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All Journal Marinade Jurnal Informatika dan Rekayasa Elektronik Journal of the Indonesian Medical Association : Majalah Kedokteran Indonesia Canrea Journal: Food Technology, Nutritions, and Culinary Journal Dirasah : Jurnal Studi Ilmu dan Manajemen Pendidikan Islam Didaktik : Jurnal Ilmiah PGSD STKIP Subang Journal Informatic, Education and Management (JIEM) JURNAL ILMIAH EDUKATIF Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer An-Nahdlah: Jurnal Pendidikan Islam AJAD : Jurnal Pengabdian kepada Masyarakat Jurnal Teknoif Teknik Informatika Institut Teknologi Padang eJournal Kedokteran Indonesia JURNAL ILMIAH PENDIDIKAN KEBUDAYAAN DAN AGAMA Jurnal Muara Medika dan Psikologi Klinis Jurnal Ilmiah Research Student Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Yusra
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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Journal : Bulletin of Computer Science Research

 1 
Analisis Perbandingan Metode DBSCAN dan Meanshift dalam Klasterisasi Data Gempa Bumi di Indonesia MHD Ade Setiawan; Fitri Insani; Yelfi Vitriani; Yusra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.605

Abstract

Indonesia is one of the countries with a high vulnerability to earthquakes due to its location at the convergence of three major tectonic plates: the Indo-Australian, Eurasian, and Pacific plates. As a result of this interaction, seismic activity is highly frequent across various regions. Understanding the distribution patterns of earthquakes is essential for disaster risk mitigation. One approach used to analyze these patterns is clustering, particularly using the DBSCAN  and Meanshift algorithms, which can group spatial data without predefining the number of clusters. This study aims to compare the effectiveness of both algorithms in clustering earthquake data based on spatial parameters, namely latitude and longitude. Evaluation was conducted using cluster visualization and the Silhouette Score as the clustering validity metric. The results show that DBSCAN  produces more optimal clustering with a Silhouette Score of 0.930028, higher than Meanshift's score of 0.90103. DBSCAN  is also capable of detecting relevant outliers in earthquake analysis, while Meanshift generates more clusters but with less separation. Using spatial parameters such as latitude and longitude, DBSCAN  is considered more effective in identifying the spatial distribution patterns of seismic activity in Indonesia based on earthquake data. This research supports the development of decision support systems for earthquake disaster mitigation and serves as a reference for selecting appropriate clustering methods for spatial data analysis.
Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra; Muhammad Fikry; Surya Agustian; Siti Ramadhani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.626

Abstract

Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
Co-Authors Aji Bayu Permadi Allevia Yumnanisha, Defin Aprilia, Risma Aria Kekalih Batubara, Jose Bin Roslan, Jumardi Binti Atun, Mazlinah Binti Julmohammad, Norliza Christianta, David Dahlan Darul Ilmi Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eliana Emeliazola Faruqi, Muhammad Febi Yanto Febi Yanto, Febi Fithri Choirun Nisa Fitri Insani Fitri Insani Herwanto Herwanto Hesti Lestari Irianti Handayani, Supri Iswantir M Iwan Iskandar Junaidi Junaidi Lawami, Reza Haya Mandalena Markarma, A. MHD Ade Setiawan Mohd Shariff, Amir Husni Muhammad Abdillah Muhammad Fikry Muhammad Iqbal Maulana Nadya, Dhifa Khairi Nazir, Alwis Nazruddin Safaat H Noval Prasetyo Nurul Huda Putra, Wahyu Eka Ramadhani Rini Sekartini Sarah Nabila Silfia Hanani Siti Nurbaya Siti Ramadhani Siti Zuhra Solly Aryza Surya Agustian Tjahjadi, Ricky Trinovita Andraini Yelfi Vitriani Yenggi Putra Dinata Zakir, Supratman Zulfani Sesmiarni