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All Journal Sainteks Jurnal Informatika Jurnal Sarjana Teknik Informatika International Journal of Advances in Intelligent Informatics 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 Journal of Information Systems and Informatics 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/ijaimi.v4i1.438

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.
Spatio-Temporal Graph-Based Hotspot Analysis of Earthquake Events Using Spatial Autocorrelation and Community Detection in Indonesia Ika Arfiani; Herman Yuliansyah; Nur Rochmah Dyah Puji Astuti; Arfiani Nur Khusna
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1641

Abstract

Analysis of clustered seismic regions is important for understanding seismic activity patterns in tectonic regions such as Indonesia. However, conventional spatial statistical approaches generally analyze earthquake events independently and fail to capture complex spatio-temporal relationships. This study proposes a graph-based spatio-temporal hotspot analysis approach integrating spatial autocorrelation and community detection to identify regional seismic interaction patterns. The dataset used consists of 3,000 earthquake events from 2008–2025. Spatial autocorrelation was analyzed using Moran’s I, while earthquake relationships were modeled using a spatio-temporal graph with spatial and temporal thresholds of ≤400 km and ≤60 days. The results showed significant positive spatial autocorrelation with Moran’s I = 0.3367 (p = 0.001). The resulting graph consisted of 3,000 nodes and 22,896 edges, revealing substantial regional-scale connectivity and 14 major clusters with a modularity score of 0.7405, indicating a strong community structure. Degree centrality analysis identified highly connected nodes with a maximum degree of 77. These findings indicate that integrating spatial autocorrelation and graph analysis provides a more comprehensive representation of seismic interaction patterns and may support future seismic risk assessment in tectonically active regions.
Random perturbation bee colony optimized k-means approach for optimized MSME data clustering Lisna lisna Zahrotun; Ika Arfiani; Dwi Normawati
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.1781

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play an important role in the economy and need strategic support to grow. The K-Means method is often used for cluster analysis, but has a weakness in determining the initial cluster centre. This research proposes the K-Means Random Perturbation Bee Colony Optimisation (RPBCO) method to overcome the problem. The test results show an increase in cluster accuracy, with the Silhouette Coefficient score increasing by 40.9% (from 0.171 to 0.245). Wilcoxon testing also showed a z-value of -1.342, confirming that RPBCO is superior to standard K-Means. This method proved effective in optimising clusters on a heterogeneous MSMEs dataset. The analysis revealed that creative MSMEs thrive on Instagram, while retail MSMEs perform best on Shopee. To expand the market reach of MSMEs beyond Java and internationally, marketing strategies tailored to the characteristics of the region and target market are required. Creative MSMEs can utilise Instagram Ads to target ASEAN countries, while retail MSMEs can focus on global platforms such as Amazon or Alibaba. Digital literacy training, platform algorithm workshops, and collaborative marketing campaigns can strengthen this strategy. These measures are expected to increase turnover and support the sustainable growth of MSMEs, positively impacting more than one million businesses.
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 Yuliansyah, Herman I Gede Iwan Sudipa Isnan Arif Cahyadi Jullev Atmadji, Ery Setiyawan Khulfani Hendrawan Lisna lisna Zahrotun 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