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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 120 Documents
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Analisis Algoritma Naive Bayes Untuk Prediksi Kepuasan Layanan Akademik Berbasis Data Multibahasa Oktafiani, Dewi; Putra, Tommy Dwi; Kusumastuti, Rajnaparamitha
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.v5i3.13456

Abstract

The quality of academic services greatly influences student satisfaction. This study predicts student satisfaction with academic services using a Naïve Bayes algorithm based on multilingual data. Data from 213 students across three departments at STMIK AMIKOM Surakarta cover five key service aspects. Student comments were processed through text preprocessing and TF-IDF weighting, then tested on both Indonesian and English-translated texts. The results showed a significant difference: the Indonesian model achieved 67.44% accuracy, 0.68 precision, 0.65 recall, and 0.66 F1-score, while the English version improved to 83.72% accuracy, 0.84 precision, 0.82 recall, and 0.83 F1-score. Statistical tests confirmed this difference as significant. The findings highlight that English NLP tools are more mature and provide empirical contributions to improving the quality of academic services in higher education.
Implementasi Groq AI untuk Otomatisasi Feedback pada Website Evaluasi Kinerja Dosen Kusumastuti, Rajnaparamitha; Oktafiani, Dewi; Dwi Putra, Tommy
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.v5i3.13458

Abstract

Lecturer performance evaluation is essential to maintain the quality of higher education, yet traditional methods often lack objectivity and provide limited feedback. This study designed a web-based evaluation system using the Simple Additive Weighting (SAW) method for decision-making, integrated with Groq AI to generate automatic feedback from students. The system was developed with a prototype approach using the Flask framework and tested on 10 courses with a total of 250 randomly selected respondents. Instrument reliability was confirmed using Cronbach’s Alpha (α = 0.84), indicating a high level of reliability. System speed evaluation through 40 trials showed an average processing time of 0.564 seconds. User satisfaction was measured with a 1–4 Likert scale and converted using the Percent of Maximum Possible (POMP), resulting in a 92.4% satisfaction rate. The AI feature successfully provided automated feedback without manual intervention, significantly improving efficiency and effectiveness. These results demonstrate that integrating SAW with Groq AI enhances objectivity, speed, and quality in lecturer performance evaluation.
Perbandingan Kinerja Support Vector Machine Dan Random Forest Untuk Klasifikasi Sentimen Pengguna Aplikasi Gojek Dengan Optimasi Smote Rihastuti, Siti; Rosyidi, Afnan
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.v5i3.13463

Abstract

This study compares the performance of Support Vector Machine (SVM) and Random Forest in classifying Gojek user sentiment using 2,000 Indonesian-language reviews (1,351 positive, 566 negative, 83 neutral). After data preprocessing and TF-IDF feature extraction, SMOTE was applied to balance the training data in each fold. Using Stratified K-Fold Cross-Validation, results showed that Random Forest achieved higher and more consistent accuracy (84.1%) than SVM (76.1%). The Paired t-test and McNemar’s Test (p-value < 0.05) confirmed that the Random Forest’s superiority was statistically significant. Overall, both models were effective, but Random Forest performed better for Gojek sentiment classification, supporting user satisfaction monitoring and complaint detection.
Pengenalan Wajah Untuk Presensi Menggunakan Metode Naive Bayes Sanders, Carmel Edra; Alamsyah, Derry; Devella, Siska
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.v5i3.13593

Abstract

Automation of the attendance process has become a necessity nowadays to facilitate the process of recording and recapitulating precise attendance data compared to conservative (manual) attendance. This process is carried out through the recognition of biometric information, namely faces, using the Naive Bayes method with Gaussian distribution and pre-trained VGG16 feature extraction. In this study, the model developed based on this method uses the public CASIA WebFace dataset which has high variation and a private dataset which has low variation. The results show that the proposed method is able to work well on datasets with low variation, with accuracy results reaching 97% supported by feature dimension reduction using the PCA method.
Pengaruh Splitting Data terhadap Akurasi Klasifikasi Demam Berdarah Dengue Menggunakan K-Nearest Neighbors Ibrahim Akbar, Muhammad; Rudi, Rudy Heriansyah; irfani, muhammad haviz
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13274

Abstract

Early detection of Dengue Hemorrhagic Fever (DHF) is crucial to prevent serious complications and improve treatment effectiveness, particularly in high-case areas such as the Dempo Primary Health Center. This study aims to develop and evaluate a DHF classification system using the K-Nearest Neighbors (K-NN) algorithm with an optimal K value of 5, determined via the Elbow Method. The dataset consists of 200 medical records with an imbalanced class distribution between positive and negative DHF cases. Three data-splitting scenarios (70:30, 80:20, and 90:10) were tested to analyze the effect of data proportion on model performance. Evaluation metrics included accuracy, precision, recall, and F1-score. Results show that the 70:30 scenario achieved the best performance, with 90% accuracy, 96.67% precision, 85.29% recall, and 90.62% F1-score. For comparison, K-NN was tested against Decision Tree and Support Vector Machine (SVM) algorithms as baselines. K-NN demonstrated competitive and more stable performance, with an average accuracy difference of ±2% compared to the other methods. These findings confirm that K-NN provides reliable results for medical data with limited sample size and imbalanced class distribution. This study contributes empirical analysis regarding the influence of varying data split ratios on classification model stability and strengthens the application of machine learning for early DHF detection based on local medical data.
Popularitas dan Tren Metode Pemodelan Expected Goals (xG): Sebuah Analisis Bibliometrik Akbar, Fadhil Raihan; Aini, Qurrotul; Hasanati, Nida'ul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13402

