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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Sainteks Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Indonesian Journal of Business Intelligence (IJUBI) Mobile and Forensics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Just TI (Jurnal Sains Terapan Teknologi Informasi) International Journal of Industrial Optimization (IJIO) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Swadaya: Indonesian Journal of Community Empowerment Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi JHCLS Prosiding Seminar Nasional Program Pengabdian Masyarakat TECHSI - Jurnal Teknik Informatika Journal of Artificial Intelligence in Architecture KOPEMAS Proceeding of Informatics Collaborations and Dessimenation Meeting (Infocoding)
Sri Winiarti
Universitas Ahmad Dahlan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Social Sciences Veterinary Other

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Optimalisasi Kinerja Convolutional Neural Networks VGG16 dalam Identifikasi Bangunan Adat Melayu Sri Winiarti; Sunardi, Sunardi; Fadlil, Abdul
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 8 No. 3 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v8i3.58210

Abstract

Penggunaan deep learning dalam mendeteksi berbagai objek sudah banyak diterapkan, namun untuk identifikasi kemiripan bangunan untuk gaya arsitektur masih terbatas. Analisis klasifikasi model desain arsitektur bangunan adat Melayu dapat dilakukan dengan menerapkan metode Convolusional Neural Network (CNN). Pendekatan yang digunakan untuk menganalisis klasifikasi dan kemiripan model bangunan adat melayu menggunakan model arsitektur VGG16. Ekstraksi fitur menggunakan model deep learning untuk mengidentifikasi jenis bangunan adat Melayu menggunakan parameter atap, jendela, dan ornamen bangunan. Dataset citra bangunan adat Melayu didapatkan dari pengambilan langsung ke lokasi bangunan adat melayu Riau di Kawasan Jalan Muhammad Arifin Pekanbaru Riau untuk training sebanyak 644 gambar dan testing model sebanyak 106 gambar. Model yang digunakan adalah VGG16. Parameter ukuran kinerja meliputi accuracy, precision, recall, dan F1-score. Akurasi yang didapatkan dalam penelitian ini adalah 98,77% dari total 106 data yang diuji, sedangkan precision 0,8678, recall 0,9633, dan F1-score 0,9877. Hasil yang didapatkan ini melalui setting parameter learning rate 0,0001, drop out 0,20, dan epoch sebesar 25. Secara keseluruhan model VGG16 yang digunakan dalam penelitian ini menghasilkan akurasi yang baik.
Analisis Efektivitas Pelatihan Kecerdasan Artifisial untuk Peningkatan Kompetensi Guru dalam Pengembangan Media Kreatif Sutikno, Tole; Ayuningtyas, Astika; Riadi, Imam; Winiarti, Sri; Rochmadi, Tri; Rosad, Safiq
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 4 (2025): Edisi Oktober - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i4.7546

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan menganalisis efektivitas pelatihan pemanfaatan teknologi kecerdasan buatan (Artificial Intelligence/AI) dalam meningkatkan kompetensi guru, khususnya dalam pengembangan media pembelajaran kreatif. Pelatihan dilaksanakan di SMA Negeri 7 Yogyakarta dengan melibatkan guru-guru dalaliterasiemaparan teori dan praktik langsung menggunakan berbagai platform AI, seperti ChatGPT, Canva AI, serta generator gambar dan video berbasis AI. Evaluasi dilakukan melalui pre-test dan post-test untuk mengukur perubahan kemampuan peserta sebelum dan sesudah pelatihan. Hasil analisis menunjukkan peningkatan rata-rata skor dari 5,95 menjadi 9,50, yang menandakan adanya peningkatan signifikan pada aspek pengetahuan, keterampilan, dan sikap guru dalam menerapkan AI. Guru mampu menghasilkan media pembelajaran digital yang lebih interaktif dan relevan dengan kebutuhan siswa. Temuan ini menunjukkan bahwa pelatihan AI berperan penting dalam meningkatkan literasi teknologi guru sekaligus mendukung transformasi digital di lingkungan pendidikan menengah.
Sistem Pendukung Keputusan untuk Menilai Kesejahteraan Wilayah DIY dengan Metode SMART Muhammad Salman Al Farisy; Sri Winiarti
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3753

