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All Journal Informatika Journal of Computer Science and Information Systems (JCoInS) Journal of Student Development Information System (JoSDIS)
Angga Putra Juledi
Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Library & Information Science Other

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PERANCANGAN SISTEM INFORMASI AKADEMIK SMA PERTIWI 2 PADANG MENGGUNAKAN BAHASA PEMOGRAMAN PHP DAN MYSQL Angga Putra Juledi
Jurnal Informatika Vol 9, No 2 (2021): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v9i2.1988

Abstract

Technology is growing rapidly in the midst of society today, making people competing to master and implement it in everyday life such as students. Students are entitled to master the current technology activity, not just to earn income or mere skill, but science is currently used for things that lead to advancements and skills that are useful for the future. Similarly, the teachers and students, for the ease and smoothness of teaching and learning process, it takes a simple system application and can help the learning process in teaching and learning activities. With the development of technology in the world of computerization and application of Academic Information system applications in SMA Pertiwi 2 Padang can help manage academic data effectively and efficiently because it can be accessed online and academic data stored electronically.
Analisis Pola Tidur Dalam Produktivitas Belajar Menggunakan Algoritma Decision Tree Dan Random Forest Pada Mahasiswa Universitas Labuhanbatu Lisa Ariani; Angga Putra Juledi; Syaiful Zuhri Harahap; Sudi Suryadi
Journal of Computer Science and Information System(JCoInS) Vol 7, No 3: JCoIns | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i3.9706

Abstract

Sleep patterns are one of the important factors that support cognitive function and students' learning productivity. Irregular sleep habits, insufficient sleep duration, poor sleep quality, and excessive use of gadgets at night can affect students' learning productivity. This study aims to analyze the influence of sleep patterns on the learning productivity of students at Universitas Labuhanbatu using the Decision Tree and Random Forest algorithms, as well as to compare the performance of both algorithms in classification tasks. The variables used in this study consist of Sleep Duration (X1), Sleep Quality (X2), Sleep Time (X3), Sleep Consistency (X4), and Nighttime Gadget Usage (X5) as independent variables, while Learning Productivity (Y) serves as the dependent variable. The research data were collected through questionnaires distributed to 80 students, which were then divided into 50 training data and 30 testing data. Data processing was carried out using the Orange Data Mining application through the stages of data preprocessing, model construction, and model evaluation using the Test and Score method with the evaluation metrics of Area Under Curve (AUC), Classification Accuracy (CA), F1-Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The results showed that the Decision Tree algorithm achieved an AUC of 0.931, CA of 0.920, F1-Score of 0.921, Precision of 0.925, Recall of 0.920, and MCC of 0.834. Meanwhile, the Random Forest algorithm achieved an AUC of 0.986, CA of 0.920, F1-Score of 0.921, Precision of 0.925, Recall of 0.920, and MCC of 0.834. Based on these results, the Random Forest algorithm demonstrated better performance in terms of the AUC value, making it more suitable for classifying students' learning productivity based on sleep patterns.
Analisis Tingkat Kepuasan Mahasiswa Pada Aplikasi Sistem Informasi Terpadu (SITU) di Universitas Labuhanbatu Menggunakan Metode Naïve Bayes Dan Decision Tree Rista Andini Ritonga; Syaiful Zuhri Harahap; Irmayanti Irmayanti; Angga Putra Juledi
Journal of Computer Science and Information System(JCoInS) Vol 7, No 3: JCoIns | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i3.9708

