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Classification of X-Ray Images of Normal, Pneumonia, and Covid-19 Lungs Using the Fuzzy C-Means (FCM) Algorithm Dini Rohmayani; Ayu Hendrati Rahayu
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5512

Abstract

Lung disease has a very serious impact on the respiratory system and can be dangerous if not treated immediately. At this time, lung diseases that are often encountered by the public include pneumonia and 2019 coronavirus. Many people mistake the disorder that occurs to him because the symptoms of Covid-19 and pneumonia are very similar. Thus, it is very important to know the difference between the two diseases so that early treatment can be carried out. Based on the problems that have been described, the author will propose a study entitled "Classification of X-ray Images of Normal Lungs, Pneumonia, and Covid-19 Using the Fuzzy C-Means (FCM) Algorithm". The aim of this study is to assist in classifying normal, pneumonia, and Covid-19 lungs. The reason for choosing this algorithm is that this algorithm has advantages in grouping cluster centers which are more optimal than other methods.
BEBAN KERJA PETUGAS FILING TERHADAP RATA-RATA WAKTU PENYEDIAAN DOKUMEN REKAM MEDIS RAWAT JALAN Rizqy Dimas Monica; Fathia Mawar Firdaus; Intan Puji Lestari; Yesti Suryati; Dini Rohmayani; Ayu Hendrati
Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Vol 3, No 2 (2015)
Publisher : Asosiasi Perguruan Tinggi Rekam Medis dan Informasi Kesehatan Indonesia- APTIRMIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33560/.v3i2.90

Abstract

AbstractThe number of officers in RSUI Yakssi Filing Gemolong Sragen as many as 5 people. Based on the preliminary survey, the average time of provision of outpatient medical record documents in RSUI Yakssi Gemolong Sragen is 13.5 minutes in which time is not in accordance with minimum service standards set by the Minister of Health No: 129 / Menkes / SK / II / 2008. The purpose of this study was to determine the effect of workload attendant to the average filing time provision of medical record documents in RSUI Yakssi Gemolong Sragen. This type of research is analytic research by testing the effect of workload attendant to the average filing time provision of medical record documents in RSUI Yakssi Gemolong Sragen. Data collection methods used were observation and interviews, while the instruments used are guidelines for observation, interview and observation time of provision of the document sheet. The data obtained will be analyzed bivariate with Simple Linear Regression. The analysis showed that the workload of officers filing very strong influence on the average time providing outpatient medical record documents in RSUI Yakssi Gemolong Sragen. Based on the analysis, the authors provide suggestions for RSUI Yakssi Gemolong Sragen in order to analyze the workload and labor requirements in the filing, made the job description for each piece and give motivation to the filing officer to speed up the provision of medical record documents.Keywords: Workload, Filing Officer, Document Delivery TimeAbstrakJumlah petugas Filing di RSUI Yakssi Gemolong Sragen sebanyak 5 orang. Berdasarkan survei pendahuluan, rata-rata waktu penyediaan dokumen rekam medis rawat jalan di RSUI Yakssi Gemolong Sragen adalah 13,5 menit di mana waktu tersebut belum sesuai dengan standar pelayanan minimal yang ditetapkan oleh KepMenKes RI No: 129/Menkes/SK/II/2008. Tujuan penelitian ini adalah untuk mengetahui pengaruh beban kerja petugas filing terhadap rata-rata waktu penyediaan dokumen rekam medis di RSUI Yakssi Gemolong Sragen. Jenis penelitian yang digunakan adalah penelitian analitik dengan menguji pengaruh beban kerja petugas filing terhadap rata-rata waktu penyediaan dokumen rekam medis di RSUI Yakssi Gemolong Sragen. Metode pengumpulan data yang digunakan yaitu observasi dan wawancara, sedangkan instrumen yang digunakan adalah pedoman observasi, pedoman wawancara dan lembar pengamatan waktu penyediaan dokumen. Data yang diperoleh akan dianalisis bivariat dengan Regresi Linier Sederhana. Hasil analisis menunjukkan bahwa beban kerja petugas filing berpengaruh sangat kuat terhadap rata-rata waktu penyediaan dokumen rekam medis rawat jalan di RSUI Yakssi Gemolong Sragen. Berdasarkan hasil analisis tersebut, penulis memberikan saran untuk RSUI Yakssi Gemolong Sragen agar melakukan analisis beban kerja dan kebutuhan tenaga kerja di filing, membuat job description untuk masing-masing bagian dan memberikan motivasi kepada petugas filing untuk meningkatkan kecepatan penyediaan dokumen rekam medis. Kata kunci:Beban Kerja, Petugas Filing, Waktu Penyediaan Dokumen
Analisis Perbedaan Tarif Riil Rumah Sakit dengan Tarif Ina-CBG’s Berdasarkan Kelengkapan Medis Pasien Rawat Inap pada Kasus Persalinan Sectio Caesarea guna Pengendalian Biaya Rumah Sakit TNI AU Dr. M. Salamun Bandung Rizqy Dimas Monica; Fathia Mawar Firdaus; Intan Puji Lestari; Yesti Suryati; Dini Rohmayani; Ayu Hendrati
Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Vol 9, No 1 (2021)
Publisher : Asosiasi Perguruan Tinggi Rekam Medis dan Informasi Kesehatan Indonesia- APTIRMIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33560/jmiki.v9i1.289

