Articles
<![CDATA[Deteksi Kapal di Laut Indonesia Menggunakan YOLOv3 ]]>
<![CDATA[Adam Fahmi Fandisyah]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Wiwiek Setya Winahju]]>
<![CDATA[ Jurnal Sains dan Seni ITS Vol 10, No 1 (2021) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS ]]>
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DOI: 10.12962/j23373520.v10i1.59312
<![CDATA[Indonesia adalah negara kepulauan terbesar di dunia yang memiliki kandungan kekayaan dan sumber daya alam laut yang sangat berlimpah. Hal ini memicu terjadinya peristiwa seperti illegal fishing, illegal mining, illegal logging, drugs trafficking dan people smuggling yang menunjukkan bahwa kurang maksimalnya pengawasan wilayah laut Indonesia. Pesatnya perkembangan teknologi di bidang kecerdasan buatan mendorong ditemukannya deep learning, salah satunya yaitu metode You Only Look Once (YOLO) yang dikembangkan dengan algoritma untuk mendeteksi sebuah objek secara realtime. Dalam penelitian ini, deteksi tipe kapal dilakukan dengan menggunakan YOLOv3 dan dievaluasi dengan menghitung nilai Mean Average Precision (mAP) yang dibandingkan hasilnya dengan ground truth. Hasil deteksi tipe kapal menggunakan YOLOv3 dengan k-means anchor box dapat mengenali tipe kapal pada citra satelit, diperoleh nilai mAP hingga 95,06% pada data training serta 50,41% pada data testing.]]>
<![CDATA[Model Components Selection in Bayesian Model Averaging Using Occam's Window for Microarray Data ]]>
<![CDATA[Ani Budi Astuti]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[irhamah Irhamah]]>;
<![CDATA[Heri Kuswanto]]>
<![CDATA[ Journal of Natural A Vol 1, No 2 (2014) ]]>
Publisher : <![CDATA[Fakultas MIPA Universitas Brawijaya ]]>
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<![CDATA[Microarray is an analysis for monitoring gene expression activity simultaneously. Microarray data are generated from microarray experiments having characteristics of very few number of samples where the shape of distribution is very complex and the number of measured variables is very large. Due to this specific characteristics, it requires special method to overcome this. Bayesian Model Averaging (BMA) is a Bayesian solution method that is capable to handle microarray data with a best single model constructed by combining all possible models in which the posterior distribution of all the best models will be averaged. There are several method that can be used to select the model components in Bayesian Model Averaging (BMA). One of the method that can be used is the Occam's Window method. The purpose of this study is to measure the performance of Occam's Window method in the selection of the best model components in the Bayesian Model Averaging (BMA). The data used in this study are some of the gene expression data as a result of microarray experiments used in the study of Sebastiani, Xie and Ramoni in 2006. The results showed that the Occam's Window method can reduce a number of models that may be formed as much as 65.7% so that the formation of a single model with Bayesian Model Averaging method (BMA) only involves the model of 34.3%. Keywords— Bayesian Model Averaging, Microarray Data, Model Components Selection, Occam's Window Method.]]>
<![CDATA[PENERAPAN METODE REGRESI LOGISTIK PADA APLIKASI SPREADSHEET SEBAGAI ALAT BANTU PENGAMBILAN KEPUTUSAN (STUDI KASUS DATA BUMN DI BPK RI) ]]>
<![CDATA[Indira Swa Buana]]>;
<![CDATA[Mahendrawathi Mahendrawathi]]>;
<![CDATA[Nur Iriawan]]>
<![CDATA[ Seminar Nasional Informatika (SEMNASIF) Vol 1, No 5 (2010): Information System And Application ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika ]]>
