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All Journal Jurnal Informatika Jurnal Teknologi JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING SEIKO : Journal of Management & Business Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi)
Heliza Rahmania Hatta, Heliza Rahmania
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Land Suitability for Mustard Plants Using Multi-Objective Optimization by Ratio Analysis Method Hatta, Heliza Rahmania; Ariani, Riska; Khairina, Dyna Marisa; Maharani, Septya; Kamila, Vina Zahrotun; Wijayanti, Arini
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1290

Abstract

Sawi dapat dikembangkan atau dikembangkan dari sudut pandang finansial dan bisnis untuk memenuhi permintaan pembeli dan menangkap peluang pasar yang signifikan. Sawi merupakan tanaman hortikultura yang mempunyai daya adaptasi tinggi dan waktu panen yang relatif singkat. Sawi ini menawarkan banyak keuntungan bagi petani. Misalnya saja banyak petani yang menanam sawi di Samarinda, Kalimantan Timur, Indonesia. Meskipun sangat mudah beradaptasi, beberapa spesies sawi tidak tumbuh subur di tanah tertentu. Tanah yang baik sangat penting untuk hasil optimal saat menanam sawi. Sawi yang ditanam dapat diseleksi dengan menggunakan pendukung keputusan berdasarkan kriteria lahan untuk mendapatkan hasil terbaik. Tujuan dari penelitian ini adalah untuk merekomendasikan tanaman sawi yang cocok berdasarkan kebutuhan luas dengan menggunakan pendekatan multi-objective optimize by ratio analysis (MOORA). MOORA merupakan suatu metode pengambilan keputusan yang membantu dalam memilih alternatif terbaik dari beberapa pilihan atau alternatif berdasarkan beberapa kriteria atau tujuan. Pengamatan ini menggunakan lima kriteria yaitu jenis tanah, pH tanah, curah hujan, suhu, ketinggian lokasi, dan enam alternatif sawi. Berdasarkan uji lahan, sawi yang direkomendasikan metode MOORA adalah Sawi Sendok atau Pak Choy dengan nilai Yi sebesar 7,6698. Jadi yang dipilih sebagai sawi yang ditanam di lahan tersebut adalah Sawi Sendok atau Pak Choy. Untuk penelitian selanjutnya perlu dilakukan penambahan atau penyesuaian kriteria dan sensor baru secara real-time yang dapat diterapkan untuk meningkatkan efisiensi sawi menuju smart farming yang fokus pada hasil yang lebih baik dengan tetap menjaga keseimbangan alam.
Grade Classification of Agarwood Sapwood Using Deep Learning Hatta, Heliza Rahmania; Nurdiati, Sri; Hermadi, Irman; Turjaman, Maman
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2257

Abstract

The agarwood tree (Aquilaria sp.) is a tree that produces agarwood, which is a black resin that has a distinctive fragrant smell. In Indonesia, one that is commonly traded is sapwood agarwood. Agarwood sapwood is black or brownish-black wood obtained from the parts of the agarwood-producing tree containing a strong aromatic mastic. Based on the Indonesian National Standard (SNI) 7631:2018, agarwood sapwood has three classes: Super Double, Super A, and Super B. However, many agarwood farmers need to learn to differentiate and classify the agarwood sapwood classes, and traders exploit this to buy cheap. So, deep learning can be used to classify the agarwood sapwood class. One of the uses of deep learning is in image processing. Image processing is used to help humans recognize or classify objects quickly and precisely and can process many data simultaneously. One of the deep learning algorithms used in image processing is the Convolutional Neural Network (CNN). In this study, it is proposed that the deep learning model used is CNN with batch normalization. The dataset used is 72 agarwood sapwood images with a white background, each consisting of 24 Super A, 24 Super B data, and 24 Super Double data. The dataset is divided into 80% training and 20% testing data. The evaluation results of the proposed method at 100 epochs show an accuracy of 87.5%. The research implications will help agarwood tree farmers differentiate and classify agarwood sapwood so that farmers get the right price from buyers.
Diagnosis of Diseases in Rubber Stems Using the Dempster Shafer Method Sukmono, Yudi; Pratiwi, Sinthya Ayu; Hatta, Heliza Rahmania; Septiarini, Anindita; Padmo Azam Masa, Amin; Wijayanti, Arini
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3474

Abstract

Rubber (Hevea Brasiliensis) is a non-timber forest product originating from the Americas and is currently widely distributed worldwide, including in East Kalimantan, Indonesia. In their management in East Kalimantan, farmers often encounter diseases in rubber plants, especially diseases of the stems, which can cause plant death. This disease requires treatment, but if it is too severe, it can harm farmers economically and in production, so it is essential for farmers to recognize the symptoms of this disease early from changes in the rubber plant stems. This study aims to diagnose diseases of rubber stems using the Dempster Shafer method. Dempster Shafer is a relevant method for overcoming the uncertainty of symptoms and rules, enabling expert systems to generate conclusions with certainty. This method has advantages in solving various problems and simultaneously combining evidence (facts) from several sources. This research was conducted by analyzing a dataset of 80 data, covering 7 types of diseases and 27 different symptoms. The accuracy test results show that the research has an accuracy rate of 96.25%. The implications of this research are significant. It is hoped that it can significantly help rubber plantation farmers in East Kalimantan and also make a valuable contribution to agricultural and plantation extension agents in overcoming the challenges faced due to diseases in rubber plant stems. Thus, this research could increase the productivity and sustainability of the rubber plantation sector in this region.
Co-Authors Anindita Septiarini, Anindita Ariani, Riska Arini Wijayanti Awang Harsa Kridalaksana Baskoro, Roby Gunawan Budi Gunawan Dyna Marisa Khairina Fahrul Agus Fazari, Alawiyah Nur Firdha Ayu Selvyani, Firdha Ayu Firman Firman Haqqi, Kamalul Irman Hermadi Kamila, Vina Zahrotun Kurniati, Wendy Maman Turjaman Nasution, Ali Napiah Nataniel Dengen Padmo Azam Masa, Amin Pratiwi, Sinthya Ayu Puspita Puji Rahayu, Puspita Puji Puspitasari, Novianti Ramadiani - Rizqoni, Rizqoni Sampe, Ferdinandus Santosa, Sonny Sembiring, Rinawati Septya Maharani, Septya Shapanara, Rhenaldi Octa Sri Nurdiati Yudi Sukmono, Yudi Yunizar Fahmi Badron, Yunizar Fahmi Zainal Arifin