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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 1,150 Documents
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Klasifikasi Sentimen Ulasan GoFood di Google Play Store dengan Metode Naive Bayes Dwi Diva Teresia Situngkir; Anita; Dheo Laurenz Purba
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3479

Abstract

GoFood is a food delivery feature within the Gojek application that has received numerous user reviews on the Google Play Store. The high volume of reviews creates a need for efficient automated sentiment analysis. This study aims to classify the sentiment of GoFood reviews using the Multinomial Naive Bayes method with TF-IDF weighting. A total of 1,649 Indonesian-language reviews were collected through web scraping from the Google Play Store, then processed through preprocessing and sentiment labeling stages, with an 80 percent training data and 20 percent testing data split. The evaluation results show an accuracy of 78.14 percent, with negative sentiment precision of 0.76 and recall of 1.00, as well as positive sentiment precision of 0.96. The low positive recall was caused by data imbalance and the absence of data balancing techniques such as SMOTE. The scientific contribution of this study is the provision of a sentiment map based on Multinomial Naive Bayes and TF-IDF as a reference for GoFood service evaluation and the development of Indonesian-language text sentiment analysis.
K-Means Clustering Menghasilkan Tiga Segmen Layanan Sertifikasi Kapal Berbasis Karakteristik Operasional Hermina; Handoyo Widi Nugroho
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3486

Abstract

Uneven workload distribution in ship certification services is a major operational challenge for port authorities. This study applies K-Means Clustering to 18 types of certification services at KSOP Class I Panjang, involving 1,506 certificates issued from January to December 2025, using four operational variables: application frequency, completion time, number of documents, and service complexity. The Elbow Method identified K = 3 as the optimal number of clusters. Three groups were formed: Regular Services, consisting of 6 low-complexity services; Complex Services, consisting of 10 services with high technical requirements; and Dominant Services, consisting of 2 services that accounted for 62.4 percent of total certificate issuance. Cluster quality was confirmed by a Silhouette Score of 0.6221 and a Davies-Bouldin Index of 0.484. These findings contribute methodologically by demonstrating that service population-based segmentation produces valid clusters that can be directly applied as a basis for human resource reallocation, digitalization prioritization, and the formation of specialist teams within port authorities.
Klasifikasi Risiko HIV Berbasis Data Agregat Populasi Menggunakan Random Forest, SVM, dan Logistic Regression Altarik Aziz; Heni Sulistiani
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3492

Abstract

This study aims to analyze the performance of machine learning algorithms in classifying HIV-related risk based on opportunistic infection indicators in population-level aggregate data. The dataset was obtained from Kaggle and contains global mortality data, with the target variable constructed as a binary classification based on the median number of HIV/AIDS deaths to represent high- and low-risk categories at the population level, rather than individual diagnosis. The research stages include data preprocessing, handling class imbalance using SMOTE on the training data, feature selection based on clinical domain knowledge, and model training using Random Forest, Support Vector Machine (SVM), and Logistic Regression with GridSearchCV optimization and cross-validation. The results show that Random Forest achieved the best performance, with an accuracy of 98.56 percent and an AUC of 0.99. However, this performance should be interpreted cautiously because it is influenced by the high correlation among features and the median-based target construction, which may simplify the classification patterns. This study demonstrates that machine learning can be used to identify HIV-related risk patterns in population-level aggregate data. However, the resulting model is not intended for individual clinical diagnosis, but rather as a risk analysis tool at the population level.
Optimasi CatBoost Menggunakan Grid Search pada Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa Christopher Radha; Evi Maria
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3517

Abstract

On-time student graduation is one of the important indicators in evaluating higher education performance. However, the classification of on-time graduation in academic data often faces the problem of imbalanced class distribution, which can affect the performance of machine learning models. This study aims to develop a classification model for student on-time graduation using the CatBoost algorithm optimized through Grid Search Cross-Validation within the Educational Data Mining framework. The research dataset consists of 951 students with a class imbalance ratio of 1.83:1. Imbalance handling was performed using the Synthetic Minority Oversampling Technique (SMOTE), while hyperparameter optimization was conducted using stratified 5-fold cross-validation with a total of 108 parameter combinations. The results show that the optimized CatBoost model achieved better performance than the default model, with an accuracy of 91.10 percent, a weighted F1-score of 91.20 percent, and a ROC-AUC of 96.09 percent, improving from the default model’s accuracy of 89.01 percent, weighted F1-score of 89.20 percent, and ROC-AUC of 95.39 percent. Feature importance analysis shows that accumulated credits and academic performance in the middle to final semesters are the most influential factors in the classification results. This study demonstrates that hyperparameter optimization plays an important role in improving CatBoost performance on academic data with imbalanced class distribution. The developed model represents classification based on students’ longitudinal academic records, rather than an early prediction system based on initial-semester data, because the features with the highest predictive contribution come from the middle to final semesters.
Prediksi Penyakit Asma Menggunakan Naïve Bayes dan Random Forest Berbasis Data Klinis Multivariat Salsabila Ainur Hidayah; Zaenal Abidin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3521

