Abstract:Abstract: Query performance is a critical factor in managing large-scale databases. One of the most widely used optimization techniques is indexing. This study aims to analyze the impact of indexing on query performance…
in PostgreSQL, compare the effectiveness of B-Tree and Hash indexes, and evaluate their influence on query planner decisions. A quantitative experimental approach was employed using the TPC-H benchmark dataset at scale factors SF0.1, SF1, and SF10. Experiments were conducted using EXPLAIN ANALYZE on exact match, range, and join queries under three conditions: without indexing, with B-Tree indexing, and with Hash indexing. The results demonstrate that indexing significantly improves query performance. For exact match queries on the SF10 dataset, execution time decreased from 93.36 ms without indexing to 0.034 ms using B-Tree and 0.045 ms using Hash indexes. For join queries, execution time was reduced from 857.77 ms to 0.180 ms using B-Tree and 0.079 ms using Hash indexes. B-Tree showed consistent performance across different query types, while Hash achieved the best results for equality-based queries. Furthermore, index usage influenced query planner decisions in selecting more efficient execution strategies. These findings indicate that appropriate index selection can substantially improve data access efficiency in PostgreSQL.
Keywords: b-tree index; hash index; PostgreSQL; query optimization; query planner
Abstrak: Performa query merupakan faktor penting dalam pengelolaan basis data berskala besar. Salah satu teknik optimasi yang umum digunakan adalah indexing. Penelitian ini bertujuan menganalisis pengaruh penggunaan indexing terhadap performa query pada PostgreSQL, membandingkan efektivitas B-Tree dan Hash index, serta mengevaluasi pengaruhnya terhadap keputusan query planner. Penelitian menggunakan metode eksperimen kuantitatif dengan dataset benchmark TPC-H pada skala SF0.1, SF1, dan SF10. Pengujian dilakukan menggunakan EXPLAIN ANALYZE pada exact match query, range query, dan join query dalam kondisi tanpa index, menggunakan B-Tree index, dan Hash index. Hasil penelitian menunjukkan bahwa indexing meningkatkan performa query secara signifikan. Pada exact match query dataset SF10, execution time menurun dari 93,36 ms tanpa index menjadi 0,034 ms menggunakan B-Tree dan 0,045 ms menggunakan Hash index. Pada join query, execution time berkurang dari 857,77 ms menjadi 0,180 ms menggunakan B-Tree dan 0,079 ms menggunakan Hash index. B-Tree menunjukkan performa yang konsisten pada berbagai jenis query, sedangkan Hash index memberikan performa terbaik pada query berbasis equality. Selain itu, penggunaan index memengaruhi keputusan query planner dalam memilih strategi eksekusi yang lebih efisien. Hasil penelitian menunjukkan bahwa pemilihan metode indexing yang tepat dapat meningkatkan efisiensi akses data pada PostgreSQL
Kata kunci: b-tree index; hash index; optimasi query; PostgreSQL; query planner
Abstract:Abstract: Existing IoT anomaly detection studies have achieved high classification performance, but most focus on accuracy and F1-score without explicitly controlling the false positive rate (FPR). In addition, many approaches…
oaches rely on a single detection perspective, limiting their operational reliability. To address this gap, this study proposes a hybrid anomaly detection framework integrating Long Short-Term Memory (LSTM), Shannon entropy, and autoencoder reconstruction error. Shannon entropy is incorporated as an additional feature, while LSTM and the autoencoder capture temporal and reconstruction characteristics. The resulting hybrid representation is processed by a constraint-based threshold selection mechanism that enforces FPR . Experiments on the TON-IoT and Edge-IIoTset datasets achieved average F1-scores of 0.9250 and 0.9934, while maintaining average FPR values of 0.0091 and 0.0714, respectively. Analysis of entropy distributions showed consistent differences between normal and anomalous traffic across both datasets, indicating that Shannon entropy provides discriminative information for anomaly detection. These results demonstrate strong detection performance with controlled false alarms, while ablation studies confirm the significant contribution of Shannon entropy to overall model performance.