Abstract

The Integrated Information System (SITU) is an application used to support various academic services at Universitas Labuhanbatu. The quality of services provided by this application needs to be evaluated to determine the level of student satisfaction as its users. This study aims to analyze student satisfaction with the use of the Integrated Information System (SITU) using the Naïve Bayes and Decision Tree methods. The research applies the Knowledge Discovery in Databases (KDD) process, which consists of Selection, Preprocessing, Transformation, Data Mining, Evaluation, and Interpretation. The research data were collected through questionnaires distributed to 50 students of the Faculty of Science and Technology at Universitas Labuhanbatu. The research variables include System Ease of Use, System Access Speed, Information Accuracy, User Interface, and System Reliability, while the target variable is the student satisfaction level, classified into Satisfied and Dissatisfied categories. The classification process was carried out using Orange Data Mining software and evaluated using a Confusion Matrix. The interpretation results based on 27 testing data showed that the Decision Tree algorithm classified 19 instances as Satisfied and 8 instances as Dissatisfied, while the Naïve Bayes algorithm classified 18 instances as Satisfied and 9 instances as Dissatisfied. Furthermore, the Confusion Matrix evaluation indicated that the Naïve Bayes method achieved a 96.8% prediction accuracy for the Satisfied category, outperforming the Decision Tree method, which achieved 81.6%. Based on these results, the Naïve Bayes method demonstrated superior classification performance in analyzing student satisfaction with the Integrated Information System (SITU). The findings of this study are expected to serve as a reference for Universitas Labuhanbatu in evaluating and improving the quality of services provided through the Integrated Information System (SITU).
Penerapan Algoritma Naïve Bayes Classifier dan Decision Tree untuk Memprediksi Tingkat Kepuasan Pelanggan Teras Coffe Rantauprapat Elfi Zahra Yuni; Syaiful Zuhri Harahap; Ibnu Rasyid Munthe; Angga Putra Juledi
Journal of Computer Science and Information System(JCoInS) Vol 7, No 3: JCoIns | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i3.9709

Abstract

The increasingly fierce competition in the coffee shop business requires business owners to understand customer satisfaction levels as a basis for improving service quality and maintaining customer loyalty. Therefore, an approach capable of accurately identifying and predicting customer satisfaction levels based on customer data is needed. Data mining is a data processing technique that can be used to discover patterns and important information from data sets to support the decision-making process. In this study, the Naïve Bayes Classifier and Decision Tree algorithms were used because both are classification methods capable of generating predictions based on the characteristics of the data. The research method used was a quantitative method by utilizing Teras Coffee Rantauprapat customer questionnaire data which was then processed using the Orange Data Mining application. The research data was divided into training data and testing data to build and test the classification models generated by both algorithms. The results showed that the Naïve Bayes and Decision Tree algorithms were able to classify customer satisfaction levels into satisfied and dissatisfied categories with a good level of accuracy. Based on the model evaluation results, the Naïve Bayes algorithm obtained superior performance compared to Decision Tree based on higher AUC, Precision, F1-Score, and MCC values. Thus, both algorithms can be applied to predict customer satisfaction levels, but Naïve Bayes proved more optimal in generating predictions on the dataset used in this study. The results of this study are expected to serve as a reference for Teras Coffee Rantauprapat in continuously improving service quality and customer satisfaction.
Penerapan Sistem Informasi Perpustakaan Berbasis Web Pada Perpustakaan Umum Rantauprapat Reyfo Irfankha; Tiara Syavitri Rambe; Marnis Nasution; Angga Putra Juledi; Syaiful Zuhri Harahap
Journal of Student Development Information System (JoSDIS) Vol 6, No 1: JoSDIS | Januari 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/josdis.v6i1.9022

Abstract

The implementation of a Web-Based Library Information System at the Rantauprapat Public Library aims to increase efficiency and effectiveness in managing library resources. This information system integrates various library functions, such as book cataloging, borrowing, returns, and member management, into a single web-based platform. By using web-based technology, libraries can make access easier for visitors to search for information and borrow online, without being limited by time and place. This research identifies the system's technical and functional requirements and evaluates its impact on library services and visitor satisfaction. The results of implementing this system show an increase in data accuracy, a reduction in service times, and a reduction in administrative errors. Apart from that, this system also makes it easy for visitors to access library information more efficiently. It is hoped that the implementation of this web-based library information system can become a model for other libraries in an effort to improve the quality of library information services in the digital era.
Analisis Data Penjualan Menggunakan Algoritma Apriori pada Analisis Kopi Tomi Hidayat; Ibnu Rasyid Munthe; Angga Putra Juledi
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6064

