cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hafiz Irsyad
Contact Email
hafizirsyad@mdp.ac.id
Phone
+6281373740969
Journal Mail Official
hafizirsyad@mdp.ac.id
Editorial Address
Universitas Multi Data Palembang, Kampus Rajawali. Jl. Rajawali no 14 Palembang
Location
Kota palembang,
Sumatera selatan
INDONESIA
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
 7   8   9   10   11 
Analisis Sentimen Supporter Sriwijaya FC Berbasis Manchine Learning Farhan, Muhammad; Puspasari, Shinta; Gasim, Gasim
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v6i1.11288

Abstract

This study analyzes the sentiment of Sriwijaya FC supporters toward the club's management through comments on Instagram. Data was collected from 6,601 comments on the official @SriwijayaFC account and processed through text preprocessing stages with an 80:20 split for training and testing data. The analysis was conducted using four machine learning algorithms: SVM, Random Forest, Naïve Bayes, and KNN. The results indicate that neutral sentiment dominates (50.92%), followed by positive (25.07%) and negative (24.01%) sentiment, suggesting that most comments are informative or impartial, although there are both supporting and opposing opinions. Model performance evaluation using a confusion matrix and accuracy, precision, recall, and F1-score metrics shows that SVM achieved the highest accuracy (89%), followed by Random Forest (82%), Naïve Bayes (74%), and KNN (65%). These findings demonstrate that machine learning is effective in classifying social media sentiment. Future research may explore deep learning algorithms and expand data sources to other platforms for a more comprehensive analysis.
Implementasi Proxmox Untuk High Availability Dan Load Balancing Pada Sistem Siak Undiksha Saskara, Gede Arna Jude; Listartha, I Made Edy; Resika Arthana, I Ketut
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v6i1.11313

Abstract

In the digital era, Information and Communication Technology infrastructure has become crucial for organizations. Virtualization serves as a primary solution to enhance server efficiency; however, increasing workloads can impact system performance. Cluster computing is essential to maintain service availability and improve processing speed. Proxmox Cluster with Ceph as a distributed storage solution offers a viable implementation.This study analyzes the performance of Proxmox Ceph Cluster in handling traffic increases to ensure optimal service delivery. The research employs the Network Development Life Cycle (NDLC) methodology, consisting of analysis, design, prototype simulation, implementation, monitoring, and management. Testing was conducted at UPA TIK Undiksha, which previously utilized a monolithic server. The evaluation was based on service availability, resource utilization, throughput, and latency, using Apache JMeter.The results indicate that implementing Proxmox Ceph Cluster improves service availability and optimizes workload distribution compared to monolithic systems. The high availability implementation with Ceph can also handle node failures without disrupting core services. Therefore, adopting Proxmox Ceph Cluster presents a reliable solution for supporting a more efficient and resilient IT infrastructure
Klasifikasi Emosi Publik di Instagram Terhadap Uji Coba Jalan Satu Arah di Palembang dengan Naive Bayes Ariantoro, Tri Rizqi; Setiadi, Arif; Cecilia, Cecilia; Fadhliazi, Fadhliazi; Sri Handini, Nurul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v5i3.11498

Abstract

Traffic congestion in Palembang City remains a major issue despite various traffic engineering policies, including the implementation of a one-way system trial. This study aims to classify public emotions toward the policy based on comments from Instagram users. The research method includes data collection through web scraping, translation of local dialects into Indonesian using Generative AI, manual emotion labeling, text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), and classification with the Multinomial Naive Bayes algorithm optimized by hyperparameter tuning. The evaluation results show an accuracy of 69.66%, precision of 63.09%, recall of 69.66%, and F1-score of 62.86%. The “anticipation” emotion emerged as the most dominant class, while “love” and “annoyance” were underrepresented. These findings indicate that the Naive Bayes-based text mining approach is effective for analyzing public emotional responses to government policies.
Sistem Pengendalian Nutrisi Hidroponik Menggunakan Logika Fuzzy Berbasis Internet of Things Fauzan, Farhan; Lesmana, Iwan; Nugraha, Nunu
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v5i3.11740

