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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Informatika Jurnal Sistim Informasi dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Internet of Things and Artificial Intelligence Journal
Juledi, Angga Putra
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Journal : Journal of Computer Science and Information Systems (JCoInS)

 1 
Implementasi Algoritma Apriori Menggunakan Tanagra Pada Coffe Ayos Untuk Mengetahui Pola Penjualan Rambe, Inny Rahayu; Juledi, Angga Putra; Nasution, Marnis
Journal of Computer Science and Information System(JCoInS) Vol 5, No 1: JCOINS | 2024
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v5i1.5491

Abstract

Caffe Ayos is a cafe that is quite famous in the Rantauprapat area, but that doesn't mean it doesn't have competition in business. With so many new cafes popping up, of course this could have quite a significant impact if Ayos cafe doesn't first create a strategy to stay afloat. Apart from improving service and maintaining the taste of the menu, there needs to be other things done by Café Ayos, and with the many transactions that occur every day, of course there is a pile of data. From this pile of data, it is hoped that it can provide new knowledge that will be useful later for Café Ayos. Knowledge can be extracted from daily sales transaction data using Acsociation Rule data mining. From the extraction results, we obtained several foods and foods that depend on each other, which have a confidence of up to 70% to 100%. It would be better to create special packages for several menus that are related to high confidence values, thereby providing more attraction.
Perancangan Sistem Informasi Pendistribusian Beras Miskin Pada Kantor Kelurahan Sirandorung Berbasis Web Zuraidah, Zuraidah; Nasution, Marnis; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 3, No 4: JCoInS | 2022
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v3i4.6205

Abstract

The development of today's increasingly advanced technology such as the use of computers has become popular in the community, the use of computers is very important because the computer is a tool in carrying out data processing activities, so that a job can be completed properly. How the distribution of rice for the poor can work properly using this application. The purpose of the study is to assist the process of distributing rice for the poor at the Sirandorung Village Office. The system analysis stage is a very important stage because errors at this stage will result in errors in the next stage. In analyzing the system, several methods are used, among others. Based on the results of research and discussion that the results of the analysis of the implementation of the distribution of rice for poor households (Raskin) in Sirandorung Village, Rantau Utara District, Labuhanbatu Regency, the implementation is still not precise and has not been implemented properly. This is based on the analysis above and based on the results of questionnaires and interviews with researchers from the Sirandorung sub-district office. All people in the Sirandorung sub-district should be collected and provided with detailed information about the Raskin program so that there is no misunderstanding or receiving inaccurate information about Raskin.
Peningkatan Efisiensi dan Penjualan Toko Fashion Outlet Rantauprapat di Jalan Sisingamangaraja Melalui Implementasi E-Commerce PrestaShop Nasution, Intan Baiduri; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 5, No 4: JCoInS | 2024
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v5i4.6805

Abstract

The purpose of this study is to assess the impact of implementing the PrestaShop e-commerce platform on the efficiency and sales of Fashion Outlet Rantauprapat on Sisingamangaraja. Using a case study approach using quantitative and qualitative data, this study analyzes purchase data before to and after PrestaShop implementation, as well as conducts research with store owners. The results of the study show that PrestaShop significantly improves operational efficiency, reduces market volatility, and increases sales. However, challenges such as a lack of technical knowledge and skills must be addressed. This study found that PrestaShop has a large potential to become an effective tool for Fashion Outlet Rantauprapat in terms of increasing sales in the digital era.
Implementasi K-Means Dalam Menentukan Tingkat Kepuasan Pelanggan Pada Bengkel Rizal Rantauprapat Rambey, Khiarul Akhyar; Suryadi, Sudi; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7937

Abstract

The growing automotive industry demands workshops to improve the quality of service for customer satisfaction. However, manual measurement of satisfaction is often inefficient and subjective. This study proposes the application of machine learning algorithms K-Means Clustering to analyze customer satisfaction data in Rizal workshop. This method is used to Group customers into several clusters based on similar satisfaction characteristics. The results of this grouping are expected to provide more objective and in-depth insights to identify patterns of satisfaction, thus enabling the workshop to formulate a more effective and targeted service quality improvement strategy.
Penerapan Data mining Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Naïve Bayes Dan Support Vector Machine (Studi Kasus Program Studi Sistem Informasi Universitas Labuhanbatu) Antika, Dewi; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7917

