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All Journal IPTEK Journal of Proceedings Series Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer Network Engineering Research Operation [NERO] Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Jurnal INFOTEL
Bain Khusnul Khotimah
Universitas Trunojoyo Madura
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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IDENTIFIKASI HAMA DAN PENYAKIT TANAMAN JAGUNG DENGAN MENGGUNAKAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) Bain Khusnul Khotimah; Eko Setiawan; Verdi Sasmeka; Aulya Fridayanti; Ikbar Maulana; Arwinda Mifta Zulfida
Network Engineering Research Operation Vol 7, No 1 (2022): NERO
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v7i1.272

Abstract

Produksi hasil panen tanaman Jagung di Madura dipengaruhi oleh adanya wabah Hama dan penyakit, sehingga menyebabkan produksi jagung menurun. Permasalahan tersebut salah satunya dapat diatasi dengan adanya mengidentifikasi awal gejala tanaman yang terserang hama dan penyakit dengan menggunakan klasifikasi machine learning.  Penelitian ini menggunakan metode Support Vector Machine (SVM) untuk klasifikasi hama dan penyakit pada tanaman jagung. Sedangkan data yang digunakan berupa 200 data jenis kategori dengan 25 variabel pertanyaan, yang didiagnosa berupa penyakit, dan hama tanaman jagung. Metode ini menggunakan fungsi pemisah  supaya lebih optimal ketika memisahkan jenis data dari dua kelas yang berbeda. SVM dapat mendeteksi jenis hama dan penyakit tanaman jagung dengan masukan gejala dari pengguna. Penelitian ini telah menghasilkan akurasi  sistem yaitu perbedaan hasil perhitungan identifikasi dengan hasil perhitungan pada perbandingan data 60:40 dengan menggunakan perubahan parameter σ dan d, dengan memilih fungsi Kernel Gaussian Radial Basic telah menghasilkan akurasi klasifikasi senilai 94.29%.
Optimasi Bobot K-Means Clustering untuk Mengatasi Missing Value dengan Menggunakan Algoritma Genetica Bain Khusnul Khotimah; Muhammad Syarief; Miswanto Miswanto; Herry Suprajitno
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 4: Agustus 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021844912

Abstract

Nilai yang hilang membutuhkan preprosesing dengan teknik imputasi untuk menghasilkan data yang lengkap. Proses imputasi membutuhkan initial bobot yang sesuai, karena data yang dihasilkan adalah data pengganti. Pemilihan nilai bobot yang optimal dan kesesuaian nilai K pada metode K-Means Imputation (KMI) merupakan masalah besar, sehingga menimbulkan error semakin meningkat. Model gabungan algoritma genetika (GA) dan KMI atau yang dikenal GAKMI digunakan untuk menentukan bobot optimal pada setiap cluster data yang mengandung nilai yang hilang. Algoritma genetika digunakan untuk memilih bobot dengan menggunakan pengkodean bilangan riel pada kromosom. Model hybrid GA dan KMI dengan pengelompokan menggunakan jumlah jarak Euclidian setiap titik data dari pusat clusternya. Pengukuran kinerja algoritma menggunakan fungsi kebugaran optimal dengan nilai MSE terkecil. Hasil percobaan data hepatitis menunjukkan bahwa GA efisien dalam menemukan nilai bobot awal optimal dari ruang pencarian yang besar. Hasil perhitungan menggunakan nilai MSE =0.044 pada K=3 dan replika ke-5 menunjukkan kinerja GAKMI menghasilkan tingkat kesalahan yang rendah untuk data hepatitis dengan atribut campuran. Hasil penelitian dengan menggunakan pengujian tingkat imputasi menunjukkan algoritma GAKMI menghasilkan nilai r = 0.526 lebih tinggi dibandingkan dengan metode lainnya. Penelitian ini menunjukkan GAKMI menghasilkan nilai r yang lebih tinggi dibandingkan metode imputasi lainnya sehingga dianggap paling baik dibandingkan teknik imputasi secara umum.  AbstractMissing values require preprocessing techniques as imputation to produce complete data. Complete data imputation results require the appropriate initial weights, because the resulting data is replacement data. The choice of the optimal weighting value and the suitability of the network nodes in the K-Means Imputation (KMI) method are big problems, causing increasing errors. The combined model of Genetic Algorithm (GA) and KMI is used to determine the optimal weights for each data cluster containing missing values. Genetic algorithm is used to select weights by using real number coding on chromosomes. GA is applied to the KMI using clustering calculated using the sum of the Euclidean distances of each data point from the center of the cluster. Performance measurement algorithms using the fitness function optimally with the smallest MSE value. The results of the hepatitis data experiment show that GA is efficient in finding the optimal initial weight value from a large search space. The results of calculations using the MSE value = 0.04 for K = 3 and the 5th replication. So, GAKMI resulted in a low error rate for mixed data. The results of research using imputation level testing performed GAKMI  produced r = 0.526 higher than the other methods. Thus, the higher the r value, the best for the imputation technique.
SISTEM PAKAR DIAGNOSA HAMA DAN PENYAKIT PADI DENGAN METODE BAYESIAN BERBASIS CERTAINTY FACTOR Dwi Puji Raharjo; Andharini Dwi Cahyani; Bain Khusnul Khotimah
Jurnal Simantec Vol 8, No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v8i1.8749