Abstract

Berdasarkan studi pendahuluan yang dilakukan penulis di Instalasi Rekam Medis RS AU TNI Dr. M. Salamun Bandung, ditemukan perbedaan tarif riil rumah sakit dan tarif INA-CBG’s yang dilihat dari software INA-CBG’s terhitung dari bulan Januari sampai dengan Desember Tahun 2019 memiliki perbedaan tarif yang signifikan sehingga dapat menyebabkan kerugian pada pihak rumah sakit.Metode penelitian yang digunakan adalah metode deskriptif dengan pendekatan kuantitatif. Teknik pengumpulan data yaitu dengan observasi, wawancara, studi kepustakaan, dan dokumentasi. Instrumen penelitian menggunakan tabel pengolah data, pedoman wawancara, dan alat tulis.Hasil penelitian dari 77 pasien rawat inap pada kasus persalinan sectio caesarea yang diteliti penulis menemukan 4 pasien (5,20%) yang tarif INA-CBG’s melebihi tarif riil rumah sakit, dan 73 pasien (94,80%) yang tarif INA-CBG’s kurang dari tarif riil rumah sakit. Hal ini menunjukan bahwa selisih antara tarif riil rumah sakit dengan tarif INA-CBG’s tidak sedikit sehingga dapat merugikan bagi rumah sakit, maka rumah sakit harus melakukan pengendalian biaya dengan menerapkan standarisasi pelayanan agar biaya rumah sakit menjadi efisien dan mengurangi variasi dalam pelayanan sehingga biaya lebih mudah di prediksi serta pelayanan lebih terstandarisasi. Kesimpulan dan saran yang diberikan sebaiknya upaya pengendalian biaya dengan menerapkan standarisasi pelayanan dan melakukan evaluasi bulanan dengan pihak terkait baik dokter yang memberikan pelayanan atau seluruh PPA (Profesional Pemberi Asuhan) terhadap pasien rawat inap kasus persalinan sectio caesarea serta reward dan punishment terhadap pelaksanaan standarisasi pelayanan. Kata Kunci : Tarif riil rumah sakit, tarif INA-CBG’s, Persalinan, Sectio CaesareaKepustakaan: 56, 2004-2020
Fuzzy Logic for Determination of Community Assistance Using the Tsukamoto Method for Residents of Kasreman Village, Rembang Dahlan Dahlan; Dini Rohmayani; Rachmat Iskandar
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7162

Abstract

The obstacle to regional progress and the main cause of social problems is due to the large number of poor people, so there must be a poverty management program by the government, one of which is citizen assistance. The selection process by the local village apparatus is very much needed in the process of determining the recipients of citizen assistance, because the quota for the recipients of citizen assistance is less than that of registrants for citizen assistance. The distribution of aid does not fall to the right party resulting in injustice to other underprivileged families so that it creates several problems, where the method that will be used is Tsukamoto's Fuzzy Logic. In this study, the data used are land area, income of residents, number of dependents of the family. The evaluation method carried out in this study is using a confusion matrix, for one test the level of accuracy produced is 92.74%. Based on the experiment, it can be concluded that the Tsukamoto algorithm is quite accurate in determining citizen assistance to the residents of Kasreman Village, Rembang.
Aplikasi Cargiver Berbasis Mobile Renol Burjulius; Dini Rohmayani; Sonty Lena; Lutfi Samsul Ma’arif
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8419

