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INDONESIA
Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,026 Documents
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Komparasi Algoritma Naïve Bayes dan SVM untuk Identifikasi Cyberbullying Selebriti di Media Sosial Twitter Lukman, Khairil; Novianto, Sendi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2196

Abstract

Tingginya aktivitas di media sosial diikuti dengan meningkatnya kasus cyberbullying yang meresahkan masyarakat, terutama di kalangan selebriti. Penelitian ini bertujuan untuk membandingkan performa algoritma Naïve Bayes dan Support Vector Machine (SVM) dalam mengklasifikasi kalimat berbahasa Indonesia yang mengandung unsur cyberbullying. Metode yang digunakan meliputi preprocessing data (tokenization, stemming, dan stopwords removal), serta ekstraksi fitur dengan TF-IDF dan Bag of Words. Dataset berjumlah 650 komentar Twitter berlabel dari situs Kaggle yang bisa diakses pada tautan sebagai berikut: https://www.kaggle.com/datasets/markini/cyberbullying-twitter-bahasa-indonesia. Hasil eksperimen menunjukkan bahwa SVM mencapai akurasi 88 persen, lebih tinggi dibandingkan Naïve Bayes yang mencapai 82. Evaluasi juga dilakukan berdasarkan metrik precision, recall, dan F1-score. Penelitian ini memberikan kontribusi terhadap pengembangan sistem deteksi otomatis cyberbullying berbasis NLP, serta implikasi praktis dalam membantu moderasi konten media sosial secara lebih akurat dan efisien.
Klasifikasi Citra Batik Menggunakan Local Binary Pattern (LBP) dan Support Vector Machine (SVM) Fatimah, Nuris Sayyidatul; Agustin, Soffiana
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2208

Abstract

Image classification is one of the important branches in digital image processing that aims to recognize and classify objects based on certain features. This research seeks to preserve Indonesian batik culture through digital documents of batik motifs that can be applied in museums and research institutions. The main objective of this research is to overcome the difficulty in classifying batik motifs by applying Local Binary Pattern (LBP) as a feature extraction technique and Support Vector Machine (SVM) as a classification algorithm. The batik motifs used are Batik Kawung, Batik Megamendung, and Batik Parang. By using 720 batik images. This research was conducted in four main stages, namely pre-processing, feature extraction, classification and evaluation. The results showed an accuracy of 88.89%, with varying precision, recall, and F1-Score. The results show that texture analysis extracted through LBP contributes significantly to the accuracy of batik motif recognition.
Deteksi dan Pencegahan Web Defacing Judi Online dengan Wazuh SIEM dan Snort IDS Berbasis Signature Reza Pahlevi, Mohammad Rizky; Umam, Chaerul; Handoko, L. Budi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2220

Abstract

Web defacing attacks, where websites are replaced with unwanted content, such as online gambling advertisements, pose a serious threat to the integrity and reputation of websites, especially those belonging to government agencies. This research aims to detect and prevent web defacing attacks containing online gambling content by combining Wazuh Security Information and Event Management (SIEM) and Snort signature-based Intrusion Detection System (IDS). Wazuh is used to monitor and collect activity logs in real-time when suspicious activity is detected. Meanwhile, Snort IDS acts as a signature-based intrusion detection system that can recognize web defacing attack patterns through predefined rules for online gambling content. This research was conducted by building a web defacing attack simulation environment on the server, then testing the response and effectiveness of Wazuh and Snort in detecting and preventing attacks. The test results show that the combination of Wazuh SIEM and Snort IDS can detect and prevent web defacing attacks with a very high accuracy rate, namely 100% of attacks can be detected by Wazuh File Integrity Monitoring and 76% for Snort IDS. The implementation of this system is expected to help improve website security, especially those managed by public institutions, from web defacing threats.
Penentuan Jumlah Project Implementation Staff dengan Metode Workload Analysis Full Time Equivalent (FTE) dan Analytical Hierarchy Process (AHP) : (Studi Kasus: PT Indodev Niaga Internet) Hakim, Sulaiman; Subekti, Yogi Agung; Triyono, Gandung
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2235

