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All Journal Jurnal Sains dan Seni ITS Teknika: Jurnal Sains dan Teknologi JUSIFO : Jurnal Sistem Informasi Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah Universitas Batanghari Jambi Syntax Literate: Jurnal Ilmiah Indonesia JURNAL INSTEK (Informatika Sains dan Teknologi) IJISTECH (International Journal Of Information System & Technology) Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Varian Indonesian Journal of Electrical Engineering and Computer Science IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Fundamental Sciences eProceedings of Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Production, Enterprise, and Industrial Applications
Riska Yanu Fa’rifah
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices Suta Wijaya, I Gede Pasek; Mulyana, Heru; Kadriyan, Hamsu; Fa'rifah, Riska Yanu
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1591

Abstract

An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early.
Artificial Intelligence for Precision Livestock Farming: A Systematic Review of Applications, Models, and Evaluation Metrics Widyatasya Agustika Nurtrisha; Luthfi Ramadani; Riska Yanu Fa’rifah; Faqih Hamami; Nur Ichsan Utama
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31179

Abstract

The increasing demand for animal-based food products has intensified the need for efficient, data-driven livestock management practices. Artificial Intelligence (AI) has emerged as a key enabling technology within Precision Livestock Farming (PLF), supporting automated monitoring, prediction, and decision-making processes. This study presents a Systematic Literature Review (SLR) of AI applications in livestock farming, focusing on application domains, AI models, and evaluation metrics. Following the PRISMA 2020 guidelines, relevant studies published between 2013 and 2024 were systematically identified, screened, and assessed across major scholarly databases, resulting in 20 eligible articles for qualitative synthesis. The findings indicate that AI is primarily applied to animal identification, body weight estimation, disease detection, behavior analysis, and feed management. Deep learning models, particularly Convolutional Neural Networks, dominate image-based tasks, while traditional machine learning approaches remain effective for structured sensor and tabular data. Common evaluation metrics include accuracy, precision, recall, R², and Mean Absolute Error. Despite promising results, the review reveals substantial heterogeneity in datasets, evaluation protocols, and livestock sector coverage, which limits cross-study comparability. This review highlights methodological trends, identifies key research gaps, and provides insights to guide future AI-driven PLF research and implementation.
Exploring the Affordances of AI-Enabled Livestock Monitoring Systems in Rural Agricultural Communities Luthfi Ramadani; Widyatasya Agustika Nurtrisha; Faqih Hamami; Nur Ichsan Utama; Riska Yanu Fa’rifah
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31181

Abstract

Productivity and sustainability remain persistent challenges in livestock farming across developing countries, particularly in rural contexts where digital transformation progresses unevenly. Advances in artificial intelligence (AI) offer opportunities to support livestock management; however, empirical understanding of how such technologies are perceived and utilized in rural settings remains limited. This study examines the perceived affordances of an AI-enabled livestock monitoring system in a rural community in Central Java, Indonesia. Guided by the Technology–Organization–Environment (TOE) framework, a qualitative case study approach was employed using semi-structured interviews with livestock farmers and local government officials. The findings indicate that the realization of AI-related affordances is shaped by technological conditions, including system capabilities, infrastructure limitations, and user readiness. Organizational factors—such as innovation awareness, government–community relationships, and the continuity of support programs—also influence affordance realization. Environmental conditions, particularly training adequacy, public trust, and rural geographic characteristics, further affect technology use. Overall, the study highlights that AI affordances in rural livestock systems are socio-technical and context-dependent, emphasizing the importance of context-sensitive design and implementation strategies to support sustainable livestock management.
Empowering MSMEs with Data-Driven Insights: Mobile Sales Dashboard Application for MSMEs Zalina Fatima Azzahra; Riska Yanu Fa'rifah; Syfa Nur Lathifah
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 1 (2025): April 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i01.2025.1-8

