Claim Missing Document
Check
Articles

Found 13 Documents
Search

IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN UNTUK DISTRIBUSI LOGISTIK KEPOLISIAN DENGAN METODE MOORA Safaria, Sayendra; Rahim, Radiyan; Wendra, Yumai; Hariska Putra, Ruri Pratama
J-Com (Journal of Computer) Vol. 4 No. 3 (2024): NOVEMBER 2024
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v4i3.3595

Abstract

Abstract: The distribution of police logistics involves various locations with differing needs, making the decision-making process highly complex. Manual decision-making often faces challenges, such as numerous factors or criteria that need to be considered, including the distance to the location, the level of logistic needs, the number of personnel at the location, and the availability of logistics in the warehouse. Additionally, difficulties frequently arise in determining priority locations during emergencies or disasters, as well as vulnerabilities to subjective errors due to human limitations in analyzing complex data. Another issue is the unequal allocation of logistics, where some locations receive more logistics than needed, while others experience shortages. A Decision Support System (DSS) is a computer-based system designed to assist decision-makers in analyzing data, evaluating various alternatives, and providing recommendations. DSS aims to simplify the decision-making process to make it more effective. Various methods are used in the decision-making process, one of which is the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. This system aims to reduce subjectivity, increase efficiency, and ensure a more equitable allocation of logistics based on needs. It is expected that this DSS can serve as a solution that facilitates faster, more accurate, and transparent decision-making.   Keywords: decision support system; moora; logistics Abstrak: Distribusi logistik kepolisian melibatkan berbagai lokasi dengan kebutuhan yang berbeda-beda, yang membuat pengambilan keputusan menjadi sangat kompleks. Pengambilan keputusan secara manual sering kali menghadapi tantangan, seperti banyaknya faktor atau kriteria yang harus dipertimbangkan, misalnya jarak lokasi, tingkat kebutuhan logistik, jumlah personel di lokasi, dan ketersediaan logistik di gudang. Selain itu, sering kali ada kesulitan dalam menentukan prioritas lokasi yang harus didahulukan dalam situasi darurat atau bencana, serta adanya kerentanannya terhadap kesalahan subjektif akibat keterbatasan manusia dalam menganalisis data yang kompleks.Permasalahan lain yang muncul adalah ketidakmerataan alokasi logistik, di mana beberapa lokasi menerima logistik melebihi kebutuhan, sementara lokasi lain mengalami kekurangan.  Sistem Penunjang Keputusan (SPK) adalah sistem berbasis komputer yang dirancang untuk mendukung pengambil keputusan dalam menganalisis data, menilai berbagai alternatif, dan memberikan rekomendasi. SPK bertujuan untuk mempermudah proses pengambilan keputusan agar lebih efektif. Terdapat berbagai metode yang digunakan dalam proses pengambilan keputusan, salah satunya adalah metode Multi Objective Optimization on the Basis of Ratio Analysis (MOORA). Sistem ini bertujuan mengurangi subjektivitas, meningkatkan efisiensi, dan memastikan alokasi logistik yang lebih merata berdasarkan kebutuhan. Diharapkan, SPK ini dapat menjadi solusi yang mampu memberikan pengambilan keputusan yang lebih cepat, akurat, dan transparan.Kata kunci: sistem penunjang keputusan; MOORA; logistik
Sistem Pakar Diagnosa Kerusakan Mobil Avanza Dengan Metode Certainty Factor Menggunakan Bahasa Pemrograman PhP Dan Database MySQL Cahyadi, Edo; Rahim, Radiyan
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.792

Abstract

The development of automotive technology has led to increased vehicle system complexity, creating the need for intelligent solutions to assist in fault diagnosis. One of the common problems faced by Avanza car users is the difficulty in detecting engine damage at an early stage. Therefore, this study aims to develop an expert system capable of diagnosing Avanza car malfunctions using the Certainty Factor method in a web-based environment. The system was developed using PHP as the programming language and MySQL as the database to manage symptoms, malfunction data, and certainty values for each diagnosis. The Certainty Factor method was implemented to calculate the level of confidence in potential malfunctions based on user-inputted symptoms. The results indicate that the method provides accurate diagnostic outcomes by presenting certainty values for each possible malfunction. This expert system helps both technicians and users obtain preliminary information about the vehicle’s condition before performing direct inspections at the workshop. Therefore, the developed expert system improves efficiency, accuracy, and service quality in diagnosing Avanza car malfunctions.
The Implementation of the K-Means Clustering Algorithm Based on the Severity Level of Diabetes in Patients Using a Website Platform Maharani, Syaputri; Wendra , Yumai; Melladia, Melladia; Rahim, Radiyan
The Future of Education Journal Vol 4 No 7 (2025): #2
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.