Keywords: false positive rate; hybrid deep learning; Internet of Things; network anomaly detection; Shannon entropy
Abstrak: Penelitian deteksi anomali Internet of Things (IoT) telah menunjukkan performa klasifikasi yang tinggi, namun sebagian besar masih berfokus pada accuracy dan F1-score tanpa mengendalikan false positive rate (FPR) secara eksplisit. Selain itu, banyak pendekatan hanya memanfaatkan satu perspektif deteksi sehingga reliabilitas operasionalnya masih terbatas. Untuk mengatasi kesenjangan tersebut, penelitian ini mengusulkan kerangka deteksi anomali hybrid yang mengintegrasikan Long Short-Term Memory (LSTM), Shannon entropy, dan autoencoder reconstruction error. Shannon entropy digunakan sebagai fitur tambahan, sedangkan LSTM dan autoencoder menangkap karakteristik temporal dan deviasi rekonstruksi. Representasi hybrid yang dihasilkan kemudian diproses melalui mekanisme constraint-based threshold selection dengan batas FPR . Hasil pengujian pada dataset TON-IoT dan Edge-IIoTset menghasilkan F1-score rata-rata sebesar 0,9250 dan 0,9934, dengan FPR rata-rata sebesar 0,0091 dan 0,0714. Perbedaan nilai entropy yang konsisten antara trafik normal dan anomali pada kedua dataset menunjukkan bahwa Shannon entropy menyediakan informasi diskriminatif untuk deteksi anomali. Hasil tersebut menunjukkan performa deteksi yang kuat dengan false alarm yang terkendali, sementara studi ablasi mengonfirmasi kontribusi signifikan Shannon entropy terhadap performa model.
Kata kunci: deteksi anomali jaringan; false positive rate; hybrid deep learning; Internet of Things; Shannon entropy
Abstract:This study aimed to compare the performance of machine learning algorithms and user experience in predicting students’ academic achievement. The research is motivated by the need for prediction systems that are not only…
y highly accurate but also easily interpretable by users. The proposed methodology involved the implementation of two algorithms, namely Decision Tree and Random Forest, using an academic dataset that included grade point average, attendance, and assessment scores. Model performance was evaluated using accuracy, precision, recall, and F1-score, while user experience was assessed through the System Usability Scale (SUS) based on a simple user interface. The findings revealed that Random Forest achieved higher predictive accuracy, whereas Decision Tree provided better interpretability and ease of understanding for users. These results indicated a trade-off between model performance and user experience, suggesting that algorithm selection should consider both aspects in order to develop an effective and user-friendly academic prediction system
Abstract:Abstract: Job training is one of the government’s efforts to improve the quality of human resources so that they possess competencies that meet labor market demands. The process of selecting training participants at the…
e Department of Manpower of Asahan Regency is still carried out manually, which can lead to subjectivity and inefficiency in determining the most eligible candidates. This study aims to develop a decision support system using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to assist the selection process objectively and systematically. The study applies four evaluation criteria, namely education level, age, work experience, and interview, with a dataset consisting of 31 training candidates. The system is developed as a web-based application using PHP programming language and MySQL database. The TOPSIS method is applied through decision matrix normalization, weighting, determination of positive and negative ideal solutions, and preference value calculation to produce a ranking of candidates. The results show that the proposed system can provide objective recommendations for selecting training participants, improve the efficiency of the selection process, and support decision makers in producing more accurate and reliable decisions.
Keywords: decision support system; selection; training; TOPSIS.
Abstrak: Pelatihan tenaga kerja merupakan salah satu upaya pemerintah dalam meningkatkan kualitas sumber daya manusia agar memiliki kompetensi yang sesuai dengan kebutuhan dunia kerja. Proses pemilihan calon peserta pelatihan di Dinas Tenaga Kerja Kabupaten Asahan selama ini masih dilakukan secara manual sehingga berpotensi menimbulkan subjektivitas dan kurang efektif dalam menentukan peserta yang paling layak. Penelitian ini bertujuan untuk membangun sistem pendukung keputusan menggunakan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk membantu proses seleksi peserta pelatihan secara objektif dan sistematis. Penelitian ini menggunakan empat kriteria penilaian yaitu pendidikan, usia, pengalaman kerja, dan wawancara dengan jumlah data sebanyak 31 calon peserta pelatihan. Sistem dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan database MySQL. Metode TOPSIS digunakan untuk melakukan normalisasi matriks keputusan, pembobotan, penentuan solusi ideal positif dan negatif, serta perhitungan nilai preferensi untuk menghasilkan perankingan peserta pelatihan. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu memberikan rekomendasi peserta pelatihan secara objektif, meningkatkan efisiensi proses seleksi, serta membantu pihak dinas dalam pengambilan keputusan yang lebih akurat.
