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All Journal Jurnal EECCIS Respati Journal of Soft Computing Exploration
Sumarni Adi, Sumarni
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Computer Science & IT Engineering

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SISTEM INFORMASI AKADEMIK SISWA SMP NEGERI BASIS KKM DALAM BENTUK RAPORT Adi, Sumarni
Jurnal Teknologi Informasi RESPATI Vol 10, No 29 (2015)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (762.975 KB) | DOI: 10.35842/jtir.v10i29.135

Abstract

ABSTRAKSistem informasi akademik dalam bentuk raport siswa merupakan sebuah sistem yang digunakan untuk mencatat nilai siswa, yang outputnya adalah raport siswa yang terstandarisasi menurut instansi pendidikan Sekolah Menengah Pertama (SMP), Sistem ini menggabungkan antara nilai mata pelajaran, nilai extrakurikuler, nilai pembiasaan dan nilai perilaku serta ketidak hadiran siswa menjadi satu kesatuan dalam database yang ditampilkan menggunkan interface, sehingga menghasilkan bentuk raport siswa untuk mengetahui prestasi akademik maupun non akademik siswa.Kata Kunci : sistem informasi, databse, nilai
Eye disease classification using deep learning convolutional neural networks Rachmawanto, Eko Hari; Sari, Christy Atika; Krismawan, Andi Danang; Erawan, Lalang; Sari, Wellia Shinta; Laksana, Deddy Award Widya; Adi, Sumarni; Yaacob, Noorayisahbe Mohd
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.493

Abstract

This study begins with the analysis of the growing challenge of accurately diagnosing eye diseases, which can lead to severe visual impairment if not identified early. To address this issue, we propose a solution using Deep Learning Convolutional Neural Networks (CNNs) enhanced by transfer learning techniques. The dataset utilized in this study comprises 4,217 images of eye diseases, categorized into four classes: Normal (1,074 images), Glaucoma (1,007 images), Cataract (1,038 images), and Diabetic Retinopathy (1,098 images). We implemented a CNN model using TensorFlow to effectively learn and classify these diseases. The evaluation results demonstrate a high accuracy of 95%, with precision and recall rates significantly varying across classes, particularly achieving 100% for Diabetic Retinopathy. These findings highlight the potential of CNNs to improve diagnostic accuracy in ophthalmology, facilitating timely interventions and enhancing patient outcomes. For future research, expanding the dataset to include a wider variety of ocular diseases and employing more sophisticated deep learning techniques could further enhance the model's performance. Integrating this model into clinical practice could significantly aid ophthalmologists in the early detection and management of eye diseases, ultimately improving patient care and reducing the burden of ocular disorders.
Naive Bayes Method to Analyze Sentiment Accuracy on YouTube Comments Rahman, Aditiya; Rahmat, Fadhil; Adi, Sumarni
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 14 No. 1 (2020)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v14i1.627

Abstract

The revolution on social media has attracted users to video sharing sites like YouTube. This site is the most popular social media site where people see, share and interact by commenting on videos. There are various types of videos shared by users such as songs, movie trailers, news, entertainment etc. Some time ago the most trending video was a video about World War III (WWIII / WW3). Analyzing comments from videos about WW3 gives viewers opinions about WW3. Study the sentiments expressed in this commentary whether WW3 gets positive or negative feedback. The machine learning algorithm, Naive Bayes, is used in comments to find out its sentiments. The test results of 1500 data produced 30.3% positive sentiment and 60.6% negative sentiment, with an accuracy of 78.17%.
Co-Authors Christy Atika Sari Eko Hari Rachmawanto Krismawan, Andi Danang Laksana, Deddy Award Widya Lalang Erawan Rahman, Aditiya Rahmat, Fadhil Wellia Shinta Sari Yaacob, Noorayisahbe Mohd