Agus Subekti
Balai Pengkajian Teknologi Pertanian Kalimantan Barat Jalan Budi Utomo No. 45, Siantan Hulu, Kotak Pos 6150, Pontianak 78061, Kalimantan Barat, Indonesia

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Perancangan dan Implementasi Mapper dan Demapper untuk DVB-T Suyoto, Suyoto; Subekti, Agus; Lukman, Arif
INKOM Journal Vol 5, No 2 (2011)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (328.607 KB) | DOI: 10.14203/j.inkom.152

Abstract

Pada penelitian ini dilakukan perancangan dan implementasi mapper dan demapper untuk DVB-T (Digital Video Broadcaster-Terrestrial). Mapper digunakan untuk memetakan deretan bit digital kedalam symbol-simbol OFDM yang akan masuk ke IFFT, sedangkan demapper digunakan untuk memetkan simbol-simbol OFDM yang keluar dari FFT ke dalam deretan bit digital. Mapper dan demapper menggunakan konstelasi 16 QAM (Quadrature Amplitude Modulation). 4 bit digunakan untuk memetakan setiap simbol OFDM. Perancangan dilakukan dengan menggunakan ISE 9.2i Xilinx. Hasil dari perancangan diimplementasikan pada virtex-4 development board.
Metoda Adaptive Beamforming untuk Cognitive Radio Subekti, Agus
INKOM Journal Vol 8, No 1 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.786 KB) | DOI: 10.14203/j.inkom.385

Abstract

Salah satu peluang pemanfaatan spektrum secara bersama antara  secondary users dan primary users adalah  dengan memanfaatkan  perbedaan sudut datang sinyal (Angle  of  Arrival-  AoA).  Dengan  aplikasi multi antena, arah berkas dari masing-masing dibentuk dan dapat diatur sehingga terfokus dan tidak saling mengganggu karena memberikan interferensi. Pada tulisan ini diusulkan teknik beamforming di sisi penerima. Arah berkas dari larik dibuat maksimum pada arah datang sinyal dan minimum pada arah referensi. Dengan algoritma LMS (Least Mean Square), pembobot dihitung secara iteratif agar memberikan nilai MSE (Minimum Square Error) dari sinyal keluaran larik dan sinyal referensi yang minimum. Algoritma yang diusulkan selanjutnya dicoba disimulasikan untuk beberapa nilai parameter step size.
Penerapan Metode Random Over-Under Sampling dan Random Forest Untuk Klasifikasi Penilaian Kredit Syukron, Akhmad; Subekti, Agus
Jurnal Informatika Vol 5, No 2 (2018): Jurnal INFORMATIKA
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (596.63 KB) | DOI: 10.31294/ji.v5i2.4158

