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Indra Nasution
Universitas Pembangunan Panca Budi
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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IMPLEMENTASI DAN EVALUASI WEB SCRAPING PADA DATA JURNAL SINTA Indra Nasution; Muhammad Iqbal
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6810

Abstract

Abstract: The development of digital technology has increased the need for access to scientific information, especially related to academic journal publications. The Science and Technology Index (SINTA) website is one of the main platforms that provides information on accredited scientific journals in Indonesia. However, the process of searching and collecting journal data on the website is still done manually, so it is less efficient when the amount of data needed is large enough. This research aims to implement and evaluate web scraping techniques in collecting journal data on the SINTA website so that the data acquisition process can be carried out automatically, quickly, and in a structured manner. The research method uses an implementive approach through the stages of literature study, analysis of the structure of website pages, development of a system using the Python programming language with the BeautifulSoup library, data collection, database storage, and evaluation of extraction results. The results of the study show that the implementation of the web scraping technique  succeeded in obtaining as many as 15,456 journal data from the SINTA website in 2026 which includes information on journal names, publishing institutions, accreditation categories, ISSN, fields of science, and journal website links. The developed system is also capable of storing data in a structured manner into a database and supports regular information updates. The results of the evaluation show that the web scraping technique is able to improve the efficiency of data collection, minimize manual processes, and produce more systematic journal data. Thus, the implementation of web scraping techniques can be an effective solution in supporting the needs of academics and researchers to obtain journal information quickly and accurately. Keywords: Web Scraping, SINTA, Scientific Journals, Python, Beautifulsoup, Data Extraction.   Abstrak: Perkembangan teknologi digital telah meningkatkan kebutuhan akses terhadap informasi ilmiah, terutama yang berkaitan dengan publikasi jurnal akademik. Situs web Indeks Sains dan Teknologi (SINTA) merupakan salah satu platform utama yang menyediakan informasi tentang jurnal ilmiah terakreditasi di Indonesia. Namun, proses pencarian dan pengumpulan data jurnal di situs web tersebut masih dilakukan secara manual, sehingga kurang efisien ketika jumlah data yang dibutuhkan cukup besar. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi teknik web scraping dalam pengumpulan data jurnal di situs web SINTA sehingga proses akuisisi data dapat dilakukan secara otomatis, cepat, dan terstruktur. Metode penelitian menggunakan pendekatan implementasi melalui tahapan studi pustaka, analisis struktur halaman web, pengembangan sistem menggunakan bahasa pemrograman Python dengan pustaka BeautifulSoup, pengumpulan data, penyimpanan basis data, dan evaluasi hasil ekstraksi. Hasil penelitian menunjukkan bahwa implementasi teknik web scraping berhasil memperoleh sebanyak 15.456 data jurnal dari situs web SINTA pada tahun 2026 yang mencakup informasi tentang nama jurnal, lembaga penerbitan, kategori akreditasi, ISSN, bidang ilmu, dan tautan situs web jurnal. Sistem yang dikembangkan juga mampu menyimpan data secara terstruktur ke dalam basis data dan mendukung pembaruan informasi secara berkala. Hasil evaluasi menunjukkan bahwa teknik web scraping mampu meningkatkan efisiensi pengumpulan data, meminimalkan proses manual, dan menghasilkan data jurnal yang lebih sistematis. Dengan demikian, implementasi teknik web scraping dapat menjadi solusi efektif dalam mendukung kebutuhan akademisi dan peneliti untuk memperoleh informasi jurnal dengan cepat dan akurat. Kata kunci: Web Scraping, SINTA, Jurnal Ilmiah, Python, Beautifulsoup, Ekstraksi Data.
PREDICTING STUDENTS AT RISK OF DROPPING OUT USING XGBOOST BASED ON ACADEMIC DATA Indra Nasution; Muhammad Syahputra Novelan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6822