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Indonesia have made a large contribution to GDP and the workforce but still face challenges in managing sales data and making data-driven decisions. Manual recording often causes operational inefficiencies, recording errors, and delays in business analysis. Based on the previous problem, this study develops a mobile-based sales dashboard application to help MSMEs analyse data in real-time and improve business strategies. The methodology used is Design Science Research (DSR) with a Rapid Application Development (RAD) approach for rapid and iterative development. This application was developed using Java and Firebase and provides sales summary features, best-selling cashiers, best-selling products, and less popular products, with time filters and graphical data visualization. Testing using Black Box Testing shows that all features run well, while the results of the User Acceptance Test (UAT) show that 90.625% of users feel that this application is easy to use and suits their needs. These results indicate that the application can improve operational efficiency and business transparency and support data-driven decision-making for MSMEs.
Classification of High School History Questions Based on Cognitive Level Revised Bloom's Taxonomy Using K-Nearest Neighbor Method Salma, Farah Sherina; Pratiwi, Oktariani Nurul; Fa’rifah, Riska Yanu
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.254

Abstract

Education plays an important role in transmitting knowledge to its students and to measure how well the student understands, testing is needed based on the cognitive level of knowledge. In measuring the cognitive level, it can be applied with reference to the Revised Bloom's Taxonomy which explains the regulation of learning processes and targets. Then by testing knowledge through the questions that have been made, it is necessary to classify the questions into several cognitive levels according to Revised Bloom's Taxonomy to determine the learning process and understanding of each individual. Many types of questions that are formed make classification difficult because the method is still done manually, therefore machine learning is needed. This study will focus on the classification of questions from the History subject in high school. The dataset used is downloaded from internet searches of USBN, PAS, PTS, and other exams. This study focuses on RBT C4 to C6 only. This study uses the K-Nearest Neighbor algorithm to obtain accuracy and with the imbalance of data in the dataset, an oversampling method using SMOTE will also be used. The accuracy results obtained are precision is 76%, recall is 76%, f1-score is 74%, accuracy is 76%. Keywords—question classification, KNN, high school history, SMOTE, RBT, oversampling
Implementasi Model Deep Learning Pada Sistem Deteksi dan Klasifikasi Kualitas Batang Tebu untuk Optimasi Penentuan Kualitas Anggiat Pandu Daniel Siregar; NUR ICHSAN UTAMA; Riska Yanu Fa'rifah
Journal of Production, Enterprise, and Industrial Applications Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jpeia.v4i1.11019

Abstract

Industri gula nasional mengalami penurunan produksi sebesar 7,01% pada tahun 2023, salah satunya disebabkan oleh rendahnya efisiensi pascapanen akibat proses klasifikasi mutu batang tebu yang masih dilakukan secara manual. Proses ini menimbulkan inkonsistensi, potensi konflik antara petani dan petugas lapangan, serta peningkatan biaya operasional. Penelitian ini mengembangkan sistem klasifikasi mutu batang tebu berbasis deep learning menggunakan pendekatan dua tahap. Tahap pertama menggunakan YOLOv11 untuk mendeteksi batang tebu, sedangkan tahap kedua menggunakan arsitektur EfficientNet (B0–B3) untuk mengklasifikasikan mutu ke dalam lima kategori (A–E). Dataset citra diperoleh dari jalur produksi PT Sinergi Gula Nusantara dan diproses melalui tahapan Knowledge Discovery in Database (KDD), meliputi data preprocessing, augmentasi, resizing, dan splitting. Hasil evaluasi menunjukkan bahwa YOLOv11 mencapai akurasi 93,5%, precision 95,7%, recall 94,4%, mAP@0.5 sebesar 97,8%, dan mAP@0.5:0.95 sebesar 89,4%. Sementara itu, EfficientNet-B2 menghasilkan akurasi klasifikasi tertinggi sebesar 88,57% setelah proses fine-tuning. Sistem yang dikembangkan mampu beroperasi pada kondisi visual yang kompleks dan dinamis, serta memberikan hasil klasifikasi yang konsisten. Studi ini menunjukkan potensi teknologi deep learning dalam mendukung otomasi dan peningkatan objektivitas proses penilaian mutu di industri agroindustri.
Implementasi Deep Learning Untuk Klasifikasi Produk Grocery dan Deteksi Tanggal Kedaluwarsa Menggunakan Resnet50 dan Easyocr Faza Hanif Suwanda; Ekky Novriza Alam; Riska Yanu Fa'rifah
Jurnal Ilmiah Universitas Batanghari Jambi Vol 26, No 2 (2026): Juli
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v26i2.6571

