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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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Analisis Clustering Gaji Karyawan Menggunakan K-Means dan Elbow Method Rumah Sakit XYZ Hudri Ritonga, Adrian Marsa; Safrizal
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2915

Abstract

Transformasi digital pada sektor layanan kesehatan menuntut rumah sakit untuk mengelola data secara lebih efektif, termasuk dalam manajemen sumber daya manusia. Salah satu aspek penting dalam pengelolaan karyawan adalah analisis distribusi gaji, karena ketidakseimbangan gaji dapat memengaruhi motivasi, kinerja, dan retensi karyawan. Penelitian ini bertujuan untuk menganalisis pola distribusi gaji karyawan dan mengelompokkan karyawan berdasarkan tingkat gaji guna mendukung pengambilan keputusan manajemen SDM. Metode yang digunakan adalah K-Means Clustering dengan bantuan Elbow Method untuk menentukan jumlah klaster optimal. Dataset terdiri dari data gaji karyawan Rumah Sakit XYZ periode Januari–Desember 2024. Proses penelitian mencakup pengumpulan data, preprocessing, penentuan jumlah klaster optimal, penerapan K-Means, serta visualisasi hasil menggunakan scatter plot, diagram batang, dan tabel ringkasan. Hasil analisis menunjukkan bahwa data gaji karyawan terbagi menjadi tiga klaster utama: Cluster 0 dengan gaji rendah, Cluster 1 dengan gaji menengah, dan Cluster 2 dengan gaji tinggi. Visualisasi hasil analisis mempermudah manajemen dalam memahami distribusi gaji dan mengambil keputusan terkait strategi kompensasi. Penelitian ini memberikan kontribusi dalam membantu rumah sakit memanfaatkan analisis data berbasis machine learning untuk meningkatkan transparansi pengelolaan gaji dan mendukung pengambilan keputusan yang lebih tepat sasaran. Selain itu, penelitian ini juga memberikan kontribusi teoretis dengan memperluas literatur mengenai penerapan data mining dalam manajemen sumber daya manusia, khususnya pada sektor kesehatan, sehingga memperkaya perspektif akademik dalam kajian manajemen SDM berbasis teknologi.
Deteksi Penyakit Hawar Daun Bakteri pada Tanaman Padi Menggunakan Algoritma Data Mining Umbu Zogara, Lukas; Rindi Widya Yato, Dhimas Buing
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2917

Abstract

Penyakit tanaman merupakan tantangan serius dalam sektor pertanian, khususnya Hawar Daun Bakteri (HDB) pada padi yang dapat menurunkan produktivitas dan menimbulkan kerugian ekonomi. Penelitian ini bertujuan mengembangkan model klasifikasi HDB berbasis algoritma Gaussian Naive Bayes yang ringan dan dapat diterapkan di wilayah dengan keterbatasan teknologi. Data citra daun padi dikumpulkan dari lapangan, diproses melalui tahap preprocessing dan ekstraksi fitur visual, lalu diklasifikasikan menggunakan Gaussian Naive Bayes dengan evaluasi berbasis akurasi, precision, recall, F1-score, dan AUC. Hasil menunjukkan akurasi 63,07%, precision 56,16%, recall 90,64%, F1-score 69,35%, dan AUC 0,7728. Nilai recall yang tinggi menegaskan kemampuan model dalam mendeteksi sebagian besar daun terinfeksi, sementara AUC menunjukkan performa klasifikasi yang cukup baik. Model ini juga telah diintegrasikan ke dalam prototipe aplikasi web dengan antarmuka pengguna yang sederhana, di mana pengguna dapat mengunggah citra daun untuk dianalisis secara otomatis. Hasil penelitian ini diharapkan dapat digunakan untuk mendukung sistem peringatan dini penyakit tanaman dan membantu petani dalam pengambilan keputusan pengendalian penyakit secara cepat dan efisien. Penelitian ini berkontribusi pada pengembangan sistem deteksi dini berbasis machine learning untuk meningkatkan produktivitas pertanian berkelanjutan.
Implementasi Yolo Untuk Menghitung Kepadatan Kendaraan Tempat Parkir Hidayat, Ferdian Afza; Umbara, Fajri Rakhmat; Ilyas, Ridwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2919

