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All Journal ILKOM Jurnal Ilmiah Jambura Journal of Electrical and Electronics Engineering Jurnal Pengabdian Masyarakat Asia International Journal Education and Computer Studies Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Perbal: Jurnal Pertanian Berkelanjutan
Zulfrianto Yusrin Lamasigi
Universitas Ichsan Gorontalo
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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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 Bode, Andi; Lamasigi, Zulfrianto Y; Drajana, Ivo Colanus Rally
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

Abstract

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.
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 Lamasigi, Zulfrianto Yusrin; Serwin, Serwin; Malago, Yusrianto
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

Abstract

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.
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 : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

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

Abstract

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.
Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor Lamasigi, Zulfrianto Yusrin; Bode, Andi
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

Abstract

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.
Implementasi Sistem Pakar dengan Certainty Factor untuk Identifikasi Akurat Hama dan Penyakit Tanaman Jagung di Provinsi Gorontalo : Implementation of an Expert System with Certainty Factor for Accurate Identification of Corn Pests and Diseases in Gorontalo Province Muhammad Iqbal Jafar; Zulfrianto Y Lamasigi; Indah Puspitasari; Mayasari Yamin
Perbal: Jurnal Pertanian Berkelanjutan Vol. 14 No. 2 (2026): Perbal: Jurnal Pertanian Berkelanjutan
Publisher : Fakultas Pertanian Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/hkadzs91

Abstract

Penelitian ini bertujuan untuk mengembangkan dan menguji sistem pakar untuk mengidentifikasi hama dan penyakit pada tanaman jagung di Provinsi Gorontalo, Indonesia, dengan menerapkan metode Certainty Factor (CF). Sistem ini dibangun berdasarkan basis pengetahuan yang diperoleh dari wawancara mendalam dengan para ahli, termasuk akademisi, penyuluh pertanian, dan peneliti. Metode CF digunakan untuk mengatasi ketidakpastian dalam diagnosis dengan menghitung nilai kepercayaan dari kombinasi gejala yang diamati oleh pengguna (petani/penyuluh). Hasil uji menunjukkan kemampuan sistem untuk mendiagnosis penyakit seperti penggerek batang dengan tingkat kepercayaan hingga 98,5%. Analisis ini menegaskan efektivitas metode CF dalam memberikan diagnosis yang terukur dan akurat (>80%), yang difasilitasi oleh antarmuka yang ramah pengguna. Sistem ini berfungsi tidak hanya sebagai alat diagnostik cepat di lapangan tetapi juga sebagai media pendidikan, menjembatani kesenjangan pengetahuan antara para ahli dan petani. Keberhasilan implementasi merekomendasikan metode CF sebagai pendekatan yang layak untuk sistem identifikasi penyakit tanaman, dengan catatan bahwa basis pengetahuan memerlukan pembaruan berkala. Penelitian ini berkontribusi untuk meningkatkan upaya ketahanan pangan melalui deteksi dini yang tepat. This study aims to develop and test an expert system for identifying pests and diseases in corn crops in Gorontalo Province, Indonesia, by applying the Certainty Factor (CF) method. The system was built on a knowledge base derived from in-depth interviews with experts, including academics, agricultural extension workers, and researchers. The CF method was employed to address uncertainty in diagnosis by calculating confidence values from combinations of symptoms observed by users (farmers/extension workers). Test results demonstrated the system's capability to diagnose diseases such as stem borer with a confidence level of up to 98.5%. The analysis confirms the effectiveness of the CF method in providing measurable and accurate diagnoses (>80%), facilitated by a user-friendly interface. This system functions not only as a rapid diagnostic tool in the field but also as an educational medium, bridging the knowledge gap between experts and farmers. The successful implementation recommends the CF method as a viable approach for plant disease identification systems, with the note that the knowledge base requires periodic updates. This research contributes to enhancing food security efforts through precise early detection.
Development of a Real-Time Face Recognition Attendance System Based on Face Embedding Using the FaceNet Architecture Irvan Abraham Salihi; Irma Surya Kumala Idris; Yasin Aril Mustofa; Zulfrianto Yusrin Lamasigi; Ardiansyah Kadir
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 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.v8i2.38763

Abstract

This study aims to develop and evaluate an efficient and accurate face embedding-based attendance system to address the limitations of the fingerprint-based attendance system still in use at Ichsan Gorontalo University. The system was developed using the FaceNet model for 512-dimensional face embedding extraction, with facial similarity comparison performed using Cosine Distance. The system development followed the Waterfall methodology, encompassing analysis, design, implementation, and testing phases. Testing was conducted through three approaches: White Box Testing to evaluate programming logic, Black Box Testing for functional validation, and User Acceptance Testing (UAT) to measure user satisfaction. Accuracy testing was performed under three different conditions involving 10 volunteers (7 registered, 3 unregistered): neutral facial expression (at 1 meter distance), smiling expression (at 1 meter distance), and 5-meter distance. The results demonstrate that the system exhibits low logical complexity with a Cyclomatic Complexity (CC) value of 7, all functional components operate without significant errors, and it achieved a user satisfaction rate of 84.53% (Grade B). Accuracy testing yielded 90% accuracy under both neutral and smiling expression conditions, but decreased to 60% at 5-meter distance. The system achieved an average response time of 1.2 seconds with memory usage below 2 GB. This study concludes that the face embedding-based attendance system is effective and efficient for use under normal facial expression conditions and close-range scenarios, and is recommended for implementation as a more accurate and hygienic modern attendance solution.
Classification of Chili Plant Diseases Through GLCM Feature Selection and the K Parameter in the K-Nearest Neighbor Ratna A. Fi’Nawu; Irvan Abraham Salihi; Zulfrianto Yusrin Lamasigi; Irma Surya Kumala Idris
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

Abstract

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.
Co-Authors Ade Moh Faridz Amiruddin Bengnga Andi Bode Apriyanto Alhamad Ardiansyah Kadir Drajana, Ivo Colanus Rally Efendi Lasulika, Mohamad Firmansyah Kadir Haba, Abd. Rahmat Karim Hulukati, Stephan A. Husdi Husdi Indah Puspitasari Irma Surya Kumala Idris Irvan Abraham Salihi Jafar, Muhammad Iqbal Kolopita, Roji Anugrah Maryam Hasan Mayasari Yamin, Mayasari Mooduto, Sarlis Mundok, Geby Mustofa, Yasin Aril Pontoh, Iftahul Farhan Ratna A. Fi’Nawu Roys Pakaya Serwin SERWIN, SERWIN Syamsir Syamsir Yasin Aril Mustofa Yulianti Lasena Yusrianto Malago