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All Journal JOIV : International Journal on Informatics Visualization JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah JPPI (Jurnal Pendidikan Islam Pendekatan Interdisipliner) Journal Peqguruang: Conference Series Journal of Industrial Automation and Electrical Engineering
Assidiq, Muhammad
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Multilayer Perceptron Model with Feature Extraction for Potassium Deficiency Identification of Cocoa Plants Basri, Basri; Karim, Harli A; Assidiq, Muhammad; Arafah, Muhammad; Rahmadani, Fitria
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2829

Abstract

The development of Multilayer Perceptron (MLP) models for networked learning systems heavily relies on the specific application case study and the accurate parameterization aligned with the chosen computer vision feature extraction models. This study proposes an MLP model for identifying potassium deficiency in cocoa plants. The feature extraction methodology employs object feature extraction that commonly used in computer vision, including Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), and Hue Saturation Value (HSV) models. These computer vision techniques aid in analyzing leaf characteristics classified into two categories: normal conditions and leaves identified with potassium deficiency. The dataset used in this research comprises two conditions: with a white background and without any specific background. The study evaluates various feature extraction techniques based on MLP parameters, incorporating network learning rates and optimizing solvers. Employing the ROC analysis method throughout the data collection, algorithm development, validation, and analysis phases reveals that the most effective classification performance, reaching up to 93.33% accuracy on the background dataset and 90.00% on the non-background dataset, is achieved using HSV-based color feature extraction with MLP parameters set at an initial learning rate of 10-3 and employing the Adam optimization solver. These outcomes underscore the suitability of HSV color feature extraction for identifying potassium deficiency in cocoa plant leaves. However, optimizing parameters remains crucial to maximize its application in real-time identification systems. Future research should refine these parameters to enhance the model's robustness and efficacy across broader agricultural contexts.
Penerapan Codeigniter Dalam Pengembangan Pembelajaran Daring Di SMKN 4 Majene SHIHAB, ALWI; ASSIDIQ, MUHAMMAD; ASQALANI, MUH AL HIJR
Journal Peqguruang: Conference Series Vol 7, No 1 (2025): Peqguruang, Volume 7 Nomor 1 Mei 2025
Publisher : Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jp.v7i1.5474

Abstract

Perkembangan teknologi digital merupakan bagian dari sistem informasi yang mendukung sistem tersebut dalam menghasilkan berbagai informasi yang diperlukan oleh suatu perusahaan untuk mencapai tujuan tertentu. Pertumbuhan teknologi digital di Indonesia telah mengalami kemajuan yang signifikan, didukung oleh perkembangan teknologi komunikasi yang sangat berarti merupakan pilihan yang tepat bagi bidang pendidikan untuk mendukung kinerja dari penyelenggaraan pendidikan tersebut agar dapat beroperasi dengan efisien. Pengembangan Sistem Pembelajaran Online menggunakan CodeIgniter membantu sekolah dalam mengawasi proses pembelajaran secara langsung, termasuk mengunggah materi, video pembelajaran, mengumpulkan tugas dari siswa dan guru, sehingga mendukung pengawasan pembelajaran online. Hasil pengujian menggunakan model TAM untuk penerimaan teknologi mencapai 85,41% dengan kualifikasi Sangat Baik untuk penerimaan teknologi dalam aplikasi pembelajaran online.
PENERAPAN UNIFIED MODELLING LANGUAGE (UML) PADA ANALISIS SISTEM SERTA PERANCANGAN DATABASE TIMBULAN SAMPAH HERLINA, HERLINA; ASSIDIQ, MUHAMMAD
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 6 No 2 (2021): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v6i2.23994