Abstract

Dalam suatu proyek yang dikerjakan oleh Tim Implementasi pada PT. Indodev Niaga Internet terkadang Implementation Staff yang ada pada Tim Implementasi sering kali mengalami beban kerja yang berlebih ataupun sebaliknya beban kerja terlalu rendah dengan tidak adanya kesesuaian jumlah Implementation Staff yang terlibat dalam suatu proyek yang dapat mengakibatkan terjadinya ketidakefisienan dalam pengerjaan proyek. Oleh karena itu diperlukan suatu cara untuk mengoptimalkan jumlah Implementation Staff yang ada pada Tim Implementasi agar masalah beban kerja yang berlebih ataupun terlalu rendah dapat teratasi. Cara yang digunakan untuk menyelesaikan masalah tersebut ialah dengan menggunakan Analisis Beban Kerja (Workload Analysis). Analisis Beban Kerja akan mencari kesesuaian jumlah tugas dan beban kerja yang ada dalam suatu organisasi yang akan diekuivalenkan dengan satuan waktu dengan jumlah pegawai yang dimiliki oleh suatu perusahaan dan dikombinasikan dengan Analytical Hierarchy Process (AHP) yang akan menentukan siapa saja Implementation Staff yang tepat dan ideal guna untuk mengisi kekurangan Implementation Staff. 7 kriteria yang telah didapatkan dari hasil kuesioner dan sudah dilakukan pengujian menggunakan Chocran Q-Test ketujuh kriteria yang didapat ialah Tanggung Jawab, Kerjasama Tim, Kemampuan Analisis, Programming Skills, Orientasi terhadap Pencapaian Target, Product Knowledge, dan Kemampuan Berkomunikasi. Dengan menggunakan kedua model tersebut dapat membantu efisiensi Sumber Daya Manusia (Implementation Staff) pada proyek teknologi informasi berbasis layanan.
Prediksi Fluktuasi Berat Badan Berdasarkan Pola Hidup Menggunakan Model XGBoost dan Deep Learning Mujiyono, Sri; Sanjaya, Ucta Pradema; Wibisono, Iwan Setiawan; Setyowati, Heni
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2253

Abstract

The global obesity rate has tripled since 1975, driving the development of technology-based solutions for predicting body weight to mitigate disease risks. This study implements three models—Decision Tree Regressor, XGBoost Regressor, and Deep Learning—to project final body weight based on physiological variables (age, gender, BMR), nutritional factors (caloric intake, surplus/deficit), and lifestyle factors (physical activity, sleep, stress). The multidimensional dataset from community health posts includes TDEE calculations and BMR estimates using the Harris-Benedict Equation. Evaluation using RMSE and R² indicates XGBoost as the best-performing model (RMSE: 5.65; R²: 0.974), outperforming the Decision Tree (RMSE: 10.68; R²: 0.908) and Deep Learning (RMSE: 10.4; R²: 0.913) models. Key challenges include overfitting in the Decision Tree and Deep Learning's inability to capture outliers due to vanishing gradients. The analysis identifies energy balance, representation of extreme data, and regularization as critical factors for model stability. Hyperparameter optimization (learning rate, max\_depth) and data augmentation are recommended to enhance generalization. These findings offer an innovative framework for data-driven health technologies, reinforcing the role of artificial intelligence in precision public health interventions. Practically, the study advocates for the adoption of optimized predictive models integrating multidimensional variables for high accuracy, while highlighting the need for outlier handling and further clinical validation to ensure relevance in real-world scenarios.
Analisis Sentimen Penilaian Pengguna Aplikasi ACI – Ojek Online Indonesia dengan Metode Naïve Bayes Irawan, Michael Aria; Aryanny, Enny
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2254

Abstract

The advancement of industrial technology and current lifestyle changes are rooted in the industrial revolution. The widespread penetration of the internet and the advancement of smartphone technology have played a crucial role in the development of this business model, driving overall industry growth. With the support of GPS technology, digital payments, and mobile applications, consumers can easily order these services. The ACI – Indonesian Online Ojek Application is one example of a platform that provides this service. However, with the surge of users in this digital era, the ACI – Indonesian Online Ojek Application faces significant challenges in competing with more established competitor applications. As can be seen on the Google Play Store platform, this application still has a lower rating compared to other competitors such as Gojek, Grab, Maxim, inDrive, and Omega. The determination of the rating count on the Google Play Store has several determining factors, one of which is user evaluation in using the application. Therefore, this research aims to check whether these problems exist, by analyzing user comment data using the Naïve Bayes method, and propose improvements using the 5W+1H method. The research results show that the sentiment analysis classification analysis using the Naïve Bayes method is 0.83 or 83%, which shows that the classification results can predict 83% correctly from the overall data, and in negative sentiment, there are 4 words that appear most often, in this case, the most complained about by users are the words application, driver, restaurant, and order. Thus, it can be concluded that there is a large amount of negative sentiment from application users, and it is best for the ACI application to improve the quality of its application performance.
Rancang Bangun Prototipe Sistem Deteksi Dini Retinopathic Diabetic Berbasis Website Muhajir, Daud; Mustaqim, Tanzilal; Safitri, Pima Hani; Oktavia, Vessa Rizky
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2255

