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Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor Lamasigi, Zulfrianto Y; Hasan, Maryam; Lasena, Yulianti
ILKOM Jurnal Ilmiah Vol 12, No 3 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i3.667.208-218

Tanaman obat tradisional merupakan jenis tanaman yang mengandung zat aktif yang berfungsi mengobati ataupun mencegah dari berbagai macam penyakit. Oleh karena itu dilakukan penelitian untuk menguji metode Local Binary Pattern untuk ektraksi ciri dari setiap tanaman obat tradisional dan K-Nearest Neighbor pada proses klasifikasi setelah dilakukan ektraksi dari metode Local Binary Pattern. Dari pengujian menggunakan metode Local Binary Pattern dan K-Nearest Neighbor mampu menghasilkan akurasi yang cukup baik yaitu sebesar 96.67%, nilai akurasi tersebut didapat dari perhitungan manual convusion matrix dengan nilai k=9. Sementara itu hasil akurasi terendah ada pada nilai k=1 yaitu 0%. Hasil ektraksi dan klasifikasi dari metode Local Binary Pattern dan K-Nearest Neighbor menggunakan 120 dataset yang dibagi menjadi 90 data training dengan 6 jenis daun tanaman obat yang terdiri dari 15 daun bayam duri, 15 daun binahong, 15 daun jarak, 15 daun afrika, dan 15 daun sirih dengan percobaan 30 data testing.

Identifikasi Tingkat Kesegaran Ikan Tuna Menggunakan Metode GLCM dan KNN Zulfrianto Yusrin Lamasigi; Serwin -; Husdi -; Yulianti Lasena
Jambura Journal of Electrical and Electronics Engineering Vol 4, No 1 (2022): Januari - Juni 2022
Publisher : Teknik Elektro - Universitas Negeri Gorontalo

Abstrak-Dari potensi perikanan dan kelautan secara Nasional, Provinsi Gorontalo memiliki potensi perikanan dan kelautan cukup besar yang dapat dikelola untuk menunjang pembangunan Gorontalo. Potensi perikanan tangkap Provinsi Gorontalo tidak bisa dipisahkan dari potensi perikanan tangkap yang berbasis pada WPP (Wilayah Pengelolaan dan Pemanfaatan) dan diakui secara Nasional maupun Internasional. Provinsi Gorontalo merupakan salah satu provinsi penghasil ikan tuna di Indonesia, hasil tangkapan ikan tuna di gorontalo telah diekspor keberbagai negara. Tuna merupakan salah satu komoditi andalan perikanan di Gorontalo yang juga banyak melibatkan nelayan kecil. Penelitianini bertujuan untuk melakukan identifikasi tingkat kesegaran ikan tuna dengan menggukanan metode Gray LevelCo-Occurrence Matrix(GLCM)sebagai metode ektraksi fitur dan K-Nearest Neighbour (K-NN) digunakan sebagai metode klasifikasi. Padapenelitian ini, akan dilakukan 5 kali percobaan berdasarkan sudut 0°, 45°, 90°, 135° dan 180° pada nilai k=1, 3, 5, dan 7. Sementara itu, untuk menghitung tingkat akurasi dari klasifikasi K-NN akan menggunakan confusion matrix. Dari uji coba yang di lakukan dengan menggunakan jumlah data training sebanyak 130 citra dan data testing 45 citra pada semua kelas dan sudut mendapatkan hasil akurasi tertinggi pada sudut 0° dengan nilai k=1 yaitu sebesar 82,28% dan yang paling rendah ada pada sudut 135° dan 180° dengan nilai k=1 yaitu sebesar 53,71%. Berdasarkan hasil akurasi yang didapatkan menunjukkan bahwah GLCM bekerja dengan baik untuk meningkatkan hasil akurasi klasifikasi K-NN yang dibuktikan dengan hasil rata-rata akurasi yang diperoleh mencapai 50%.Abstract-From the national fisheries and marine potential, Gorontalo Province has a large enough fishery and marine potential that can be managed to support the development of Gorontalo. The capture fisheries potential of Gorontalo Province cannot be separated from the potential of capture fisheries based on the WPP (Management and Utilization Area) and is recognized both nationally and internationally. Gorontalo province is one of the tuna-producing provinces in Indonesia, tuna catches in Gorontalo have been exported to various countries. Tuna is one of the mainstay fisheries commodities in Gorontalo which also involves many small fishermen. This study aims to identify the freshness level of tuna by using the Gray Level Co-Occurrence Matrix (GLCM) method as a feature extraction method and K-Nearest Neighbor (K-NN) is a classification method. In this experiment, 5 experiments were conducted based on the angles of 0°, 45°, 90°, 135° and 180° at the values of k=1, 3, 5, and 7. Meanwhile, to calculate the accuracy level of the K-NN classification, we will use a confusion matrix. From the trials carried out using the amount of training data as many as 130 images and testing data 45 images against all classes based on angles 0°, 45°, 90°, 135°, and 180° at the values of k=1, 3, 5, and 7, the highest accuracy obtained is at an angle of 0° with a value of k=1 which is 82.28% and the lowest is at an angle of 135° and 180° with a value of k=1 which is 53.71%. The results of the trials conducted show that GLCM works well to improve the accuracy of the K-NN classification as evidenced by the average accuracy of 50%.