Abstract

                                         AbstrakPenilaian kredit telah menjadi salah satu cara utama bagi sebuah lembaga keuangan untuk menilai resiko kredit,  meningkatkan arus kas, mengurangi kemungkinan resiko dan membuat keputusan manajerial. Salah satu permasalahan yang dihadapai pada penilaian kredit yaitu adanya ketidakseimbangan distribusi dataset. Metode untuk mengatasi ketidakseimbangan kelas yaitu dengan metode resampling, seperti menggunakan Oversampling, undersampling dan hibrida yaitu dengan menggabungkan kedua pendekatan sampling. Metode yang diusulkan pada penelitian ini adalah penerapan metode Random Over-Under Sampling Random Forest untuk meningkatkan kinerja akurasi klasifikasi penilaian kredit pada dataset German Credit.  Hasil pengujian menunjukan bahwa klasifikasi tanpa melalui proses resampling menghasilkan kinerja akurasi rata-rata 70 % pada semua classifier. Metode Random Forest memiliki nilai akurasi yang lebih baik dibandingkan dengan beberapa metode lainnya dengan nilai akurasi sebesar 0,76 atau 76%. Sedangkan klasifikasi dengan penerapan metode Random Over-under sampling Random Forest  dapat meningkatkan kinerja akurasi sebesar 14,1% dengan nilai akurasi sebesar 0,901 atau 90,1 %. Hasil penelitian menunjukan bahwa penerapan  resampling dengan metode Random Over-Under Sampling pada algoritma Random Forest dapat meningkatkan kinerja akurasi secara efektif pada klasifikasi  tidak seimbang untuk penilaian kredit pada dataset German Credit. Kata kunci: Penilaian Kredit, Random Forest, Klasifikasi, ketidakseimbangan kelas, Random Over-Under Sampling                                                  AbstractCredit scoring has become one of the main ways for a financial institution to assess credit risk, improve cash flow, reduce the possibility of risk and make managerial decisions. One of the problems faced by credit scoring is the imbalance in the distribution of datasets. The method to overcome class imbalances is the resampling method, such as using Oversampling, undersampling and hybrids by combining both sampling approaches. The method proposed in this study is the application of the Random Over-Under Sampling Random Forest method to improve the accuracy of the credit scoring classification performance on German Credit dataset. The test results show that the classification without going through the resampling process results in an average accuracy performance of 70% for all classifiers. The Random Forest method has a better accuracy value compared to some other methods with an accuracy value of 0.76 or 76%. While classification by applying the Random Over-under sampling + Random Forest method can improve accuracy performance 14.1% with an accuracy value of 0.901 or 90.1%. The results showed that the application of resampling using Random Over-Under Sampling method in the Random Forest algorithm can improve accuracy performance effectively on an unbalanced classification for credit scoring on German Credit dataset. Keywords: Imbalance Class, Credit Scoring, Random Forest, Classification, Resampling
Inverse Modeling Using Taylor Expansion Approach and Jacobi Matrix on Magnetic Data (Dyke/Magma Intrusion Cases) Suprianto, Agus; Wahyudi, Wahyudi; Suryanto, Wiwit; Setiawan, Ari; Adhi, Aryono; Priyantari, Nurul; Supriyadi, Supriyadi; Subekti, Agus
Scientific Journal of Informatics Vol 6, No 2 (2019): November 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i2.19798

Abstract

The mathematical modelling of geological structures, i.e. magma intrusion or dyke, has been done,  based on magnetic data with inversion techniques using MatLab. The magnetic equation is a non-linear equation, and completion is done using a linear approach to non-linear mathematical models of magnetic data using the Taylor expansion approach and Jacobi Matrix. The first step of this research is to make synthetic data forward modelling from the magnetic equation of magma intrusion or dyke cases without errors, and the next stepping then add errors to the data. The next step is to do an inversion to get the parameters sought, i.e. depth and angle of the magma intrusion, by giving initial guesses, and then re-correct iteratively until convergent results are obtained. Finally, parameters of slope dyke or thin magma intrusion and its depth can be determined. The results obtained indicate that this technique can be used to get physical parameters sought from magnetic data for simple geological cases, i.e. dyke and magma intrusion.
PRODUKSI GUM ARABIC BALURAN SEBAGAI PENDUKUNG PENGEMBANGAN WISATA KAMPUNG BANTENG DI KARANG TEKOK SEBAGAI WILAYAH PENYANGGA TN BALURAN Kusumah, Maulana S.; Wiyono, Hidayat Teguh; Subekti, Agus; Muzakhar, Kahar; Winarsa, Rudju
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 4 No 1 (2020): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Forum Dosen Indonesia JATIM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36339/je.v4i1.272

Abstract

This article is the result of PPDM (Mitra Desa Service Program) about Baluran bos javanicus (Banteng) village tourism. Development of bull village tourism is an effort to solve the problem of wild grazing in Bunaken National Park. In the event of Banteng Village Tourism, it is necessary to support tourism, namely creative industries, agro-tourism, and NTFP production (non-timber forest products). One of the NTFPs that is relied upon is Arabic gum. Currently, cattle breeders in the banteng village area have been able to produce Arabic gum as a result of the introduction of tapping technology by the 2019 PPDM team. The dedication method is in the form of dissemination and field practice. Three groups representing breeders were trained to tap acacia gum through a drilling method combined with ethephon induction as GIS. One week after application, the group begins harvesting gum and submits the results to the group leader. Then the group leader sends the results to the Cooperative in Pondok Pesantren Assalam, Sumberanyar, Banyuputih Situbondo. The amount of Baluran Arabic gum that was collected by the group for three months reached 143.9 kg. This service activity concludes that the strength in producing Baluran Arabic gum is significant in improving the welfare of breeders in supporting the maintenance and retention of a Banteng.
Penerapan Algoritma Random Forest dengan Kombinasi Ekstraksi Fitur Untuk Klasifikasi Penyakit Daun Tomat Khultsum, Umi; Subekti, Agus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2624