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<![CDATA[Decision Support System (DSS) merupakan alat bantu pengambilan keputusan berbasis komputer atau komputasi untuk membantu manajemen dalam memproses data menjadi informasi yang berguna untuk pengambilan keputusan. Salah satu komponen dari DSS adalah model yang digunakan sebagai abstraksi dari dunia nyata. Model yang sering dipakai adalah model matematika dan statistika untuk membantu pengolahan data menjadi informasi. BPK RI terutama pada unit BUMN belum memiliki dukungan model untuk membantu pimpinan dalam mengambil keputusan pemeriksaan. Jumlah BUMN yang banyak dan jenis pemeriksaan yang beraneka ragam membutuhkan penilaian obyektif dalam pengambilan keputusan pemeriksaan dengan menggunakan data yang dimiliki. Data BUMN adalah data keuangan yang wajib untuk disampaikan kepada BPK RI setiap tahun. Data keuangan dapat diolah menjadi rasio keuangan sebagai wujud analisis data sebagai dasar penilaian BUMN. Salah satu model pengambilan keputusan menggunakan rasio keuangan adalah model rating menggunakan metode regresi logistik pada emiten di Bursa Efek Surabaya (BES) oleh Iriawan (2005a). Model tersebut dapat juga diterapkan di BPK RI yang memiliki data keuangan BUMN yang dapat diolah menjadi rasio keuangan terutama untuk mendukung pengambilan keputusan pemeriksaan di BPK RI. Penelitian dengan menggunakan data rasio keuangan BUMN di BPK RI dan jenis keputusan yang dapat didukung dari model seperti ini pada lingkungan kerja BPK RI belum pernah dilakukan.Metode regresi logistik dapat membantu memodelkan BUMN ke dalam klasifikasi tingkat kesehatan menggunakan prediktor berupa rasio-rasio keuangan BUMN. Model regresi ini dapat menghasilkan peringkat BUMN, indikasi pergeseran tingkat kesehatan BUMN dan mengetahui kontribusi pengaruh masing-masing rasio keuangan terhadap tingkat kesehatan BUMN. Informasi tersebut dapat digunakan oleh BPK RI sebagai dasar pengambilan keputusan dalam perencanaan pemeriksaan di BPK RI. Metode regresi logistik tersebut diterapkan dalam aplikasi spreadsheet EWS emiten (Iriawan, 2005b) dengan modifikasi sebagai penyesuaian terhadap informasi yang dibutuhkan.]]>
<![CDATA[Data Mining Approach for Educational Decision Support ]]>
<![CDATA[Sinta Septi Pangastuti]]>;
<![CDATA[Kartika Fithriasari]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Wahyuni Suryaningtyas]]>
<![CDATA[ EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 1, February 2021 ]]>
Publisher : <![CDATA[Fakultas Matematika dan Ilmu Pengetahuan Alam ]]>
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DOI: 10.20885/EKSAKTA.vol2.iss1.art5
<![CDATA[data mining techniques in education sector have begun to evolve, along with the development of technology and the amount of data that can be stored in an education database storage system. One of them is a database of Bidikmisi scholarships in Indonesia. The Bidikmisi data used in this study will be classified using classification data mining technique. The technique that used in this study is random forest in combination with boosting algorithm and bagging algorithms. These algorithms also combine with SMOTE algorithm to handling the imbalance class in dataset. Based on the performance criteria G-mean and AUC, the algorithm combines with SMOTE tended to be better. The classification accuracy of each method being more than 90%]]>
<![CDATA[Small Area Estimation Of Expenditure Per-capita in Banyuwangi with Hierarchical Bayesian and Empirical Bayes Methods ]]>
<![CDATA[Wirajaya Kusuma]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Irhamah Irhamah]]>
<![CDATA[ IPTEK Journal of Science Vol 2, No 3 (2017) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian kepada Masyarakat ]]>
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DOI: 10.12962/j23378530.v2i3.a3185
<![CDATA[One of the economic indicators that are widely used to measure the level of prosperity and welfare is per capita income. However, an accurate income data is difficult to be obtained. In Susenas this data is approached by using data on expenditures per capita. This study employ Hierarchical Bayes (HB) and Empirical Bayes (EB) methods to be applied to Small Area Estimation (SAE) to estimate the expenditure per-capita in Banyuwangi. The results showed indirect estimation using hierarchical Bayes and Empirical Bayes produce RMSE values smaller than the direct estimation. The HB method, on the other hand, produces smaller RMSE value than the EB method. Finally, this research suggests to use HB method to estimate the expenditure per-capita in Banyuwangi rather than direct estimation which is used nowadays.]]>