Abstract

This study was conducted to classify public satisfaction levels using the Support Vector Machine (SVM) algorithm as the primary data analysis method. The objective of this study was to obtain an accurate and reliable prediction model for determining the Satisfaction and Dissatisfaction categories based on the available data. The theoretical basis used refers to the concept of machine learning, specifically SVM, which works by forming an optimal hyperplane to separate data classes. In addition, model evaluation theories such as the Confusion Matrix were used to objectively measure prediction performance. The research methodology included data collection, pre-processing, dividing the dataset into training and test data, and training the SVM model. Evaluation was conducted using accuracy, sensitivity, and specificity metrics to assess the model's ability to predict data accurately. The results and discussion indicate that the SVM successfully classified the majority of data correctly, with the Satisfaction class having a perfect prediction rate while the Dissatisfaction class still had a small error. Further analysis indicated the need for SVM parameter optimization to improve accuracy in the minority class. The conclusion of this study states that the SVM has good performance in classifying public satisfaction data, although it still requires refinement in recognizing certain class patterns. This finding opens up opportunities for developing more adaptive methods to improve predictive performance.
Penerapan Algoritma Random Forest untuk Klasifikasi Tingkat Keparahan Penyakit pada Data Rekam Medis Nasution, Fitri Aini; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7993

Abstract

Accurate determination of disease severity is an important step in supporting medical decision-making. This study aims to classify the severity of patients’ diseases into three categories—Mild, Moderate, and Severe—using the Random Forest algorithm. The data used were obtained from patients’ medical records containing structured clinical parameters and have undergone a preprocessing stage, including data cleaning, variable transformation, and splitting into training data (80%) and testing data (20%). The test results show that the Random Forest model achieved an accuracy of 74.77%. The best performance was obtained in the Mild class with a recall value of 0.95 and an f1-score of 0.84. The Moderate class achieved a recall of 0.71 and an f1-score of 0.73, while the Severe class showed perfect precision (1.00) but a low recall (0.12), indicating the model’s limited ability to detect cases in this class. The macro average values for precision, recall, and f1-score were 0.83, 0.60, and 0.59 respectively, while the weighted average values were 0.78, 0.75, and 0.71 respectively. These findings indicate that Random Forest can be used to classify disease severity based on medical records with relatively good performance for the majority class, but further optimization—such as data balancing or parameter adjustment—is needed to improve sensitivity toward classes with fewer samples.
Optimalisasi Kinerja Tenaga Kependidikan di MTSN 1 Labuhanbatu Selatan Studi Kasus Penggunaan Algoritma Naïve Bayes Rambe, Aida Zahrah Hasanati Br; Juledi, Angga Putra; Irmayani, Deci; Harahap, Syaiful Zuhri
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.8034

Abstract

This study aims to optimize the performance of Education personnel in MTsN 1 Labuhanbatu Selatan through the application of Naive Bayes algorithm for performance classification. The performance of Education personnel, including administrative, administrative, and service staff for one school year was analyzed using data involving attributes such as attendance, punctuality, productivity, and work attitude. Naive Bayes algorithm was chosen because of its ability to classify data accurately and efficiently despite the large amount of data. The results showed that the use of this algorithm can produce a more objective, accurate, and data-based evaluation system, as well as provide clearer insights in improving work efficiency and service to teachers and students. The evaluation of the model was conducted using accuracy, precision, recall, and F1-score metrics to ensure that the classification of educational staff performance can be done appropriately. The study also provides recommendations to improve data quality and the use of additional attributes to improve model performance.
Co-Authors Ah, Rahma Muti Alam, Dewi Pathimah Andriyani, Wahyu Fitri Angreani Hulu, Linca Elma Popi Anjar, Agus ANTIKA, DEWI Aris Munandar Aurianda, Rieke Dini Hariyati Adam Harahap, Tongku Hamonangan Harefa, Sinema Harist N, Abdul Hasibuan, Elysa Rohayani Hia, Faomaha Irmayani, Deci Lubis, Rapida Hanim Manullang, Dosma Parningotan Munthe , Ibnu Rasyid Munthe, Ibnu Rasyid Muti'ah, Rahma Mutia, Rahma Muti’ah, Rahma Naibaho, Restu Fauzy Nasution, Fitri Aini Nasution, Intan Baiduri Nasution, Marnis Nazliah, Rahmi Patriya, Angga Prayoga Pransiska, Apprillia Yudha PRATAMA, ZAKARIA Putri, Kirana Dwi Rambe, Aida Zahrah Hasanati Br Rambe, Inny Rahayu Rambey, Khiarul Akhyar Rezeki, Imam Dwi Ritonga, Muhammad Zakuan Rohani Rohani Sarjon Defit Sihombing, Volvo Sipahutar, Alwidahyani Sipahutar, Rizka Nurfatni Subianto, Roby Suryadi, Sudi Syahfitri, Nur’aini Indah Syaiful Zuhri Harahap Syaputra, Panca Tomi Hidayat, Tomi Yuhandri Yuhandri, Yuhandri Yunus, Yuhandri ZURAIDAH ZURAIDAH