluded that fuzzy k-NN method achieve the best performance with accuracy 77.5%. The result suggests that the enhanced development of bankruptcy prediction model could use the improvement or modification of fuzzy k-NN.]]>
<![CDATA[Perbandingan Metode Single Linkage, Complete Linkage, dan Average Linkage pada Kesejahteraan Masyarakat pad a Kabupaten dan Kota di Jawa Timur ]]>
<![CDATA[Yanuwar Reinaldi]]>;
<![CDATA[Nurissaidah Ulinnuha]]>;
<![CDATA[Moh. Hafiyusholeh]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 1 (2021): September 2021 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v18i1.14228
<![CDATA[Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.]]>
<![CDATA[Prediksi Biaya Konsumsi Bahan Bakar Gas Menggunakan Metode Backpropagation Neural Network (Studi Kasus: PLTU PT. Pembangkit Jawa Bali Unit Pembangkitan Gresik) ]]>
<![CDATA[Uswatun Khasanah]]>;
<![CDATA[Nurissaidah Ulinnuha]]>
<![CDATA[ Jurnal Sains Matematika dan Statistika Vol 5, No 2 (2019): JSMS Juli 2019 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/jsms.v5i2.7630
<![CDATA[Dalam pembangkitan energi listrik diperlukan bahan bakar yang memadai karena bahan bakar merupakan komponen utama dalam pembangkitan energi listrik. Penggunaan bahan bakar yang efektif dan efisien tentu saja disesuaikan dengan kebutuhan beban yang diminta sehingga tidak ada energi yang terbuang ataupun kekurangan bahan bakar dalam proses pembangkitan. Maka dibutuhkan suatu perencanaan yang baik dengan melakukan prediksi terhadap biaya konsumsi bahan bakar gas yang dikeluarkan oleh Unit PLTU PT. PJB UP Gresik dengan menggunakan kecerdasan buatan yaitu metode Backpropagation Neural Network. Hasil Prediksi biaya konsumsi bahan bakar gas pada Unit PLTU PT. PJB UP Gresik selama tahun tahun 2019 yaitu sebesar Rp 379.039.171.701 dengan MAPE sebesar 10.418%.]]>
<![CDATA[Analisis Cluster dalam Pengelompokan Provinsi di Indonesia Berdasarkan Variabel Penyakit Menular Menggunakan Metode Complete Linkage, Average Linkage dan Ward ]]>
<![CDATA[Nurissaidah Ulinnuha]]>;
<![CDATA[Rafika Veriani]]>
<![CDATA[ InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 5, No 1 (2020): InfoTekJar September ]]>
Publisher : <![CDATA[Universitas Islam Sumatera Utara ]]>
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DOI: 10.30743/infotekjar.v5i1.2464
<![CDATA[Penyakit adalah salah satu indikator dalam indeks pembangunan manusia bidang kesehatan. Mengingat bahwa pembangunan bidang kesehatan di Indonesia sedang mengalami beban ganda dimana penyakit menular masih menjadi masalah yang belum dapat diselesaikan, dan masih terdapat penyakit menular yang awalnya masih mampu dikendalikan kini muncul kembali. Hal ini seharusnya mendapatkan perhatian lebih khususnya bagi Dinas Kesehatan maupun Kementerian Kesehatan Republik Indonesia mengenai penyebaran penyakit menular ataupun penyakit tidak menular. Salah satu upaya yang dapat dilakukan adalah dengan membentuk suatu pengelompokan provinsi dalam suatu kelompok yang memiliki karakteristik yang sama dengan maksud memberikan informasi terkait dengan kesehatan pada masing-masing provinsi. Pada penelitian ini bertujuan untuk membentuk suatu cluster provinsi di Indonesia berdasarkan variabel jenis penyakit menggunakan metode Complete Linkage, Average Linkage, dan Ward. Ukuran jarak yang digunakan dalam penelitian ini adalah jarak Euclidean dan Squared Euclidean, dan untuk menentukan cara kerja metode yang terbaik dengan melihat dari nilai simpangan baku dalam kelompok (Sw) yang minimum, simpangan baku antar kelompok (Sb) yang maksimum, dan rasio Sw terhadap Sb yang minimum. Hasil analisis cluster yang terbaik adalah dengan metode Ward menggunakan 6 cluster dengan nilai Sw sebesar 0,18405, Sb sebesar 2,12284 serta rasio Sw terhadap Sb sebesar 0,08670.]]>
<![CDATA[Regency grouping in East Java based on Variable Type of Agriculture uses Hybrid Hierarchical Clustering Via Mutual Cluster Method ]]>
<![CDATA[Sulthan Fikri Mu'afa]]>;
<![CDATA[Nurissaidah Ulinnuha]]>
<![CDATA[ InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020) ]]>
Publisher : <![CDATA[Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah ]]>
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DOI: 10.15408/inprime.v2i1.14167
<![CDATA[AbstractEast Java Province is one of the provinces that has the largest agricultural resources in Indonesia. The Government of East Java needs to produce superior commodities in each region. This study aims to group districts in East Java Province based on variable types of agriculture with the hybrid hierarchical clustering via mutual cluster method that combines the merging of bottom-up clustering advantages and top-down clustering advantages. Mutual cluster is a grouping with the largest distance between small groups of the shortest distance for each point outside the group. In this research, the calculation uses Euclidean distance. The data used in this study are from the East Java Central Statistics Agency (BPS) in 2017. The division calculation is obtained by finding the minimum (standard deviation of intra cluster) value and the maximum (standard deviation of inter clusters) value and using the analysis of variance calculation. The grouping results obtained were nine groups with value of 725.934, value of 1.475.978 and value of 7,908.Keywords: agriculture; Hybrid Hierarchical Clustering; mutual cluster; Euclidean distance; analysis of variance. AbstrakProvinsi Jawa Timur merupakan salah satu provinsi yang memiliki sumber daya pertanian terbesar di Indonesia. Pemerintah Jawa Timur perlu mengembangkan komoditi unggulan di tiap daerah di Jawa Timur. Penelitian ini bertujuan untuk mengelompokkan kabupaten di Provinsi Jawa Timur berdasarkan variabel jenis pertanian dengan metode hybrid hierarchical clustering via mutual cluster yaitu menggabungkan kelebihan bottom-up clustering dan kelebihan top-down clustering. Mutual cluster yakni pengelompokkan dengan jarak terbesar antara bagian dalam kelompok yang kecil dari jarak yang terpendek kepada tiap titik di luar kelompok. Dalam penelitian ini, perhitungan jarak menggunakan jarak Euclidean. Data yang digunakan dalam penelitian ini dari Badan Pusat Statistik Jawa Timur tahun 2017. Perhitungan pembagian didapat dengan mencari nilai (simpangan baku dalam klaster) yang minimal dan nilai (simpangan baku antar klaster) yang maksimal, serta digunakan perhitungan analyze of varians. Hasil pengelompokkan yang diperoleh didapatkan sebanyak sembilan kelompok dengan nilai sebesar 725.934, nilai sebesar 1.475.978 dan nilai sebesar 7,908.Kata Kunci: pertanian; Hybrid Hierarchical Clustering; mutual cluster; jarak Euclid; analisis variansi.]]>
