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All Journal IPTEK Journal of Proceedings Series Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Network Engineering Research Operation [NERO] Jurnal Kepariwisataan Indonesia: Jurnal Penelitian dan Pengembangan Kepariwisataan Indonesia Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal INFOTEL
Bain Khusnul Khotimah
Universitas Trunojoyo Madura
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics 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%.
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 : Cv. Utility Project Solution

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.
The Key Success Factor for Sustainable Rural Tourism Development in Madura Using Integration of the IPA-Kano Model Fitri Agustina; Bain Khusnul Khotimah; Dhony Susilowanto; Putri Rahma Maulida
Jurnal Kepariwisataan Indonesia: Jurnal Penelitian dan Pengembangan Kepariwisataan Indonesia Vol. 18 No. 2 (2024): JKI Edisi Desember 2024
Publisher : Ministry of Tourism and Creative Economy/Tourism and Creative Economy Agency Republic of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47608/jki.v18i22024.315-330

Abstract

The momentum for the rise of global tourism after the COVID-19 pandemic has become an opportunity for developing local tourism and Indonesia's creative economy. One of the promising tourism regions in Indonesia is Madura. The tourism potentials on the island of Madura are historical, religious, natural, and cultural. Unfortunately, Madura's tourism potential has not been fully utilized, and as a result, the benefits have not emerged to strengthen the Madurese community's economy. In addition, tourism development directions are also partial without looking at environmental sustainability, community welfare, and other social aspects. Therefore, this research was conducted to determine the key success factors of sustainable tourism by integrating the Important-Performance Analysis method with the Kano model. This study was conducted in Sampang, Madura, specifically in a tourist village called Bira Tengah. The result of the study is that the tourist village should adopt key success factors such as leading to maintaining the destination environment, improving the contribution of tourists and local communities in area conservation, increasing the competitiveness of tourist destinations, conducting more intensive tourism promotions, creating fun tourism activities, increasing participation of the community and stakeholders of tourist villages, using local and environmentally friendly materials, improving the cleanliness of destinations, disseminating local cultural information and increasing employment opportunities. The cultural factors are a highlight in improving sustainable tourism performance. In addition, to enhance tourism performance, it needs to benchmark with developed and autonomous tourist villages that have demonstrated success in sustainable tourism.
Co-Authors Ahmad Ubaidillah Andharini Dwi Cahyani Arwinda Mifta Zulfida Aulya Fridayanti Budi Dwi Satoto Dhony Susilowanto Diana Rahmawati Rahmawati Dwi Puji Raharjo EKO SETIAWAN Fitri Agustina Haryanto Haryanto Ikbar Maulana Koko Joni Lilik Anifah Miftahul Ulum Muslimatur Rosidah Nur Hasanah Putri Lailatul Maghfiroh Putri Rahma Maulida Rima Tri Wahyuningrum Riza Alfita Syakur, Muhammad Ali Verdi Sasmeka Wahyu Wiyanda