Abstract

Abstrak - Perawatan jangka panjang (PJP) bagi lansia sangat dibutuhkan untuk mempertahankan tingkat kemandirian, mengurangi ketergantungan, dan mencegah komplikasi penyakit. Untuk mencari perawat lansia tidak bisa sembarangan dan harus sesuai dengan kriteria. Pemesanan perawat yang dilakukan secara manual tanpa adanya aplikasi belum sesuai dengan yang diinginkan dimana terdapat beberapa permasalahan yaitu  pengguna jasa harus menggambarkan kriteria Cargiver yang diinginkan kemudian admin harus mengirimkan informasi Cargiver sesuai kriteria kepada pengguna jasa,  serta admin mengalami  kesulitan dalam melakukan pengecekan perawat yang tersedia jika permintaan Cargiver sedang banyak. Dari permasalahan tersebut penulis membuat aplikasi Cargiver berbasis Mobile yang dapat memudahkan calon Cargiver untuk melamar pekerjaan, pengguna jasa dalam pemesanan perawat serta membantu admin Yayasan dalam pengelolaannya. Dalam penelitian ini penulis menggunakan metode Software Development Life Cycle (SDLC) dengan model Waterfall.Tahapan Waterfall yang terdiri dari 4 (empat) tahap yaitu requirement analysis, design, implementation dan testing. Sistem ini dibangun menggunakan framework flutter serta menggunakan library CSS dan PHP versi 7.2 serta basis data MySQL. Dari hasil pengujian Black Box testing untuk 3 (tiga) hak akses yaitu, User, pelamar dan admin setiap menu telah sesuai dan berhasil dengan persentasi 100%. Hasil pengujian User Acceptance Test (UAT) menunjukkan nilai rata-rata sebesar 88,61%, nilai tersebut diperoleh dari nilai rata-rata desain (88.42%), Fitur (89.82%), dan Kepuasan (90.88%).Kata kunci: Android Studio, Cargiver, Waterfall, Flutter, MySQL. Abstract - Long-term care (LTC) for the elderly is very much needed to maintain the level of independence, reduce dependency, and prevent complications of the disease. Finding an elderly nurse cannot be arbitrary and must be in accordance with the criteria. Ordering a nurse manually without an application is not in accordance with what is desired where the problem is that the service user must describe the desired Cargiver criteria then the admin must send Cargiver information according to the criteria to the service user, and the admin has difficulty in checking the available nurses if the Cargiver request is high. From these problems, the author created a Mobile-based Cargiver application that can make it easier for prospective Cargivers to apply for jobs, service users in ordering nurses and assisting the Foundation admin in managing it. In this study, the author used the Software Development Life Cycle (SDLC) method with the Waterfall model. The Waterfall stages consist of 4 (four) stages, namely requirement analysis, design, implementation and testing. This sistem is built using the flutter framework and uses the CSS and PHP libraries version 7.2 and the MySQL database. From the results of the Black Box testing for 3 (three) access rights, namely, User, applicant and admin, each menu has been appropriate and successful with a percentage of 100%. The results of the User Acceptance Test (UAT) showed an average value of 88.61%, the value was obtained from the average value of design (88.42%), Features (89.82%), and Satisfaction (90.88%).Keywords: Android Studio, Cargiver, Waterfall, Flutter, MySQL.
Interpretable Deep Learning Model for Grape Leaf Disease Classification Based on EfficientNet with Grad-CAM Visualization Castaka Agus Sugianto; Dini Rohmayani; Jhoanne Fredricka; Mohamed Doheir
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2745

Abstract

Grape leaf diseases pose a significant threat to agricultural productivity, especially in regions with fluctuating climatic conditions that create favorable environments for pathogen growth. Early and accurate disease detection is essential for preventing severe crop losses. Traditional manual inspection methods are inefficient and prone to human error, highlighting the need for an automated approach. This study proposes a computer vision-based solution using Convolutional Neural Networks (CNN) improved by EfficientNetB0 to classify grape leaf diseases. The model was trained on a publicly available dataset from Kaggle, which consists of 9,027 images in four classes: ESCA, Leaf Blight, Black Rot, and Healthy. Each image has a resolution of 300 × 300 pixels with a 24-bit color depth, ensuring sufficient detail for analysis. To enhance model performance, data augmentation and hyperparameter tuning were applied. The EfficientNetB0 model was employed due to its strong feature extraction capabilities and computational efficiency. The proposed model achieved 99.36% accuracy, with evaluation metrics including precision (99%), recall (99%), and F1-score (99%), demonstrating its reliability in distinguishing disease categories. Further analysis using a confusion matrix and Grad-CAM visualization provided insights into the model’s decision-making process. The results indicate that this deep learning-based approach is highly effective for grape leaf disease classification. Future research can explore real-time field data collection, attention mechanisms, and self-supervised learning to further improve classification accuracy and model generalization for large-scale agricultural applications.