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All Journal Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Journal of Computer Science and Informatics (JOCSI)
Pramestiawan, Rico
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Computer Science & IT

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IMPLEMENTASI MANAJEMEN TABUNGAN SISWA PADA RAUDLATUL ATHFAL SUBULUSSALAM BERBASIS WEB MOBILE Sasmito, Angger; Pramestiawan, Rico; Ratnasari, Mei; Trinovita, Diyah
Journal of Computer Science and Informatics (JOCSI) Vol 2 No 2 (2025): February
Publisher : EDU PARTNER INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69747/jocsi.v2i2.80

Abstract

Di masa kini, teknologi berkembang dengan sangat cepat, terutama teknologi informasi, dan memengaruhi banyak aspek kehidupan, termasuk pendidikan. RA Sabulussalam adalah lembaga pendidikan Islam yang berlokasi di Kampung Sri Purnomo, Kecamatan Kalirejo, Kabupaten Lampung Tengah. Selama ini, lembaga ini membantu sistem penyimpanan data yang masih berupa kertas dan buku, yang membuat dokumen kurang aman dan terjamin penyimpanannya. Salah satu masalah yang muncul dengan metode ini adalah seringnya kehilangan buku tabungan karena buku-buku kecil itu mudah sobek dan kotor. Untuk merancang buku-buku yang lebih besar, lembaga ini harus menggunakan Diagaram Arus Data (DAD), Diagaram Konteks, dan Diagaram Arus Data (DFD), serta Flowchart adalah komponen sistem yang digunakan untuk menggambarkan model sistem SDLC (Software Development Life Cycle). Selanjutnya, desain web dilakukan dengan menggunakan Macromedia Dreamweaver, Xampp, web browser, dan Photoshop. Hasil penelitian berikut menunjukkan bahwa proses menabung siswa saat transaksi berlangsung dan pencetakan laporan, serta proses penarikan atau setor uang siswa saat transaksi berlangsung dan pencetakan laporan. Kelemahan sistem ini termasuk masalah teknis seperti tampilan data tidak fullscreen pada smartphone Android. Nowadays, technology is developing very rapidly, especially information technology, and affects many aspects of life, including education. RA Sabulussalam is an Islamic educational institution located in Kampung Sri Purnomo, Kalirejo District, Central Lampung Regency. So far, this institution has helped the data storage system which is still in the form of paper and books, which makes documents less safe and secure in storage. One of the problems that arises with this method is the frequent loss of savings books because the small books are easily torn and dirty. To design larger books, this institution must use Data Flow Diagrams (DAD), Context Diagrams, and Data Flow Diagrams (DFD), and Flowcharts are system components used to describe the SDLC (Software Development Life Cycle) system model. Furthermore, web design is done using Macromedia Dreamweaver, Xampp, web browsers, and Photoshop. The following research results show that the process of saving students during transactions and printing reports, as well as the process of withdrawing or depositing student money during transactions and printing reports. The weaknesses of this system include technical problems such as the data display is not fullscreen on Android smartphones.
Analisis Komparatif Algoritma Machine Learning dengan Metrik Akurasi, Presisi, Recall, dan F1-Score pada Dataset Kacang Kering Helmiyah, Siti; Pramestiawan, Rico
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.2031

Abstract

This study aims to compare the performance of five machine learning algorithms in classifying dry bean varieties as an effort to support quality detection systems for agricultural products. Issues related to authenticity and food safety that frequently occur, such as rice adulteration, highlight the importance of fast and accurate methods for variety identification. The study utilizes the Dry Bean Dataset from the UCI Machine Learning Repository, which consists of 13,611 samples with 16 numerical features and 7 classes of bean varieties. Five algorithms were tested, including K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). The data were divided into 80% for training and 20% for testing, and evaluated using accuracy, precision, recall, and F1-Score metrics. The results show that the SVM algorithm achieved the best performance with an accuracy of 92.43% and an F1-Score of 93.61%, followed by Logistic Regression and Random Forest. The confusion matrix analysis indicates that most varieties were correctly classified, although some misclassifications occurred among classes with similar morphological characteristics such as Dermason, Seker, and Sira. Based on these findings, it can be concluded that selecting the appropriate algorithm is crucial in applying machine learning for agricultural product classification. Evaluation using multiple metrics provides a more comprehensive performance overview compared to relying solely on accuracy. This approach has the potential to support more efficient automation in the identification of agricultural product varieties.
Co-Authors Helmiyah, Siti Ratnasari, Mei Sasmito, Angger Trinovita, Diyah