Abstract

Hydroponics, using water instead of soil for cultivation, often faces nutrient instability issues. This research developed an IoT-based nutrient control system using the Mamdani Fuzzy Logic algorithm to maintain nutrient stability for optimal plant growth. The system includes a microcontroller, sensors, and the Fuzzy Logic algorithm, tested through a prototype. Results showed the system effectively maintained nutrient stability, with pH levels between 5.5 and 6.5 and Total Dissolved Solids (TDS) between 650 and 850. Pumps adjusted based on nutrient conditions, demonstrating good performance. This Fuzzy Logic-based control system is ready for widespread use, ensuring reliable nutrient stability for hydroponic plants.
Rekomendasi Produk Fashion Berdasarkan Rating Menggunakan Metode Singular Value Decomposition Buleuw, Zulaeva Rajiah; Agustining Tyas, Windi; Aryanto, Aryanto
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v6i1.12154

Abstract

The growth of e-commerce in the fashion industry has significantly increased the number of digital products offered to consumers. This situation leads to an information overload problem, where users find it difficult to choose products that match their preferences. To address this issue, a recommendation system is needed to filter information and provide relevant product suggestions. This study aims to develop a fashion product recommendation system based on user ratings using the Singular Value Decomposition (SVD) method. The data used is secondary data sourced from the Kaggle platform, containing user interactions with various fashion products. The research process includes data collection, preprocessing, rating matrix construction, data decomposition, and model performance evaluation. The evaluation results show a Mean Absolute Error of 0.47 and a Root Mean Squared Error of 0.67, indicating a relatively low prediction error. The system also successfully recommends items with high predicted ratings, such as a rating of 4.30, demonstrating strong relevance to user preferences. These findings confirm that the applied method is effective in building an accurate and relevant recommendation system that assists users in making fashion product purchase decisions more efficiently.
Perbandingan Algoritma Naïve Bayes dan Random Forest untuk Klasifikasi Intent Chatbot Layanan Pelanggan Meinita, Rizkia; Fardian Anshori, Iedam
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v5i3.12639

Abstract

In recent years, chatbots have become one of the key innovations in customer service due to their ability to provide fast, accurate, and consistent responses. However, selecting the most suitable machine learning algorithm to accurately classify customer inquiries remains a challenge. This study compares the Naïve Bayes and Random Forest algorithms in intent classification for an Indonesian language-based customer service chatbot. Using a dataset of 26,873 conversations processed through preprocessing stages and TF-IDF vectorization, the evaluation results show that Random Forest achieved an accuracy of 96%, compared to 95% for Naïve Bayes, although both yielded nearly similar precision, recall, and f1-score values. These findings highlight that both algorithms remain relevant, but Random Forest delivers more consistent performance in improving classification accuracy. Practically, this research provides a reference for selecting algorithms in developing customer service chatbots that are more efficient, accurate, and adaptive to user needs, thereby enhancing interaction quality and reducing the workload of human operators.
Analisis Sentimen Ulasan Aplikasi SAMBARA Menggunakan Pendekatan Natural Language Processing Athoilah, M. Fakhrizal Nur; Fardian Anshori, Iedam
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v5i3.12640

Abstract

SAMBARA is a digital service application developed by the West Java Provincial Government for motor vehicle tax payments, which has received a low rating of 2.4 from more than 39,000 reviews on the Google Play Store, making it a critical issue that undermines public trust in government digital services. This study analyzes 21,572 user reviews using a Natural Language Processing (NLP) approach with the Long Short-Term Memory (LSTM) algorithm through several preprocessing steps including cleaning, stopword removal, stemming, normalization, and Word2Vec numerical representation. The model was trained for 20 epochs with a batch size of 64 and evaluated using accuracy, precision, recall, and F1-score. The results show that negative reviews (46.8%) dominate over positive (45.9%) and neutral (7.3%) reviews, with the model achieving 81.1% accuracy and an F1-score of 0.81. Dominant negative words include “error” and “server”, while positive reviews highlight “tax” and “helpful”. These findings indicate that the main weakness of the application lies in technical aspects such as server stability, while its strength is in the core function of online tax payment. This study provides concrete recommendations for developers to improve system stability and offers methodological contributions for future research by adopting transformer-based models.
Analisis Sentimen Ulasan Mobile Legend Menggunakan Algoritma Naive Bayes, SVM, Logistic Regression Alengka, Son Gohan; Putra, Jordy Lasmana; Setiyorini, Tyas
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v5i3.12915

