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All Journal Jupiter Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Sains Teknologi dan Sistem Informasi Jurnal Pengabdian Kepada Masyarakat J-Com (Journal of Computer) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Sains Informatika Terapan (JSIT) Journal of Scientech Research and Development Jurnal Manajemen Informatika dan Bisnis Digital Jurnal Teknologi Komputer dan Informasi TOFEDU: The Future of Education Journal Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi)
Melladia, Melladia
Universitas Nahdlatul Ulama Sumatera Barat
Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

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The Implementation of the K-Means Clustering Algorithm Based on the Severity Level of Diabetes in Patients Using a Website Platform Syaputri Maharani; Yumai Wendra; Melladia Melladia; Radiyan Rahim
The Future of Education Journal Vol 4 No 7 (2025): Continued
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Yayasan Pendidikan Tumpuan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61445/tofedu.v4i7.933

Abstract

Diabetes mellitus is a chronic non-communicable disease with a steadily increasing prevalence worldwide, posing a significant public health challenge due to its potential for severe complications if not managed properly. In many healthcare facilities, including RSUD Pariaman, there is still no structured system to classify patients according to the severity of their condition, which hampers timely intervention and optimal resource allocation. This study aims to develop and implement a web-based system for clustering the severity levels of type 2 diabetes mellitus using the K-Means Clustering algorithm as a decision support tool for medical staff. A quantitative system development research design was applied, utilizing secondary medical records from January 2023 to December 2024, with five clinical variables: Hemoglobin A1c (HbA1c), Fasting Blood Glucose (GDP), Systolic Blood Pressure (TDS), Diastolic Blood Pressure (TDD), and Body Mass Index (BMI). The system was built using the CodeIgniter PHP framework, MySQL database, and Bootstrap-based interface, following the Knowledge Discovery in Database (KDD) process for data preprocessing. K-Means clustering was configured into three categories (mild, moderate, and severe). Validation using RapidMiner confirmed that the clustering results from the web-based system were consistent with the benchmark model, ensuring the correctness of the algorithm’s implementation. The developed system enables real-time data processing, displays results in both tabular and graphical forms, and provides an intuitive interface for medical personnel, thus supporting clinical decision-making and improving healthcare service quality.
The Application of Expert System in Diagnosing Catfish Diseases Using Forward Chaining Method Muhammad Afdal; Melladia
The Future of Education Journal Vol 4 No 8 (2025): Continued
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Yayasan Pendidikan Tumpuan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61445/tofedu.v4i8.1019

Abstract

Indonesia, as an archipelagic nation, holds significant potential for the development of freshwater commodities, particularly catfish. Catfish farming is feasible in limited spaces but is susceptible to various diseases, resulting in substantial losses for farmers. This study aims to develop an expert system based on the forward chaining method, designed to diagnose catfish diseases according to observed symptoms and provide treatment solutions without requiring direct expert consultation. A qualitative approach was employed, with data collected through literature reviews and expert interviews. The developed expert system was implemented using MATLAB to generate accurate diagnoses and treatment recommendations. The results show that the system can accurately recognize different types of catfish diseases based on the symptoms provided. It also gives suitable treatment options, helping farmers learn about the disease, its symptoms, and the right ways to treat it. This makes farming more efficient and reduces the damage caused by diseases.
The Application of Expert System for Diagnosing Diseases in Corn Plants Using Forward Chaining Method Tiara Tiara; Melladia
The Future of Education Journal Vol 4 No 8 (2025): Continued
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Yayasan Pendidikan Tumpuan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61445/tofedu.v4i8.1041

Abstract

Corn is one of the most important food crops in Indonesia, but it is highly susceptible to various diseases that can significantly reduce yield. Diagnosing corn diseases typically requires agricultural experts, whose availability is often limited in rural areas. This research aims to develop an expert system to diagnose corn diseases using the Forward Chaining method. The system was built using PHP and MySQL with the CodeIgniter framework. The methodology included requirement analysis, system design using Unified Modeling Language (UML), implementation, and testing. The knowledge base was obtained from agricultural experts and existing literature, consisting of symptoms, diseases, and their corresponding solutions. Forward Chaining was applied to match user-selected symptoms with predefined rules to produce a diagnosis. The test results indicate that the system can accurately diagnose diseases based on the rules, helping farmers identify problems early and providing recommended solutions without requiring direct expert assistance. This study demonstrates the effectiveness of expert systems in agricultural disease management and offers a potential solution for farmers facing limited access to agricultural experts.
GEOGRAPHIC INFORMATION SYSTEM APPLICATION FOR TRADITIONAL MARKET MAPPING IN PADANG CITY BASED ON ANDROID Melladia Melladia; Fadila Afriansyah
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3656

Abstract

This study aims to develop an Android-based Geographic Information System (GIS) application for mapping traditional markets in Padang City. The main issue addressed is the lack of accessible spatial information regarding market locations for the general public. The research employed field observations, interviews with the Trade Office and market vendors, and literature study. The system development followed the waterfall model, comprising requirement analysis, system design, implementation, testing, and maintenance. The application was built using Android Studio, LeafletJS, and QGIS. The results show that the application successfully presents market location information in an interactive map format, displays detailed market data, and facilitates administrative data management. This application is expected to assist users in locating traditional markets and support local government efforts in spatial data management more efficiently.Keyword: Georaphic Information System, Padang City, QGIS, Leaflet, Android
Co-Authors Afriansyah, Fadila Ahmad Sarjianto Annisa, Indah Febri Dertha Mukhtar desi, desipermatasari Eka Putra, Dian Fadila Afriansyah Febri Annisa, Indah Febriyon Pratama, Vicky Hendri Putrananda Iis Roza Mardani Indah Febri Annisa Leila Muhelni Leila Muhelni Maulana Sukma, Ilham Muhammad Afdal Muhelni, Leila Putrananda, Hendri Radiyan Rahim Reni Handayani Siregar Restu Restu Siska Aprilia Sumiati Sumiati Surya Kharisma Karnefo Syaputri Maharani Tiara Tiara Yumai Wendra