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Algoritme Jurnal Mahasiswa Teknik Informatika
Published by Universitas Multi Data Palembang
ISSN : -     EISSN : 27758796     DOI : https://doi.org/10.35957/algoritme.v2i2
Core Subject : Science,
Computer Science & IT
Jurnal Algoritme menjadi sarana publikasi artikel hasil temuan Penelitian orisinal atau artikel analisis. Bahasa yang digunakan jurnal adalah bahasa Inggris atau bahasa Indonesia. Ruang lingkup tulisan harus relevan dengan disiplin ilmu seperti: - Machine Learning - Computer Vision, - Artificial Inteledence, - Internet Of Things, - Natural Language Processing, - Image Processing, - Cyber Security, - Data Mining, - Game Development, - Digital Forensic, - Pattern Recognization, - Virtual & AUmented Reality,. - Cloud Computing, - Game Development, - Mobile Application, dan - Topik kajian lainnya yang relevan dengan ilmu teknik informatika.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 104 Documents
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Pengaruh Image Size pada Penghitung Jumlah Orang Dalam Ruangan Menggunakan Metode YOLOv8 Akbar, Naufal; Rahman, Abdul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9035

Abstract

Currently, technological advancements have led to the creation of various products that impact company performance. One of the products used in shopping malls is a monitoring application. A temporary closure occurred at one of the stores in a shopping mall due to a surge in visitors caused by a large discount offer. The manual methods currently in use are highly prone to errors. Knowing the number of people in a room is crucial for maintaining service quality. Information about the number of visitors is essential for companies to optimize resource utilization and conduct operational evaluations. This study aims to develop an application for counting the number of people in a room using the YOLOv8 (You Only Look Once) method. Model testing shows that an image size of 640x640 yields a higher mAP compared to the 416x416 size, achieving a precision of 79.6%, a recall of 61.7%, a mAP of 72.3%, and a f1-score of 69.52%. Accuracy testing for counting the number of people shows an accuracy rate of 82.67%.
Penerapan SMOTE dan Regresi Logistik Pada Website Skrining Awal Kesehatan Mental Mahasiswa Wijaya, Vannes; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9046

Abstract

Mental health is a very important aspect in realizing overall health. Students are an age group that experiences a transition from adolescence to adulthood, students tend to experience stress, especially those originating from the academic process. In this study, a website-based questionnaire system was developed to predict mental health profiles consisting of optimal mental health profiles (+-), maximum mental health profiles (++), minimal mental health profiles (--), and minimal mental health profiles (-+). The questionnaire questions and grouping of mental health profiles used the SKM-12 mental health measurement tool. The dataset used was obtained from 78 students at Multi Data University Palembang. The method used in this research is Logistic Regression using the data imbalance method, namely SMOTE with parameter solver newton-cg with data division, 70% training data and 30% test data. The results obtained in this study using confusion matrix model evaluation obtained an accuracy of 89.28% and model evaluation using K-fold cross validation obtained an accuracy of 87.43% for training data and 82.66% for test data.
Deteksi Kategori Sampah Menggunakan Metode You Only Look Once Ja'far, Ja'far; Udjulawa, Daniel
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9047

Abstract

Waste is the residue generated from human daily activities or natural processes that is no longer needed. According to data from the Ministry of Environment and Forestry, waste generation in Indonesia reaches 36 million tons per year, with unmanaged waste totaling 13 million tons annually. Inadequate waste management can lead to various issues. One significant source of unmanaged waste is found in schools, attributed to lack of awareness and low concern, resulting in student’s laziness in disposing of waste properly. This project aims to develop a system that assists students in identifying specific waste categories and incorporates a royalty points system to boost student motivation. The method employed is You Only Look Once Version 5 with the Darknet architecture, utilizing an 80% training, 10% validation, and 10% testing dataset split. The results of testing this method at the development stage showed a recall value of 92.4%, precision of 88.4%, and mAP of 96.4%. Meanwhile, at the implementation stage on smartphones, the recall value reached 89.9% with a precision of 94.2%, and an average detection speed of around 1.68 seconds.
Pengenalan Wajah Untuk Sistem Absensi Sekolah Menggunakan YOLOv8 Fernando, Fernando; Al Rivan, M Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9652