Abstract

The significant increase in the number of vehicles entering the Universitas Jenderal Achmad Yani area—especially after the construction of the Faculty of Science and Informatics building—has caused congestion at several strategic points on campus, including the area in front of the campus mosque. This study aims to develop a real-time vehicle density monitoring system to support more efficient campus traffic management. The method used involves applying the YOLOv5 object detection algorithm to identify and count vehicles from video recordings in selected monitoring areas. The system is designed to deliver fast and accurate detection while providing real-time vehicle density information. Testing results show that the system achieved strong detection performance, with a maximum precision value of 1.00 at a confidence threshold of 0.983. The maximum recall value of 0.90 was obtained at a lower confidence threshold, reflecting the system’s ability to detect most objects present. These findings highlight the trade-off between model confidence in predictions and its ability to avoid missing relevant objects. The contribution of this study is the development of a prototype system capable of automatically and in real time monitoring vehicle density in campus areas. This system has the potential to become part of a smarter, data-driven campus traffic management solution to reduce congestion and improve the comfort and mobility of the academic community.
Klasifikasi Gangguan Mental Menggunakan Metode TabularBERT Dengan Explainable AI Untuk Model Interpretabilitas Aufa, Hans Adiyatma Putra; Rahmayanti Setyaning Nastiti, Vinna
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2925

Abstract

Mental disorders are health problems that require early detection and appropriate treatment. This study developed a classification model for mental disorders using the TabularBERT method and evaluated the effect of Principal Component Analysis (PCA) in improving performance. A mental disorder symptom dataset was used with a train-test split scheme, and evaluation was conducted using accuracy, precision, recall, F1-score metrics, and confusion matrix visualization. Model interpretability was analyzed using Local Interpretable Model-Agnostic Explanations (LIME). The results show that the application of PCA consistently improves model performance. LIME analysis revealed differences in feature contributions between models with and without PCA. This study confirms that the combination of TabularBERT, PCA, and LIME not only produces a high-performance classification model but also supports interpretability, making it potentially applicable in decision support systems in the field of mental health to improve the quality of mental disorder detection and treatment.
Pendekatan Linguistik dalam Klasifikasi Emosi Depresi untuk Deteksi Dini Kesehatan Mental di Reddit Fitriyani, Annisaa Salsabila Shafiyyah; Setyaning Nastiti, Vinna Rahmayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2927

Abstract

In the digital era, social media has become a primary means for individuals to express emotions, including symptoms of depression. Posts reflecting feelings of despair and loneliness are increasingly common, particularly on platforms like Reddit. This phenomenon underscores the importance of automatically detecting depressive emotions at an early stage through technology-based approaches, to mitigate negative impacts on mental health. This study employs three linguistic approaches—Lexical Base, WordNet, and GLUE—to enrich semantic understanding and enhance model performance in multilabel classification of depressive emotions. A total of 6,037 text data points were used and split into training, validation, and test sets with a ratio of 70%:15%:15%, following initial processing and linguistic preprocessing stages. Evaluation was conducted using precision, recall, and F1-score metrics on both macro and micro averages. Overall, the study indicates that while linguistic approaches such as Lexical Base, WordNet, and GLUE can enrich text representation, their performance does not always surpass BERT without preprocessing. This suggests that the effectiveness of integrating linguistic information is highly dependent on data context, and further research could explore combining it with multimodal data or advanced mechanisms such as attention to improve depressive emotion classification performance. These findings have potential applications in AI-based mental health monitoring systems, such as chatbots or early detection platforms, to assist in automatically identifying depression symptoms in social media users.
Pengembangan Dashboard Prediksi Penggunaan Transportasi Umum Berbasis Business Intelligence dan Random Forest di Jakarta Ahmad, Alifio Fikra Ahmad; Budy Santoso, Cahyono
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2941

Abstract

This study applies the concept of Business Intelligence (BI) to predict and visualize trends in public transportation usage in Jakarta to support data-driven decision making. Secondary data from the Satu Data Jakarta portal was analyzed using the Random Forest algorithm due to its ability to process complex variables with accurate prediction results (R² = 0.978). The results show that TransJakarta, MRT, and KRL have stable passenger trends, while LRT, KCI Commuter Bandara, ships, and school buses are more volatile. These results are visualized in a web-based dashboard that facilitates fleet planning and public transportation operational policies. This research contributes to the application of BI in the transportation sector by presenting a prediction model that supports data-driven policy formulation.
SmartTraffic-CNN: Deteksi dan Estimasi Jumlah Kendaraan Secara Otomatis Menggunakan Deep Learning dan Ekstraksi Fitur Putri, Marsiska Ariesta; Riyono; Setiawan Wibisono, Iwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2943

