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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Journal of Information Systems Engineering and Business Intelligence JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal E-Komtek JIKA (Jurnal Informatika) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Teknika JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Informatika Jurnal Masyarakat Madani Indonesia eProceedings of Engineering Jurnal Informatika: Jurnal Pengembangan IT Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Jnanadharma Transaction on Informatics and Data Science Jurnal Teknokes KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Yohani Setiya Rafika Nur
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry 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 Mechanical Engineering Public Health Social Sciences Other

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Inovasi Penetas Telur Cerdas Berbasis IoT sebagai Strategi Pemberdayaan dan Kemandirian Ekonomi Perempuan di Desa Muntang Dasril Aldo; Yohani Setiya Rafika Nur; Ajeng Dyah Kurniawati; Afifah Naurah Hidayat; Dio Syahputra; Faizal Burhani Ulil Fathan; ⁠Ihsan Maulana; Riftian Dimas Adriano
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/a95a4s30

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kemandirian ekonomi perempuan di Desa Muntang, Kecamatan Kemangkon, Kabupaten Purbalingga, melalui penerapan inovasi penetas telur cerdas berbasis Internet of Things (IoT). Permasalahan utama mitra adalah proses penetasan ayam kampung yang masih manual, kurang efisien, serta rendahnya keterlibatan perempuan dalam usaha produktif. Kolaborasi dilakukan antara Komunitas Limbah Pustaka sebagai fasilitator sosial, kelompok Petet Ayam Lestari sebagai mitra teknis, dan warga desa sebagai peserta utama. Kegiatan mencakup sosialisasi, pelatihan teknis dan manajemen usaha, implementasi alat di lapangan, serta evaluasi melalui observasi dan kuesioner. Hasil menunjukkan peningkatan signifikan pada pemahaman peserta terhadap teknologi dan peluang ekonomi desa. Rata-rata tingkat pemahaman meningkat dari 46% menjadi 91%, sedangkan tingkat kepuasan terhadap kegiatan mencapai skor 3,69 dari skala 4,0 (kategori sangat baik). Program ini menghasilkan alat penetas telur cerdas yang mudah digunakan dan sesuai dengan kebutuhan masyarakat pedesaan. Kesimpulannya, penerapan teknologi tepat guna berbasis IoT terbukti efektif dalam mendorong pemberdayaan perempuan, peningkatan literasi teknologi, serta penguatan ekonomi lokal yang berkelanjutan.
Edukasi Pencegahan Diabetes Dini pada Anak melalui Multimedia Interaktif TOMATOSMART KIDS Berbasis Pangan Lokal Adanti Wido Paramadini; Ajeng Dyah Kurniawati; Yohani Setiya Rafika Nur; Dasril Aldo; M. Hanif Al Faiz; Ichya Ulumiddiin; Muhammad Nafal Fiqrian
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/cgwdj470

Abstract

Peningkatan risiko diabetes melitus pada usia anak menjadi permasalahan kesehatan yang perlu mendapatkan perhatian serius, terutama akibat pola konsumsi tinggi gula dan rendahnya literasi kesehatan sejak dini. Kegiatan pengabdian masyarakat ini dilaksanakan di [nama sekolah/lokasi kegiatan] dengan melibatkan 60 peserta anak usia sekolah dasar. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan kesadaran anak dalam pencegahan diabetes dini melalui multimedia interaktif TOMATOSMART KIDS berbasis pangan lokal. Metode yang digunakan adalah pendekatan community-based dengan strategi edukasi berbasis multimedia interaktif dan learning by playing. Kegiatan dilaksanakan melalui tahapan persiapan, pelaksanaan edukasi, dan evaluasi. Evaluasi dilakukan menggunakan pre-test dan post-test serta observasi keterlibatan peserta. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan peserta, dengan rata-rata skor pre-test sebesar 43,2% meningkat menjadi 90,8% pada post-test. Selain itu, peserta menunjukkan keterlibatan aktif dan sikap positif terhadap penerapan pola makan sehat. Penggunaan multimedia interaktif terbukti efektif dalam meningkatkan pemahaman peserta karena mampu menyajikan materi secara menarik, interaktif, dan sesuai dengan karakteristik anak. Dengan demikian, program TOMATOSMART KIDS berpotensi menjadi media edukasi kesehatan yang inovatif dan aplikatif dalam upaya pencegahan diabetes sejak usia dini berbasis pemanfaatan pangan lokal.
Expert System for Diagnosing Autoimmune Diseases Using Dempster–Shafer and Fuzzy Logic: A Case Study of Prof. Dr. Margono Soekarjo Regional Hospital Rahmadani, Ragil Putri; Nur, Yohani Setiya Rafika; Utami, Annisaa
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5585

