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All Journal ILKOM Jurnal Ilmiah Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jambura Journal of Electrical and Electronics Engineering Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Minfo Polgan (JMP) Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Journal Of Informatics And Busisnes Empiris Jurnal Pengabdian Pada Masyarakat (EJPPM) Jurnal Pengabdian Pada Masyarakat Eldimas
Maryam Hasan
Universitas Ichsan Gorontalo
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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The Implementation of GLCM and ANN Methods to Identify Dragon Fruit Maturity Level Faisal, Muhammad; Hasan, Maryam; Pelangi, Kartika Candra
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.v15i1.1504.64-71

Abstract

The identification of the maturity level of dragon fruit in this study was divided into two groups of ripeness: the unripe and the ripe. This study aims to classify the maturity level based on dragon fruit images using the feature extraction method, the gray level co-occurrence matrix (GLCM). This research method consists of converting RGB data to grayscale, image normalization, detection of dragon fruit maturity, feature extraction, and identification. Data collection from real data totaled 60 images used in this study consisting of 40 training data and 20 testing data which are RGB image data in JPG format. Each data consists of 2 maturity categories. Training data consists of 20 images of 99% ripe dragon fruit and 20 images of 85%. Meanwhile, the testing data consisted of 10 of 99% ripe dragon fruit images and 10 of 85% ripe dragon fruit images. The image data is processed into a grayscale image which then detects the ripeness of the dragon fruit. After the maturity of the dragon fruit is obtained, segmentation is carried out on the location of the dragon fruit found. Then the feature calculation is performed using the Gray Level Co-Occurrence Matrix (GLCM). The Artificial Neural Network (ANN) algorithm is used for the identification process. The final test results show that the proposed method has been able to detect dragon fruit maturity level with an accuracy of = 9/10* 100% = 90%, calculated using the confusion matrix. Thus, implementing the Gray Level Co-Occurrence Matrix and Artificial Neural Network methods to the maturity level problem dragon fruit needs to be developed.
Sistem Pakar Diagnosa Penyakit Tanaman Kacang Panjang Menggunakan Metode Forward Chaining Siti Azizah Tondako; Irma Surya Kumala Idris; Maryam Hasan
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 5 No 1 (2026): Mei 2026
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/d2qjgt53

Abstract

Penelitian ini merancang dan mengimplementasikan sistem pakar untuk mendiagnosis penyakit pada tanaman kacang panjang menggunakan metode forward chaining. Sistem ini bertujuan untuk membantu petani dalam mengidentifikasi dan mengendalikan penyakit pada tanaman kacang panjang. Metode forward chaining digunakan sebagai mekanisme inferensi, di mana pengetahuan dalam basis data diproses secara bertahap berdasarkan data yang diberikan menuju kesimpulan diagnosis. Hasil penelitian menunjukkan bahwa aplikasi yang dirancang efektif dalam mendiagnosa penyakit tanaman kacang panjang, dibuktikan dengan pengujian White Box yang menghasilkan nilai Cyclomatic Complexity (CC), Volume Graph (VG), dan Region (R) sebesar 4, serta pengujian Black Box yang menunjukkan semua fungsi berjalan sesuai harapan. Dengan demikian, penerapan metode forward chaining dalam sistem pakar ini terbukti efektif dan memberikan alat yang bermanfaat bagi petani dalam menjaga kesehatan tanaman kacang panjang.
Analisis Kualitas Website UMKM Nasional Menggunakan Metode Heuristic Evaluation dan Google Lighthouse Apriyanto Alhamad; Sudirman Melangi; Maryam Hasan; Sitti Nur Avifa Olii; Warid Yunus; Hastuti Dalai
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16223

Abstract

This study analyzes the quality of SMEsta, a national MSME digital portal, by combining user-based Heuristic Evaluation and Google Lighthouse auditing. The background of the study is the strategic role of public websites in supporting MSME digitalization and the need to evaluate not only interface convenience but also technical readiness on mobile access. The objective is to describe SMEsta usability, identify technical quality gaps, and formulate improvement priorities from both sources of evidence. The research used a quantitative descriptive case-study approach. Users evaluated three pages, namely Home, Export MSME Recommendations, and FAQ, through a Likert questionnaire mapped to Nielsen's ten heuristics, while the same pages were audited through Google Lighthouse mobile configuration. The instrument was validated by experts and showed reliable internal consistency with Cronbach's alpha of 0.910. From 105 responses, 100 valid responses were analyzed. The findings show that SMEsta usability is in the high category, with an overall mean of 4.19 and an index of 79.80. Error prevention obtained the highest mean of 4.32, whereas help users recognize, diagnose, and recover from errors was the lowest with a mean of 3.73. Lighthouse results indicate uneven technical quality, especially performance: the Export MSME Recommendations page scored 56, the Home page scored 36, and the FAQ page scored 23, while SEO reached 100 on all pages. The study concludes that SMEsta is generally usable, but performance optimization and clearer contextual error support should become priority improvements
Co-Authors Almer Hassan Ali Amiruddin Bengnga Apriyanto Alhamad Bahrin Bahrin Betrisandi, Betrisandi Gaffar, Gaffar Haba, Abd. Rahmat Karim Hastuti Dalai Husdi Irma Surya Kumala Idris Ismail S Manto Juriko Abdussamad Mahmud, Mohamad An Nafli Muhammad Erdiansyah Muhammad Faisal Panna, sudirman S. Pelangi, Kartika Candra Ramadhani, Syarifah Fitrah Rifdah Rofifah Faruk Abdullah Salihi, Irvan Siska Udilawati Siti Azizah Tondako Sitti Nur Avifa Olii Sudirman Melangi Sudirman S Panna Swastiani Dunggio Syahrir Abdussamad Warid Yunus Yulianti Lasena Zulfrianto Yusrin Lamasigi