Abstract

Data analysis has fundamentally altered contemporary football performance assessment, with Expected Goals (xG) emerging as a key metric. Despite its widespread use, comprehensive documentation of its modeling methodologies remains scarce. This study aims to map the global xG research landscape using bibliometric analysis. Scopus data was analyzed using VOSviewer to identify trends, prevalent methodologies, and implementation domains. Findings indicate a significant rise in publications from 2020 to 2025, signaling growing scholarly interest. Random Forest is identified as the most widely used technique, though recent trends suggest a resurgence of Logistic Regression. The primary application domain is Performance Analysis, followed by Tactical & Strategic Analysis. Keyword analysis reveals three main clusters: machine learning, regression models, and deep learning. It is concluded that current xG research trends are moving towards a balance between algorithmic complexity and model interpretability, while expanding beyond mere shot evaluation to more holistic performance metrics.
Implementasi Algoritma Machine Learing Pada Data Tidak Seimbang Menggunakan SMOTE Untuk Klasifikasi Kemiskinan Di Indonesia Ningsih, Desi; Maimunah, Maimunah; Sukmasetya, Pristi
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13518

Abstract

Poverty in Indonesia requires precise analysis based on socio-economic indicators. This study develops classification models using Naïve Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The primary focus is addressing class imbalance through the SMOTE technique. Utilizing 2021 BPS data from 515 regencies, the research incorporates 13 indicators, including education and infrastructure. Models were evaluated using accuracy, precision, recall, and F1-score across multiple data-split scenarios. Results indicate that SMOTE significantly enhances Naïve Bayes and KNN performance in identifying minority classes by reducing data bias. Conversely, SVM maintained consistent performance across all scenarios without SMOTE, attributed to its robust margin-based separation mechanism against distribution shifts. Overall, integrating SMOTE with machine learning algorithms improves classification reliability. This provides a crucial data-driven foundation for the government to formulate more targeted and equitable poverty alleviation policies across Indonesia, ensuring resources are allocated to the regions that need them most.
Penggunaan MobileNetV2 untuk Klasifikasi Penyakit Daun Cabai Saputra, Muhammad Redho; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13562

Abstract

Chili peppers (Capsicum annuum L.) are an important horticultural commodity in Indonesia with high economic value, but they are susceptible to leaf diseases such as leaf spots, curled leaves, yellowing leaves, and whitefly pests. This study aims to classify chili leaf diseases using a MobileNetV2-based Convolutional Neural Network (CNN) architecture utilizing the Depthwise Separable Convolution mechanism for filter decomposition and model complexity reduction. Based on previous studies, MobileNetV2 has been proven to maintain a highly competitive level of accuracy. The dataset used consisted of 6000 images from five categories: healthy, leaf spot, leaf curl, yellowish, and whitefly, which were taken from open sources and equalized in number for each class. The data was divided into training, validation, and testing sets with a ratio of 80:10:10. The training process used depthwise separable convolution, dropout, and Adam and SGD optimization techniques to prevent overfitting. Model evaluation was carried out through 12 scenarios with variations in batch size, dense layer, optimizer, and epoch. The results show the highest accuracy of 98.40% in the scenario with a batch size configuration of 32, a dense layer of 128, a learning rate of 0.001, an Adam optimizer, and 20 epochs. Most scenarios achieved an accuracy above 96%, proving that MobileNetV2 is effective for classifying chili leaf diseases. The contribution of this study is the identification of an optimal and efficient MobileNetV2 parameter configuration for chili leaf disease classification.
Analisis Komparasi Kinerja Model SVM-TF-IDF dan LSTM dengan Embedding BERT untuk Klasifikasi Tingkat Literasi Digital christianto, yudhi; Crysdian, Cahyo; Abidin, Zainal
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13597

Abstract

This study compares the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) with BERT embedding for classifying users’ digital literacy levels from textual digital footprints, dataset of 1,500 Indonesian-language texts from platform X was annotated by three experts into low, medium, and high literacy categories. After text preprocessing, TF-IDF features were applied to SVM and BERT tokenization to LSTM. Models were evaluated using 5-Fold Cross-Validation to ensure reliability. Results show that LSTM-BERT achieved the highest performance (F1-Score = 73.8%) compared to SVM (70.50%), with confusion-matrix analysis indicating better accuracy in detecting high-literacy texts. These findings confirm that contextual linguistic patterns effectively represent digital literacy levels and highlight the potential of deep-learning approaches for scalable, objective, and automated literacy assessment based on text data.
Personalisasi Jalur Pembelajaran Mahasiswa Sistem Informasi dengan Recurrent Neural Network Caesar Ananta, Firzian; Irsyad, Akhmad; Labib Jundillah, Muhammad
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13631

Abstract

Personalized learning faces challenges when Information Systems students must choose a study path among many specialization options, while existing systems often fail to map student interests accurately. Static preference data are commonly treated as independent features, which prevents models from capturing relationships between interest scores. This study proposes a solution using a Simple Recurrent Neural Network that represents seven interest scores as a single sequence to capture positional context across features. A dataset of 318 respondents was used for training, and SMOTE was applied to address label imbalance. The model was compared with a Dense Neural Network to evaluate the impact of the sequential representation. SimpleRNN achieved an accuracy of 90.10 percent at 100 epochs, outperforming the DNN result of 80.20 percent. Evaluation using the confusion matrix along with precision, recall, and F1-score showed that SimpleRNN offers more stable classification, especially for interest categories with similar characteristics. These results indicate that applying a sequential approach to static data improves interest classification performance and supports more accurate personalized learning path recommendations.

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