Kata kunci: pelatihan; seleksi; sistem pendukung keputusan; TOPSIS.
Abstract:Abstract: Mental health issues, particularly depression among young adult university students, are often detected late due to stigma and reluctance to seek medical consultation. The objective of this study is to develop…
an early screening model employing machine learning techniques, specifically the random forest algorithm, on a dataset of 268 students (aged 17-29 years; consisting of 98 males and 170 females) within a multicultural educational setting. The principal challenges associated with this dataset are class imbalance and the potential for data leakage from clinical scores. This study implements a rigorous feature selection approach that involves the elimination of depression score features and the utilization of the Synthetic Minority Over-sampling Technique (SMOTE) to balance the training data distribution. Furthermore, a Threshold Tuning strategy is employed to prioritize detection sensitivity (Recall). The findings indicate that reducing the decision threshold to an optimal value of 0.25 led to a substantial enhancement in the recall value, increasing it from 36% (baseline) to 77%. A feature importance analysis was conducted, the results of which indicated that Total Social Connectedness (ToSC) is the most dominant predictor. In summary, the present study corroborates the notion that optimizing sensitivity through threshold tuning is of paramount importance for medical screening. Furthermore, social isolation factors emerge as more significant indicators of depression risk than demographic attributes.
Keywords: data mining; depression; imbalanced data; random forest; smote; threshold tuning
Abstrak: Masalah kesehatan mental, khususnya depresi di kalangan mahasiswa dewasa muda, sering terdeteksi terlambat akibat stigma dan enggan mencari konsultasi medis. Tujuan studi ini adalah mengembangkan model skrining dini menggunakan teknik machine learning, khususnya algoritma random forest, pada dataset 268 mahasiswa (usia 17-29 tahun; terdiri dari 98 laki-laki dan 170 perempuan) dalam lingkungan pendidikan multikultural. Tantangan utama yang terkait dengan dataset ini adalah ketidakseimbangan kelas dan potensi kebocoran data dari skor klinis. Studi ini menerapkan pendekatan seleksi fitur yang ketat, yang melibatkan eliminasi fitur skor depresi dan penggunaan Teknik Over-sampling Minoritas Sintetis (SMOTE) untuk menyeimbangkan distribusi data pelatihan. Selain itu, strategi Penyesuaian Ambang Batas diterapkan untuk memprioritaskan sensitivitas deteksi (Recall). Hasil penelitian menunjukkan bahwa mengurangi ambang batas keputusan ke nilai optimal 0,25 menyebabkan peningkatan signifikan dalam nilai recall, dari 36% (dasar) menjadi 77%. Analisis pentingnya fitur dilakukan, hasilnya menunjukkan bahwa Total Social Connectedness (ToSC) adalah prediktor yang paling dominan. Secara ringkas, studi ini membenarkan bahwa mengoptimalkan sensitivitas melalui penyesuaian ambang batas sangat penting untuk skrining medis. Selain itu, faktor isolasi sosial muncul sebagai indikator risiko depresi yang lebih signifikan daripada atribut demografis.
Kata kunci: penambangan data; depresi; data tidak seimbang; hutan acak; smote; penyesuaian ambang batas
Abstract:Abstract: Managing fast-moving drug inventory requires accurate supplier selection to ensure product availability and minimize the risk of overstock and out-of-stock conditions. At Annisa Pharmacy, the supplier selection…
process has traditionally relied on experience and subjective judgment, which may lead to less optimal decisions. This study aims to design and implement a Decision Support System (DSS) for selecting fast-moving drug suppliers using the Weighted Product (WP) method. The WP method is applied because it is capable of processing multiple criteria simultaneously through structured weighting, including demand frequency, delivery lead time, remaining shelf life, purchase price, and profit margin. The system is developed as a web-based application using PHP and MySQL. The results show that the implementation of the Weighted Product method successfully produces preference values and accurate supplier rankings, enabling the system to correctly determine the most optimal fast-moving drug supplier based on the defined criteria. Therefore, the developed system can assist the owner of Annisa Pharmacy in making more precise, objective, and structured inventory procurement decisions.
