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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Indonesian Journal of Artificial Intelligence and Data Mining InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Indonesian Journal of Social and Environmental Issues (IJSEI) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Soft Computing Exploration Jurnal Pepadun Malcom: Indonesian Journal of Machine Learning and Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Andrian, Rico
Ilmu Komputer FMIPA Unila
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Tree Damage Type Classification Based on Forest Health Monitoring Using Mobile-Based Convolutional Neural Network Gandadipoera, Faishal Hariz Makaarim; Andrian, Rico; safei, rahmat
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29421

Abstract

One of the fundamental parts of surveying forest health conditions with Forest Health Monitoring (FHM) is to visually assess the damage experienced by trees under certain conditions. This visual assessment can be facilitated using a Convolutional Neural Network (CNN) which involves building the MobileNetV2 model architecture. The model was trained using 1600 image data with 16 classes. The image data was pre-processed by resizing it to 224x224. The data was categorized into three categories: 80% was allocated for training, 10% for validation, and testing with 10% also. Training was done by changing the values from batches with a maximum of 100 epochs. The model was then incorporated into a mobile application using TensorFlow Lite and testing the application gave satisfactory results.  The model results get the best accuracy rate of 98.75% and a loss of 0.0497. This research concludes that the classification of tree damage types based on FHM with CNN can be done. For accurate results, the image provided by the user must be clear and reflect the actual damage observed on the tree.
Identifikasi Kupu-Kupu Menggunakan Ekstraksi Fitur Deteksi Tepi (Edge Detection) dan Klasifikasi K-Nearest Neighbor (KNN) Rico Andrian; Saipul Anwar; Meizano Ardhi Muhammad; Akmal Junaidi
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 2 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i2.1744

Abstract

Lampung has the only breeding of in situ butterflies engineered in Indonesia namely Gita Persada Butterfly Park, which has approximately 211 butterfly species. Butterflies can be classified according to patterns found on the wings of a butterfly. The weakness of the human eye in distinguishing patterns on butterflies is a foundation in building butterfly identification based on pattern recognition. This study uses 6 species of butterflies: Papilio memnon, Troides helena, Papilio nephelus, Cethosia penthesilea, Papilio peranthus, and Pachliopta aristolochiae. The butterfly dataset used is 600 images. The butterfly image used is in the form of the upper wing side. The pre-processing stage uses the method of scaling, segmentation, and grayscale. The feature extraction stage uses the canny edge detection method by applying smoothing, edge strength, edge direction, non maximum suppression, and hyterisis thresholding. The classification phase uses the K-Nearest Neighbor (KNN) method with values k = 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 obtained under the Rule of Thumb. The identification of butterfly require a classification time of 8 seconds. The highest accuracy is obtained from testing with a value of k = 5 by 80%.
Pengembangan Sistem Informasi Manajemen Supplier dan Barang dengan Extreme Programming Astria Hijriani; Jannati Asri Safitri; Raden Irwan Adi Pribadi; Rico Andrian
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.2132

Abstract

The case study was taken from one of trading companies in Lampung. The company sells Muslim fashion products from a large number of suppliers. Suppliers data is recorded in detail manually, as well as products recorded. Manual data collection can result in recording errors, data easily tucked, or not recorded. This research develops an information system to help the company in data collection of suppliers and products automatically based on web using Laravel as a framework. This system is built using extreme programming methods and has features that focus on collecting suppliers, products, and product shipments. The results of system testing using the black box testing method shows that the system has fulfilled functional requirements and user needs. Keywords— Management Information System; Product; Supplier.
Development of EfficientNet Model on Broad and Needles Leaved Species Tree Crowns with Forest Health Monitoring Method Hernani, Livia Ayu Istoria; Andrian, Rico; Safei, Rahmat; Tristiyanto, Tristiyanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37463

Abstract

Forest Health Monitoring (FHM) is a method for monitoring forest health conditions using various ecological indicators, such as tree canopy density and transparency. This research aims to evaluate the performance of the EfficientNet model in classifying the density and transparency values of broadleaf and coniferous tree canopies. The dataset consists of 3,956 tree canopy images collected from Tahura Wan Abdul Rachman (WAR), a conservation forest in Lampung, and is divided into 10 classes based on magic cards. Magic cards are a learning medium in the form of picture cards containing values of density and transparency. This research uses the EfficientNet-B0 architecture with certain training parameters. The results show that the EfficientNet-B0 model provides the best performance with an accuracy of 90.00%, a precision of 97.00%, a recall of 97.00%, and an F1-score of 97.00%. This research shows that EfficientNet can be used effectively to assist decision making related to automatic visual monitoring of forest health.
Implementasi YOLOv10 untuk Deteksi Kerapatan dan Transparansi Tajuk Pohon melalui Aplikasi Mobile Alkhadafi Saddam Simparico; Rico Andrian; Rahmat Safe'i; Admi Syarif
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9581