Abstract

The Erythemato-Squamous Dermatology Diseases Severity Determination using Self-Organizing Map Haryanto Haryanto; Miftahul Ulum; Diana Rahmawati Rahmawati; Koko Joni; Ahmad Ubaidillah; Riza Alfita; Lilik Anifah; Bain Khusnul Khotimah
IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2014i1.358

Abstract

A new approach based on the implementation of Self Organizing Map is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is in order to determinate the severity of erythemato-squamous dermatology diseases. The studied domain contained records of patients with known diagnosis. Self-Organizing  Map algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the six clusters (psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, dan pityriasis rubra pilaris). The algorithm was used to detect the six erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and SOM algorithm's task achieved high classification accuracies. The best accuration for  psoriasis 85,94%, seboreic dermatitis 40,48%, lichen planus 56,25%, and pityriasis rosea 82,61%, with learning rate value were 0,1, 0,2, 0,9, and 0,4
Pendampingan Persiapan Eduwisata Kampong Batik Podhek Berbasis Kearifan Lokal Bain Khusnul Khotimah; Muhammad Ali Syakur; Nur Hasanah; Wahyu Wiyanda; Muslimatur Rosidah; Putri Lailatul Maghfiroh
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 3 No. 2.1 Desember (2022): SPECIAL ISSUE
Publisher : Sistem Informasi dan Teknologi (Sisfokomtek)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Batik Podhek Pamekasan, Madura, Jawa Timur hampir punah. Padahal, produk batik dari desa ini sangat berharga karena semua produknya adalah produk batik yang digambar dengan tangan. Untuk itulah pemerintah berusaha melestarikan salah satu seni budaya desa Podhek. Tujuan Program Pengabdian pendampingan eduwisata untuk memberikan informasi edukasi kepada masyarakat dengan memberdayakan masyarakat melalui diskusi kelompok terarah tentang pengelolaan batik, kewirausahaan, seni budaya lokal dan keindahan desa setempat. Oleh karena itu, perwakilan desa Batik Podhek diharapkan dapat membantu meningkatkan kesejahteraan masyarakat setempat. Metode Pengabdian kepada Masyarakat ini meliputi pendampingan wisata delegasi melalui kegiatan FGD dan simulasi pembelajaran menggunakan Flash. Pelaksanaan kegiatan nirlaba dimulai dengan analisis situasi dan kebutuhan, identifikasi masalah, definisi program aksi, pelaksanaan, pemantauan dan evaluasi. Layanan nirlaba ini mengembangkan kegiatan wisata yang representatif sebagai dasar pendirian Kampong Batik Podhek, Desa Rang Perang Daya, Kecamatan Proppo, Kabupaten Pamekasan.
Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation Budi Dwi Satoto; Rima Tri Wahyuningrum; Bain Khusnul Khotimah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26222

Abstract

Corn is one of the essential commodities in agriculture. All components of corn can be utilized and accommodated for the benefit of humans. One of the supporting components is the quality of corn seeds, where a specific source has the physiological qualities to survive. The problem is how to get information on the quality of corn seeds at agricultural locations and get information through the physical image alone. This research tries to find a solution to obtain high accuracy in classifying corn kernels using a convolutional neural network because there is a profound training process. The problem with convolutional neural networks is the training process takes a long time, depending on the number of layers in the architecture. This research contributes to increasing the computing time with the proposed contribution by adding Region proposals with a convex hull to use on a custom layer. The method's purpose is a region proposal area with a convex hull to increase the focus on the convolution multiplication process. It affected reducing unnecessary objects in background images. A custom layer architecture by maintaining the priority layer is an option to get a shorter computational time in constructing a model. In addition, the architecture that is made still considers the stability of the training process. The results on the classification of corn seeds are obtained by a model with an average accuracy of 99.01%—the Computational training time to get the model is 2 minutes 30 seconds. The average error value for MSE is 0.0125, RMSE is 0.118, and MAE is 0.0108. The experimental data testing process has an accuracy ranging from 77% -99%. In conclusion, using region proposals can increase accuracy by around 0.3% because focused objects assist the convolution process
Performance of the K-Nearest Neighbors method on identification of maize plant nutrients Bain Khusnul Khotimah
JURNAL INFOTEL Vol 14 No 1 (2022): February 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i1.735

Abstract

Maize is one kind of commodity consumption in domestic as well as export that has high economic value. However, the low productivity is caused by the main factor, namely the decreased level of soil fertility, so that it has the same effect on crop yields. These problems require the application of technology with the K-Nearest Neighbor (KNN) method. The method of study is based on 17 signs of nutrient deficiencies with Minkowski distance calculation process, calculation of deficiency of soil nutrients based on the value of K determined. The test results of the research use K = 75 to get an accuracy of 92.40. Comparative analysis of the K-nearest neighbor (K-NN) and NB methods by looking for the closeness between the criteria for new cases and old case criteria based on the criteria for the closest cases. The results showed that the K-Nearest Neighbor (K-NN) Algorithm had a better accuracy value than NB.
Co-Authors Ahmad Ubaidillah Andharini Dwi Cahyani Arwinda Mifta Zulfida Aulya Fridayanti Budi Dwi Satoto Diana Rahmawati Rahmawati Dwi Puji Raharjo EKO SETIAWAN Haryanto Haryanto Herry Suprajitno Ikbar Maulana Koko Joni Lilik Anifah Miftahul Ulum Miswanto Muhammad Syarief Muslimatur Rosidah Nur Hasanah Putri Lailatul Maghfiroh Rima Tri Wahyuningrum Riza Alfita Syakur, Muhammad Ali Verdi Sasmeka Wahyu Wiyanda