Abstract

Asthma is a chronic respiratory disease that can significantly reduce a patient’s quality of life if it is not detected and treated properly. This study aims to compare the performance of Gaussian Naïve Bayes and Random Forest in predicting asthma using the Asthma Disease Dataset from Kaggle, which contains 2,392 patient records with an imbalanced class distribution: 2,268 non-asthma cases and 124 asthma cases. The steps taken include feature selection, preprocessing using StandardScaler, handling class imbalance with SMOTE applied exclusively to the training data, the classification process, and model evaluation using the metrics accuracy, precision, recall, F1-score, ROC AUC, Cohen’s Kappa, MCC, and 5-Fold Cross Validation. Test results showed that Random Forest achieved the highest accuracy of 0.904 with a precision of 0.080, while Gaussian Naïve Bayes produced a recall of 0.520, an F1-score of 0.134, and an ROC AUC of 0.638. These findings indicate that Random Forest is superior in terms of overall accuracy, while Gaussian Naïve Bayes is more effective in detecting asthma cases in the dataset used. The results of this study can serve as a reference in the development of decision support systems for asthma risk identification, although further validation using more diverse clinical data is still required.
Deteksi Plagiarisme Tugas Mahasiswa Menggunakan Sentence Embedding Berbasis Transformer dan Metode Cosine Similarity Nawfal Tamim Syuja'i; Dennis Imanuel Daeli; Ichsan Alfarizi Darnela; Eveline Ardhelie Thio Candra; Juliansyah Putra Tanjung
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3523

Abstract

Academic plagiarism is becoming increasingly difficult to detect because it is not only carried out through direct copying but also through paraphrasing that changes sentence structure without altering meaning. This study develops a student assignment plagiarism detection system using Transformer-based Sentence Embeddings and Cosine Similarity. The dataset used consists of 600 Indonesian text pairs manually annotated as ground truth with plagiarism and non-plagiarism labels. The research stages include data collection, selective preprocessing, subword tokenization in the Transformer model, embedding vector formation, Cosine Similarity calculation, threshold determination, and performance evaluation using accuracy, precision, recall, and F1-score. This study compares the baseline TF-IDF method with two Transformer models, namely all-MiniLM-L6-v2 and paraphrase-multilingual-MiniLM-L12-v2. The test results show that the paraphrase-multilingual-MiniLM-L12-v2 model achieved the best performance, with an accuracy of 86.67 percent, precision of 0.83, recall of 0.95, and F1-score of 0.89. Meanwhile, all-MiniLM-L6-v2 achieved an accuracy of 74.67 percent, and TF-IDF achieved an accuracy of 62.00 percent. A threshold value of 0.70 was determined based on the analysis of changes in precision, recall, and F1-score, so that plagiarism decisions were not based merely on assumptions but on quantitative evaluation results. Therefore, the Transformer-based Sentence Embedding approach proved to be more effective than lexical methods in detecting paraphrase-based plagiarism in Informatics Engineering student assignments.
Determinan Perilaku, Etika, dan Finansial terhadap Penggunaan Buy Now, Pay Later (BNPL): Tinjauan Literatur Sistematis Bayu Liano Leader Habibullah; Mudjahidin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3524

