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All Journal Jurnal Media Infotama Jurnal Pseudocode International Journal of Artificial Intelligence Research JUTIM (Jurnal Teknik Informatika Musirawas) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JUKI : Jurnal Komputer dan Informatika GATOTKACA Journal (Teknik Sipil, Informatika, Mesin dan Arsitektur) Jurnal Multidisiplin Dehasen (MUDE) Jurnal Dehasen Untuk Negeri Jurnal Media Computer Science Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Komputer Indonesia Jurnal Karya Nyata Pengabdian Journal of Engineering and Technology Innovation Jurnal Komputer, Informasi dan Teknologi
Devi Sartika
Dehasen University
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Implementation of XGBoost for Classifying Student Interest in Informatics at SMP Negeri 30 Bengkulu Selatan Ocy Lara Putri; Devi Sartika; Dimas Aulia Trianggana
Jurnal Komputer Indonesia Vol. 5 No. 3 (2026): September
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jki.v5i3.1859

Abstract

Student interest in Informatics is still commonly identified through teacher observation, creating a need for a more objective data-driven approach. This study develops an XGBoost classification model and explains the factors underlying its decisions using SHAP. A quantitative data-analytics design followed the CRISP-DM stages using 27 student records and seven features: assignment scores, daily tests, practical scores, midterm scores, final scores, attendance, and attitude. The dataset was split into 80% training and 20% testing data. On six held-out cases, the model correctly classified two students as Interested and four as Not Interested, producing 100% Accuracy, Precision, Recall, and F1-Score, with an AUC of 1.00. SHAP analysis identified daily-test scores as the strongest contributor (1.1179), followed by assignment, final-exam, practical, and midterm scores. These findings indicate that XGBoost combined with SHAP can support measurable and transparent identification of student interest, although the limited dataset requires caution when generalizing the performance.
Implemetasi Machine Learning Metode K-Nearest Neighbor Untuk Memprediksi Status Kelulusan Mahasiswa Program Studi Informatika Universitas Dehasen Bengkulu AL Kausar AH; Devi Sartika; Ockhy Jey Fhiter
Jurnal Media Infotama Vol 22 No 2 (2026): Oktober 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i2.12098

Abstract

Student graduation rates are a key performance indicator that’s really crucial for the quality and reputation of a university. However, study delays and the risk of dropping out are still serious challenges for the Informatics Study Program at Dehasen University Bengkulu. This research focuses on tackling these issues by developing an early prediction system for student graduation status to help support more proactive academic decision-making. This research uses an Educational Data Mining (EDM) approach with the Knowledge Discovery in Databases (KDD) framework to process student academic datasets. It implements the K-Nearest Neighbor (KNN) algorithm. Based on the testing results with the hyperparameter K=5, the KNN model achieved an accuracy rate of 50%, which indicates that the decision boundary between students who pass and those who don't cannot be significantly separated by the numeric academic variables currently used. This finding suggests that further development is needed by adding latent variables or sociological data, as well as more specific feature selection techniques to better capture student behavior patterns and improve the model's performance in the future.
Implementasi Sistem Ekstraksi Data Otomatis Dari Formulir Administrasi Penduduk Menggunakan Optical Character Recognition Tesseract Di Kelurahan Berkas Kota Bengkulu Ramalia Apriani; Khairil Khairil; Devi Sartika
Jurnal Media Infotama Vol 22 No 2 (2026): Oktober 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i2.13309