Abstract

Abstract: Student dropout has become a significant challenge for higher education institutions because it negatively affects academic performance, institutional reputation, and resource allocation. Early identification of students at risk of dropping out enables universities to implement timely interventions and improve student retention. This study proposes a predictive model for identifying students at risk of dropping out using the Extreme Gradient Boosting (XGBoost) algorithm based on academic data. The dataset consists of student academic records, including grade point average (GPA), course completion rate, attendance, accumulated credits, failed courses, and semester performance. The data were preprocessed through data cleaning, feature selection, and normalization to improve model performance. The dataset was then divided into training and testing sets using an 80:20 ratio. The XGBoost model was trained and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The experimental results demonstrate that XGBoost effectively captures complex relationships among academic variables and provides high predictive performance in identifying students with dropout risk. Feature importance analysis further reveals that GPA, accumulated credits, attendance rate, and the number of failed courses are the most influential factors affecting student dropout. The proposed approach offers a practical decision-support tool for higher education institutions by enabling early detection of at-risk students and facilitating targeted academic support programs. The findings contribute to the application of machine learning in educational data mining and learning analytics, providing valuable insights for improving student retention strategies and academic success. Future research may integrate non-academic factors such as socioeconomic background, psychological characteristics, and student engagement to further enhance prediction accuracy and model generalizability across different educational institutions. Keywords: Student Dropout Prediction; XGBoost; Machine Learning; Educational Data Mining; Learning Analytics.   Abstrak: Putus kuliah merupakan tantangan signifikan bagi institusi pendidikan tinggi karena berdampak negatif terhadap kinerja akademik, reputasi institusi, dan alokasi sumber daya. Identifikasi dini terhadap mahasiswa yang berisiko putus kuliah memungkinkan universitas untuk menerapkan intervensi yang tepat waktu dan meningkatkan retensi mahasiswa. Penelitian ini mengusulkan model prediktif untuk mengidentifikasi mahasiswa yang berisiko putus kuliah menggunakan algoritma Extreme Gradient Boosting (XGBoost) berdasarkan data akademik. Kumpulan data mencakup rekam jejak akademik mahasiswa, termasuk Indeks Prestasi Kumulatif (IPK), tingkat penyelesaian mata kuliah, kehadiran, akumulasi kredit, mata kuliah yang gagal, dan kinerja semester. Data diproses melalui tahapan pembersihan data, seleksi fitur, dan normalisasi untuk meningkatkan kinerja model. Selanjutnya, kumpulan data dibagi menjadi set pelatihan dan pengujian dengan rasio 80:20. Model XGBoost dilatih dan dievaluasi menggunakan metrik akurasi, presisi, recall, skor-F1, dan Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Hasil eksperimen menunjukkan bahwa XGBoost secara efektif menangkap hubungan kompleks antarvariabel akademik dan memberikan kinerja prediksi yang tinggi dalam mengidentifikasi mahasiswa dengan risiko putus kuliah. Analisis kepentingan fitur mengungkapkan bahwa IPK, akumulasi kredit, tingkat kehadiran, dan jumlah mata kuliah yang gagal merupakan faktor paling berpengaruh terhadap risiko putus kuliah mahasiswa. Pendekatan yang diusulkan ini menawarkan alat pendukung keputusan yang praktis bagi institusi pendidikan tinggi dengan memungkinkan deteksi dini mahasiswa berisiko serta memfasilitasi program dukungan akademik yang terarah. Temuan ini berkontribusi pada penerapan machine learning dalam penambangan data pendidikan dan analitik pembelajaran, serta memberikan wawasan berharga untuk meningkatkan strategi retensi mahasiswa dan keberhasilan akademik. Penelitian di masa mendatang dapat mengintegrasikan faktor non-akademik—seperti latar belakang sosial-ekonomi, karakteristik psikologis, dan keterlibatan mahasiswa—untuk lebih meningkatkan akurasi prediksi dan kemampuan generalisasi model di berbagai institusi pendidikan. Kata kunci: Prediksi Putus Kuliah Mahasiswa; XGBoost; Machine Learning; Penambangan Data Pendidikan; Analitik Pembelajaran.
Co-Authors Muhammad Iqbal Muhammad Syahputra Novelan