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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri INOVTEK Polbeng - Seri Informatika Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik Penusa JURTEKSI Jurnal Riset Informatika Jurnal Mantik Ubiquitous: Computers and its Applications Journal Jurnal Sistem Informasi Computer Science (CO-SCIENCE) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Paradigma Info Abdi Cendekia Justika : Jurnal Sistem Informasi Akuntansi Journal of Accounting Information System The Indonesian Journal of Computer Science Jurnal Komtika (Komputasi dan Informatika)
Siti Nurajizah
Universitas Bina Sarana Informatika
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Jurnal Riset Informatika

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Image Segmentation Analysis Using Otsu Thresholding and Mean Denoising for the Identification Coffee Plant Diseases Ami Rahmawati; Yulianti, Ita; Nurajizah, Siti
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.261

Abstract

In Indonesia, coffee is one of the plantation products with a relatively high level of productivity and is a source of foreign exchange income for the country. However, unfortunately, certain factors can threaten productivity and quality in cultivating coffee plants, one of which is rust leaf disease. This disease causes disturbances in photosynthesis, thereby reducing plant yields. Therefore, to maintain and control productivity in coffee cultivation, this research carried out the process of observing coffee leaf images through segmentation using the Otsu Thresholding and Mean Denoising methods. The entire series of processes in this research was carried out using the Python programming language and succeeded in providing output in the form of image comparisons showing areas affected by Rust Leaf disease using the Otsu thresholding method alone and the Otsu thresholding method combined with a non-local means denoising algorithm. The test results prove that the Otsu thresholding method with the non-local means denoising algorithm has a smaller MSE value. It is the most optimal method for handling coffee leaf disease image segmentation with an accuracy level of 88%. It is hoped that this research can support farmers in providing insight into early detection of coffee plant diseases and increasing productivity through visual analysis.
ENHANCING SLEEP QUALITY PREDICTION THROUGH SMOTE-BASED DATA BALANCING AND HYBRID MACHINE LEARNING MODELS Rahmawati, Ami; Yulianti, Ita; Oktarini Sari, Ani; Nurajizah, Siti; Hikmatulloh
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.456

Abstract

Sleep is a vital aspect in maintaining a person's physical and psychological balance. Poor sleep quality can reduce physical and cognitive performance, increasing the risk of various health problems. This study aims to develop a predictive model for sleep quality based on factors such as lifestyle, stress, daily activities, and caffeine consumption, using XGBoost combined with Recursive Feature Elimination (RFE). XGBoost was chosen for its ability to handle imbalanced datasets and heterogeneous features, while RFE helps simplify the model without losing important information. In the data pre-processing stage, a class imbalance was found, so the Synthetic Minority Over-sampling Technique (SMOTE) process was carried out to balance the proportion of the minority class. The dataset in this study was divided into two parts, namely 80% as training data and 20% as testing data, and validated using cross-validation to ensure generalization. The results show very high model performance with an accuracy of 99.79% on training data, 99.63% on cross-validation, and 99.10% on testing data. This model was then developed into a web application for practical use in analyzing sleep quality prediction. This study emphasizes the methodological contribution of a SMOTE-based hybrid machine learning model and its ready-to-use application implementation, while also opening opportunities for further testing on more diverse datasets and evaluating biases caused by synthetic data.
Co-Authors Adli, Idham Abdi Al Alifia Ramadhanti Ami Rahmawati ANI OKTARINI SARI, ANI OKTARINI Arinda Salbinda Bambang Rizki Wicaksono Dewi, Rani Kurnia Divika Namastute Duwik Sudarsono Ervan Aziz Fani Roziqi Fajri Fitria Dara Kurnia Fitrika, Sonia Hari Sugiarto - UBSI Henny Leidiyana Hikmatulloh Hikmatulloh, Hikmatulloh Irlia Oktavianisa Ita Yulianti Ita Yulianti Jestin Anttares Lis Saumi Ramdhani Maria, Vivi Marijan Marijan Maulana Saputra Mawadatul Maulidah Nadiyah Hidayati Nila Alis Ambarwati Normah Normah Normah Normah Normah Normah Nurfitriani Nurfitriani Oktarini Sari, Ani Panca Saputra, Elin Rahmawati Maulida, Nurul Rahmawati, Ami Ramdiani Azzahra, Dinda Rifa Nurafifah Syabaniah Rizqitha Maula Safitri Linawati Sahrul, Nizar Saputra, Maulana Saumi Ramdhani, Lis Sri Muryani Supriatiningsih Supriatiningsih Susilawati, Susilawati Taufik Hidayatulloh Taufik Rahman Titik Misriati Wahyuni, Ayu Dewi Sri Wahyuni Yusti Farlina