Abstract

Penelitian ini bertujuan membangun aplikasi digitalisasi dokumen administrasi penduduk di Kelurahan Berkas Kota Bengkulu menggunakan Optical Character Recognition (OCR) dan Rule-Based Text Extraction. Aplikasi dikembangkan menggunakan PHP dan MySQL untuk mengubah dokumen citra menjadi data terstruktur. Tesseract OCR digunakan sebagai metode utama dalam proses pembacaan teks, sedangkan EasyOCR digunakan sebagai metode pembanding untuk mengevaluasi hasil pembacaan pada kondisi dokumen yang berbeda. Pengujian dilakukan menggunakan Black Box Testing terhadap 31 dokumen administrasi. Hasil pengujian menunjukkan seluruh fungsi aplikasi berjalan sesuai rancangan. Tesseract menghasilkan rata-rata 921,88 karakter dan 122,98 kata per dokumen, sedangkan EasyOCR menghasilkan rata-rata 908,26 karakter dan 121,16 kata per dokumen. Hasil perbandingan menunjukkan bahwa EasyOCR memberikan hasil pembacaan yang lebih baik pada beberapa kondisi dokumen, yaitu kertas bercak atau bernoda, kusut atau kerumuk, miring, dan buram, berdasarkan kesesuaian hasil pembacaan terhadap ground truth serta tingkat kesalahan pembacaan. Selanjutnya, Rule-Based Text Extraction berhasil mengenali 230 dari 248 field dengan tingkat keberhasilan sebesar 92,74%. Hasil tersebut menunjukkan bahwa aplikasi dapat membantu mempercepat proses digitalisasi dan pengolahan data dokumen administrasi penduduk
Analisis Pola Kriminalitas Menggunakan Data Science Dengan Metode Catboost Classifier Di Polres Kota Bengkulu Santiya Saputri; Devi Sartika; Abdussalam Al Akbar
Jurnal Media Infotama Vol 22 No 2 (2026): Oktober 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i2.13318

Abstract

Crime data management at the Bengkulu City Police Station currently faces several operational constraints, including underutilized historical data and conventional analytical methods that prolong the crime-pattern mapping process. This study aimed to analyze crime patterns through a data science approach using the CatBoost Classifier method to support data-driven tactical decision-making within the police department. A quantitative methodology using an Applied Data Science framework was executed following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The study analyzed crime report data extracted from the Bengkulu City Police database, focusing on temporal (time of occurrence) and spatial (district/kecamatan) variables. Model performance was comprehensively evaluated using a Confusion Matrix. Findings revealed that Motor Vehicle Theft (Curanmor) was the most dominant crime category, with peak vulnerability observed in Selebar District and the majority of incidents concentrated during the early morning hours. Feature Importance analysis indicated that the temporal dimension (Time Category) contributed the most (30.37%), followed by geographical location (District) at 29.30% identifying them as the primary predictors shaping crime patterns. Model evaluation for the CatBoost Classifier yielded an overall accuracy of 49%, reflecting performance limitations due to the small dataset size and imbalanced class distribution. Nevertheless, the model demonstrated a high recall value (89%) specifically in detecting Motor Vehicle Theft. maintaining its mility as an early warning signal for street crimes. Conversely, the model completely failed to predict fraud offenses (0% accuracy) because fraud characteristics are not tied to spatial-temporal patterns. The application of the CatBoost Classifier in this study ts best suited as a tactical intelligence support instrument for street crime mitigation, enabling police administrators to optimize directed patrol strategies in high-risk zones and time windows
Analysis Of The Image Quality Of Public Works And Housing Infrastructure Documentation Using The Discrete Cosine Transform Method Belly Prayoga; Devi Sartika; Ockhy Jey Fhiter
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12135

Abstract

Visual documentation of Public Works and Public Housing (PUPR) infrastructure projects produces high-resolution images that function as progress evidence, technical verification, and audit archives; however, large file sizes create storage and transmission constraints. This study aims to analyze the quality of infrastructure documentation images using a web-based Discrete Cosine Transform (DCT) approach. The research employed a quantitative experimental design supported by the Waterfall development model. Infrastructure documentation images were processed through DCT transformation, quantization, reconstruction, and evaluation using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), compression ratio, and processing time. The system was implemented using PHP and MySQL. Testing on five images produced an average MSE of 45.29 and an average PSNR of 34.86 dB, indicating that reconstructed images remained acceptable for visual documentation because most PSNR values exceeded 30 dB. Nevertheless, the average compression ratio of -68.98% indicated data expansion under the current implementation. These findings confirm that DCT is effective in preserving visual quality, but quantization parameters, output encoding strategy, and file-format handling require further optimization to achieve actual compression efficiency.
The Application of XGBoost to Classify Students’ Interests in Computer Science at SMP Negeri 30 in South Bengkulu Ocy Lara Putri; Devi Sartika; Dimas Aulia Trianggana
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12643

