Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Kecerdasan Buatan dan Teknologi Informasi Journal of Computer Science and Informatics (JOCSI)
Pramestiawan, Rico
Unknown Affiliation
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
COMPARATIVE STUDY OF CLASSIFICATION MODELS IN PROCESSING STUDENT TEST SCORES DATASETS Pramestiawan, Rico; Verdian, Arry; Bhuana, Chindu Lintang; Susanto, Lilik Joko
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.475

Abstract

The development of Machine Learning (ML) has contributed significantly to the field of education, particularly in analyzing student academic data to support data-driven decision-making. Predicting student exam results is important for identifying academic performance patterns, detecting potential failures, and improving learning interventions. However, variations in student characteristics and dataset complexity require the selection of appropriate classification models to achieve optimal prediction performance. This study aims to compare the effectiveness of several ML classification models in predicting student exam results using a student academic dataset. The dataset consists of 306 records, seven attributes, and five grade classes (A, B, C, D, and E), including attendance, quiz scores, midterm examination scores, final examination scores, and assignment scores. Data preprocessing was conducted to handle missing values, duplication, inconsistencies, and outliers. The dataset was split into training and testing data with a ratio of 75:25 and evaluated using 10-fold cross-validation. Several classification models were applied, including k-Nearest Neighbour (kNN), Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that Random Forest achieved the best performance with an accuracy of 73.9%, precision of 74.0%, recall of 73.9%, and F1-score of 73.9%, followed by Naive Bayes and Decision Tree. Meanwhile, SVM produced the lowest performance among the tested models. The findings indicate that Random Forest is the most effective method for predicting student exam results and has strong potential to support educational decision-making systems.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION (GINI DECREASE AND RELIEF-F) IN HEART DISEASE DATASET Bhuana, Chindu Lintang; Pramestiawan, Rico; Susanto, Lilik Joko; Verdian, Arry
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.477

Abstract

Heart disease remains one of the leading causes of mortality worldwide and presents a major challenge in healthcare systems. Early detection plays an essential role in improving survival rates and minimizing complications through timely intervention. Recent advances in Machine Learning (ML) have provided new opportunities for developing accurate and efficient prediction systems for heart disease detection. However, one of the major challenges in ML-based prediction is identifying the most relevant features to improve classification performance while reducing computational complexity and noise. This study aims to evaluate the effectiveness of two feature selection techniques, namely Gini Decrease (GD) and ReliefF, combined with several ML models, including Support Vector Machine (SVM), Tree, Naïve Bayes, and Random Forest, for heart disease classification. The study employed the UCI Heart Disease Dataset consisting of 303 records and 14 attributes. Data preprocessing included handling missing values using mean imputation, followed by feature selection and classification using 10-fold cross-validation with an 80:20 training-testing ratio. Experimental results showed that ReliefF outperformed GD, achieving the highest average accuracy of 0.796, compared to GD with 0.767 and all features with 0.771. The SVM model achieved the highest accuracy using GD (0.833), while Random Forest demonstrated optimal performance with ReliefF (0.817). Furthermore, the Tree model exhibited the fastest computational time among all evaluated models. These findings indicate that integrating suitable feature selection methods with ML models significantly enhances heart disease classification performance, particularly in improving predictive accuracy and computational efficiency for early medical diagnosis applications.
Co-Authors Bhuana, Chindu Lintang Helmiyah, Siti Ratnasari, Mei Sasmito, Angger Susanto, Lilik Joko Trinovita, Diyah Verdian, Arry