Abstract

Welfare in the Special Region of Yogyakarta (DIY) is disrupted by urbanization and development inequality, which leads to poverty and other social problems. This research uses the Waterfall method which includes requirements analysis, design, implementation, and testing. Data were collected through interviews, and literature studies. The results of the study show that: first, the system developed is able to provide welfare ratings with high accuracy. Second, system validation through MAE calculation produces a value of 0.285, indicating a minimum error rate, so that the system can provide reliable predictions and support data-driven decision-making. Usability evaluation using the System Usability Scale (SUS) obtained a score of 73.9 which is in the good category and shows that the system is easy to use, and the functionality test with the Black Box method shows a 100% success rate. In conclusion, SPK based on the SMART method is effective in supporting social policies.
Analisis Spasio-Temporal Berbasis Data Video untuk Identifikasi Bangunan Melayu Menggunakan Metode Hybrid CNN-LSTM Ines Triseptiani; Sri Winiarti
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9917

Abstract

Malay buildings have distinctive architectural characteristics and require technology-based identification systems to support cultural documentation and preservation. This study aims to develop an identification system for Malay and non-Malay buildings using video data extracted into frame-by-frame images. The use of video data in this study is not intended to analyze the physical movement of buildings, but to utilize visual variations caused by changes in camera angle, recording distance, lighting, object composition, and visible building elements. The proposed method is CNN-LSTM, where CNN extracts visual features from each frame, while LSTM learns inter-frame feature relationships as a sequence of visual information. To reduce redundant information between adjacent frames and minimize the risk of excessive similarity between training and testing data, the number of frames was limited to a maximum of 15 frames per video folder, and data splitting was performed by considering video source groups. The dataset consists of Riau Malay buildings, Kalimantan Malay buildings, and non-Malay buildings. The research stages include frame extraction, image resizing to 224×224 pixels, normalization, data augmentation, class labeling, train-test splitting, modeling, evaluation, GroupKFold validation, and web-based system implementation. The best testing scenario was obtained using an 80:20 data split, 80 maximum epochs, and a batch size of 16, achieving an accuracy of 0.9916 and a test loss of 0.0852. GroupKFold validation produced an average accuracy of 99.1% with a standard deviation of 0.5%. These results indicate that the model can recognize architectural visual patterns, such as roofs, windows, doors, ornaments, and overall building appearance, while the performance should still be interpreted within the scope of the dataset and evaluation scenario used in this study.
Sistem Rekomendasi Produk Skincare Halal Berbasis Komposisi Bahan menggunakan Metode Simple Additive Weighting Velisa Amanda Putri; Sri Winiarti; Nurkhasanah Nurkhasanah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.38877

Abstract

The rapid development of the skincare industry has increased the complexity of product ingredient composition, making it difficult for consumers to determine products that suit their skin needs. This challenge is even more complex for Muslim consumers who must consider the halal aspects of skincare product ingredients. Lack of understanding regarding ingredient content, skin problems, skin type, and product halal status leads to a high risk of errors in skincare selection. Therefore, this study aims to implement a halal skincare product recommendation system based on ingredient content using the Simple Additive Weighting (SAW) method. The system uses seven criteria: ingredient content, halal logo, BPOM logo, ingredient safety, skin problems, skin type, and age. Alternatives consist of 61 skincare products, 22 facial washes, 23 moisturizers, and 16 sunscreens obtained from questionnaires with 300 respondents. The novelty of this study lies in the integration of halal aspects, skin conditions, and government regulations in a multi-criteria decision-making (MCDM)-based decision-making model. The SAW method is used to weight and rank alternatives based on preference values. The results show that the SAW method is able to generate product recommendations according to user characteristics. For normal-dull skin conditions, the best recommendations are Emina Bright Prebiotic Tranexamic (0.7800), Somethinc Skin Goals Moisture Silk Creme (0.8300), and Wardah UV Shield Active Protection Serum SPF 50 PA++++ (0.7050). For sensitive-acne skin conditions, the best recommendations are Skintific 3x Acid Acne Gel Cleanser (0.9750), Something Acne Treatment Moisturizer Gel (1.0000), and Wardah UV Shield Acne Calming Sunscreen SPF 50 PA++++ (0.8300). The results of the Black Box Testing showed that all system functions were running well, while the expert judgment by a dermatology specialist showed that the system was running well.
Analisis Komparatif Random Forest dan Support Vector Machine untuk Klasifikasi Tingkat Keparahan Serangan Siber Reyhanssan Islamey; Sri Winiarti; Imam Riadi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36558