Abstract

Data Mining is a technique for finding, searching, or extracting new information or knowledge from a very large set of data, by integration or merging with other disciplines such as statistics, artificial intelligence, and machine learning, making Data Mining as one of the tools to analyze data and then produce useful information. Association Rule is a process in Data Mining to determine all associative rules that meet the minimum requirements for support (minsup) and confidence (minconf) in a database. In Association Rule, there are 2 methods that can be used, namely a priori method and FP-Growth method, where FP-Growth method is the development of a priori method where a priori method there are still some shortcomings such as there are many patterns of data combinations that often appear (many frequent patterns), many types of items but low minimum support fulfillment, it takes quite a long time because database scanning is done repeatedly to get the ideal frequent pattern. In this study the method used is a priori algorithm method, a priori algorithm method is one of the alternative ways to find the most frequently appearing data sets (frequent itemset) without using candidate generation that is suitable for analyzing a transaction data. Coffee analysis is a Cafe Shop engaged in the sale of food and beverages that many food and beverage sales transactions. Open on November 7, 2021 coffee analysis penetrates 245 sales transactions and this transaction data continues to grow every day.
Implementasi Data Mining Menggunakan Metode Algoritma FP-Growth Dan Algoritma Apriori Pada Toko IBR Jaya Untuk Meningkatkan Penjualan Restu Fauzy Naibaho; Syaiful Zuhri Harahap; Angga Putra Juledi
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6128

Abstract

Dian trading business is one of the grocery stores engaged in buying and selling the main household needs of nine basic ingredients which have been doing a lot of grocery sales transactions. This transaction Data continues to grow every day and in the IBR Jaya store sales transaction data is only presented as an archive or report and it is not mentioned what the benefits of these data are. Nah, the problem at the IBR Jaya store is the improvement of improvements due to the shortage of basic food stocks that are often purchased by consumers are not available which results in improvements and usability improvements then the FP-Growth algorithm is used to analyze patterns of improvement and a priori algorithms for comparison through archived transaction data goods that will be purchased later as a reference to increase food stocks so as to increase sales at the IBR Jaya Food Store in the hope that this increase can help this is one of many ways to make money online. Association rules are a process in Data Mining to establish all associative policies that meet the minimum requirements for support (minsup) and trust (minconf) in a database . In association rules, there are 2 methods that can be used, namely a priori method and FP-Growth method. In this study the method used is FP-Growth algorithm and a priori algorithm, FP-Growth algorithm and a priori method is a method to find the most frequently appearing data set (frequent itemset) without using candidate generation that is suitable to analyze a data transaction.
Rancang Bangun Sistem Informasi Penjualan Sepatu Menggunakan Bahasa Pemograman PHP Dan MySQL Pada Aman Store Rantau Prapat Nia Edi Putri; Syaiful Zuhri Harahap; Irmayanti Irmayanti; Angga Putra Juledi
Journal of Computer Science and Information System(JCoInS) Vol 7, No 1: JCoInS | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i1.9025

Abstract

The rapid development of information technology has pushed various business sectors, including Aman Store Rantau Prapat, to adapt digital technology to enhance operational efficiency and market reach. Currently, Aman Store still relies on a manual system, which results in inefficiencies in monitoring transactions and stock, as well as a limited market reach. This research aims to design and build a web-based shoe sales information system using PHP and a MySQL database. The system development follows the Waterfall method, which includes stages of requirement analysis, system design, implementation, testing, and maintenance. System modeling is represented using the Unified Modeling Language (UML), including Use Case, Activity, and Sequence Diagrams, to visualize actor-system interactions and business processes. The result of this research is a web-based application that provides digital product catalogs, online ordering and payment features, and automated stock management. Implementation of this system is expected to accelerate the transaction process, reduce human error, and expand market coverage beyond the local area, thereby increasing the store's competitiveness.
Co-Authors Elfi Zahra Yuni Ibnu Rasyid Munthe Irmayanti Irmayanti Lisa Ariani Marnis Nasution Nia Edi Putri Restu Fauzy Naibaho Reyfo Irfankha Rista Andini Ritonga Sudi Suryadi Syaiful Zuhri Harahap Tiara Syavitri Rambe Tomi Hidayat