Abstract

This study presents a Systematic Literature Review (SLR) of 30 peer-reviewed articles on the adoption, behavioral dynamics, and financial implications of Buy Now, Pay Later (BNPL) services. Using the SPAR-4-SLR protocol and the TCCM framework, this review integrates findings from fintech, consumer behavior, behavioral economics, and digital credit research. The results show that BNPL studies primarily employ behavioral and technology acceptance theories, such as TAM, TPB, UTAUT, and impulsive buying theory. Research is largely concentrated in Indonesia, Malaysia, Australia, the United States, and the United Kingdom. The main factors influencing BNPL usage intention include perceived usefulness, trust, affordability, convenience, impulsive tendency, and financial literacy. Most studies use quantitative survey-based methods, while qualitative approaches, mixed methods, and secondary data remain limited. This review identifies gaps in theory, context, methodology, and constructs, while also opening opportunities for future research, such as multi-theory integration, cross-country comparisons, and studies on financially vulnerable groups. Practical implications for policymakers and BNPL service providers include improving transparency, promoting responsible lending practices, and strengthening consumer protection. Research limitations, such as the focus on B2C BNPL, the exclusion of B2B models, and reliance on secondary data, provide opportunities for further empirical research.
Peningkatan Klasifikasi Biner Glioma pada MRI Otak Menggunakan CLAHE dan Transfer Learning Devi Dian Aprilia Kusuma Sari; Agus Eko Minarno
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3357

Abstract

The classification of gliomas based on Magnetic Resonance Imaging (MRI) still faces challenges due to image quality issues such as low contrast, noise, and intensity variations. This study aims to evaluate the impact of applying Contrast Limited Adaptive Histogram Equalization (CLAHE), transfer learning architectures, and optimization strategies on the performance of binary glioma classification using an ablation study framework with a patient-level data split to prevent data leakage. This study uses a dataset consisting of 4,000 MRI images from 1,402 unique patients. The results show that the combination of CLAHE, ResNet50, and the Adam optimizer delivers the best performance with an accuracy of 100% on the dataset and under the experimental conditions used, while Grad-CAM visualizations qualitatively demonstrate the model’s focus on relevant anatomical areas. This study contributes through an integrated evaluation framework that combines preprocessing, transfer learning, optimization strategies, and model interpretability. However, validation on a multi-institutional dataset and quantitative XAI evaluation are still needed to test generalization capabilities and strengthen the interpretation of results.
Implementasi Sistem Klasifikasi Kualitas Udara Menggunakan Sensor Gas dan Jaringan Saraf Tiruan Anifatul Faricha; Dimas Adiputra; Rifki Dwi Putranto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3388

Abstract

Indoor air quality is an important factor for human respiration. In enclosed spaces, especially with increased activity and the number of occupants, pollutants such as carbon dioxide may increase, while oxygen levels may decrease. Therefore, a classification system is needed to determine indoor air quality. In this study, we designed an air quality measurement device using two types of gas sensors, namely carbon dioxide and oxygen sensors. The carbon dioxide gas sensor used was the MG-811, while the oxygen gas sensor used was the Gravity I2C. In addition, an artificial neural network was implemented as the air quality classification method, divided into three categories: normal, wary, and dangerous. The classification process was divided into two stages: training and testing. Based on the experimental results, an error value of 0.016 was obtained with 274 epochs during the training process. Meanwhile, in the testing process, the achieved accuracy was above 90 percent, indicating that the artificial neural network was successfully implemented for air quality classification.
Analisis Kesuksesan Aplikasi Food Delivery Menggunakan Model DeLone & McLean: Studi Empiris Pengguna ShopeeFood Erina Setyawati; Berlilana; Dhanar Intan Surya Saputra
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3425

Abstract

The digital transformation in the food delivery industry is driving service providers to offer high-quality information systems while ensuring user security and privacy so that satisfaction levels can be maintained. This study was conducted to examine the influence of Information System Quality and Security & Privacy on User Satisfaction among ShopeeFood app users by adopting the DeLone and McLean information system success model and using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) method. The study employed a quantitative approach by collecting data through a questionnaire distributed to 100 ShopeeFood users. The analysis results show that Information System Quality and Security & Privacy have a positive and significant influence on User Satisfaction. An R-squared value of 0.642 indicates that these two variables account for 64.2% of the variation in user satisfaction. These findings confirm that optimal information system quality, supported by adequate security and privacy protections, plays a crucial role in enhancing ShopeeFood user satisfaction. From a theoretical perspective, this study reinforces the relevance of the DeLone and McLean model in the context of digital food delivery services, while from a practical standpoint, the research findings can serve as a reference for service providers to improve system quality, service security, and the overall user experience.

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