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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 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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Development of Geographic Information Systems in Mapping Village-Owned Enterprises in Sleman Regency Ramadhan, Imam; Arfiani, Ika
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4513

Abstract

The population increase in the Special Region of Yogyakarta poses challenges, including developing Village-owned Enterprises or BUM Desa in Sleman Regency to enhance rural community welfare. BUM Desa data management currently relies on manual spreadsheets and lacks a dynamic data storage system, hindering access to accurate information. This study employed the Scrum methodology, gathering data through literature reviews, interviews, and observations to assess the current state of BUM Desa. A product backlog guided the development of a web-based GIS application through sprint planning, resulting in an application that maps BUM Desa locations in the Sleman Regency based on coordinates and provides detailed development classifications. This application enhances data management and decision-making for BUM Desa development, simplifies government data management, and improves public access to BUM Desa locations. Black box testing confirmed its functionality, with 100% validity. End-user computing Satisfaction (EUCS) surveys indicated high user satisfaction, emphasizing the application's usability and alignment with user expectations in providing accurate and accessible BUM Desa information.
Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment Iwan Sudipa, I Gede; Azdy, Rezania Agramanisti; Arfiani, Ika; Setiohardjo, Nicodemus Mardanus; Sumiyatun
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.125

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.
Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Tarigan, Thomas Edyson; Susanti, Erma; Siami, M. Ikbal; Arfiani, Ika; Jiwa Permana, Agus Aan; Sunia Raharja, I Made
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): 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.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Predictive Modeling of Air Quality Levels Using Decision Tree Classification: Insights from Environmental and Demographic Factors Iwan Sudipa, I Gede; Habibi, Muhammad; Jullev Atmadji, Ery Setiyawan; Arfiani, Ika
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.201

Abstract

Air pollution poses a significant global challenge, adversely impacting public health and environmental sustainability. Understanding the factors influencing air quality is essential for developing effective mitigation strategies. This study aims to analyse key environmental and demographic factors, such as PM2.5 concentration, population density, and proximity to industrial areas, to predict air quality levels using a Decision Tree model. The dataset, comprising 5000 samples, was pre-processed by encoding the target variable and applying Z-score normalization to numerical features. The model was trained on 80% of the data and evaluated on the remaining 20%, achieving an accuracy of 93%. Evaluation metrics, including a classification report and confusion matrix, demonstrated the model's effectiveness in distinguishing between four air quality categories: Good, Moderate, Poor, and Hazardous. PM2.5 emerged as the most critical predictor, followed by demographic and industrial factors. These findings underscore the potential of machine learning models in providing actionable insights for air quality management. The results contribute to public policy by highlighting the need for targeted interventions in high-risk areas and the importance of incorporating environmental data into urban planning. Future work should focus on expanding the feature set and exploring ensemble techniques to further enhance predictive accuracy and robustness.
Pengenalan Landmark Pariwisata di Kulon Progo Berbasis Augmented Reality ika arfiani; Murien Nugraheni; Muhammad Dzikrullah Suratin
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 4 No 3 (2023)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.4.3.142