Abstract

The escalating volume and sophistication of cyberattacks on network infrastructures processing massive daily traffic have overwhelmed security teams in prioritizing incident responses rapidly and accurately, a phenomenon known as alert fatigue. This study aims to analyze and compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for classifying cyberattack severity levels (Low, Medium, and High). The study uses the public Cyber Security Attacks dataset, consisting of 40,000 network traffic records reduced to 20,000 clean entries through preprocessing and feature engineering. The methodology includes data cleaning, selecting 10 significant features using SelectKBest, standardizing numerical features, and evaluating models across three data split scenarios (70:30, 80:20, and 90:10) using a stratified splitting approach. Experimental results show that SVM consistently outperforms RF across all scenarios, with the best performance in the 80:20 split, achieving 98.92% accuracy and a weighted average F1-Score of 0.99 using hyperparameter configurations of C = 100 and gamma = 0.01. The superiority of SVM lies in its ability to model non-linear relationships and complex feature interactions in data with overlapping class boundaries. In contrast, RF exhibits an over-prediction bias toward the minority class (’Low’) due to the class_weight=’balanced’ mechanism and limitations of axis-based separation. These findings confirm that SVM with a Radial Basis Function (RBF) kernel is more suitable for cyberattack severity classification, particularly in automated incident detection systems requiring balanced precision and recall as well as reliable decision-making.
Artificial Intelligence-Based Aircraft Detection for Enhanced Aviation Safety and Air Traffic Management Ayuningtyas, Astika; Novelia Gunawan, Saomi; Ira Candra Dewi Wulan, Puspa; Medianto, Rully; Winiarti, Sri; Rakhmadi, Aris
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5661

Abstract

The rapid growth of international air traffic has made maintaining aviation safety and managing air traffic efficiently increasingly complex, particularly in identifying aircraft in constantly changing airspace. Traditional monitoring systems such as radar and Automatic Dependent Surveillance-Broadcast (ADS-B) have limitations in operating at low altitudes, in adverse weather, and in overcrowded environments, which can reduce the ability to understand surrounding conditions. This research proposes an artificial intelligence-based visual detection system aimed at enhancing real-time aircraft identification and improving air traffic monitoring. The system uses a YOLO-based deep learning model enhanced with a special attention mechanism and data augmentation to increase accuracy, flexibility, and operational resilience. The dataset used covers various flight situations, such as variations in light, viewing angles, and background complexity, to train the model. The model's test results show that it can correctly identify 95.24% of passenger planes, 92.4% of blimps, and 90% of fighter planes. The average overall precision (mAP) is over 90%. This system is also capable of real-time inference with precision and recall consistently above 85% under various conditions. Compared with conventional vision-based detection methods, this system demonstrates superior localization capabilities and robustness, making it suitable for use in real-world flight surveillance and air traffic management. In conclusion, this AI-based framework provides a practical and scalable solution that can improve flight safety and promote smarter air traffic management.
AUTOMATED ACNE TYPE IDENTIFICATION THROUGH FORWARD CHAINING APPROACH Aris Rakhmadi; Naura Fikamelyalla; Sri Winiarti; Esi Putri Silmina; Umi Fadlillah; Yusuf Sulistyo Nugroho
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 1 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i1.5377