<![CDATA[Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah ]]>
<![CDATA[Siti Aisyah]]>;
<![CDATA[Nurissaidah Ulinnuha]]>;
<![CDATA[Abdulloh Hamid]]>
<![CDATA[ KUBIK Vol 7, No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika ]]>
Publisher : <![CDATA[Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/kubik.v7i2.20460
<![CDATA[The need for crude palm oil has increased due to the large demand for vegetable oils in various parts of the world. Beginning in March 2022, the price of crude palm oil set a record high which caused international cooking oil prices to soar, especially for Indonesia. This study aims to predict the price of crude palm oil with test parameters, namely hidden neurons and activation functions. The method used is Extreme Learning Machine (ELM). This method is a development of the artificial neural network (ANN) method which can overcome weaknesses in the learning speed process. There are several stages in this study: (1) pre-processing the data by normalizing the data and dividing the data using the time series split method, (2) analyzing the data using the ELM method by testing parameters, namely hidden neurons and activation functions, (3) analyzing the results of the best parameter trials, (4) calculating forecasting data using the best parameters that have been obtained, and (5) analyzing the forecasting results that have been obtained. This study uses daily data on the price of crude palm oil from April 1 2021 to April 14 2022 obtained from the Investing website. The results of the research that has been carried out obtained MAPE and RMSE values of 0.0173 and 0.0308 with the best parameters namely the number of hidden neurons of 5 and the binary sigmoid activation function. Based on the results obtained, it is hoped that it will make it easier for the government to determine the price of crude palm oil in the future.]]>
<![CDATA[Klusterisasi Penyandang Masalah Kesejahteraan Sosial (PMKS) Di Kabupaten Bojonegoro Menggunakan Algoritma K-Medoids ]]>
<![CDATA[Elisa Syafaqoh]]>;
<![CDATA[Nurissaidah Ulinnuha]]>;
<![CDATA[Lutfi Hakim]]>
<![CDATA[ KUBIK Vol 7, No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika ]]>
Publisher : <![CDATA[Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/kubik.v7i2.21653
<![CDATA[Persons with Social Welfare Problems (PMKS) are individuals, community groups, or families who cannot adequately and properly meet their economic, physical, mental, and social needs, both spiritually and physically, because of an obstacle, difficulty, or disturbance. This study aimed to classify sub-districts in Bojonegoro Regency based on the level of social welfare problems using the K-Medoids Clustering (PAM) Analysis method. There are 2 clusters formed with an Average Silhouette of 0.73. Cluster 1 is a sub-district group with common social welfare problems, and Cluster 2 is a sub-district group with high social welfare problems. Each silhouette value of the cluster is 0.74 and 0.70 with the specifications of a well-formed and strong structure.]]>
<![CDATA[Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah ]]>
<![CDATA[Siti Aisyah]]>;
<![CDATA[Nurissaidah Ulinnuha]]>;
<![CDATA[Abdulloh Hamid]]>
<![CDATA[ KUBIK Vol 7, No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika ]]>
Publisher : <![CDATA[Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/kubik.v7i2.20460
<![CDATA[The need for crude palm oil has increased due to the large demand for vegetable oils in various parts of the world. Beginning in March 2022, the price of crude palm oil set a record high which caused international cooking oil prices to soar, especially for Indonesia. This study aims to predict the price of crude palm oil with test parameters, namely hidden neurons and activation functions. The method used is Extreme Learning Machine (ELM). This method is a development of the artificial neural network (ANN) method which can overcome weaknesses in the learning speed process. There are several stages in this study: (1) pre-processing the data by normalizing the data and dividing the data using the time series split method, (2) analyzing the data using the ELM method by testing parameters, namely hidden neurons and activation functions, (3) analyzing the results of the best parameter trials, (4) calculating forecasting data using the best parameters that have been obtained, and (5) analyzing the forecasting results that have been obtained. This study uses daily data on the price of crude palm oil from April 1 2021 to April 14 2022 obtained from the Investing website. The results of the research that has been carried out obtained MAPE and RMSE values of 0.0173 and 0.0308 with the best parameters namely the number of hidden neurons of 5 and the binary sigmoid activation function. Based on the results obtained, it is hoped that it will make it easier for the government to determine the price of crude palm oil in the future.]]>