Abstract

Diabetic Retinopathic (DR) is one of the retinal disorders caused by high blood sugar levels. There are fewer ophthalmologists available, and treating DR patients manually is a time-consuming process. Therefore, there is a need for an automatic DR early detection method using Deep Learning. The purpose of this research is to build a web-based DR early detection prototype with retinal image classification using the DenseNet121 Deep Learning model and the Stochastic Gradient Descent (SGD) optimizer to improve the accessibility and efficiency of screening. The software development method used in this research is waterfall which consists of analysis phase, design phase, implementation phase, and testing phase. To ensure the prototype runs as planned, black-box testing is carried out on each of its features to ensure system functionality in accordance with predetermined specifications. This research produces a RD early detection prototype that has been tested with all 16 test cases and has a suitable status. Future research can be carried out further system development by involving real users such as ophthalmologists and can be applied in hospitals.
Augmented Reality Menggunakan Marker Based Tracking Untuk Informasi Sejarah Kain Tapis Lampung Setiawan, Asep Trisna; Dika Hastanto; Dian Resha Agustina; Adi Permana; Dewi Tresnawati, Dewi Tresnawati
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2261

Abstract

Lampung Tapis fabric is one of Indonesia's cultural heritages that possesses its own unique charm and beauty. This fabric originates from Lampung, a province on the island of Sumatra, Indonesia. Lampung Tapis fabric features distinctive patterns, motifs, and production techniques, and is believed to hold significant historical and symbolic value for the Lampung community. In the era of rapidly advancing digital technology, Augmented Reality (AR) has emerged as an innovative tool for preserving, promoting, and safeguarding cultural heritage. AR is a technology that blends the real world with virtual or additional elements through the use of electronic devices such as smartphones or tablets, enabling users to interactively experience digital content within a real-world physical context. One of the main issues is the lack of a complete history of the Lampung Tapis Fabric exhibited at the Ruwa Jurai Museum in Lampung, which poses a challenge as it results in insufficient historical information to understand the origins, development, and value contained within this cultural heritage. As a result, there is a risk of losing valuable information about local cultural heritage. This research has successfully developed an Augmented Reality application that provides information about the Lampung Tapis Cloth, thereby reducing the potential loss of cultural heritage information in Lampung.
Konsultasi Katarak Dengan Prosedur Certainty Factor Naiborhu, Rinda Anisa; Agus, Raja Tama Andri; Mardalius, Mardalius
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2276

Abstract

Cataract is one of the main causes of visual impairment, especially in the elderly, and requires a quick and accurate diagnosis to prevent further complications. To support the diagnosis process, a web-based expert system with the Certainty Factor (CF) method was developed that is able to calculate the certainty level of diagnosis based on the symptoms experienced by the patient. The system was built using the PHP programming language and MySQL database, with a waterfall development model. Testing was conducted on 50 patient data that had been validated by an ophthalmologist. The evaluation results showed that the system accuracy rate reached 92%, with 46 out of 50 diagnoses in accordance with the results of manual medical examinations. The system is not only able to provide diagnosis recommendations quickly and efficiently, but also improve the accuracy of initial medical decision-making. The practical implication of this research is to increase the efficiency of health services, especially in facilities with limited specialists, and provide convenience for patients in obtaining initial information related to cataract conditions. This application has the potential to be a supporting solution in the digital transformation of health services in the field of eye health.
Analisis Sentimen Ulasan Pengguna iPhone dengan Pendekatan Hibrida RoBERTa dan XGBoost Zain, Affa Fahmi; Azies, Harun Al; Ananda, Imanuel Khrisna
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2277

Abstract

User reviews play an important role in shaping perceptions of products, including the iPhone. Sentiment analysis of these reviews can provide valuable insights for companies to improve product and service quality. This study explores sentiment analysis of iPhone user reviews using a hybrid approach that combines RoBERTa and XGBoost to improve classification accuracy. The model was built and tested on a public dataset containing 2,960 reviews obtained from the Kaggle platform, following data cleaning processes. Preprocessing steps included handling missing values, encoding, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). RoBERTa was used to extract text features and understand contextual meaning, while XGBoost served as the classification algorithm. The evaluation showed an accuracy of 99.74%, with an increase in the F1-score from 0.99 to 1.00 after applying SMOTE, particularly in the minority class. These findings demonstrate the superiority of the RoBERTa-XGBoost approach over traditional methods and contribute to the development of more balanced and adaptive classification models for imbalanced data.

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