Abstract

With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. As a result, various traffic-related problems have emerged, such as congestion and excessive vehicle volume and types. To address these issues, comprehensive road data collection is essential. Therefore, in this study, we developed an intelligent traffic monitoring system based on You Only Look Once (YOLO) and a Fuzzy Convolutional Neural Network (CFNN), which records traffic volume and vehicle-type information from the roadway. In this system, YOLO is first used for vehicle detection and combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector CFNN) along with a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed methods achieved an accuracy of 90.45% on a public dataset. On this dataset, the average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods reached 99%, outperforming other approaches. On real highways, the proposed YOLO-CFNN and YOLO-VCFNN methods not only attained high F1-scores for vehicle classification but also demonstrated remarkable accuracy in vehicle counting. Furthermore, the system maintained a detection speed of over 30 frames per second. Thus, the proposed intelligent traffic monitoring system is well-suited for real-time vehicle classification and counting in real-world environments.
Optimalisasi Akurasi Model Identifikasi Penyakit Pada Daun Padi Dengan Fine-Tuning YOLOv11 Untuk Ketahanan Pangan Berkelanjutan Harsanto; Pradana, Afu Ichsan; Wahyu Pamekas, Bondan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2945

Abstract

Rice is one of Indonesia's main food commodities, whose productivity often declines due to leaf disease. Early detection of rice leaf disease is an important aspect of maintaining sustainable food security. This study aims to optimize the accuracy of early identification of rice leaf disease by fine-tuning the YOLOv11 model. The research stages included dataset collection, annotation, data preprocessing, data augmentation, model training, fine-tuning, and model performance evaluation. The results showed an improvement in model performance after fine-tuning, with the overall recall value increasing from 0.760 to 0.788 and mAP from 0.764 to 0.785. The confusion matrix also shows a more stable prediction distribution in the fine-tuned model compared to the initial model. Thus, fine-tuning YOLOv11 has proven to be effective in improving the accuracy of early identification of rice leaf diseases and has the potential to support the application of artificial intelligence in the agricultural sector to strengthen food security in Indonesia.
Penerapan Transformer-Based Neural Machine Translation untuk Bahasa Bima Julkarnain, M; Mardinata, Erwin; Susilowati, Rina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2949

Abstract

The Bima language is one of the regional languages in Indonesia that still lacks support in natural language processing technology. The application of Transformer architecture for regional languages has not been widely researched, especially for the Bima language. This study aims to develop an automatic translation system from Bima to Indonesian using a Transformer-Based Neural Machine Translation (NMT) approach. The methods used include the collection and processing of parallel corpora, training NMT models using the OpenNMT framework, and evaluating translation results using metrics such as BLEU and TER. The data used will be collected from various sources, including manually translated texts by linguists and available local documents. The model evaluation results indicate that the model was successfully developed and can translate sentences well. This approach is expected to become the foundation for the development of automatic translation technology for other regional languages in Indonesia, while also contributing to the preservation and digitization of local languages through artificial intelligence technology.
Analisis Sentimen Terhadap Ulasan Aplikasi Deepseek AI Menggunakan Model Bidirectional LSTM dan IndoBert Mahendra, Lucky Syahroni; Herry Chrisnanto, Yulison; Yuniarti, Rezki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2950

Abstract

Advancements in Natural Language Processing (NLP) technology have progressed rapidly, marked by the emergence of various Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek AI. One particularly popular model is DeepSeek AI due to its ability to understand and respond to natural language text more contextually. The increasing popularity of this application is accompanied by a growing number of user reviews, which serve as an important source of data for capturing their experiences and perceptions. This study aims to analyze user sentiment toward the DeepSeek AI application using a deep learning approach. Specifically, the research focuses on evaluating the performance of sentiment classification models in the context of Indonesian-language data, which is relatively limited and imbalanced. The dataset was collected from user reviews on the Google Play Store and categorized into three sentiment classes: positive, negative, and neutral. The method employed is a combination of IndoBERT and Bidirectional Long Short-Term Memory (BiLSTM). IndoBERT is used to generate contextual text representations in Indonesian, while BiLSTM is utilized to recognize sequential word patterns. Experimental results show that this hybrid model achieves an accuracy of 45%, with the highest F1-score of 0.66 in the positive class. Meanwhile, a macro-average F1-score of 0.33 and a ROC-AUC of 0.546 indicate that the model’s performance remains limited in distinguishing the three classes evenly. Nevertheless, the main contribution of this study lies in the development of a new dataset consisting of 1,774 Indonesian-language reviews related to LLM-based applications, which can be used for further research in the field of Natural Language Processing (NLP). The study also demonstrates the effectiveness of integrating IndoBERT and BiLSTM for sentiment analysis of Indonesian text with imbalanced data distribution.

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