Abstract

Masyarakat Sulawesi Barat memiliki kebiasaan untuk membakar sampah, baik sampah organik maupun sampah non-organik. Padahal kebiasaan membakar sampah dapat meningkatkan pemanasan global. Kebiasaan ini tidak dapat diubah seketika dengan edukasi saja. Sebenarnya pemerintah sudah melaksanakan beberapa program penanggulangan sampah, tetapi belum tepat sasaran. Hal ini disebabkan karena validnya data timbulan sampah yang dihasilkan rumah tangga setiap hari. Solusinya adalah dengan terciptanya sebuah aplikasi pendataan timbulan sampah rumah tangga berbasis database sehingga memudahkan stakeholder dalam hal ini pemerintah daerah, terkait pengambilan keputusan perencanaan pembangunan program persampahan serta pemilihan teknologi yang akan diterapkan. Untuk analisis dan perancangan sistem timbulan sampah ini, peneliti menggunakan pemodelan Unified Modelling Language (UML) karena UML memiliki kemampuan fleksibilitas yang baik untuk pengembangan konsep perangkat lunak.Kata Kunci : Database, UML, Timbulan Sampah
Customer Relationship Management Sistem Untuk Optimalisasi Sistem Rantai Pasok Pada Usaha Tambak Perikanan Sahrul, Muh; Assidiq, Muhammad; Qashlim, Akhmad
JPPI (Jurnal Pendidikan Islam Pendekatan Interdisipliner) Vol 9 No 1 (2025): JPPI Volume 9 Nomor 1 Juni 2025
Publisher : UI DDI AGH AD Polewali Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36915/jppi.v9i1.173

Abstract

This research aims to develop and implement a Customer Relationship Management (CRM) system to optimize the supply chain system in fish farming businesses. CRM systems are designed to increase operational efficiency, strengthen customer relationships, and optimize the flow of information throughout the supply chain. In the context of fish farming businesses, CRM can help in monitoring market needs, real-time stock management, and improving customer service through more effective communication. The research results show that implementing a CRM system is able to reduce operational costs, increase customer satisfaction, and strengthen the competitiveness of fish farming businesses in an increasingly competitive market. Through the integration of information technology, the CRM system provides a comprehensive solution to overcome challenges in supply chain management, while supporting the sustainability and growth of fisheries businesses.
IoT Based Digital Weight Scale in Rice Inventory for Small and Medium Entreprises Assidiq, Muhammad; Yelfianhar, Ichwan; Mirshad, Emilham
Journal of Industrial Automation and Electrical Engineering Vol. 1 No. 1 (2024): Vol 1 No 1 (2024): June 2024
Publisher : Department of Electrical Engineering Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the current digital era, Internet of Things (IoT) technology offers innovative solutions to increase efficiency and accuracy in various sectors, including the rice sales business. This journal discusses the design of IoT-based scales specifically designed to help with bookkeeping in rice sales businesses. The main objective of this research is to develop a weighing system that can automatically measure the weight of rice and integrate it with a digital bookkeeping system, thereby minimizing manual errors and increasing operational efficiency. The research methodology includes design of weighing hardware and software, implementation of weight sensors, and development of IoT applications that connect scales with bookkeeping systems. The system is designed to send real-time weight data to a cloud-based bookkeeping platform, enabling more accurate and faster monitoring and reporting. Test results show that this IoT-based scale can provide weight data with high accuracy and seamless integration with the bookkeeping system. Users report increased efficiency in the process of recording and managing rice sales data. In conclusion, IoT-based scales offer an effective solution for modernizing the bookkeeping process in rice sales businesses, supporting better decision making and improving the accuracy of financial reports.
Extreme learning machine with feature extraction using GLCM for phosphorus deficiency identification of cocoa plants Basri, Basri; Assidiq, Muhammad; Karim, Harli A.; Nuraisyah, Andi
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1226.112-119

Abstract

This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.
Co-Authors Akhmad Qashlim, Akhmad Ardi, Lobianus ASQALANI, MUH AL HIJR Asrina Asrina Bandu, Berti D. Basri Basri Fadriany, Fadriany Firman, M Herlina Herlina Iqra, Muhammad Karim, Harli A Karim, Harli A. Khairat, UL Mansur Mansur Marni Marni, Marni Mirshad, Emilham Muammar Muammar, Muammar Muhammad Arafah Muslihan, Muslihan Noprianto, Noprianto Nuraisyah, Andi Nurdina Nurdina Paidah, Patihatul Paramita, Paramita Qashlim, A. Akhmad Qaslim, Akhmad Rahmadani Rahmadani Rahmadani, Fitria S, Amriyani Amir Sahrul, Muh Sairah, St. Shihab, Alwi Sopyan, Edy Sutriani, Chichi Syamriadi, Syamriadi Syarli, Syarli Tamin, Rosmawati Utami, Putri Aisyah Warlia, Rika Yelfianhar, Ichwan Zaenuddin Zaenuddin, Zaenuddin