<![CDATA[The Distribution of Damage to District Roads in Karang Penag Sub-District in Sampang District Uses Pavement Condition Index (PCI) and Spatial Poisson Point Process (SPPP) ]]>
<![CDATA[Sulhan Sulhan]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Ervina Ahyudanari]]>
<![CDATA[ IPTEK Journal of Proceedings Series No 3 (2020): International Conference on Management of Technology, Innovation, and Project (MOTIP) 2 ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/j23546026.y2020i3.11193
<![CDATA[Karang Penang sub-district is a new sub-district after the division of territory in the Sampang Regency. This sub-district consists of seven villages. Sampang Regency is an area that is categorized as underdeveloped regions in East Java Province. This is indicated by the economic conditions and the low quality of human resources, and inadequate infrastructure. The last indication needs to be carried out regularly to ensure its continued functioning in supporting economic movements in this area. Delay in the identification of damage that often occurs in this area has caused delays in the maintenance of the facility. To overcome the delay in preparing the repair schedule, it is necessary to make a faster and more accurate assessment of road conditions. This study aims to assess the condition of the road pavement by combining the two approaches, namely the Pavement Condition Index (PCI) method and the Spatial Poisson Point Process (SPPP). The results of the modeling are expected to be able to identify the distribution pattern of the locations of damage points of the highway, to identify what parameters have a significant role in the distribution of the points of damage to the highway, and to be able to identify the level or condition of district highway conditions in Karang Penang District. The results of this study are able to provide predictions of the pattern of road damage conditions with varying intensity and influential factors between highway locations. These results, in turn, can provide information on the pattern of corrective actions that are more appropriate according to the damage criteria at these locations.]]>
<![CDATA[Pemodelan Harga Cryptocurrency Menggunakan Markov Switching Autoregressive ]]>
<![CDATA[Akhmad Ridho Ashariansyah]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Adatul Mukarromah]]>
<![CDATA[ Inferensi Vol 3, No 2 (2020): Inferensi ]]>
Publisher : <![CDATA[Department of Statistics ITS ]]>
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DOI: 10.12962/j27213862.v3i2.7726
<![CDATA[Perdagangan merupakan sebuah kegiatan tukar menukar barang atau jasa yang dilakukan manusia untuk memenuhi kebutuhan hidup. Perkembangan sistem pembayaran yang dilakukan umat manusia dimulai dari sistem pertukaran barang atau barter, logam mulia seperti emas dan perak, koin, uang kartal, uang giral, dan uang elektronik (e-money). Selain itu, muncul cryptocurrency yaitu mata uang digital dengan sistem kriptografi dalam setiap proses transaksi datanya tanpa melalui pihak ketiga. Namun cryptocurrency memiliki kelemahan perubahan harga yang sangat besar dalam waktu yang sangat cepat. Pergerakan harga yang berfluktuasi sangat tinggi tersebut menyebabkan kekhawatiran pemilik aset kripto mengalami kerugian, maka pemodelan harga cryptocurrency sangat penting untuk dilakukan agar meminimalisir risiko kerugi-an. Berdasarkan pola pergerakan harga yang berfluktuasi sangat tinggi yang berbeda tiap periodenya maka dilakukanlah pemodelan harga cryptocurrency mengguna-kan Markov Switching Autoregressive (MSAR) dengan algoritma Expectation Maximization. Selain meminimkan risiko kerugian, penelitian ini juga ingin mengetahui model MSAR mana yang mampu mengklasifikasikan state dengan baik. Data yang digunakan yaitu harga harian cryptocurrency dengan nilai kapitalisasi pasar terbesar dari September 2015 hingga Januari 2020. Hasil penelitian menunjukkan bahwa bitcoin dan ripple menggunakan model MS(8)AR(1), sedangkan ethereum menggunakan model MS(9)AR(1). Selain itu model MS(8)AR(1) pada data ripple menjadi model dengan nilai akurasi tertinggi dibandingkan model lainnya dalam hal klasifikasi state.]]>
