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All Journal ITEj (Information Technology Engineering Journals) JET (Journal of Electrical Technology) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Engineering, Science and Information Technology JURNAL ILMIAH GLOBAL EDUCATION International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma Jurnal Teknologi Terapan and Sains 4.0 Buletin Pengabdian Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Solusi Masyarakat Dikara INOVTEK Polbeng - Seri Informatika
Fuadi, Wahyu
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Clustering Agricultural Productivity by Type and Results Using K-Medoids Method in Districts North Aceh Zahara, Mutia; Fuadi, Wahyu; Meiyanti, Rini
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.699

Abstract

This research aims to develop a web-based application that can cluster sub-districts in North Aceh District based on the type and yield of agricultural productivity, focusing on increasing the ease of visualization and data analysis by users. The method applied in this research is K-Medoids, a clustering technique used to group sub-districts based on high, medium, and low harvest levels. The application will use data from the North Aceh District Agriculture Office, covering 2021 to 2023, including various food crops such as rice, corn, peanuts, green beans, cassava, sweet potatoes, and soybeans. This research will analyze the sub-district name, type of agriculture, year of production, planting area, and harvest area to identify clusters of sub-districts with similar agricultural yield patterns. The system is developed using the PHP programming language to facilitate implementation and data access by stakeholders. As an evaluation tool for clustering results, the Davies-Bouldin Index (DBI) is used to measure the quality of clustering results. The results of this study are expected to provide insights into agricultural productivity in North Aceh District and assist policymakers in designing more effective strategies to increase agricultural yields, especially in low-yielding sub-districts. In addition, this application also provides an interactive platform for users to analyze agrarian data quickly and efficiently.
Sistem Pendeteksi Tingkat Kesegaran Daging Ayam pada Citra Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android Naturizal, Rayhan; Fuadi, Wahyu; Rosnita, Lidya
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp301-312

Abstract

This research develops a chicken meat freshness detection system based on image processing, implemented on an Android platform using the Convolutional Neural Network (CNN) method optimized with TensorFlow Lite. The system classifies chicken meat into three categories: fresh, less fresh, and rotten. The CNN model uses 32 filters to enhance feature extraction from the meat images. Testing on 30 samples, with each category tested 10 times, showed an accuracy of 90%, with 27 correct detections and 3 errors in the less fresh category. While the system effectively identifies fresh and rotten categories, there is a challenge in distinguishing the less fresh category due to its ambiguous visual characteristics. One limitation is the lack of a bounding box, causing the application to still provide detection results even when the scanned object is not chicken meat. This application is specifically designed to detect chicken meat pieces, so it is not recommended for use outside this context.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JURUSAN SEKOLAH PADA MAN MENGGUNAKAN METODE WASPAS Marpaung, Rifky Firzani; Fuadi, Wahyu; Aidilof, Hafizh Al Kautsar
TECHSI - Jurnal Teknik Informatika Vol. 15 No. 1 (2024)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v15i1.19471

Abstract

Penentuan jurusan sekolah pada Madrasah Aliyah Negeri (MAN) merupakan proses penting yang harus mempertimbangkan berbagai faktor seperti nilai akademik, minat, dan bakat siswa. Namun, penentuan jurusan sering kali masih dilakukan secara manual, sehingga kurang efisien dan memerlukan waktu yang lama. Penelitian ini bertujuan untuk merancang dan membangun Sistem Pendukung Keputusan (SPK) berbasis web yang menggunakan metode Weighted Aggregated Sum Product Assessment (WASPAS) guna membantu pihak sekolah dalam menentukan jurusan yang sesuai dengan kemampuan siswa. SPK ini menggabungkan berbagai kriteria seperti nilai raport, hasil tes akademik, serta kemampuan praktek untuk memberikan rekomendasi penjurusan yang optimal. Metode WASPAS dipilih karena kemampuannya dalam mengkombinasikan metode Weighted Sum Model (WSM) dan Weighted Product Model (WPM), yang menghasilkan perhitungan yang lebih akurat. Hasil pengujian menunjukkan bahwa sistem yang dibangun mampu memberikan rekomendasi jurusan dengan lebih cepat dan efektif dibandingkan dengan metode manual. Sistem ini diharapkan dapat membantu sekolah dalam mengelola penjurusan siswa dengan lebih efisien dan objektif.
Cataract Eye Disease Diagnosis Using the Random Forest Method Novita, Lilis; Fuadi, Wahyu; Kurniawati, Kurniawati
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.777