Abstract

The tourism sector in Kulon Progo is showing a decline in terms of regional tourism development. The causative factor is the promotion that has not been able to maximize the availability of information technology. Several unmanaged tourist attractions such as Pringtali Temple, Lawangsih Cave and Kiskendo Cave. Disadvantages that make tourism objects less accessible to tourists include difficult access roads, uphill and steep terrain, damaged roads, and the lack of street lighting. Then an AR-based tourism introduction application was made. The method used is the Multimedia Development Lifecycle (MDLC) starting from the creation of the application concept, design, collection of materials, system implementation, and testing. This research produces an application based on Augmented Reality (AR) regarding the Introduction of Tourist Attractions in Kulon Progo which has been tested with an assessment percentage of 76.9%, which means the application is acceptable.
Peningkatan Kompetensi Guru SMK Muhammadiyah Kalibawang melalui Pelatihan Google Apps for Education (GAFE) Arfiani, Ika; Rochmah Dyah Pujiastuti, Nur; Normawati, Dwi
ABDIMASTEK Vol. 2 No. 2 (2023): Desember
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pendidikan di era digital membutuhkan perubahan paradigma dalam metode pengajaran dan pembelajaran. Google Apps for Education (GAFE) menjadi salah satu alat yang efektif untuk meningkatkan kualitas pendidikan. Kegiatan ini bertujuan untuk mengevaluasi dampak pelatihan GAFE terhadap peningkatan kompetensi guru di SMK Muhammadiyah Kalibawang, Kulonprogo. Kegiatan ini menggunakan desain eksperimental pre-test dan post-test. Sebelum pelatihan, dilakukan pengukuran awal kompetensi guru menggunakan kuesioner dan observasi. Setelah itu, menjalani pelatihan intensif GAFE dan menerapkannya dalam pekerjaan sehari-hari. Setelah dilakukan monitoring dan evaluasi, hasil pelatihan menunjukkan adanya peningkatan yang signifikan dalam pemahaman dan penerapan GAFE. Guru yang mengikuti pelatihan menunjukkan peningkatan kemampuan dalam merancang dan melaksanakan pembelajaran berbasis teknologi. Selain itu, terlihat peningkatan motivasi dan partisipasi aktif dari siswa dalam proses pembelajarannya.
Optimalisasi Keterampilan Literasi dan Numerasi Guru Melalui Pelatihan Aplikasi Literanum Arfiani, Ika; Pujiastuti, Nur Rochmah Dyah; Normawati, Dwi
ABDIMASTEK Vol. 3 No. 2 (2024): Desember
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pendidikan di era digital menuntut peningkatan kompetensi guru dalam literasi dan numerasi untuk mendukung pembelajaran yang relevan dan efektif. Aplikasi Literanum hadir sebagai alat yang inovatif untuk meningkatkan kemampuan literasi dan numerasi guru. Kegiatan ini dilaksanakan di SMK Muhammadiyah Kalibawang, yang berlokasi di Kecamatan Kalibawang, Kabupaten Kulonprogo, Daerah Istimewa Yogyakarta. Program ini bertujuan untuk mengevaluasi dampak pelatihan aplikasi Literanum terhadap peningkatan kompetensi literasi dan numerasi guru di sekolah tersebut. Desain pelatihan ini menggunakan pendekatan eksperimental pre-test dan post-test. Sebelum pelatihan, dilakukan pengukuran awal kompetensi guru menggunakan kuesioner dan observasi. Selanjutnya, guru menjalani pelatihan intensif Literanum dan menerapkannya dalam aktivitas mengajar sehari-hari. Setelah melalui proses monitoring dan evaluasi, hasil pelatihan menunjukkan peningkatan yang signifikan dalam pemahaman dan penerapan aplikasi Literanum. Guru yang mengikuti pelatihan menunjukkan peningkatan kemampuan dalam merancang dan melaksanakan pembelajaran berbasis literasi dan numerasi. Selain itu, tercatat peningkatan motivasi dan partisipasi aktif siswa dalam proses pembelajaran, yang turut mendukung tercapainya tujuan pendidikan di era digital.
A Hybrid Model of Graph Attention Networks and Random Forests for Link Prediction in Co-Authorship Networks Arfiani, Ika; Yuliansyah, Herman
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1382

Abstract

Co-authorship prediction is important in academic network analysis due to it helps to understand patterns of scientific collaboration and supports collaboration recommendation systems. Topology-based approaches, such as connectivity metrics and node distance, have been widely used to model new relationships in networks. However, these approaches often overlook relevant author attributes, such as reputation and productivity. This study develops a co-authorship prediction model by combining a Graph Attention Network (GAT) and a Random Forest. GAT is used to extract topological features from the co-authorship graph, while Random Forest leverages additional attributes such as h-index and the number of publications to improve prediction accuracy. Experiments were conducted on a co-authorship dataset comprising over 10,000 authors and 50,000 publications. The results show that GAT achieved 85% accuracy, while Random Forest reached 80%. The combination of the two yielded 90% accuracy and a higher F1-score, indicating a better balance between precision and recall. The combined model also proved more accurate in predicting collaborations involving highly productive authors. These findings suggest that a hybrid approach can more comprehensively capture the dynamics of academic collaboration and may serve as a foundation for developing more effective collaboration prediction systems in the future.
Sistem Informasi Geografis Pemetaan Dampak Tsunami di Kota Pangandaran Berbasis Web Apriatna, Sendy; Arfiani, Ika
Jurnal Sarjana Teknik Informatika Vol. 13 No. 1 (2025): Februari
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v13i1.29183

Abstract

Pemahaman rendah masyarakat terkait mitigasi bencana alam akan sangat berpotensi mengakibatkan jumlah korban jiwa dengan jumlah yang tinggi. Pemerintah memiliki peran untuk memberikan edukasi terkait bencana alam melalui pemasangan rambu bahaya seperti tanda arah evaluasi, banner peta ancaman bencana, dan pemasangan alarm bencada. Upaya pemerintah terkait sosialisasi dan mitigasi bencana dirasa belum efektif akibat penolakan dari masyarakat yang merasa informasi bencana terlalu menakutkan. Penelitian ini bertujuan menghasilkan sistem informasi geografis untuk pemetaan dampak bencana alam tsunami di wilayah kota Pangandaran. Pelaksanaan penelitian ini akan menggunakan metode Waterfall yang terdiri dari tahap requirement analysis, design, development, testing, dan maintenance. Penelitian ini telah berhasil membangun sistem informasi geografis yang memvisualisasi atribut distribusi spasial potensi acaman bencana alam tsunami di kota Pangandaran melalui penerapan representasi peta. Hasil pengujian fungsional dengan black box test mendapatkan tingkat kesesuaian 100%. Hasil pengujian disimpulkan bahwa sistem informasi dapat digunakan dan diterima secara positif.
Co-Authors Abdullah Sayfei Nurdin Agus Aan Jiwa Permana Alfian, Mukhsin Apriatna, Sendy Arfiani Nur Khusna Bambang Sugiantoro Dwi Normawati, Dwi Erma Susanti Herman Yuliansyah Herman Yuliansyah, Herman I Gede Iwan Sudipa Isnan Arif Cahyadi Jullev Atmadji, Ery Setiyawan 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 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