Abstract

Autoimmune diseases, particularly lupus, pose a major challenge in healthcare because their symptoms are highly variable and often mimic other medical conditions. Delayed diagnosis can worsen patient outcomes, increase the risk of severe complications, and even lead to death, especially in healthcare facilities with limited autoimmune subspecialists, such as Prof. Dr. Margono Soekarjo Regional Hospital. This study aims to develop a web-based expert system to support early screening for lupus by combining the Fuzzy Tsukamoto method and the Dempster-Shafer theory. The Fuzzy Tsukamoto method is used to represent symptom uncertainty through fuzzification, while the Dempster-Shafer theory is used to combine evidence from individual symptoms to produce confidence levels for possible diagnoses. The research process included a literature review, expert interviews, construction of a symptom–disease knowledge base, design of fuzzy rules, implementation of mass function calculations, and development of a web-based diagnostic application. Testing was conducted using ten patient test cases with confirmed expert diagnoses. The test results showed an accuracy of 100%, with all system diagnoses matching the experts’ diagnoses. The strength of this research lies in the integration of two inference methods to improve the accuracy of evidence calculation, and in the use of symptom uniqueness and occurrence parameters that were validated directly by experts. This system has the potential to serve as an effective early screening tool for healthcare providers and patients, particularly in resource-limited settings. From an informatics perspective, this study contributes to the development of intelligent decision support systems by demonstrating the effectiveness of a hybrid reasoning approach in handling uncertainty in medical diagnosis. The integration of Fuzzy Tsukamoto and Dempster–Shafer methods enhances diagnostic consistency and reliability, making the proposed system relevant for research in expert systems and medical informatics.
Food Detection to Estimate Calories Using Detection Transformer Joshua Putra Fesha Kristanto; Dedy Agung Prabowo; Yohani Setiya Rafika Nur
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.132

Abstract

Accurately estimating calorie intake remains a common challenge, as many individuals have limited understanding of portion sizes and the caloric content of foods. This lack of nutritional knowledge is a major cause of both over- and under-calorie consumption and contributes to significant public health problems, including obesity, cardiovascular disease, and chronic metabolic disorders. Although computer vision–based approaches for dietary assessment have advanced, many methods still rely on handcrafted features, anchor-based CNN detectors, or controlled geometric assumptions. This indicates a practical gap in developing a fully functional system that operates on basic RGB images captured under everyday conditions. This study aims to develop an end-to-end food detection and calorie estimation system using the Detection Transformer (DETR) to predict calorie values directly from food images. The main contributions of this study include: (1) employing DETR to address non-maximum suppression limitations and improve the stability of multi-food recognition; (2) using a bounding box area-to-weight ratio as a low-complexity alternative to segmentation-based food portion estimation; and (3) developing a user-friendly interface for output visualization that displays detected food items and their estimated calorie values in real-world scenarios involving irregular food shapes and varying focal lengths. A DETR-based detector was trained using 2,228 COCO-formatted images across six distinct food classes. Calorie values were estimated by predicting food weight based on bounding box measurements, followed by calorie calculation using standardized reference weights. The method assessed robustness by evaluation on both controlled and real-life food images. Experimental results demonstrated moderate performance, with 0.617 mean Average Precision (mAP) and 0.656 mean Average Recall (mAR). The weight prediction module served as the primary estimation component, achieving a mean absolute residual of 8.7. These findings suggest that bounding box area is a reliable estimator of serving size. This study serves as a proof of concept for monitoring individual food intake and provides a foundation for further improvement in sub-item recognition, three-dimensional volume estimation, and the inclusion of broader food classes.
Machine Learning Approaches with Random Forest and XGBoost for Sustainable Tourism Forecasting in Bali Destinations Nadia Nabila; Yohani Setiya Rafika Nur; Maie Istighosah
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5612