Abstract

Students' attendance at school is still recorded manually using books. This has weaknesses such as the attendance book being susceptible to damage and loss which can cause loss of important attendance data and requires a long time to recapitulate the results of the attendance recapitulation ahead of the semester increase. Therefore, this research discusses the application of the You Only Look Once (YOLO) method to recognize students' faces because YOLO is a real-time object detection method that is very fast and has high accuracy. The dataset used consists of 1,250 images with 70% train data, 20% valid data, and 10% test data which was trained with epoch 50, epoch 75, and epoch 100 resulting in an accuracy of 100% for each epoch. The model that has been trained can recognize students' faces well and can be applied to computer vision-based software to assist teachers in taking attendance and recapitulating attendance results in a certain period so that it doesn't take a long time to recap.
Analisis Sentimen Komentar Pada Saluran Youtube Beauty Vlogger Berbahasa Indonesia Menggunakan Metode Support Vector Machine Tjut Adek, Rizal; Fitri, Zahratul; Siregar, Siti Chairani
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9692

Abstract

products, where user comments often provide valuable feedback. This research aims to analyze the sentiment of user comments on Indonesian-language Beauty Vlogger channels, specifically regarding reviews of powder and skincare products, to identify the positive or negative responses that emerge. Utilizing the Support Vector Machine (SVM) method as a classification algorithm and TF-IDF as a weighting technique, this study involves 1,000 comments divided into 800 training data and 200 testing data. The data is analyzed through the text preprocessing stage, followed by sentiment classification using SVM. The results indicate that the model achieved an accuracy of 97%, with a precision of 98% and a recall of 96%, demonstrating that SVM is effective in identifying sentiment in user comments. This system is expected to provide in-depth insights for Beauty Vloggers in understanding opinions regarding powder and skincare products, as well as contribute to the development of similar applications in other industries
Pengenalan Makanan Khas Palembang Secara Realtime Menggunakan Yolov8 dan Text to Speech Valentino, Calvin Bertnas; Hermanto, Dedy
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9937

Abstract

The introduction of traditional Palembang food has an important role in preserving local cultural and culinary heritage. As interest in object recognition technology grows, challenges arise in creating a system that is able to recognize typical types of Palembang food effectively and efficiently. This research aims to overcome these challenges by developing a food detection system based on the You Only Look Once (YOLO) algorithm, which is known for its ability to detect objects in real-time with high accuracy. The dataset used consists of 1,234 images, which are divided into three parts: 70% for training data, 20% for validation data, and 10% for test data. By utilizing YOLO, this system can detect and recognize typical Palembang food in an average time of 3.15 seconds, and achieve an accuracy of 99.28%. Apart from that, this research also integrates a Text-to-Speech feature which provides a verbal description of the detected food, thereby increasing interaction and convenience for users.
Analisis Perbandingan Model CNN Terhadap Klasifikasi Citra Komponen Elektronika Arrosyid, Muhammad Zydane; Hermawan, Arief; ., Sutarman
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10259

Abstract

This study compares various Convolutional Neural Network (CNN) models in classifying electronic component images. The background of this research stems from the need to automatically identify and classify components in environments with limited computational resources. The data used in this research was collected through image scraping from the internet, supplemented by direct image acquisition using a camera. The data was then processed and trained using several CNN models, including MobileNet, NASNetLarge, VGG16, and others, as well as a custom CNN model developed by the researcher. The results show that NASNetLarge achieved the highest test accuracy of 79.31%, while MobileNet demonstrated high efficiency in computational resource usage. This study highlights that model size does not always correlate with accuracy, and models with fewer parameters can provide effective solutions for resource-constrained conditions.
Analisis Clustering Data Gizi Pada Puskesmas 1 Ulu Menggunakan Metode Algoritma K-Means Edwardo, Edwardo; Damayanti, Nita Rosa
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10707