DCT Untuk Ekstraksi Fitur Berbasis GLCM Pada Identifikasi Batik Menggunakan K-NN Zulfrianto Yusrin Lamasigi
Jambura Journal of Electrical and Electronics Engineering Vol 3, No 1 (2021): Januari - Juni 2021
Publisher : Teknik Elektro - Universitas Negeri Gorontalo

Batik merupakan kain yang dibuat khusus, batik sendiri terbilang unik karena memiliki motif tertentu yang dibuat berdasarkan unsur budaya dari daerah asal batik itu dibuat. setiap motif dan warna batik berbeda-beda sehingga sulit untuk dikenali asal dari motir batik itu sendiri. penelitian ini bertujuan untuk meningkatkan hasil ektraksi fitur pada identifikasi motif batik. metode yang digunakan dalam penelitian ini adalah Discrete Cosine Transform bertujuan untuk meningkatkan hasil ektraksi fitur Gray Level Co-Occurrence Matrix untuk mendapatkan hasil akurasi identifikasi motif batik yang lebih baik, sedangkan untuk mengetahui nilai kedekatan antara data training dengan data testing citra batik akan menggunakan K-Nearest Neighbour berdasarkan nilai ekstraksi fitur yang diperoleh. dalam eksperimen ini dilakukan 4 kali percobaan berdasarkan sudut 0°, 45°, 90°, dan 135° pada nilai k=1, 3, 5, 7, dan 9. sementara itu, untuk menghitung tingkat akurasi dari klasifikasi KNN akan menggunakan confusion matrix. Dari uji coba yang di lakukan dengan menggunakan jumalah data training sebanyak 602 citra dan data testing 344 citra terhadap semua kelas berdasarkan sudut 0°, 45°, 90°, dan 135° pada nilai k=1, 3, 5, , dan 9 akurasi tertinggi yang diperoleh DCT-GLCM ada pada sudut 135° dengan nilai k=3 sebesar 84,88% dan yang paling rendah ada pada sudut 0° dengan nilai k=7 dan 9 sebesar 41,86%. Sedangkan hasil uji dengan hanya mennggunakan GLCM akurasi tertinggi ada pada sudut 135° dengan nilai k=1 sebesar 77,90% dan yang paling rendah ada pada sudut 90° dengan nilai k=7 sebesar 40,69%. Dari hasil uji coba yang dilakukan menunjukkan bahwah DCT bekerja dengan baik untuk meningkatkan hasil ekstraksi fitur GLCM yang dibuktikan dengan hasil rata-rata akurasi yang diperoleh.Batik is a specially made cloth, batik itself is unique because it has certain motifs that are made based on cultural elements from the area where the batik was made. each batik motif and color is different so it is difficult to identify the origin of the batik motir itself. This study aims to improve the feature extraction results in the identification of batik motifs. The method used in this research is Discrete Cosine Transform, which aims to increase the extraction of the Gray Level Co-Occurrence Matrix feature to obtain better accuracy results for identification of batik motifs, while to determine the closeness value between training data and batik image testing data will use K- Nearest Neighbor based on the feature extraction value obtained. In this experiment, 4 experiments were carried out based on angles of 0 °, 45 °, 90 °, and 135 ° at values of k = 1, 3, 5, 7, and 9. Meanwhile, to calculate the level of accuracy of the KNN classification, confusion matrix will be used. . From the trials carried out using the total training data of 602 images and testing data of 344 images for all classes based on angles of 0 °, 45 °, 90 °, and 135 ° at values of k = 1, 3, 5, and 9 accuracy The highest obtained by DCT-GLCM was at an angle of 135 ° with a value of k = 3 of 84.88% and the lowest was at an angle of 0 ° with values of k = 7 and 9 of 41.86%. While the test results using only GLCM, the highest accuracy is at an angle of 135 ° with a value of k = 1 of 77.90% and the lowest is at an angle of 90 ° with a value of k = 7 of 40.69%. From the results of the trials conducted, it shows that the DCT works well to improve the results of the GLCM feature extraction as evidenced by the average accuracy results obtained.

Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor Zulfrianto Yusrin Lamasigi; Andi Bode
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.1025.322-333

Batik is one type of fabric that is unique because it has a special motif, in Indonesia itself batik is unique because it has certain motifs that are made based on the culture from which batik was made. This study aims to examine the effect of the texture feature extraction method on the identification of batik motifs from five major islands in Indonesia. The method used in this study is the Gray Level Co-occurrence Matrix as the texture feature extraction of batik motifs to obtain good batik motif identification accuracy results and to determine the value of the proximity of the training data and image testing of batik motifs, the K-Nearest Neighbor classification method will be used based on texture feature extraction value obtained. In this experiment, 5 experiments will be carried out based on angles 0degrees, 45degrees, 90degrees, 135degrees, and 180degreesusing the values of k is1, 3, 5, and 7. The confusion matrix will be used to calculate the accuracy level of the K-Nearest Neighbor classification. From the results of experiments carried out using training data as many as 607 images and testing as many as 344 images in five classes used with angles of 0 degrees, 45degrees, 90degrees, 135degrees, 180degrees, and values of k are 1, 3, 5, and 7, getting the highest accuracy results is at an angle of 135degreesand 180degreeswith a value of k is 1 of 89.24% and the lowest is at an angle of 90degreeswith a value of k is 3 of 67.44%. This shows that the Gray level co-occurrence matrix method is good for extracting the texture features of batik motifs from five major islands in Indonesia, it is evidenced by the results of the average accuracy of the classification obtained.

Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor Zulfrianto Y Lamasigi; Maryam Hasan; Yulianti Lasena
ILKOM Jurnal Ilmiah Vol 12, No 3 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i3.667.208-218

Tanaman obat tradisional merupakan jenis tanaman yang mengandung zat aktif yang berfungsi mengobati ataupun mencegah dari berbagai macam penyakit. Oleh karena itu dilakukan penelitian untuk menguji metode Local Binary Pattern untuk ektraksi ciri dari setiap tanaman obat tradisional dan K-Nearest Neighbor pada proses klasifikasi setelah dilakukan ektraksi dari metode Local Binary Pattern. Dari pengujian menggunakan metode Local Binary Pattern dan K-Nearest Neighbor mampu menghasilkan akurasi yang cukup baik yaitu sebesar 96.67%, nilai akurasi tersebut didapat dari perhitungan manual convusion matrix dengan nilai k=9. Sementara itu hasil akurasi terendah ada pada nilai k=1 yaitu 0%. Hasil ektraksi dan klasifikasi dari metode Local Binary Pattern dan K-Nearest Neighbor menggunakan 120 dataset yang dibagi menjadi 90 data training dengan 6 jenis daun tanaman obat yang terdiri dari 15 daun bayam duri, 15 daun binahong, 15 daun jarak, 15 daun afrika, dan 15 daun sirih dengan percobaan 30 data testing.

Game Edukasi Sebagai Media Pembelajaran Fisika Untuk Siswa Kelas X SMK Negeri 1 Boalemo Berbasis Android Firmansyah Kadir; Zulfrianto Y Lamasigi; Serwin Serwin
Jurnal Cosphi Vol 4, No 2 (2020): Agustus-Desember 2020
Publisher : Teknik Elektro - Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.749 KB)

Dalam bidang pendidikan, pemanfaatan teknologi informasi dan komunikasi mendorong penciptaan inovasi-inovasi dalam pembelajaran, memberikan solusi dan kemudahan untuk memfasilitasi peserta didik agar dapat belajar dimana saja dan kapan saja tanpa dibatasi ruang dan waktu dengan mudah dan terjangkau. Penggunaan game edukatif sebagai media pembelajaran sudah banyak dilakukan dan dapat membantu meningkatkan minat belajar peserta didik. Penelitian game edukatif ini dapat diimplementasikan dilihat dari hasil pengujian dengan menggunakan metode user acceptance testing yang di lakukan pada 10 orang siswa sebagai sampel dan mendapatkan nilai skor rata-rata 9 dapat disimpulkan bahwa Game Edukasi media pembelajaran Fisika ini Menarik, mudah dipahami, mudah dioperasikan, mendukung kebijakan, membantu/memudahkan, aplikasi ini baik, dokumentasi baik, teknologi aplikasi canggih, bebas dari error dan perlu diimplementasikan. Serta Kelayakan dan keefektifan game edukatif ini dinilai layak berdasarkan hasil pengujian black box yang telah dilakukan, terlihat bahwa semua pengujian black box yang diperoleh sudah dites satu kali. Maka berdasarkan ketentuan tersebut dari segi kelayakan aplikasi, maka aplikasi ini sudah memenuhi syarat