Abstract

Bali has experienced rapid tourism growth, reaching more than 6.3 million international visitors in 2024, which has increased the risk of overtourism and created challenges for sustainable destination management. Despite tourism being a major contributor to Bali’s economy, planning practices have not fully adopted data-driven prediction approaches, resulting in uncertainty in infrastructure development, service capacity, and resource allocation. This study aims to compare the performance of Random Forest and XGBoost algorithms in predicting the popularity of tourist destinations in Bali to support evidence-based decision-making. The research utilizes historical tourist visitation data from 2018 to 2023, obtained from the Bali Provincial Tourism Office. Data preprocessing includes data cleaning, normalization, feature encoding, and dimensionality reduction using Principal Component Analysis. Three data split schemes (80:20, 75:25, and 90:10) are evaluated. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that XGBoost outperforms Random Forest, achieving the best performance with a MAPE of 5.32% using the 90:10 data split. The selected model is then applied to project tourism demand for 2024–2026, indicating that nature-based and cultural tourism destinations remain dominant, particularly in Badung, Jembrana, and Gianyar Regencies. This study contributes to informatics by providing a machine-learning-based prediction model to support sustainable tourism management.
Co-Authors Adanti Wido Paramadini Adanti Wido Paramadini Ade Prasetyo, Ade Affriza Brilyan Relo Pambudi Agus Putra Afifah Naurah Hidayat Afifatul Fajri, Nabila Ajeng Ayu Suryani Ajeng Dyah Kurniawati Al Faiz, M. Hanif Alfonsus Simbolon Alika, Shintia Dwi Amalia Beladinna Arifa Aminatus Sa’adah Andre Citro Febriliyan Lanyak Angga Kurniawan Audrey Hillary Auliya Burhanuddin Azmi, Wifqi Wifakul Bachrul Restu Bagja Bidayatul Masulah Bita Parga Zen Christantie Effendy Christian Tambunan, Gerry Claudio Felle, Roland Dading Qolbu Adi Dasril Aldo Dedi Rahman Habibie Dedy Agung Prabowo Deni Romadan, Muhamad Dio Syahputra Dwi Putro Wicaksono, Aditya Edelin Gultom Endraswari, Putri Mentari Eryan Ahmad Firdaus Evia Zunita Dwi Pratiwi Faisal Dharma Adhinata Faiz, M. Hanif Al Faizah Faizah Faizal Burhani Ulil Fathan Fathan, Faizal Burhani Ulil Fau, Andrew Filfimo Yulfiz Ahsanul Hulqi Firmansyah, Muhammad Raafi'u Gusla Nengsih, Yeyi Gusnita Linda Harald Riandi Rantetana Purukan Hasan, Faiz Hidayat, Afifah Naurah Ichya Ulumiddiin Imam Ghozali J. Manurung, Barnes Joshua Putra Fesha Kristanto Lina Fatimah Lishobrina Luqman Wahyudi M Yoka Fathoni M. Hanif Al Faiz Maie Istighosah Maulana, Ihsan Melinda Br Ginting Miftahul Ilmi Muadin, Dika Alim Muhamad Azrino Gustalika Muhammad Nafal Fiqrian Muhammad Nazmi Al Faiz Muhammad Raafi'u Firmansyah Muhammad Zaky Mubarok Nadia Ayu Isroh Nadia Nabila Nia Annisa Ferani Tanjung Nur Ghaniaviyanto Ramadhan Nurhaeka Tou Nurul Latifasari Pamuji, Yanuar Ikhsan Paradise Rahmadani, Ragil Putri Ramadhani, Rima Dias Rania Nur Hikmah Rianto Putra, Frederick Ridho Rahmadi Riftian Dimas Adriano Sa'adah, Aminatus Sahara Sahara Sapta Eka Putra Sulaeman, Gilang Suprapto, Amelia Rut Trihastuti Yuniati Ummi Athiyah Usman, Muhammad Lulu Latif Utami, Annisaa Wahyu Adi Prabowo Wanda Ilham Warto Widya Lelisa Army Yasin, Feri Yehezekiel Ramasyah Putra Haloho Yoka Fathoni, M. Yuan Sa'adati Zahirah, Regina Putri Wanda ⁠Ihsan Maulana