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All Journal JSiI (Jurnal Sistem Informasi) Jurnal Sistem Komputer & Kecerdasan Buatan
Kisnu Darmawan, Aang
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Pengembangan Sistem Informasi Manajemen Skripsi Berbasis Web: Pengembangan Sistem Informasi Manajemen Skripsi Berbasis Web Khoir, Afiful; Hoiriyah, Hoiriyah; Kisnu Darmawan, Aang
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 8 No. 1 (2024): Volume VIII - Nomor 1 - September 2024
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v8i1.657

Abstract

Abstrak Skripsi merupakan salah satu syarat untuk mendapatkan gelar sarjana. Mahasiswa harus menyelesaikan skripsi sebagai mata kuliah akhir agar memenuhi syarat tersebut. Di Universitas Islam Madura (UIM), setiap fakultas umumnya melalui beberapa tahapan dalam pelaksanaan ujian skripsi, yaitu pengajuan judul skripsi, pengajuan proposal skripsi, seminar proposal skripsi, penelitian, dan sidang skripsi. Namun, proses ini sering kali terhambat oleh ketiadaan sistem informasi yang memadai, sehingga memperlambat pengerjaan skripsi. Penelitian ini bertujuan untuk mengembangkan sistem informasi manajemen skripsi berbasis web dengan studi kasus di Universitas Islam Madura (UIM), menggunakan metode pengembangan sistem waterfall. Pengumpulan data dilakukan melalui observasi dan wawancara. Hasil penelitian menunjukkan bahwa sistem manajemen skripsi berbasis web yang dihasilkan dapat memberikan kemudahan bagi Universitas Islam Madura (UIM) dan mahasiswa dalam proses penulisan skripsi. Pengujian aplikasi menggunakan metode blackbox testing menunjukkan bahwa aplikasi tersebut valid. Hal ini membuktikan bahwa sistem ini berfungsi dengan baik dan memberikan kepuasan yang tinggi kepada pengguna. Keywords: Skripsi, Universitas Islam Madura, Sistem Informasi, Website
APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS (CNN) FOR HEPATITIS C VIRUS (HCV) DISEASE DETECTION Fathorrozi, Fathorrozi Ariyanto; Ariyanto, Fathorrozi; Maulana, Indra; Hamzah, Moh. Aminollah; Kisnu Darmawan, Aang
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10542

Abstract

Hepatitis C is a disease that attacks the liver and can progress to more serious conditions, such as cirrhosis or liver cancer, if not diagnosed and treated properly. Conventional diagnostic methods for Hepatitis C often face challenges in terms of efficiency and accuracy, so an innovative AI-based approach is needed to improve early detection. In this study, we apply a 1D Convolutional Neural Network (CNN) to classify Hepatitis C patients, using a dataset from Kaggle consisting of 615 samples with various medical parameters. The dataset goes through a series of preprocessing stages, including data cleaning, normalization, and feature transformation, before being applied to a 1D CNN model. The model is trained using the Adam optimizer, with ReLU activation functions in the convolution layer and sigmoid in the output layer. Model performance is evaluated through accuracy, precision, recall, and F1-score metrics. The results show that the developed 1D CNN model achieves an accuracy of 75% in detecting Hepatitis C. Although these results show promising potential, there is still room for improvement through exploration of more complex architectures or the use of larger datasets. Thus, this research is expected to make artificial intelligence an effective tool in the diagnosis of Hepatitis C, increasing accuracy and efficiency in the process. Keywords: Hepatitis C, 1D CNN, Deep Learning, Disease Classification, Medical Diagnosis
Co-Authors Fathorrozi Ariyanto Hamzah, Moh Aminollah Hoiriyah, Hoiriyah Indra Maulana, Indra Khoir, Afiful