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All Journal ILKOM Jurnal Ilmiah Ilmu Komputer untuk Masyarakat
Kasmira Kasmira
Universitas Muslim Indonesia
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Bimbingan Teknis Pemanfaatan xSIA untuk Pelaporan Akademik Siswa di SDN No. 133 Kabupaten Takalar Poetri Lestari Lokapitasari Belluano; Purnawansyah Purnawansyah; Yudha Islami Sulistya; La Saiman; Kasmira Kasmira
Ilmu Komputer untuk Masyarakat Vol 2, No 1 (2021)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (202.406 KB) | DOI: 10.33096/ilkomas.v2i1.1001

Abstract

Sistem Informasi Akademik (xSIA) adalah sistem yang dibangun untuk mengelola data-data peserta ajar sehingga memberikan kemudahan kepada pengguna dalam hal ini adalah Guru dalam kegiatan administrasi akademik secara online. Sekolah perlu menyediakan layanan sistem informasi akademik dalam bentuk web application dimana Guru secara mandiri dapat melaksanakan pelaporan akademik siswa untuk kebutuhan sinkronisasi data Pelaporan Kinerja Guru (PKG) DAPODIK. Kemudahan dalam mengakses sistem informasi akademik mulai dari level Guru, Operator Sekolah sampai Kepala Sekolah diperlukan, sehingga pengembangkan xSIA untuk tingkat Pendidikan Dasar dan Menengah diterapkan sesuai spesifikasi User Experience (UX) dan Developer Experience (DX). Program Kemitraan Masyarakat (PKM) berupa bimtek pemanfaatan xSIA yang diikuti oleh Guru dilaksanakan dengan model latihan Preceptorship dan Partisipatif. sedangkan tahap peran DAPODIK dengan aplikasi digunakan model Prototyping untuk merepresentasikan secara grafis alur kerja sistem. Target luaran berupa aplikasi berbasis web xSIA untuk pelaporan data akademik siswa.
The development of Web-based information system using quick UDP internet connection Poetri Lestari Lokapitasari Belluano; Benny Leonard Enrico Panggabean; Purnawansyah Purnawansyah; Kasmira Kasmira
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1134.314-322

Abstract

The Academic Information System (xSIA) is built to its users to manage Study Program modules, including student academic grades. xSIA applying the Moodle Learning Management System (LMS) was developed by implementing Quick UDP Internet Connection (QUIC) technology with the HTTP/3 protocol which can demonstrate protocol transaction speed performance. The design of information systems and databases employs the Convention Over Configuration paradigm. The Prototyping Model is used to graphically represent the workflow of the system with an experimental research design. System modeling utilizes Unified Modeling Language (UML) tools, Data Base Management System (DBMS) using PostgreSQL, and UDP ports as a means of data communication. The implementation of Quick UDP Internet Connection (QUIC) on the xSIA moodle LMS is effective for real-time communications that do not require conditions to open, maintain, or terminate connections as in streaming video conference. It is also optimal because the UDP data is transferred individually and checked for its integrity upon arrival. When a video streaming transaction last 02:36 seconds with a file size of 4.1mb, there is a significant difference of 100.98ms in the waiting time to first byte (ttfb).
Analysis of the Ensemble Method Classifier's Performance on Handwritten Arabic Characters Dataset Abdul Rachman Manga'; Anik Nur Handayani; Heru Wahyu Herwanto; Rosa Andrie Asmara; Yudha Islami Sulistya; Kasmira Kasmira
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1357.186-192

Abstract

Arabic character handwriting is one of the patterns and characteristics of each person's writing. This characteristic makes Arabic writing more challenging if the letter recognition process is based on a dataset of Arabic scripts. This Arabic script has been presented in a dataset totaling 16800, each representing a class of hijaiyah letters starting from alif to yes, consisting of 600 data for each class. The accuracy of the data used can be increased using the ensemble method. By using multiple algorithms at simultaneously, the ensemble technique can raise the level or result of a score in machine learning. This study's primary goal is to evaluate the ensemble method classifier's performance on datasets of handwritten Arabic characters. The classifier uses the ensemble method by applying the proposed soft voting to provide a multiclass classification of three machine learning algorithms, namely, SVM, Random Forest, and Decision Tree for classification. This research process produces an accuracy value for the voting classifier of 0.988 and several other SVM algorithms with an accuracy of 0.103, a random forest with an accuracy of 1.0, and a decision tree with an accuracy of 0.134. The test results used the confusion matrix evaluation model, including accuracy, precision, recall, and f1-score of 0.99.
Co-Authors Abdul Rachman Manga' Anik Nur Handayani Benny Leonard Enrico Panggabean Heru Wahyu Herwanto La Saiman Poetri Lestari Lokapitasari Belluano Purnawansyah Purnawansyah Rosa Andrie Asmara Yudha Islami Sulistya