<![CDATA[Generalized Additive Logistic Pada Pemodelan Faktor-Faktor Yang Mempengaruhi Keuntungan PT. PDC ]]>
<![CDATA[Kartika Fithriasari]]>;
<![CDATA[Soehardjoepri Soehardjoepri]]>;
<![CDATA[Nur Iriawan]]>
<![CDATA[ Inferensi Vol 1, No 1 (2018): Inferensi ]]>
Publisher : <![CDATA[Department of Statistics ITS ]]>
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DOI: 10.12962/j27213862.v1i1.6720
<![CDATA[Generalized Additive Models (GAM) merupakan kombinasi dari model additive dan generalized linear models (GLMs). GAM dengan variabel respon bertipe biner disebut model generalized additive logistic. Perbedaan hasil model regresi logistik pada GLMs dan GAM didapatkan pada pemodelan faktor-faktor yang mempengaruhi keuntungan PT.PDC. Dari studi kasus PT.PDC. terlihat bahwa GLMs hanya menangkap hubungan linier antara log-odds dan variabel prediktor, sedangkan GAM dapat menangkap hubungan kuadratik yang digambarkan dalam grafik prediksi parsial. Sehingga dapat disimpulkan bahwa GAM mampu memodelkan hubungan yang lebih kompleks dibanding GLMs.]]>
<![CDATA[A Functional Form of The Zenga Curve Based on Rohde’s Version of the Lorenz Curve ]]>
<![CDATA[Muhammad Fajar]]>;
<![CDATA[Setiawan]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Eko Fajariyanto]]>
<![CDATA[ Jurnal Matematika MANTIK Vol. 8 No. 1 (2022): April - June ]]>
Publisher : <![CDATA[Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya ]]>
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DOI: 10.15642/mantik.2022.8.1.63-67
<![CDATA[The Zenga curve is a tool to measure income inequality that represents the income ratio between the bottom income group and the top income group. A proper Zenga curve is a Zenga curve that can detect variations in the Ratio. In this paper, we derive the functional form of the Zenga curve from Rohde's Lorenz curve model. The result of this paper is that the functional form of the Zenga curve from Rohde's version of the Lorenz curve model is a constant. It cannot represent the truly happening phenomenon of inequality.]]>
<![CDATA[VARIASI VARIABEL PENGARUH FIX DAN RANDOM TERHADAP PRODUKSI GULA DAN TETES ]]>
<![CDATA[I Nyoman Latra]]>;
<![CDATA[Nur Iriawan]]>;
<![CDATA[Purhadi Purhadi]]>;
<![CDATA[Suhartono Suhartono]]>
<![CDATA[ Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 4 No 1 (2012): Jurnal Ilmiah Matematika dan Pendidikan Matematika ]]>
Publisher : <![CDATA[Jurusan Matematika FMIPA Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jmp.2012.4.1.2946
<![CDATA[This paper presents the modeling of the amount of sugar and molasses production in Pabrik Gula Candi Baru Sidoarjo (PGCBS), East Java, by using multivariate mixedlinear models. Estimation of parameters will be done by using maximum likelihood coupled with restricted maximum likelihood methods. The amount of sugar and molasses products which have strong linear correlation, will be set as responses and are supposed to be affected by seven fixed effect variables and four random effect variables. This paper demonstrates that the seven fixed effect variables and only one random effect variable have significant influence on a single response. In the multivariate response modeling, however, all of variables fail to explain the variability of these two responses simultaneously. It is due to the factors matrix has no full rank. As a result, the model response of molasses can be explained by using a model of the amount of sugar obtained.]]>