Abstract

Puskesmas sebagai lembaga kesehatan pertama yang beroperasi untuk menyediakan layanan kesehatan dasar, puskesmas sangat penting bertanggung jawab untuk menangani topik masalah gizi. Sebagai salah satu unit pelayanan kesehatan, Puskesmas 1 Ulu memantau dan menangani penting topik masalah gizi. Pengembangan teknologi informasi telah memungkinkan pengolahan data gizi menggunakan metode analisis yang lebih canggih. Salah satunya, analisis clustering algoritma K-Means. Proses penelitian analisis clustering ini diterapkan dengan menggunakan metode KDD (Knowledge Discovery in, yang mencakup dat selecton, processing data, tranformation, data mining, interpretaion. Dari analisis yang dilakukan, data yang dikumpulkan meliputi atribut usia, berat badan, dan tinggi badan dari data balita yang ada pada puskesmas 1 ulu kota palembang. Hasil clustering menunjukkan adanya tiga cluster status gizi. Penggunaan aplikasi Orange dalam penelitian ini mempermudah proses analisis penerapan metode KDD (Knowledge Discovery in Databases) supaya menghasilkan pemahaman yang lebih baik tentang pola gizi anak-anak. Clustering menunjukkan bahwa terdapat perbedaan signifikan dalam distribusi status gizi balita, yang bisa dimanfaatkan untuk dasar menciptakan intervensi gizi yang lebih baik.
Klasifikasi Penggunaan Helm pada Citra Pengendara Sepeda Motor Menggunakan K-Means Clustering dan GLCM Antoni, Antoni; Ramadhanu, Agung
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10921

Abstract

The safety of motorcycle riders is a critical issue, particularly in countries with high traffic accident rates. In Indonesia, motorcycles make up the majority of personal vehicles, with over 130 million units as of June 2023, but many riders fail to comply with helmet usage regulations. Helmets significantly reduce the risk of fatal head injuries, yet the compliance rate remains low. To address this issue, an image classification system for motorcycle riders using helmets is proposed, leveraging image processing techniques and machine learning. By leveraging the K-Means Clustering algorithm, the system segments motorcycle images into two categories: riders with helmets and those without. The images are pre-processed, converted from RGB to LAB color space, and K-Means clustering is used to segment background and object areas. Feature extraction is applied using GLCM (Gray-Level Co-occurrence Matrix) to identify key characteristics such as texture and shape. The system compares the extracted features using Euclidean distance to classify whether a rider is wearing a helmet. Results show an accuracy rate of 94% in classifying helmet usage from 50 test images. This method can serve as an efficient and cost-effective alternative to more computationally intensive techniques like deep learning, with potential for real-time traffic surveillance applications.
Transfer Learning dengan MobileNetV3 untuk Deteksi Serangan Spoofing Wajah pada Foto Krisna Putra, Jelvin; Yoannita, Yoannita
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10954

Abstract

Deteksi serangan spoofing wajah menjadi tantangan dalam teknologi pengenalan wajah, terutama pada sistem presensi karyawan yang rentan terhadap penyalahgunaan, seperti penggunaan foto untuk memalsukan kehadiran. Penelitian ini mengembangkan metode deteksi spoofing berbasis transfer learning dengan memanfaatkan arsitektur MobileNetV3, yang dirancang untuk efisiensi pada perangkat dengan keterbatasan sumber daya, seperti perangkat seluler. Model yang dikembangkan dilatih menggunakan dataset dengan pembagian rasio 70:15:15 untuk pelatihan, validasi, dan pengujian, menggunakan pendekatan supervised learning. Hasil eksperimen menunjukkan bahwa varian MobileNetV3-Large dengan teknik fine-tuning, yang dilakukan dengan membuka kembali lapisan setelah lapisan ke-130, mampu mencapai akurasi 99,58% pada data pelatihan, 99,94% pada validasi, dan 99,77% pada pengujian. Selain itu, model ini memperoleh HTER sebesar 0,002254791432, yang menunjukkan efektivitas tinggi dalam mengidentifikasi serangan spoofing wajah.

Page 8 of 11 | Total Record : 104

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