Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Artificial Intelligence Research Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences
Mardison Mardison
Universitas Putra Indonesia YPTK Padang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Hybrid DSS for recommendations of halal culinary tourism West Sumatra Mardison Mardison; Agung Ramadhanu; Larissa Navia Rani; Sofika Enggari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp273-283

Abstract

Decision support system (DSS) is a system that design to support managers in deciding on multiple criteria and multiple attributes. This study combines two methods in the DSS, that are analytical hierarchy process (AHP) method and simple additive weighting (SAW) method. This combination of two DSS method named hybrid DSS. The AHP method is using to find the weighting or priorities of criteria in DSS and then the value will use by SAW method using to find the decision. The decision of this DSS is the recommendation of halal culinary tourism in West Sumatra Indonesia. The purpose of this study is to provide updates from previous studies, related to adding indicators of halal culinary tourism and other information updates. The number of potential culinary tourism attractions and tourism, the problems that exist in the real field, is still lack of culinary information in West Sumatra. As a result, many tourists find it difficult to find the best and economical culinary. The SAW and AHP methods become the hybrid DSS method that will be able to classify and provide information on halal tourism in West Sumatra that is precise, accurate, consistent, and validated.
Prediction of Scholarship Recipients Using Hybrid Data Mining Method with Combination of K-Means and C4.5 Algorithms Mardison Mardison; Sarjon Defit; Shaza Alturky
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.759 KB) | DOI: 10.29099/ijair.v5i2.224

Abstract

Obtaining a scholarship is the desire of every student or student who studies, especially those who come from poor families. The scholarship can lighten the burden on parents who pay for these students and can streamline the lecture process. However, students do not know exactly what they have to do to get the scholarship. Aside from that, students naturally want to know what causes and conditions have the greatest impact on achievement. The objective of this research is how to predict which number of students among them are predicted to get a scholarship at the opening of the scholarship acceptance using the K-Means and C4.5 methods. Apart from that, the aim of this research is to discover how the K-Means algorithm conducts data clustering (clustering) of student data to determine if they will succeed or not, as well as how the C4.5 algorithm makes predictions against students who have been clustered together. The Rapid Miner program version 9.7.002 was used to process the data in this report. The results of this study were that out of 100 students, 32 students were not scholarship recipients and 68 students were scholarship recipients. Another result of this research is that out of 100 students it is predicted that 9 (9%) will receive scholarships and 91 (91%) will not receive scholarships.
PENGENALAN DASAR JARINGAN KOMPUTER PADA SANTRI RAHMATAN LIL ‘ALAMIN INTERNATIONAL ISLAMIC BOARDING SCHOOL (RLA IIBS) Mardison Mardison; Rahmad Hidayat; Romi Wijaya
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2022): Volume 3 Nomor 2 Tahun 2022
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v3i2.5316

Abstract

Jaringan merupakan salah satu sarana utama dalam menunjang kehidupan di Era 4.0. Pembangunan jaringan nasional saat ini di Indonesia sangat dibutuhkan dalam menunjang hidup bermasyarakat, berbangsa dan bernegara, sebagai pemberi kemudahan bagi masyarakat dalam melakukan kegiatan sehari-hari yang berkaitan dengan penggunaan jaringan. Penggunaan jaringan Saat ini tidak hanya di manfaatkan oleh generasi muda dan dewasa Saja, tetapi juga digunakan oleh anak- anak dalam menunjang pembelajaran di Era Digital saat ini. Oleh karena itu, dalam laporan ini akan dipaparkan mengenai kegiatan pengabdian kepada masyarakat khususnya di Rahmatan Lil ‘Alamin Boarding School (RLA-IIBS) dalam rangka memberikan Sosialisai dan pengarahan kepada siswa mengenai Pengenalan Dasar dalam Penggunaan Jaringan Komputer.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; Yuhandri Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Co-Authors Febri Hadi Halifia Hendri Larissa Navia Rani Rahmad Hidayat Ramadhanu, Agung - Romi Wijaya Sarjon Defit Shaza Alturky Sofika Enggari Yuhandri Yuhandri