Abstract

The rapid growth of the mobile gaming industry in Indonesia, particularly Mobile Legends: Bang-Bang, has generated millions of user reviews on the Google Play Store, making manual analysis inefficient and prone to bias. This study compares three algorithms—Naive Bayes, Support Vector Machine (SVM), and Logistic Regression—for sentiment analysis of 52,651 reviews. Preprocessing includes text cleaning, stopword removal (Indonesian/English), Sastrawi stemming, and TF-IDF representation (min_df=3, max_df=0.9, n-gram 1–2). Binary labeling follows a rating-based approach: 1–2 stars (negative), 4–5 stars (positive), while 3-star reviews are excluded due to ambiguity. Evaluation using accuracy, precision, recall, F1-score, confusion matrix, and Cohen’s Kappa shows SVM and Logistic Regression achieving ≈90–91%, with SVM chosen as the default model for its balanced metrics and margin stability. The model can be deployed as an API service (Flask/FastAPI) for near real-time review monitoring (e.g., lag, AFK, matchmaking), enabling alert thresholds and improvement prioritization. Findings remain limited to Mobile Legends reviews on Google Play, requiring further validation across other applications.
Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Daun Padi Menggunakan Ekstraksi HOG Yana, Baktiar Yudha; Via, Yisti Vita; Nurlaili, Afina Lina
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v6i1.13306

Abstract

Rice (Oryza sativa) is a strategic Indonesian food commodity that is susceptible to leaf disease attacks, causing decreased productivity and even crop failure. Conventional detection methods based on visual observation have limited accuracy and consistency, so an automated approach based on computer vision technology is needed for more effective early detection. This study applies the K-Nearest Neighbors (KNN) algorithm in rice leaf disease classification using Histogram of Oriented Gradients (HOG) feature extraction. A secondary dataset from Kaggle of 1,400 images covers four categories: Bacterial Leaf Blight, Brown Spot, Leaf Blast, and Healthy. The methodology includes image preprocessing (resize, augmentation, grayscale conversion, normalization), HOG feature extraction, and KNN classification with evaluation on a training-test data ratio of 85:15. The results show that KNN with k=2 achieves optimal performance at a ratio of 85:15 with an accuracy of 90.24%, a precision of 90.27%, a recall of 90.24%, an F1-score of 90.23%, and an efficient computational time of 3.34 seconds. The combination of HOG and KNN is proven to be effective for the automatic classification of rice leaf diseases with high accuracy and good computational efficiency.
Analisis Ekstraksi Fitur LBP, GLCM Dan HSV Untuk Klasifikasi Kualitas Cabai Rawit Menggunakan Xgboost ZAMAZANI, ZAIN MUZADID; Puspaningrum, Eva Yulia; Via, Yisti Vita
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v6i1.13307

Abstract

Cayenne pepper (Capsicum frutescens L.) is a horticultural commodity of high economic value, so determining its quality is an important factor in determining the selling price and suitability for consumption. So far, quality assessment is still mostly done manually, but this method tends to be subjective and less efficient. To overcome this, this research evaluates the quality classification of cayenne pepper based on digital image processing using the XGBoost algorithm with three types of features, namely Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Hue, Saturation, Value (HSV). The primary dataset used consists of 1,200 images of six quality classes (raw, undercooked, cooked, dry, rotten, and anthracnose). The methodology stages include pre-processing in the form of background removal, resizing, and data augmentation. Next, LBP, GLCM, and HSV feature extraction is carried out, then classification by dividing the test training data by 80:20. The test results show that the best configuration is obtained with the HSV feature, using learning rate parameters 0.1, n_estimators 100, and max depth 12, which produces accuracy (98.92%), higher than using GLCM (88.08%) or LBP (79.17%). These findings confirm that color information is more dominant than texture in supporting automatic quality classification of cayenne peppers.

Page 10 of 11 | Total Record : 104

 7   8   9   10   11 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 2 (2023): April 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 2 (2022): April 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 1 (2021): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 2 (2021): April 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika More Issue