Abstract

The tomato plant is widely consumed by the community and is widely cultivated by farmers. Tomato plants are susceptible to disease attacks. Plant diseases cause a decrease in the quality and quantity of crops or agricultural produce. The idea of the 4.0 agricultural revolution emerged as a result of the 4.0 industrial revolution. Farmers are not ready to face increasingly rapid technological advances. It is important to identify the disease in tomato leaves correctly in the efficiency of disease management for efforts to control so that disease in tomato leaves does not develop. The main objective of the proposed method is to develop a technique for identifying foliar diseases in tomato plants by increasing the classification accuracy. The novelty of this research is a combination of several feature extractions to improve classification accuracy. The features used are the color feature, the Hu-Moment feature, and the firur haralick. In the classification process, the Random Forest algorithm and other classification algorithms are applied for comparison. In this study, the Random Forest method and the combination of extraction features have shown an increase in accuracy, the accuracy obtained is 96%.
Deep Neural Network untuk Prediksi Stroke Faisal, Anas; Subekti, Agus
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 7, No 3 (2021): Volume 7 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v7i3.50094

Abstract

Pada Tahun 2019 Organisasi Kesehatan Dunia (WHO) mendudukkan stroke sebagai tujuh dari sepuluh penyebab utama kematian. Kementerian Kesehatan menggolongkan stroke sebagai penyakit katastropik karena dampaknya luas secara ekonomi dan sosial. Oleh karena itu, diperlukan peran dari teknologi informasi untuk memprediksi stroke guna pencegahan dan perawatan dini. Analisis data yang memiliki kelas tidak seimbang mengakibatkan ketidakakuratan dalam memprediksi stroke. Penelitian ini membandingkan tiga teknik oversampling untuk mendapatkan model prediksi yang lebih baik. Data kelas yang sudah diseimbangkan diuji menggunakan tiga model Arsitektur Deep Neural Network (DNN) dengan melakukan optimasi pada beberapa parameter yaitu optimizer, learning rate dan epoch. Hasil paling baik didapatkan teknik oversampling SMOTETomek dan Arsitektur DNN dengan lima hidden layer, optimasi Adam, learning rate 0.001 dan jumlah epoch 500. Skor akurasi, presisi, recall, dan f1-score masing-masing mendapatkan 0.96, 0.9614, 0.9608 dan 0.9611.
Perbandingan Algoritma Klasifikasi K-Nearest Neighbor, Random Forest dan Gradient Boosting untuk Memprediksi Ketertarikan Nasabah pada Polis Asuransi Kendaraan Diantika, Sri; Subekti, Agus; Nalatissifa, Hiya; Lase, Mareanus
Jurnal Informatika Universitas Pamulang Vol 6, No 3 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i3.9419

Abstract

An insurance policy provides coverage for compensation for specified loss, damage, illness, or death in exchange for premium payments. Likewise for vehicle insurance, every year the customer needs to pay a premium to the insurance company so that if an accident occurs that is not profitable for the vehicle, the insurance company provides compensation to the customer. The purpose of this research is to classify the health insurance cross-sell prediction dataset so that certain patterns or relationships can be found between the data to become valuable information and build a model to predict whether policyholders (customers) from the previous year will also be interested in insurance. Vehicles provided by the company. The researcher uses the K-nearest neighbor classification algorithm, Random Forest, and gradient boosting classifier as well as Python data mining tools. After doing the research, it was found that the K-nearest neighbor classification algorithm produces a higher accuracy of 91%, when compared to the Random Forest algorithm which is 87% and the boosting classifier algorithm is 88% in classifying customer interest in taking a vehicle insurance policy.