Identification of the Freshness Level of Tuna based on Discrete Cosine Transform on Feature Extraction of Gray Level Co-Occurrence Matrix using K-Nearest Neighbor Zulfrianto Yusrin Lamasigi; Serwin Serwin; Yusrianto Malago
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1426.153-164

Gorontalo Province is one of the provinces that have fishery potential and has a large sea area that can be managed to support the economy and development of the province. Gorontalo is also one of the tuna-producing provinces in Indonesia, where tuna is also one of the mainstay fisheries commodities. This study aimed to combine transformation and texture feature extraction methods to improve the identification of the freshness level of tuna. This research used Discrete Cosine Transform as transformation detection and Gray Level Co-Occurrence Matrix as texture feature extraction. To find out the value of the proximity of the training data and image testing of tuna fish, the K-Nearest Neighbor classification method was employed. Then, the Confusion Matrix was used to calculate the accuracy level of the K-Nearest Neighbor classification. This research was carried out with 4 stages of testing, namely at angles of 0°, 45°, 90°, and 135°, and using the values of k=1, 3, 5, and 7. The test results of using training data of 428 images and testing data of 161 images in four classes used with angles of 0°, 45°, 90°, 135°, and the value of k=1, 3, 5, 7. The highest accuracy results was obtained at an angle of 0° with a value of k = 1 of 94.40%, while the lowest accuracy value was at an angle of 90° and 135° with a value of k=7 of 59%. This showed that the Discrete Cosine Transform transformation method was very effective to improve the performance of texture feature extraction of Gray Level Co-Occurrence Matrix in extracting tuna image features. It was proven from the results of the accuracy of the K-Nearest Neighbor classification obtained.

The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic Andi Bode; Zulfrianto Y Lamasigi; Ivo Colanus Rally Drajana
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1381.118-123

Academic services are actions taken by state and private universities to provide convenience for student’s academic activities. During the current covid-19 pandemic, every university remains active in academic activities. This study aimed to apply the K-Nearest Neighbor algorithm in predicting the level of student satisfaction with online lectures at University Ichsan Gorontalo. Our main aim was to obtain quantitative information to measure student satisfaction with online lectures during the pandemic, which should be taken into account when making decisions. K-Nearest Neighbor is a non-parametric Algorithm that can be used for classification and regression, but K-Nearest Neighbor are better if feature selection is applied in selecting features that are not relevant to the model. Feature Selection used in this research is Forward Selection and Backward Elimination. Seeing the results of experiments that have been carried out with the application of the K-nearest Neighbor algorithm and the selection feature, the results of the forecasting can be used for consideration or policy in decision making. The highest level of accuracy in the K-Nearest Neighbor algorithm model used Forward Selection with an accuracy rate of 98.00%. Thus, the experimental results showed that feature selection, namely forward selection, was a better model in the relevant selection variables compared to backward elimination.

Automated Drip Irrigation System Based on IoT for Chili Plants Using Solar Panel Energy Zulfrianto Yusrin Lamasigi; Abd. Rahmat Karim Haba; Muh. Iqbal Jafar; Syamsir Syamsir; Stephan A. Hulukati
Jurnal Pengabdian Masyarakat Vol 5 No 1 (2024): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v5i1.2006

Purpose: This research paper aims to address the challenges faced by horticultural farmers in Lauwonu Village, particularly regarding water scarcity exacerbated by hot weather conditions. The study emphasizes the significance of utilizing technology, specifically a drip irrigation system based on IoT and solar panel energy, to mitigate these challenges effectively. Method: The research employed a qualitative approach to investigate the impact of implementing IoT-based drip irrigation systems on chili farming productivity. Data collection methods included surveys and interviews with 15 members of the Mekar Green farmer group. Thematic analysis was utilized to interpret the gathered data. Practical Applications: The findings demonstrate the practical benefits of adopting IoT-driven irrigation technology, enhancing water efficiency and agricultural productivity. This research offers valuable insights for farmers, policymakers, and agricultural practitioners, facilitating informed decision-making and sustainable agricultural practices.Conclusion: Implementing IoT-enabled drip irrigation systems powered by solar panels presents a viable solution to address water scarcity challenges in chili farming. The study underscores the importance of leveraging technology to improve agricultural resilience and productivity, thereby contributing to sustainable food production and livelihoods in rural communities.

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