Abstract

Student interest in Informatics is still commonly identified through teacher observation, creating a need for a more objective data-driven approach. This study develops an XGBoost classification model and explains the factors underlying its decisions using SHAP. A quantitative data-analytics design followed the CRISP-DM stages using 27 student records and seven features: assignment scores, daily tests, practical scores, midterm scores, final scores, attendance, and attitude. The dataset was split into 80% training and 20% testing data. On six held-out cases, the model correctly classified two students as Interested and four as Not Interested, producing 100% Accuracy, Precision, Recall, and F1-Score, with an AUC of 1.00. SHAP analysis identified daily-test scores as the strongest contributor (1.1179), followed by assignment, final-exam, practical, and midterm scores. These findings indicate that XGBoost combined with SHAP can support measurable and transparent identification of student interest, although the limited dataset requires caution when generalizing the performance.
Classification of Eligibility for Prospective Recipients of the KIP Kuliah Scholarship Using the K-Nearest Neighbor Method at Dehasen University in Bengkulu Imam Ashari; Herlina Latipa Sari; Devi Sartika
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12720

Abstract

The Kartu Indonesia Pintar (KIP) Kuliah program aims to help high-achieving students from low-income backgrounds continue their education at the university level. At Dehasen University in Bengkulu, the high number of applicants far exceeds the available quota, while the selection process is still conducted manually by reviewing applications one by one. This situation results in a slow and inefficient selection process that is prone to human error and potentially introduces subjectivity in determining the eligibility of scholarship candidates.This study aims to apply the K-Nearest Neighbor (KNN) algorithm to classify the eligibility of KIP Kuliah scholarship candidates based on socioeconomic and academic data. The research stages included importing a dataset of 665 records, data cleaning, data selection, data transformation, Min-Max normalization, and splitting the data into training and testing sets, followed by classification by testing values of K = 3, 5, 7, and 9 using Euclidean distance calculations. The research results show that the KNN algorithm was successfully applied in the classification process, with the best accuracy achieved at K=7: 63.91%, Precision 53.90%, Recall 63.91%, and F1-Score 57.61%. Thus, the K-Nearest Neighbor (KNN)-based classification system developed can facilitate the initial selection process for prospective recipients of the KIP Kuliah Scholarship more quickly, consistently, and objectively compared to a manual selection process.
Co-Authors Abdussalam Al Akbar Achmad Rudi Antoni Adjie Danang Aprianto Agung Pradana Ahmad Arib Alfarisy Ahmad Asyhari Ahmad Meidy Ruslida AL Kausar AH Aldy Sanjaya Alviyan Sahadi Annisa Arsy Tiara Anton Saputra Artika Sari Devi Asnawati Asnawati Aziz Zurahman Belly Prayoga Bimo Restu Putra Chisillia Mayangsari Dadang Kurniawan Desi Mahdalena Devri Aryansyah Dewi Suranti Difran Hadi Nata Dimas Aldi Prandika Dimas Aulia Trianggana Doan Sefriyansyah Dwi Krismo Putra Edo Alwis Edo Angga Dwi Supardi Putra Eko Febrianto Eko Suryana Farhaan Fadhlurrahman Fauzan Latief Mukmin Feri Hari Feri Feri Saputra Firdhan Satriawan Sakti Fitri Alfarisy Haggy Sandy Prayogy Herlin Herlin Herlina Latipa Sari Ila Yati Beti Imam Ashari Intan Permata Sari Jefry Thomas Jumadi, Juju Jusuf Wahyudi Kanedi, Indra Kanedi Khairil Khairil Lena Elfianty Lidya Anggraini Liza Yulianti Lois Kapirosi Lydia Margaretha M. Iqbal Syahputra Marfuatin Muthoharoh Maryaningsih Maryaningsih Muhamad Lio Meilsandre Muhammad Baluqiah Al Ghazali Muhammad Bintang Firdaus Musa Alhadi Pulungan Nanda Eka Juniawan Nanda Pratama Putra Ockhy Jey Fhiter Ockhy Jey Fitter Ocy Lara Putri Prahasti Rahmat Hidayat Ramalia Apriani Ricky Zulfiandri Riki Sumansyah Riko Mandala Putra Riska Riska Santiya Saputri Sapri , Sapri Sarah Ulfah Al Amany Siswanto Siswanto Sri Narti Tengsi Mayang Nopita Sari Tri Ramadani Venny Novita Sari Wahyu Rizki Rasuanto Yoga Pramadana Yoli Andi Rozzi Yupianti, Yupianti