Abstract

Checking product categories and expiry dates in grocery stores is still largely done manually, making the process error-prone and time-consuming, especially for packaging with small or inconsistently formatted date prints. This research builds a two-stage pipeline that automatically classifies grocery product categories and detects expiry dates from product images. The method consists of an image classification model based on transfer learning with a frozen ResNet50 backbone, trained on three product categories (Fruits, Dairy Products, and Bread), and an expiry-date detection module leveraging the CRAFT text detector built into EasyOCR, combined with regular-expression matching and a tiered OCR fallback mechanism. The classification model was trained on 398 training images and evaluated on 98 validation images, achieving a validation accuracy of 83.67%, well above the 33.3% random-guess baseline for three classes, with the highest F1-score on the Dairy Products category (0.91). Testing of the date-detection pipeline on 63 test images showed a detection precision of 70.0% and an overall valid-date rate of 44.4%, indicating that a pragmatic approach based on a ready-made text detector can function without training a dedicated object detector, although reliability still needs improvement in filtering false-positive date candidates. Evaluation of the integrated system further found that the initial design assumption, that OCR should only run on packaged categories, was not entirely accurate, since the Fruits category showed a higher success rate (83.3%) than the packaged category (40.4%). The main contribution of this research is a pragmatic approach that leverages a ready-made text detector to locate very small date objects without training a dedicated object detector from scratch, allowing the pipeline to be implemented end-to-end under limited data and computing resources.
Transformer-based sentiment modeling for identifying cross country fintech perception gaps Kayla Zhafira Ardinov; Muhardi Saputra; Riska Yanu Fa’rifah
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp522-533

Abstract

Conventional sentiment analysis lacks granularity for capturing detailed user experiences and cross-country comparative insights in digital finance. This study identifies and maps perception gaps among ShopeePay users in Indonesia and Thailand using a topic-informed sentiment analysis pipeline inspired by aspect-based sentiment analysis (ABSA) principles. Adopting the knowledge discovery in databases (KDD) framework, over 170,000 web scraped reviews were preprocessed, automatically labeled through pseudo labeling, and balanced using random oversampling. To mitigate pseudo-label reinforcement, manual validation on 500 reviews per country achieved agreement rates of 98.80% (Indonesia) and 99.20% (Thailand) with Cohen’s Kappa above 0.97. The fine-tuned DistilBERT model achieved accuracies of 97.65% (Indonesia) and 98.38% (Thailand), though these figures should be interpreted within the pseudo-labeled evaluation context. Significant perception gaps were revealed: Thai users showed lower satisfaction with transactions (67.6% negative) while Indonesian users were more positive (93.0% positive). Process time received negative dominance in both countries, with Indonesia at 78.2% and Thailand at 50.8% negative. These findings demonstrate that user satisfaction is shaped by local infrastructure and cultural contexts, providing strategic insights for regional fintech development.
Co-Authors Afdillah, Dwivi Ahmad Fauzi Alfi Zahra Hafizhah Anggiat Pandu Daniel Siregar Ariandi, Rama Arrahmani, Farras Hilmy Azzam Imaduddin, Muhammad B. Poerwanto Deden Witarsyah Dita Pramesti Edi Triono Nuryatno Ekky Novriza Alam Faqih Hamami Faza Hanif Suwanda Febrianti, Ferda Ayu Dwi Putri Ferda Ayu Dwi Putri Febrianti Helmayanti, Sheva Aditya Hutasoit, Michael Nico I Gede Pasek Suta Wijaya Jauhari, M.Habib Kadriyan, Hamsu Kayla Zhafira Ardinov Kuswandi, Brillian Adhiyaksa Luthfi Ramadani Mufriz, Muhammad Fadwa Muhaimin, Luthfi Ahmad Muhardi Saputra Mulyana, Heru Nirsal Nisa, Amarila Zahratun Nur Ichsan Utama Nurul Hidayati Nurul Samania Oktariani Nurul Pratiwi Pariswara, Endar Poedjimartojo, Naufal Avilandi Pramudia, Rudi Guna Pramuwidya, Shinta Purhadi Purhadi Putri Kurniawati Rachmadita Andreswari Rahmah, Najma Syarifa Salma, Farah Sherina Sheva Aditya Helmayanti Sinung Suakanto Siregar , Anggiat Pandu Daniel Syfa Nur Lathifah Widyatasya Agustika Nurtrisha Zalina Fatima Azzahra ZulFiqar Busrah