Keywords: decision support system; drug inventory; supplier selection; weighted product.
Abstrak: Pengelolaan stok obat fast moving di Toko Obat Annisa memerlukan ketepatan dalam menentukan supplier agar ketersediaan obat tetap terjaga dan risiko overstock maupun out of stock dapat diminimalkan. Selama ini, proses pemilihan supplier masih dilakukan secara konvensional berdasarkan pengalaman, sehingga berpotensi menghasilkan keputusan yang kurang optimal. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Pendukung Keputusan (SPK) pemilihan supplier obat fast moving menggunakan metode Weighted Product (WP). Metode WP digunakan karena mampu mengolah beberapa kriteria secara simultan melalui pembobotan yang terstruktur, meliputi frekuensi permintaan, lead time, sisa masa kedaluwarsa, harga beli, dan margin keuntungan. Sistem dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil penelitian menunjukkan bahwa penerapan metode Weighted Product mampu menghasilkan nilai preferensi dan perankingan supplier secara objektif, sehingga sistem berhasil menentukan supplier obat fast moving yang paling optimal sesuai dengan kriteria yang telah ditetapkan. Dengan demikian, sistem yang dibangun dapat membantu pemilik Toko Obat Annisa dalam mengambil keputusan pengadaan stok obat secara lebih tepat, objektif, dan terstruktur.
Kata kunci: sistem pendukung keputusan; toko obat; pemilihan pemasok; weighted product
Abstract:Abstract: The election of the Student Council President is an important process in building a democratic, responsible, and integrity-based leadership culture in the school environment. In addition, through the main discussion…
ssion in this study aims to optimize the selection process for candidates for Student Council President at SMP Negeri 11 Tanjung Balai through the implementation of a Decision Support System (DSS) based on the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method. The MOORA method is used to normalize values, weight criteria, and calculate optimization values to produce the best alternative ranking. Data were obtained through observation, interviews with school officials, and literature studies. The results of the study indicate that the MOORA method is able to provide objective and transparent ranking results, where M. Asril Sitorus Pane obtained the highest score of 0.347 (Rank 1), followed by M. Rian with a score of 0.333 (Rank 2). The implementation of this system is expected to be able to provide recommendations for the best candidates regularly and accurately, so that it can support fairer and more accountable decision-making in the next Student Council President election.
Keywords: decision support system; osis chairman election; MOORA; SMP negeri 11 tanjung balai.
Abstrak: Pemilihan Ketua OSIS merupakan proses penting dalam membangun budaya kepemimpinan yang demokratis, bertanggung jawab dan berintegritas di lingkungan sekolah. Selain itu, melalui pokok pembahasan pada penelitian ini bertujuan untuk dapat mengoptimalkan proses seleksi calon Ketua OSIS di SMP Negeri 11 Tanjung Balai melalui penerapan Sistem Pendukung Keputusan berbasis metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis). Metode MOORA digunakan agar dapat melakukan normalisasi nilai, pembobotan kriteria serta perhitungan nilai optimasi guna menghasilkan peringkat alternatif terbaik. Data diperoleh melalui observasi, wawancara dengan pihak sekolah serta dan studi kepustakaan. Hasil penelitian menunjukkan bahwa metode MOORA mampu memberikan hasil peringkat yang objektif dan transparan, yang dimana M. Asril Sitorus Pane memperoleh nilai tertinggi sebesar 0,347 (Peringkat 1), diikuti oleh M. Rian dengan nilai 0,333 (Peringkat 2). Penerapan sistem ini diharapkan mampu memberikan rekomendasi kandidat terbaik secara sistematis dan akurat, sehingga dapat mendukung pengambilan keputusan yang lebih adil dan akuntabel dalam pemilihan Ketua OSIS selanjutnya.
Kata kunci: MOORA; pemilihan ketua osis; sistem pendukung keputusan; SMP negeri 11 tanjung balai.
