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All Journal Jurnal Riset Informatika JITPI : Jurnal Ilmu Dan Teknologi Peternakan Indonesia (Indonesian Journal of Animal Science and Technology)
Ami Rahmawati
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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Exterior and Interior Quality of Peking Duck Eggs Reared by the Monggelemong Duck Farmer Group Dasan Cermen Sandubaya Mataram Tamzil, Mohammad Hasil; Bulkaini; Budi Indarsih; Ami Rahmawati
Jurnal Ilmu Dan Teknologi Peternakan Indonesia (JITPI) Indonesian Journal of Animal Science and Technology) Vol 10 No 2 (2024): Jurnal Ilmu dan Teknologi Peternakan Indonesia (JITPI) Indonesian Journal of Ani
Publisher : Faculty of Animal Husbandry, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jitpi.v10i2.211

Abstract

Kualitas telur itik antara lain dipengaruhi oleh sistem budidaya. Tujuan penelitian ini adalah untuk mengkaji kualitas eksternal dan internal telur itik Peking yang dihasilkan di Kelompok Peternak itik Monggelemong dasan Cermen Sandubaya kota Mataram. Telur itik diperoleh dari anggota kelompok peternak yang tergabung pada Kelompok Peternak itik Monggelemong. Pengambilan telur dilakukan secara bergiliran setiap hari pada setiap anggota kelompok peternak dengan mengambil secara random 10% dari total produksi telur, sehingga terkumpul 170 butir telur. Telur-telur sampel yang diambil setiap hari selanjutnya diukur kualitas eksternal dan internalnya di Laboratorium Teknologi Pengolahan Hasil Ternak Fakultas Peternakan Universitas Mataram. Data yang diperoleh dianalisis secara deskriptif. Hasil penelitian menyimpulkan bahwa mutu internal telur itik Peking yang dihasilkan Kelompok Peternakan Itik Monggelemong termasuk dalam kategori mutu sangat baik (grade AA), namun mutu eksternalnya rendah.
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.
Integration of Adasyn Method with Decision Tree Algorithm in Handling Imbalance Class for Loan Status Prediction Ami Rahmawati; Yulianti, Ita; Mardiana, Tati; Pribadi, Denny
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (761.285 KB) | DOI: 10.34288/jri.v6i3.299

Abstract

Determining the provision of credit is generally carried out based on measuring credibility using credit analysis principles (5C principles). However, this method requires quite a long processing time and is very susceptible to subjective judgments which might influence the final results. This research uses data mining techniques by developing modeling on loan status prediction datasets. The stages in this research include data preprocessing, modeling, and evaluation using accuracy metrics and ROC graphs. In this analysis, it is known that there is a class imbalance in the processed dataset, so an oversampling technique must be carried out. This research uses the ADASYN (Adaptive Synthetic) Oversampling technique to ensure the class distribution is more balanced. Then, the ADASYN technique is integrated with the Decision Tree Algorithm to build a prediction model. The research results show that the two methods can increase prediction accuracy by 12.22%, from 73,91% to 85.22%. This improvement was obtained by comparing the accuracy results before and after using the ADASYN Oversampling technique. This finding is important because it proves that implementing such integration modeling can significantly improve the performance of classification models and provide strong potential for practical application in helping more effective loan status predictions.
Co-Authors Budi Indarsih Bulkaini (Bulkaini) Ita Yulianti Mohammad Hasil Tamzil Pribadi, Denny Siti Nurajizah, Siti Tati Mardiana, Tati