Abstract

This study developed a machine learning-based classification model using the Random Forest algorithm to detect cataract risk based on 11 variables: age, gender, family history, lens opacity, visual acuity reduction, light sensitivity, color changes, double vision, intraocular pressure, slit-lamp results, and visual acuity. Feature importance analysis revealed that lens opacity and visual acuity variables contributed most significantly to cataract risk prediction, followed by intraocular pressure and visual acuity reduction. The system was designed using Google Colab for model training and Streamlit as an interactive interface, enabling real-time predictions with intuitive result visualization. After optimization using Grid Search, the model achieved an accuracy of 92.0%, precision of 95.0%, sensitivity of 90.0%, F1 Score of 92.4%, and specificity of 98.0%. This system is expected to serve as an effective supporting tool for medical professionals in the early diagnosis of cataracts.
Design of Attendance System for Informatics Engineering Lecturers Using RFID Sensors Based on IoT and Telegram Applications Akbar, Andry Maulana; Fuadi, Wahyu; Nunsina, Nunsina
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.794

Abstract

The attendance system is an essential element in the academic environment to ensure lecturer attendance in the lecture process. However, the manual attendance method still has various weaknesses, such as the potential for data manipulation and inefficiency in recording attendance. To overcome these problems, this research designs and implements an Internet of Things (IoT)-based lecturer attendance system using Radio Frequency Identification (RFID) sensors integrated with the Telegram application. The research method includes hardware design with ESP32 microcontroller, ESP32-CAM, RFID sensor, and HC-SR04 ultrasonic sensor. This system works by detecting lecturer attendance through RFID cards confirmed by ESP32, taking pictures with ESP32-CAM, and sending automatic notifications via the Telegram bot. Lecturer attendance data is then stored in a web-based database to facilitate the monitoring and evaluation. The test results show that the developed system can detect and record lecturer attendance accurately, with the response speed of the RFID sensor in reading cards ranging from 1-5 cm. The ultrasonic sensor also successfully detects objects accurately within a predetermined distance range. Lecturer attendance notifications sent via Telegram allow administrators to conduct real-time monitoring. With this IoT-based attendance system, the attendance recording process becomes more efficient and transparent and can reduce the risk of data manipulation. Further development can be done by adding data encryption and biometric authentication features to improve system security.
Student Learning Style Decision-Making System Using the Multi-Attribute Utility Theory Method at SMA Negeri 1 Jangka Munawarah, Munawarah; Fuadi, Wahyu; Aidilof, Hafizh Al Kautsar
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.842

Abstract

Education plays a vital role in shaping individual development and national progress. One key factor influencing learning effectiveness is students' learning styles, which determine how individuals absorb, organize, and process information. Understanding these differences is crucial for designing effective teaching methods. This research develops a Decision Support System (DSS) to determine student learning styles at SMA Negeri 1 Jangka using the Multi-Attribute Utility Theory (MAUT) method. MAUT is chosen for its ability to evaluate multiple criteria, convert them into numerical values, and systematically identify the most suitable learning approach. The alternatives in this study include Project Based Learning (PBL), Problem-Based Learning (PrBL), Inquiry-Based Learning (IBL), Discovery Learning (DL), and Contextual Teaching and Learning (CTL). The MAUT analysis considers five criteria: student activeness, material understanding, collaboration, initiative and creativity, and teacher-student communication. The research stages include literature study, data collection, system and database design, MAUT implementation, and system evaluation. The results, based on MAUT calculations, show that Inquiry-Based Learning (IBL) scores the highest at 13.611, followed by Discovery Learning (DL) at 13.018, Problem-Based Learning (PrBL) at 12.975, Contextual Teaching and Learning (CTL) at 12.929, and Project Based Learning (PBL) at 12.558. This system assists educators in designing personalized learning strategies that align with students' strengths. Leveraging data-driven analysis enhances education quality, fosters a student-centred learning environment, and improves academic performance and lifelong learning habits.
Application of Ant Colony Algorithm to Determine the Shortest Route for Nature and Culinary Tourism in North Aceh Teguh, Muhammad; Fuadi, Wahyu; Fitri, Zahratul
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.857