Abstract

Kerapatan dan transparansi tajuk pohon merupakan indikator penting kesehatan hutan yang berpengaruh terhadap keseimbangan ekosistem dan keanekaragaman hayati. Penelitian ini mengembangkan sistem deteksi real-time berbasis model YOLOv10 yang dioptimalkan untuk perangkat mobile melalui konversi ke TensorFlow Lite, sehingga memungkinkan inferensi cepat dan efisien di lapangan tanpa memerlukan perangkat komputasi besar. Dataset yang digunakan terdiri dari 5.000 citra tajuk pohon yang mencakup sepuluh kelas variasi kerapatan dan transparansi, mewakili lima jenis daun jarum dan lima jenis daun lebar dengan perbedaan morfologi dan karakteristik transmisi cahaya. Pengambilan data dilakukan pada berbagai sudut pandang untuk meningkatkan ketahanan model terhadap kondisi nyata di lapangan. Data dibagi menjadi 70% untuk pelatihan, 10% untuk validasi, dan 20% untuk pengujian. Hasil evaluasi menunjukkan akurasi 97,7% dengan nilai precision, recall, dan F1-score yang tinggi di setiap kelas. Sistem ini berpotensi mempercepat proses survei lapangan, meningkatkan akurasi pemantauan ekosistem, dan menjadi alat pendukung pengambilan keputusan dalam pengelolaan hutan serta program konservasi. Pendekatan ini menawarkan solusi praktis dan terukur untuk pemantauan hutan berkelanjutan dengan memanfaatkan teknologi computer vision mutakhir di perangkat mobile
Co-Authors . Wamiliana Adi Pribadi, Raden Irwan Admi Syarif Admi Syarif Agatha Beny Himawan Ahmad Adi Wijaya, Ahmad Adi Akmal Junaidi Alkhadafi Saddam Simparico Ananto Danu Prasetyo Andikha Yunar Cornelius Dabukke Andriyan Hutomo Ardiansyah Ardiansyah Aristoteles, Aristoteles Astria Hijriani Astria Hijriani Astria Hijriani, Astria Ayu Taqiya Ulfa Basir Efendi Dedy Hermawan Dedy Miswar Destian ade anggi Sukma Diah Adi Sriatna Dian Riskiyana Didik Kurniawan Dwi Sakethi Dwi Sakethi Dwi Sakethi Dwi Sakethi Eka Fitri Jayanti Eko Septiawan Favorisen R. Lumbanraja Febi Eka Febriansyah Flaurensia Riahta Tarigan Florencia Irena Gandadipoera, Faishal Hariz Makaarim Heningtyas, Yunda Hernani, Livia Ayu Istoria Igo Febrianto Indrianti Indrianti Irawati, Anie Rose Ismail Indra Pratama Jannati Asri Safitri Kristina Ademariana Kurnia Muludi Lisa Suarni Lona Ertina M. Juandhika Rizky Machudor Yusman Maharani, Devi Malik Abdul Azis Malik Abdul Aziz Meizano Ardhi Muhammad Muhammad Chairuddin Muhammad Iqbal Muhammad Iqbal Muhammad, Meizano Ardhi Muhaqiqin Muhaqiqin Novita Dwilestari Octarina, Nur Ayu Prabowo, Rizky Prabowo, Rizky Pradana Marlando Qonitati Qonitati RA Dina Nia Pratiwi Raden Irwan Adi Pribadi Rahman Taufik Rahmat Safe'i Rahmat Safe'i Rahmat Safe'i Rahmat Safe’i Reda Meiningtiyas Rika Ningtias Azhari S Susiyani Safei, Rahmat Safitri, Jannati Asri Saipul Anwar Saipul Anwar Sholehurrohman, Ridho Sunita Agustina TANJUNG, AKBAR RISMAWAN Tri Maryono Tristiyanto Tristiyanto Utami, Noera Yudhiarti Verina, Vira Wamiliana Wamiliana Wartariyus Wartariyus Zuhri Nopriyanto