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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal MIPA Jurnal Teknik Informatika Jurnal TIMES JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah Jurnal Ilmiah Behongang Journal of Applied Data Sciences Operations Research: International Conference Series JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Journal of Agriculture (JoA) Jurnal Ilmiah Sains Bulletin of Social Informatics Theory and Application d'Cartesian: Jurnal Matematika dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Luther Latumakulita
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : JOIV : International Journal on Informatics Visualization

 1 
A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels Latumakulita, Luther Alexander; Paat, Frangky J; Budiman, Glenn; Tooy, Dedie; Koibur, Mayko Edison; Islam, Noorul
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2574

Abstract

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.
Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network Latumakulita, Luther Alexander; Arya Astawa, I Nyoman Gede; Mairi, Vitrail Gloria; Purnama, Fajar; Wibawa, Aji Prasetya; Jabari, Nida; Islam, Noorul
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1082

Abstract

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification.  Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.
Co-Authors Aji Prasetya Wibawa Altien Rindengan Altien Rindengan Alwin Melkie Sambul Ambarita, Yolanda Margareta Anastasia, Lenshy Aprisilia Andar Alwein Pinilas Arista Mandagi Arthur G. Pinaria Assa, Jan Rudolf Benny Pinontoan Bernard Bernard, Bernard Bobby Polii Budiman, Glenn Chriestie E. J. C. Montolalu Chriestie E. J. C. Montolalu Dedie Tooy Deiby Tineke Salaki Djoni Hatidja Eliasta Ketaren, Eliasta Enny Itje Sela Fajar Purnama Felliks Tampinongkol, Felliks Frangky J. Paat Gybert Saselah I Nyoman Gede Arya Astawa Islam, Noorul Jabari, Nida Jantje Pongoh Jevenston Lalenoh John Socrates Kekenusa Julana Rarung Julana Rarung, Julana Jullia Titaley Karim, Irwan Koibur, Mayko Edison Kusuma, Samuel D. A. Lapihu, Dodisutarma Lindsay Mokosuli Liwu, Suzanne L. Mairi, Vitrail Gloria Mamuaja, Christine F Manarisip, Endrue Jehezkiel Mandagi, Franklin Mans Mananohas Mans Mananohas, Mans Marni Sumarno Marni Sumarno, Marni Max R Kumaseh Miske Silangen Montolalu, Chriestie Ellyane Juliet Clara NELSON NAINGGOLAN NELSON NAINGGOLAN Ngangi, Stefano C.W. Ngangi, Stephano Caesar Wenston Noorul Islam Noviania, Reski Oessoe, Yoakhim Y.E. Paat, Frangky J Paat, Frangky Jessy Paat, Franky Pagewang, Yalon Bu'tu Pinatik, Herry F Pioh, Diane Raintung, Stephanie Marceline Rindengan, Altien J. Rinny Mamarimbing Rumambi, David P Salaky, Deiby Tineke Sandra Pakasi Sandy Laurentius Lumintang Sanriomi Sintaro Saroyo Saroyo Selvie Tumbelaka Sirait, Hasanuddin Sofia Wantasen Steven Ray Sentinuwo Sulu, Brian Sumual, Gery Josua Surahman, Ade Takaendengan, Mahardika Inra Tangkeallo, Sindy C. T. Teltje Koapaha Tenda, Edwin Tineke M. Langi Vederico Pitsalitz Sabandar Winsy Weku Winsy Weku Yohanes Langi Yohanes Langi