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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Informatika Komputer Jurnal Psikologi Jurnal Ilmiah Teknologi dan Rekayasa IPTEK Journal of Proceedings Series Semantik Jurnal Ilmiah KOMPUTASI Applied Information System and Management JURTEKSI RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Teknologi Kesehatan
Fitrianingsih Fitrianingsih
Fakultas Teknik-Jurusan Teknik Informatika , Universitas Gunadarma
Religion Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Nursing Public Health Social Sciences Other

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INTUITIVE UI DESIGN FOR MANGROVE TREE DETECTION APP Asnur, Paranita; Agushinta R, Dewi; Fitrianingsih, Fitrianingsih; Ngakasah, Siti Aliyah
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4362

Abstract

Abstract: The rapid degradation of mangrove ecosystems threatens coastal biodiversity, shoreline stability, and carbon sequestration capacity, particularly in areas experiencing intense human activity. However, community-based participatory mangrove monitoring remains limited due to the lack of accessible and user-friendly digital tools. This study aims to design an intuitive mobile application for mangrove tree detection and participatory ecological monitoring using a User-Centered Design (UCD) approach. The research was conducted iteratively through user needs analysis, prototype development, and usability evaluation involving local governments, conservation practitioners, and non-expert users. The proposed application integrates machine learning for automated mangrove recognition with geospatial visualization and real-time feedback to support field-based monitoring. Usability evaluation using the System Usability Scale (SUS) yielded an overall score of 82.3, categorized as excellent usability, indicating high user satisfaction and intuitive interaction. The results demonstrate that integrating UCD and machine learning enhances usability, user engagement, and the accuracy of mangrove documentation under real field conditions. Overall, this study presents a field-ready, user-centered mobile solution that bridges usability engineering and participatory mangrove monitoring as a replicable model for inclusive ecological application development. Keywords: Carbon sequestration; mangrove monitoring; mobile application; user-centered design; usability evaluation Abstrak: Degradasi ekosistem mangrove yang semakin cepat mengancam keanekaragaman hayati pesisir, stabilitas garis pantai, dan kapasitas sekuestrasi karbon, terutama di wilayah dengan aktivitas manusia yang intens. Namun, pemantauan mangrove secara partisipatif berbasis komunitas masih terbatas akibat kurangnya perangkat digital yang mudah diakses dan ramah pengguna. Penelitian ini bertujuan merancang aplikasi mobile yang intuitif untuk deteksi pohon mangrove dan pemantauan ekologi partisipatif dengan menggunakan pendekatan User-Centered Design (UCD). Penelitian dilakukan secara iteratif melalui analisis kebutuhan pengguna, pengembangan prototipe, dan evaluasi kegunaan dengan melibatkan pemerintah daerah, praktisi konservasi, serta pengguna non-ahli. Aplikasi yang diusulkan mengintegrasikan pembelajaran mesin untuk pengenalan mangrove secara otomatis dengan visualisasi geospasial dan umpan balik waktu nyata guna mendukung pemantauan di lapangan. Evaluasi kegunaan menggunakan System Usability Scale (SUS) menghasilkan skor keseluruhan sebesar 82,3 yang termasuk dalam kategori kegunaan sangat baik, menunjukkan tingkat kepuasan pengguna yang tinggi dan interaksi yang intuitif. Hasil penelitian menunjukkan bahwa integrasi UCD dan pembelajaran mesin meningkatkan kegunaan, keterlibatan pengguna, serta akurasi dokumentasi mangrove dalam kondisi lapangan. Secara keseluruhan, penelitian ini menyajikan solusi mobile berbasis UCD yang siap digunakan di lapangan dan menjembatani rekayasa kegunaan dengan pemantauan mangrove partisipatif sebagai model replikatif bagi pengembangan aplikasi ekologi yang inklusif. Kata kunci: Carbon sequestration; mangrove monitoring; mobile application; user-centered design; usability evaluation
Development of the web-based nutrition information system “SehatMate” for adolescents and adults using the agile method Putri Liana Suryadi; Fitrianingsih Fitrianingsih; Lestari Octavia
Jurnal Teknologi Kesehatan (Journal of Health Technology) Vol. 22 No. 1 (2026): Article in Press
Publisher : POLTEKKES KEMENKES YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29238/jtk.v22i1.3142

Abstract

Nutrition literacy and tools for calculating individual energy needs remain limited among adolescents and adults, potentially hindering balanced diet implementation in daily life. This study aims to develop SehatMate, a web-based nutrition system integrating Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE) calculations with dietary recording and a food composition database. An Agile software engineering approach was used with iterative planning, UML-based design, web framework and MySQL implementation, followed by testing, documentation, and deployment. BMR is calculated using the Harris–Benedict equation, while TDEE is derived from an activity factor. Food composition data from the Indonesian Food Composition Table were integrated into the system. System validation included comparison with manual calculations and black-box testing of user and admin functions across devices and browsers. The results show that SehatMate provides modules for registration, profile management, energy requirement calculation, food selection, daily intake recording, and nutrient adequacy recap. Findings indicate that system-generated BMR and TDEE values are consistent with manual calculations, and functional testing confirms system reliability across platforms. In conclusion, the system meets functional requirements for supporting nutrition education and dietary planning; however, further development is required to expand the nutrient database, improve personalization, and evaluate usability in real-user settings.
A Comparative Study of Machine Learning Models for Fashion Product Demand Prediction: Exploring Algorithms, Data Splitting, and Feature Engineering Reviana Siti Mardiah; Fitrianingsih Fitrianingsih
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45600

Abstract

The fashion industry faces challenges in accurately predicting demand due to inherent uncertainty, leading to suboptimal inventory and financial losses. Machine learning (ML) offers a robust solution by analyzing large and complex data, identifying non-linear patterns, and providing more accurate predictions than conventional methods that rely on limited factors.  This research aims to compare and evaluate the performance of six different ML models—XGBoost, SVM, RF, GBM, KNN, and NN, considering the influence of feature engineering and various data split ratios on predicting fashion product demand. KNN and NN were included due to distinct modeling approaches and competitive capabilities in identifying local and non-linear patterns across numerical, categorical, and time series data.  Techniques such as feature extraction and selection and various data split ratios (70:30, 80:20, 90:10) were used.  Using Adidas sales data, the models were evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the XGBoost-based model with feature engineering consistently outperforms the other models across all data split ratios. Particularly, XGBoost with feature engineering at a data split ratio of 90:10 achieved the best performance with an RMSE of 4.46 and an MAE of 1.51. Analyzing model performance shows that the predictive ability of ML models is influenced by the implementation of feature engineering and the selection of the data split ratio. These results demonstrate the potential of using feature-engineered XGBoost models and optimized data ratios to mitigate the risk of stockouts or overstocks, and reduce financial losses and environmental waste.
Co-Authors Agushinta , Dewi Andri Hanafi Andri Hanafi Betty Suswati Cahyawati Diah Kusumarini Debyo Saptono Dewi Agushinta R. Dody Pernadi Dody Pernadi Ernastuti Ernastuti Hermas Yuda Pamungkas Ihsan Jatnika Khairunnisa, Aprillia Intan Lestari Octavia Mardiah, Reviana Siti Ngakasah, Siti Aliyah Paranita Asnur Putri Liana Suryadi Quroyzhin Kartika Rini Reviana Siti Mardiah Rodiah Rodiah Sarifuddin Madenda Suryarini Widodo Syahrul, Elfitrin Tri Mardhika Sampurna Tri Wahyu R.N