Abstract

This Research aims to design and implement a shortest route determination system for natural and culinary tourism locations in North Aceh using the Ant Colony Optimization (AntCO) algorithm. The developed system is designed to help tourists plan their trips efficiently by considering the distance and travel time between tourist destinations. The system implementation using the AntCO algorithm successfully displayed optimal routes for 28 tourist destinations in North Aceh. The system successfully implemented filtering features based on tourism categories and route visualization on an interactive map using different markers (green for natural tourism and red for culinary tourism). The research results show that the system successfully optimized tourist travel routes and provided comprehensive information, including automatic location detection, a list of tourist destinations, travel route details, and optimal visit sequences based on selected tourism categories. This system proved effective in helping tourists plan their trips in North Aceh by providing efficient routes according to their preferred tourism category preferences.
PENDETEKSIAN BAHASA ISYARAT INDONESIA SECARA REAL-TIME MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM) Putri, Husna Moetia; Fadlisyah, Fadlisyah; Fuadi, Wahyu
Jurnal Teknologi Terapan and Sains 4.0 Vol 3 No 1 (2022): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v3i1.6853

Abstract

Bahasa Isyarat Indonesia (BISINDO) adalah salah satu cara teman Tuli untuk berkomunikasi. BISINDO muncul secara alami dari interaksi Tuli dengan lingkungannya dan dikenal sebagai budaya Tuli di Indonesia. Namun saat ini terdapat kendala dalam berkomunikasi antar teman Tuli dengan teman dengar dalam menggunakan fasilitas publik dikarenakan petugas pada pelayanan publik tersebut tidak dapat mengerti apa yang disampaikan oleh teman Tuli. Penelitian ini bertujuan untuk menjadi alat penghubung komunikasi satu arah antar teman Tuli dengan teman dengar yang diharapkan dapat mempermudah dalam berkomunikasi. Sistem yang dihasilkan akan mengklasifikasi dan mendeteksi gestur dari kosakata isyarat BISINDO secara langsung yang dikonversi menjadi sebuah teks. Klasifikasi BISINDO pada penelitian ini menggunakan metode Long short-term memory (LSTM) dan Mediapipe Holistic untuk mendeteksi kerangka pada tangan, wajah dan badan. Objek yang digunakan pada penelitian ini merupakan 30 kosakata isyarat BISINDO yang sering digunakan teman Tuli. Dari hasil evaluasi deteksi real-time penelitian ini mendapatkan akurasi sebanyak 92% untuk model 10 kelas dengan bidirectional layer LSTM, epoch 1000, hidden layer 64, batch size 32 dan mendapatkan akurasi sebanyak 65% untuk model 30 kelas dengan 2 layer LSTM epoch 500, hidden layer 64, batch size 64.
Classification of Coronary Heart Disease Based on Community Health Centre Medical Record Data Using SVM Algorithm Kausar, M Reza; Fuadi, Wahyu; Fitri, Zahratul
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ng11kk81

