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All Journal Sainteks Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO IJID (International Journal on Informatics for Development) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Edsence: Jurnal Pendidikan Multimedia ScientiCO : Computer Science and Informatics Journal Building of Informatics, Technology and Science International Journal of Advances in Data and Information Systems Indonesian Journal of Data and Science Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Artificial Intelligence in Medical Issues ABDIMASTEK
Ika Arfiani
Universitas Ahmad Dahlan
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Aplikasi Penilaian Studi Proyek Independen Kurikulum Merdeka Belajar Kampus Merdeka Berbasis Android Cahyadi, Isnan Arif; Arfiani, Ika
Jurnal Sarjana Teknik Informatika Vol. 11 No. 2 (2023): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v11i2.24845

Abstract

Kementerian Pendidikan dan Kebudayaan membuat kebijakan baru dibidang pendidikan yaitu Merdeka Belajar Kampus Merdeka (MBKM) dengan dilandasi oleh Peraturan Menteri Pendidikan dan Kebudayaan Republik Indonesia No.3 Tahun 2020 tentang Standar Nasional Pendidikan Tinggi. Model penilaian yang berbeda dari sebelumnya mengakibatkan pekerjaan menjadi lama, dan belum tersedianya sistem informasi dan sistem penilian otomatis untuk mendukung kegiatan belajar terutama pada program Studi Proyek Independen. Task Centered System Design (TCSD) merupakan sebuah metode yang digunakan untuk merancang user interface dari sebuah sistem. Perancangan sistem menggunakan metode waterfall sebagai alur pengerjaan dari aplikasi kemudian mnggunakan TCSD sebagai perancangan user interface. Penelitian ini menghasilkan sebuah aplikasi sistem penilaian yang dapat membantu dalam melaksanakan kurikulum MBKM. Aplikasi akan melalui dua pengujian yaitu pengujian blackbox dan System Usability Scale (SUS) dengan nilai minimal SUS yang harus dicapai adalah 80.
Classifying Honors Class Eligibility Using SVM Khulfani Hendrawan; Ika Arfiani
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.261

Abstract

Introduction: The honors class program aims to group outstanding students, but an objective data-based classification system is not yet available. This can result in high-potential students going undetected due to the selection process relying on self-registration and causing a lack of student interest. Method: This research uses the Support Vector Machine (SVM) algorithm to classify the eligibility of students to participate in the honors program based on academic and non-academic data. The dataset consists of 453 entries with an imbalanced class distribution, which was then balanced using the SMOTE technique. The model was trained using GridSearchCV to find the optimal parameters and compared with four types of SVM kernels: linear, polynomial, sigmoid, and radial basis function (RBF). Result: The RBF kernel achieved the best performance with an accuracy of 84%, precision of 0.77, recall of 0.84, and an F1-score of 0.79. However, it was found that the precision in the minority class (high-achieving students) is still lower than in the majority class. Conclusion: The SVM model, particularly with the RBF kernel, has proven effective in automating the classification of students for the honors program. However, further improvements are needed to enhance performance on the minority class to make the selection system fairer and more accurate
Machine Learning-Based Clustering of Viruses Using Taxonomic and Genomic Features for Health Informatics Applications Adityo Permana Wibowo; Made Leo Radhitya; Edi Faizal; Ika Arfiani
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/qstvhw47

Abstract

Viruses remain a major concern in global public health due to their potential to cause outbreaks, epidemics, and pandemics. The rapid organization and analysis of virus-related data are important for supporting computational virology, health informatics, and pandemic preparedness. This study proposes an unsupervised machine learning approach to cluster viruses based on taxonomic and genomic characteristics. The dataset consisted of 70 virus records with attributes including family, genus, genome type, strand type, and envelope status. Since the dataset did not contain predefined epidemiological labels or risk categories, the analysis was designed as an exploratory clustering task rather than a supervised prediction task. Data preprocessing was performed by removing duplicates, handling missing values, standardizing categorical attributes, and transforming selected features using One-Hot Encoding. Three clustering algorithms were evaluated, namely K-Means, Agglomerative Clustering, and DBSCAN. The clustering performance was assessed using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Score, while Principal Component Analysis was applied for two-dimensional visualization. The results showed that K-Means with 10 clusters achieved a Silhouette Score of 0.7725 and a Davies-Bouldin Index of 0.8186. Agglomerative Clustering obtained the highest Silhouette Score of 0.7754, while DBSCAN produced fewer clusters with lower overall performance. Several biologically meaningful groups were identified, including clusters representing Flaviviridae, Coronaviridae, Herpesviridae, Poxviridae, and enveloped RNA viruses. However, a large proportion of records contained unknown values, which influenced the formation of a dominant incomplete-data cluster. These findings indicate that taxonomic and genomic features can support machine learning-based virus grouping, although data completeness remains a critical factor. This study provides an initial computational framework for AI-driven viral data exploration and may serve as a foundation for future viral risk stratification using enriched epidemiological and clinical features.
Co-Authors Abdullah Sayfei Nurdin Adityo Permana Wibowo Agus Aan Jiwa Permana Alfian, Mukhsin Apriatna, Sendy Arfiani Nur Khusna Bambang Sugiantoro Cahyadi, Isnan Arif Dwi Normawati, Dwi Edi Faizal Erma Susanti Haidar Luthfi, Abyan Herman Yuliansyah Herman Yuliansyah, Herman I Gede Iwan Sudipa Isnan Arif Cahyadi Jullev Atmadji, Ery Setiyawan Khulfani Hendrawan Lisna Zahrotun Muhamad Fadli Muhammad Dzikrullah Suratin Muhammad Dzikrullah Suratin Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Habibi Mukhsin Alfian Murien Nugraheni Murien Nugraheni Nicodemus Mardanus Setiohardjo Nur Rochmah Dyah Pujiastuti Nur Rochmah Dyah Pujiastuti, Nur Rochmah Dyah P utra Rizqa Yasira Probo Prasetyo Radhitya, Made Leo Ramadhan, Imam Rezania Agramanisti Azdy, Rezania Agramanisti Rochmah Dyah Pujiastuti, Nur Siami, M. Ikbal Sulistyono, Danang Sumiyatun Sunia Raharja, I Made Supriyanto Tarigan, Thomas Edyson Tia Purwantias Zain Ahmad Taufik