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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.

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

Implementasi Augmented Reality Sebagai Media Pengenalan Monumen Bersejarah Gorontalo Berbasis Android Kolopita, Roji Anugrah; Y Lamasigi, Zulfriyanto; Pakaya, Roys
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 2 (2023): November 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i2.710

Abstract - Augmented Reality is a technology that combines 2-dimensional objects or 3- dimensional objects into images on markers and then projects the 3D objects directly. It is very useful as a tourism data media to attract tourists both domestic and foreign, as well as to share different experiences and guidance in mastering each place or tourism attraction visited. In making this application the researcher uses unity3D by applying the Marker-Based Tracking method. This marker method is used to detect markers in an Augmented Reality application so that it can display 3D visualizations of Gorontalo historical monuments. The utilization of Augmented Reality technology in this recognition system can provide an overview of the Gorontalo Historical Monument so that it can increase the attractiveness of the public to get to know historical monuments in Gorontalo. Keywords: augmented reality, android, marker-based tracking, Vuforia

Klasifikasi Waktu Kelulusan Mahasiswa Menggunakan Metode Decision Tree Mundok, Geby; Amiruddin; Zulfrianto Y. Lamasigi
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 3 No 1 (2024): Mei 2024
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v3i1.736

Abstract A student's graduation time is one indicator of the university's success. Student graduation time is the period required for students to complete their studies. Ideally, students can graduate on time under a specified study period. However, not all students can graduate on time. This study is aimed at classifying whether students graduate on time using the decision tree method. The data employed in this study are the Faculty of Law's graduates of 2015 - 2017, Universitas Ichsan Gorontalo. The attributes used in this classification consist of IPS1, PIS2, IPS3, IPS4, gender, and graduation information. In this study, model optimization performed is by selecting attributes, pruning trees, and measuring inside the tree. The results of this study show that the decision tree method can predict student graduation times with 92% accuracy by producing nine (9) decision rules. It indicates that the decision tree method can be a solution for predicting student graduation times, so it can be a solution to help study programs increase student success in completing their studies. Keywords: classification, graduation time, students, data mining, decision tree, accurate graduation

Prototype Alat Ukur Tingkat Pencemaran Udara Menggunakan Nodemcu Esp 8266 Dan Mq 135 Ade Moh Faridz; Husdi; Apriyanto Alhamad; Zulfrianto Y. Lamasigi
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 3 No 2 (2024): November 2024
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v3i2.1204

Pencemaran udara adalah kondisi di mana udara mengandung bahan-bahan kimia, partikel, atau mikroorganisme yang membahayakan kesehatan manusia, hewan, tumbuhan, serta merusak lingkungan. Pada era industrialisasi dan urbanisasi yang pesat, aktivitas manusia seperti pembakaran bahan bakar fosil, produksi industri, dan penggunaan kendaraan bermotor telah meningkatkan kadar polutan di atmosfer. Pada Penelitian Ini akan menggunakan Sensor MQ 135 untuk mengukur kualitas udara dan NodeMCU Esp8266 Sebagai Mikrokontoller. Kelebihan dari dari penggunaan NodeMCU ESP8266 ini lebih praktis dibandingkan membeli berbagai macam komponen dan kemudian merakitnya sendiri, NodeMCU ESP8266 dapat berfungsi sebagai mikrokontroller dan dapat mendistribusikan data dari sensor ke perangkat lain melalui distrubusi internet sehingga pengontrolan nirkabel sangat memungkinkan. Sedangkan Sensor Sensor MQ 135 adalah sebuah sensor lingkungan dengan suhu, tekanan barometrik, dan kelembaban. Sensor ini bagus untuk semua jenis penginderaan lingkungan dalam ruangan Berdasarkan uraian di atas , maka perlu bagi peneliti mengangkat judul penelitian Prototype Alat Ukur tingkat Pencemaran Udara Menggunakan Nodemcu dan esp8266. Berdasarkan hasil Penelitian Yang telah dilakukan dan pembahasan yang telah diuraikan sebelumnya, maka dapat ditarik suatu kesimpulan bahwa Perancangan Alat Ukur kualitas udara dapat dilakukan dengan menggunakan sensor MQ135 Hasil perakitan alat dapat mengetahui tingkat pencemaran udara dengan satuan PPM Kata Kunci: Pencemaran Udara, Nodemcu esp8266, Sensor Mq135

Automated Drip Irrigation System Based on IoT for Chili Plants Using Solar Panel Energy Lamasigi, Zulfrianto Yusrin; Haba, Abd. Rahmat Karim; Jafar, Muh. Iqbal; Syamsir, Syamsir; Hulukati, Stephan A.
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.

Game Edukasi Menyusun Nama Hewan Sebagai Media Pembelajaran Anak-Anak Menggunakan Construct 2 Pontoh, Iftahul Farhan; Mustofa, Yasin Aril; Lamasigi, Zulfrianto Yusrin
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 4 No 2 (2025)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v4i2.900

This research aims to develop an educational game to provide alternative learning and knowledge about animals and their types. The problem faced is the lack of interest and understanding of children's conventional learning materials that tend to be monotonous and non-interactive, which often results in decreased motivation to learn at home and school. To overcome this problem, an educational game application using Construct 2 software. The stages include system testing, development, construction, design, and analysis. This research takes place at SD Negeri 03 Kabila, engaging 30 students as respondents. The assessment of the game is carried out through a questionnaire analyzed descriptively and qualitatively to assess the feasibility of the application. The test results show that the application is free from component errors based on Black Box testing and is well received by users based on User Acceptance testing with a total score of 86.4%, categorized as very good. However, the random system used to generate the questions reappears which can reduce the variety and effectiveness of learning. The improvements to this system are proposed to enhance the learning quality. The development of this educational game application can be an effective solution to increase children's interest and understanding in learning while providing a fun learning experience while providing a fun and interactive learning experience.Keywords: educational game, Construct 2, animal recognition

Classification of Chili Plant Diseases Through GLCM Feature Selection and the K Parameter in the K-Nearest Neighbor Fi’Nawu, Ratna A.; Salihi, Irvan Abraham; Lamasigi, Zulfrianto Yusrin; Idris, Irma Surya Kumala
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34661

Chili pepper (Capsicum annuum L.) is a strategic horticultural commodity in Indonesia with high economic value. However, chili plants are often infected by diseases such as Anthracnose, Fusarium Wilt, Fruit Fly, and Thrips, which can lead to significant yield losses. Early and accurate identification of these diseases is crucial for effective control measures. This study aims to classify chili plant diseases based on leaf images using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and the K-Nearest Neighbor (K-NN) algorithm for classification. A total of 736 leaf images were used, divided into four disease classes. The pre-processing stages included resizing the images to 300×300 pixels, rotation augmentation (0°, 45°, 75°, 90°), and conversion to grayscale. Textural features were extracted using GLCM at four angles, and K-NN was applied with K values of 5, 7, and 9. The highest classification accuracy of 88.19% was achieved at a GLCM angle of 0° and K=5, with an overall average accuracy across all angles of 85.06%. These findings not only reinforce previous findings on the effectiveness of GLCM and K-NN but also contribute by identifying the optimal parameter configuration (angle 0° and K=5) for the specific chili disease dataset. The results have the potential to be applied as a foundation for developing an automated plant disease detection system in the field.

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