Abstract

Acne, a prevalent dermatological condition, poses significant physical and psychological challenges. Despite its widespread impact, timely and accessible diagnosis remained a barrier for many, emphasizing the need for innovative solutions. This study introduced an online consultation system for acne-type identification, leveraging a forward chaining approach within an AI-powered expert system. The system analyzed user-reported symptoms—such as severity, location, and appearance—using a rule-based inference mechanism to provide accurate diagnoses and tailored treatment recommendations. Developed using a prototype model, the system’s knowledge base was enriched through observations, literature reviews, and expert interviews, ensuring reliability and clinical relevance. Iterative testing, including black-box evaluations and a System Usability Scale (SUS) assessment, confirmed the system's functionality and user satisfaction, with a SUS score of 86.5, indicating high acceptance. The system bridged critical gaps in dermatological care, particularly for underserved communities, by enabling rapid, user-centric diagnostics and personalized recommendations. The research underscored the transformative potential of artificial intelligence and expert systems in healthcare. By integrating accessibility, scalability, and precision, the proposed system addressed the challenges of acne management and set a foundation for future advancements in dermatological diagnostics.
Machine Learning-Based Lifestyle Analysis for Health Risk Detection in Coffee Drinkers Sri Winiarti; Ahmad Azhari; Nathaniela Isya Nur Rofiah
Mobile and Forensics Vol. 8 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i2.16102

