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Journal : The Indonesian Journal of Computer Science

Sistem Informasi Perpustakaan Berbasis Website Menggunakan Repository Pattern Agung Stiven Cahyati Angely; Lapatta, Nouval Trezandy; Syahrullah; Andi Hendra; Ryfial Azhar
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4332

Abstract

Pengembangan Sistem Informasi Perpustakaan Sekolah menggunakan Metode Repository Pattern bertujuan meningkatkan efisiensi dan efektivitas pengelolaan perpustakaan yang sebelumnya dilakukan secara konvensional. Sistem informasi berbasis web ini mempermudah pencatatan peminjaman, pengembalian buku, dan pengelolaan data lainnya. Hasil penelitian menunjukkan peningkatan kualitas pengelolaan perpustakaan, dengan akses informasi yang lebih cepat dan akurat. Mayoritas pengguna menyatakan puas dengan antarmuka sistem yang intuitif dan user-friendly. Sistem ini juga memudahkan pustakawan dan admin dalam mengelola data, mencatat peminjaman dan pengembalian buku, serta mengorganisasikan kategori buku. Kesimpulannya, penerapan sistem informasi perpustakaan berbasis web dengan metode Repository Pattern di SMK Negeri 3 Palu berhasil meningkatkan efektivitas dan efisiensi pengelolaan perpustakaan, serta memberikan kemudahan akses bagi pengguna.
Rancang Bangun Aplikasi Diagnosa Sexually Transmitted Diseases Menggunakan Algoritma Certainty Factor Mandra; Nouval Trezandy Lapatta; Syaiful Hendra; Syahrullah
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4293

Abstract

This research aims to design and develop an Android application that can be used to diagnose results Sexually Transmitted Diseases using algorithms Certainty Factor. Sexually Transmitted Diseases is a sexually transmitted disease that can cause serious health impacts if not immediately identified and treated appropriately. This application is designed to help users carry out initial diagnoses independently. The method used in developing this application is the Certainty Factor algorithm, which is a rule-based decision support method. This algorithm utilizes knowledge from experts in the medical field and combines it with symptom data provided by users to produce more accurate diagnoses. The app will allow users to input suggested symptoms and generate a diagnosis based on that information. It is hoped that this application will be a useful tool in a self-directed approach to diagnosis Sexually Transmitted Diseases.
Analisis Sentimen Terhadap Presiden Terpilih Dimedia Sosial Twitter (X) Menggunakan Algoritma Support Vector Machine Ono, Jumaita; Anshori , Yusuf; Yudhaswana Joefrie , Yuri; Yazdi Pusadan, Mohammad; Syahrullah
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4388

Abstract

The current elected presidents of Indonesia are Prabowo and Gibran, with several work programs and visions and missions that are still being discussed on various social media, especially on Twitter. Based on the problems in this research, the Support Vector Machine method was applied with the dataset used amounting to 2000 data obtained from Twitter social media using scraping techniques, and divided into five scenarios, namely positive, very positive, neutral, negative and very negative. Data were tested from 100 datasets, 500 datasets, 1000 datasets, 1500 datasets, and 2000 datasets. The accuracy results obtained from 100 data were 0.40% accuracy, 0.08% precision, and 0.20% recall. The second test used 500 data with an accuracy of 0.67%, precision of 0.33% and recall of 0.24%. The third test used 1000 data with an accuracy of 0.73%, precision of 0.52% and recall of 0.29%. The fourth test used 1500 data with an accuracy of 0.74%, precision of 0.41% and recall of 0.29%. The fifth test with the highest level of accuracy uses 2000 data, with an accuracy of 0.75%, precision of 0.47%, and recall of 0.30%