Abstract:Abstract: Tech Kios Laptop Kisaran is a business engaged in selling used laptops with various brands and specifications to meet customer needs. However, the selection process is still conducted manually and relies on subjective…
jective judgment, which may result in less accurate recommendations. This study aims to design and implement a Decision Support System using the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method to objectively determine the best used laptop. The criteria applied in this study include brand, screen resolution, laptop size, and battery durability. The system was developed through requirement analysis, system design, implementation, and black-box testing. The results show that the system successfully generates rankings based on MOORA preference values. The highest optimization value of 0.4321 was achieved by Lenovo IdeaPad Slim (A04) and Lenovo ThinkPad (A06), indicating that these two alternatives are the best recommended used laptops. Therefore, the developed system enhances the objectivity, effectiveness, and accuracy of the laptop selection process at Tech Kios Laptop Kisaran.
Keywords: decision support system; MOORA; multi criteria; used laptop; recommendation.
Abstrak: Tech Kios Laptop Kisaran merupakan usaha yang bergerak di bidang penjualan laptop bekas dengan berbagai merek dan spesifikasi untuk memenuhi kebutuhan pelanggan. Namun, proses pemilihan laptop masih dilakukan secara manual dan bergantung pada penilaian subjektif, sehingga berpotensi menghasilkan rekomendasi yang kurang akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Pendukung Keputusan menggunakan metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) guna menentukan laptop bekas terbaik secara objektif. Kriteria yang digunakan dalam penelitian ini meliputi merek, resolusi layar, ukuran laptop, dan ketahanan daya baterai. Pengembangan sistem dilakukan melalui tahapan analisis kebutuhan, perancangan sistem, implementasi, serta pengujian menggunakan metode black-box. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan peringkat alternatif berdasarkan nilai preferensi MOORA. Nilai optimasi tertinggi sebesar 0,4321 diperoleh oleh Lenovo IdeaPad Slim (A04) dan Lenovo ThinkPad (A06), yang menunjukkan bahwa kedua alternatif tersebut merupakan rekomendasi laptop bekas terbaik. Dengan demikian, sistem yang dikembangkan mampu meningkatkan objektivitas, efektivitas, dan ketepatan dalam proses pemilihan laptop bekas di Tech Kios Laptop Kisaran.
Kata kunci: laptop bekas; MOORA; multi-kriteria; rekomendasi; sistem pendukung keputusan.
Abstract:Abstract: The growing intensity of cyber attacks, marked by rapid, large-scale, automated, and adaptive execution, requires analytical methods that represent the diversity of network environments, including variations in…
target platforms such as IoT, traditional networks, and hybrid infrastructures. This study compares machine learning models for cyber attack classification under heterogeneous environmental conditions and formulates a conceptual optimization framework based on model performance. Four publicly available benchmark datasets were used, namely UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, and a Kaggle cyber security attacks dataset, comprising approximately 40,000 to over 3.6 million records and 25 to 80 features across IoT, conventional, and mixed network environments. Random Forest, XGBoost, Multilayer Perceptron, and Transformer were implemented within a unified pipeline involving preprocessing, feature selection, and Bayesian Optimization-based hyperparameter tuning. All models achieved F1-score and Cohen's Kappa above 96%, with XGBoost performing best (97.80%, 97.26%), followed by Random Forest (97.78%, 96.96%) and Transformer (97.44%, 96.82%), while MLP scored lowest (96.74%, 96.00%), a gap below one percentage point. Confusion matrix analysis revealed persistent misclassification in minority and overlapping attack classes, informing a proposed adaptive cyber attack simulation optimization framework.
Keywords: cyber attacks; optimization; machine learning; environmental variability.