Abstract

Coronary heart disease (CHD) is one of the leading causes of death worldwide and demands a fast and accurate diagnostic system, especially in community health centres (Puskesmas) where medical resources are limited. This study aims to develop a classification system for CHD using the Support Vector Machine (SVM) algorithm based on numerical medical record data. It also addresses the gap in previous studies that rarely applied SVM to tabular data from primary healthcare facilities. The methodology includes variable weighting, min-max normalization, model training with a linear kernel, and performance evaluation using a confusion matrix. The dataset consists of 100 patient records with variables such as age, blood pressure, heart rate, respiratory rate, and chest pain. The results show that the SVM model achieved an accuracy of 95%, a precision of 100%, recall of 88.9%, and an F1-score of 94.1%. The model was further integrated into a web-based application using Flask to support automated early diagnosis. This study demonstrates that SVM is effective in classifying heart disease based on medical records and offers a practical solution to improve healthcare service quality in Puskesmas.
Development of an Expert System for Identifying Students' Learning Styles Using the Euclidean Probability Method Rahma, Putri; Fitri, Zahratul; Fuadi, Wahyu
ITEJ (Information Technology Engineering Journals) Vol 10 No 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.214

Abstract

Learning styles play an important role in determining the most effective teaching strategies by aligning instructional methods with students’ individual preferences in receiving, processing, and understanding information. However, classroom teaching is often applied uniformly, disregarding the differences in learning styles among students. This can hinder the effectiveness of the learning process. This research aims to develop a web-based expert system using the Euclidean Probability method to identify the dominant learning styles of students at SMK Negeri 3 Lhokseumawe. The system processes input data representing student characteristics and calculates the proximity to each learning style category using the Euclidean distance formula. A total of 110 student data entries were analyzed, revealing that 32 students (29.09%) had a Visual learning style, 26 students (23.64%) were Auditory, 16 students (14.55%) were Read/Write, and 36 students (32.73%) were Kinesthetic learners. The results showed that the Kinesthetic learning style was the most dominant among students. Therefore, this expert system can efficiently assist in determining students' learning styles, allowing for quick and accurate identification of their learning preferences. This supports the development of more personalized and adaptive learning strategies, which are expected to enhance student engagement and learning outcomes.
Co-Authors Aidilof, Hafizh Al Kautsar Akbar, Andry Maulana amri, maulana Andik Bintoro Angga Pratama Aqsa, Dina Alfini Asran Asran Auji, Muhammad Fathah Badriana, Badriana Bakhtiar Bakhtiar Cut Ita Erliana Dedi Fariadi Dika, Farhan Eva Ayuzar, Eva Eva Darnila Eva Darnila Fadlisyah Fadlisyah Fajar, Kasihan Muhammad Fajriana, Fajriana Halimatun Sakdiah, Halimatun Harahap, Lina Mardiana Irwanda Syahputra Irwansyah, Defi Jabbar, Salamul Jalila, Muhammad Mulkan Kamilaini, Kamilaini Karmawati Karmawati Kausar, M Reza Khairawati Khairawati, Khairawati Khairawati, K Kurniawati Kurniawati Kurniawati Lidya Rosnita Mahdaliana, Mahdaliana Marpaung, Rifky Firzani Maryana Maryana Maryana Maryana, Maryana Muhammad Luthfi MUHAMMAD TEGUH, MUHAMMAD Munawarah Munawarah, Munawarah Mutammimul Ula Muthmainnah Muthmainnah Mutia Zahara mutiara mutiara Naturizal, Rayhan Novita, Lilis Nunsina, Nunsina Pohan, Muhammad Wahyu Putri, Husna Moetia Rachmawati Rusydi, Rachmawati Rahma, Putri Rahman, Irfan Fatur Rahmaniar, R Rahmawati M, Rahmawati Rahmawati, Rahmawati Raihan Putri Ramadhansyah, Rizki Razi, Ar Rini Meiyanti Risawandi, Risawandi RISKA YANTI, RISKA Riza, Saiful Rozzi Kesuma Dinata Safwandi Sahputra, Ilham Saifuddin Muhammad Jalil Selamat Meliala Sofyan, Diana Khairani Syintia, Icut Taufik, Nugraha Muhammad Teuku Multazam wandi, risa Yesy Afrillia Zahratul Fitri, Zahratul Zara Yunizar Zulfia , Anni Zuraida Zuraida