Abstract

This study aims to develop a machine learning-based intelligent system to detect health risks in coffee drinkers through a lifestyle analysis approach. The background of this study is based on the increasing consumption of coffee as part of a modern lifestyle, which has the potential to cause various health risks if not balanced with a healthy lifestyle. The dataset was collected through a survey covering several important variables, such as coffee consumption frequency, sugar intake, sleep duration, physical activity level, body mass index (BMI), and blood pressure. The research stages included data preprocessing, normalization, and classification using three algorithms: Random Forest and Gradient Boosting. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics to ensure the system’s reliability. Test results showed that the Random Forest algorithm performed best with an accuracy of 0.91, followed by Gradient Boosting with an accuracy of 0.90. Further analysis revealed that the variables most influential on health risks are coffee consumption frequency, sleep duration, and sugar intake. The developed system proved effective in detecting health risks early and has the potential to serve as a data-driven educational tool to raise public awareness of the importance of a healthy lifestyle.
Penerapan Case-Based Reasoning dan Certainty Factor dalam Mendiagnosa Penyakit pada Cabai Sri Winiarti; Dewa Putra Hernanda
Proceeding of Informatics Collaborations and Dessimenation Meeting Vol. 1 No. 1 (2025)
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Petani cabai menghadapi masalah dalam budidaya tanaman mereka, seperti serangan hama dan penyakit. Untuk mengatasi kerugian yang disebabkan oleh masalah tersebut, penting bagi petani untuk mengidentifikasi penyakit yang menyerang tanaman cabai melalui gejala yang . Penelitian ini bertujuan untuk membangun sistem yang dapat mendiagnosis penyakit tanaman cabai menggunakan metode case based reasoning dan certainty factor. Dengan adanya penelitian ini diharapkan dapat membantu para petani dalam mengidentifikasi penyakit tanaman dan menentukan tindakan pengendalian yang sesuai. Metode yang digunakan dalam penelitian ini adalah Case Based Reasoning dipadukan dengan Certainty Factor. Certainty Factor berperan dalam proses revisi pada tahapan case based reasoning. Jika nilai Certainty Factor melebihi ambang batas, data akan otomatis dimasukkan ke dalam basis pengetahuan. Namun, jika nilai Certainty Factor berada di bawah ambang batas, pakar akan melakukan revisi. Data diperoleh melalui wawancara dengan pakar. Pengujian menerapkan metode Expert Judgment dengan membandingkan hasil diagnosis dari sistem dengan diagnosis pakar. Selain itu, dilakukan juga pengujian sistem menggunakan metode System Usability Scale dengan memberikan kuisioner kepada petani cabai untuk mencoba aplikasi dan memberikan tanggapan. Hasil penelitian berupa sistem aplikasi web untuk membantu petani mendiagnosis penyakit tanaman cabai berdasarkan gejala yang muncul. Aplikasi ini akan memberikan diagnosis penyakit dan solusi penanganannya. Pengujian dengan metode Expert Judgment menunjukkan hasil yang sesuai dengan pendapat pakar, dan pengujian System Usability Scale menunjukkan kepuasan pengguna. Aplikasi ini berpotensi meningkatkan efisiensi budidaya dan mengurangi kerugian akibat penyakit pada tanaman cabai.
Co-Authors Abdul Fadlil Adil Pratama Afiat Triyuniarta An Nur, Fitrinanda Ana Distia Diva Andina Megawati Akase Andri Pranolo Anis Nurwanto Apriliani, Evinda Ardi Pujiyanta Aris Rakhmadi Astika AyuningTyas, Astika Astreanto Habibullah Astri Yatnasari Atik Mar’atun Sholihah Augit Indatmowo Azhari, Ahmad Bagus Imam S.N. Bagus Priangga Bagus Priangga Bagus Priangga, Bagus Cendani Wukir Choirul Fajri Cindy Mayeza Putri Daffa Alif Murtaja Dedi Nugraha Dedi Nugraha Desy Widayanti Dewa Putra Hernanda Dewi Soyusiawaty Dewi, Kharisma Kusuma Dian Sulistyo Distia Diva, Ana Dwi Oktavia Andriyanti Endriyono Endriyono Enggar Novianto Enita Try Saadyah Esi Putri Silmina Faisal, Ilyas Faza Akmal Fitriana Susanti Fitrinanda An Nur Galih Oktorika Isnawan Heri Pramono Herman Yuliansyah Herman Yuliansyah Herman Yuliansyah Herman Yuliansyah, Herman Ida Widaningrum, Ida Imam Riadi Imam Riadi Ines Triseptiani Ira Candra Dewi Wulan, Puspa Irawan, Riki Izzati Muhimmah Kharisma Kusuma Dewi Lathifah Lathifah Meilawati, Noni Melanita Indrianis Miftahurrahma Rosyda Miksa Mardhia, Murein Muhammad Al Mahdi Muhammad Arifin Setyawan Muhammad Salman Al Farisy Murein Miksa Mardhia Murinto Murinto Nailut Thoyibah Nathaniela Isya Nur Rofiah Naura Fikamelyalla Nila Susanti Noni Meilawati Norma Sari Norma Sari Novelia Gunawan, Saomi Nungky Anjaswari Nur Azizah Nur Kahfi Ibrahim Nur Rachmaliany Nur Rochmah Dyah Pujiastuti Nurkhasanah Nurkhasanah Nurul Azizah Az zakiyyah Pandu Herwijaya Priranda Widara Ananta Puguh Drajat Eka Putra R. Panji Daru Tutuko Rachmaliany, Nur Rahmawati Witriani Witriani, Rahmawati Witriani Reni Wijayanti Reyhanssan Islamey Rifki Pambudi Riki Irawan Rizka Gustikasari Rochmadi, Tri Rully Medianto, Rully Rusydi Umar Safiq Rosad sapanti, intan rawit Saputro, Mochammad Yulianto Andi Sonny Zulhuda Sri Kusumadewi Sri Wahyuni Sri Wahyuni Sulistyo, Dian Sunardi Sunardi Sunardi, Sunardi Supriyanto Taufiq Ismail Taufiq Ismail Taufiq Ismail Taufiq Ismail Tole Sutikno Tri Afriliyanti Tsaqila, Siti Lathifah Ulaya Ahdiani Ulaya Ahdiani Ulaya Ahdiani Ulfah Yuraida Ulfah Yuraida Umi Fadlillah Velisa Amanda Putri Wiwik Handayani Yuliansyah, Herman Yunita Tri Hernawati Yuraida, Ulfah Yusuf Sulistyo Nugroho Zainal Ihsanul F