Abstrak: Meningkatnya intensitas serangan siber yang berlangsung cepat, masif, otomatis, dan adaptif menuntut pendekatan analitis yang merepresentasikan keragaman lingkungan jaringan, termasuk perbedaan karakteristik platform sasaran seperti Internet of Things (IoT), jaringan konvensional, dan infrastruktur hibrida. Penelitian ini membandingkan model machine learning untuk klasifikasi serangan siber pada kondisi lingkungan heterogen, sekaligus menyusun kerangka optimasi konseptual berdasarkan performa model. Empat dataset benchmark publik digunakan, yaitu UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, serta dataset Kaggle cyber security attacks, dengan jumlah data berkisar 40.000 hingga lebih dari 3,6 juta rekaman dan 25 sampai 80 fitur, mewakili lingkungan IoT, konvensional, dan campuran. Random Forest, XGBoost, Multilayer Perceptron, dan Transformer diimplementasikan melalui pipeline terpadu mencakup pra-pemrosesan, seleksi fitur, dan optimasi hyperparameter berbasis Bayesian Optimization. Seluruh model mencapai F1-score dan Cohen's Kappa di atas 96%, dengan XGBoost menunjukkan performa terbaik (97,80%, 97,26%), diikuti Random Forest (97,78%, 96,96%) dan Transformer (97,44%, 96,82%), sementara MLP mencatat skor terendah (96,74%, 96,00%), dengan selisih kurang dari satu poin persentase. Analisis confusion matrix mengungkap misklasifikasi yang konsisten pada kelas minoritas dan serangan dengan karakteristik serupa, yang menjadi dasar kerangka optimasi simulasi serangan siber adaptif yang diusulkan.
Kata kunci: serangan siber; optimasi; machine learning; variabilitas lingkungan
Abstract:Abstract: SMP Muhammadiyah 5 Samarinda still relies on manual evaluation with limited data analysis tools in predicting student academic achievement. This study aims develop a system for predicting the learning achievement…
nt of students at SMP Muhammadiyah 5 Samarinda using the Naive Bayes classification method. The dataset used consists of 192 student exam scores covering academic scores, attendance, parents’ education and income, and living conditions as independent variables, while the dependent variable is the achievement label (achieved or not achieved). The preprocessing stage includes label normalization, feature selection, and median imputation to handle missing data. The dataset was divided into 75% training data and 25%. The model was implemented as a pipeline consisting of a median imputer and a Gaussian Naive Bayes classifier. The evaluation results showed that the model achieved an accuracy of 79.2%, with a perfect recall value (1.00) in the high-achieving class and (0.64) in the low-achieving class. This shows that the model is quite effective in identifying high-achieving students. The trained model was then integrated into a Flask-based web application, which enables online predictions through a simple form interface, facilitating contextual interpretation. This system is expected to assist in educational decision-making by helping teachers identify students’ achievement levels early on and design more targeted learning interventions.
Keywords: academic performance; educational data mining; naive bayes; prediction system; student achievement
Abstrak: SMP Muhammadiyah 5 Samarinda masih bergantung pada evaluasi manual dengan alat analisis data terbatas dalam melakukan prediksi prestasi akademik siswa. Penelitian ini bertujuan mengembangkan sistem prediksi prestasi belajar siswa SMP Muhammadiyah 5 Samarinda menggunakan metode klasifikasi Naive Bayes. Dataset yang digunakan terdiri atas 192 data nilai ujian siswa yang mencakup skor akademik, kehadiran, pendidikan dan pendapatan orang tua, serta kondisi tempat tinggal sebagai variabel independen, sedangkan variabel dependen berupa label prestasi (berprestasi atau tidak berprestasi). Tahap preprocessing meliputi normalisasi label, seleksi fitur, serta imputasi median untuk menangani data yang hilang. Dataset dibagi menjadi 75% data latih dan 25%. Model diimplementasikan dalam bentuk pipeline yang terdiri atas median imputer dan Gaussian Naive Bayes classifier. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 79,2%, dengan nilai recall sempurna (1,00) pada kelas berprestasi dan lebih rendah (0,64) pada kelas tidak berprestasi. Hal ini menunjukkan bahwa model cukup efektif dalam mengidentifikasi siswa berprestasi. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi web berbasis Flask, yang memungkinkan prediksi secara daring melalui antarmuka formulir sederhana untuk mendukung interpretasi kontekstual. Sistem ini diharapkan dapat membantu untuk pengambilan keputusan dalam pendidikan dengan membantu guru mengidentifikasi tingkat prestasi siswa sejak dini dan merancang intervensi pembelajaran yang lebih terarah.
Kata kunci: prestasi akademik; penambangan data Pendidikan; naive bayes; sistem prediksi; prestasi siswa