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Syaiful Zuhri Harahap
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Program Studi Sistem Informasi, Fakultas Sains & Teknologi, Universitas Labuhanbatu Jalan Sisingamangaraja No.126 A KM 3.5 Aek Tapa, Bakaran Batu, Rantau Sel., Kabupaten Labuhanbatu, Sumatera Utara 21418
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Journal of Computer Science and Information Systems (JCoInS)
Published by Universitas Labuhanbatu
ISSN : -     EISSN : 27472221     DOI : 10.36987
Core Subject : Science,
Computer Science & IT
Journal of Computer Science and Information Systems (JCoInS) - Journal of the Information Systems Study Program seeks to facilitate critical study and in-depth analysis of information system problems, this journal is an expert computer science scientist, information system scientist. e-ISSN : 2747-2221
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 22 Documents
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Search results for , issue "Vol 6, No 3: JCoInS | 2025" : 22 Documents clear
Analisis Clustering Kepuasan Pelanggan Bengkel Mobil Auto Muara Baru Menggunakan Metode K-Means Herdiansyah, Roydido; Suryadi, Sudi; Irmayanti, Irmayanti
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.v6i3.7929

Abstract

This study aims to analyze customer satisfaction of Muara Baru Auto Repair Shop by using K-Means clustering method. Customer satisfaction is a crucial factor in maintaining loyalty and improving service quality in the automotive industry. The Data was collected through surveys involving customers who had used the workshop services, and then analyzed using the k-Means algorithm to identify patterns and clusters in satisfaction levels. The results of the analysis show that there are several clustering that reflect variations in customer satisfaction levels, providing important insights into service aspects that need to be improved as well as areas that have met customer expectations. These findings indicate that the K-Means method is effective in analyzing customer satisfaction and can be used as a basis for workshop management to formulate service improvement strategies to better meet customer expectations.
Pengembangan Program Kewirausahaan melalui Manajemen Pendidikan di SMK Amal Luhur Kota Medan Haqki, Bay
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.v6i3.7891

Abstract

This study aims to describe and analyze how educational management plays a role in the development of entrepreneurship programs at SMK Amal Luhur in Medan City. The background of this study is based on the importance of integrating entrepreneurial values in the education system, especially in Vocational High Schools (SMK) which are oriented towards producing graduates who are ready to work and are economically independent. This study uses a descriptive qualitative approach with data collection techniques in the form of observation, in-depth interviews, and documentation studies. The results show that the development of the entrepreneurship program is carried out through a managerial process that includes entrepreneurship curriculum planning, implementation of project-based learning, and continuous evaluation involving teachers, students, and industry partners. In addition, the support of school leadership and collaboration with the business world are key factors in the success of the program. This study concludes that effective educational management can create a learning ecosystem that is conducive to the growth of students' entrepreneurial spirit.
Analisis Sentimen Pelayanan Pembayaran Pajak Menggunakan Metode Algoritma Naïve Bayes Pada Kantor Badan Pendapatan Daerah Labuhanbatu Utara Dengan Menggunakan RapidMiner Purba, Mhd. Rafly; Harahap, Syaiful Zuhri; Nasution, Fitri Aini; Bangun, Budianto
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.v6i3.7959

Abstract

Improving the quality of Public Services is a major need in the era of digitalization, including in the local taxation sector related to the sentiment of services provided in tax payments. The purpose of this study was to analyze public sentiment towards tax payment services in the Office of the regional Revenue Agency (Bapenda) Labuhanbatu Utara by applying Naïve Bayes algorithm using Rapid Miner software. Data analysis through text preprocessing, feature selection, and sentiment classification into positive, negative, and neutral categories. The Data obtained consisted of 225 community comments from the SIMPATDA application and 612 tweets with the hashtag #pajakLabura from Twitter, which reflected people's opinions directly. The analysis process is carried out through the stages of text preprocessing, feature selection, to the classification of sentiments into positive, negative, and neutral categories. The results showed that the Naïve Bayes algorithm is able to classify public opinion with a high degree of accuracy and establish similarities/differences in the aspects of service that are most complained about or appreciated by the public. This study also contributes to the development of data-based evaluation system in the scope of public services.
Kepatuhan Pembayaran Pajak Kendaraan Bermotor Menggunakan Algoritma Decision Tree Dan Random Forest Di Samsat Balige Wijaya, Alief Achmad; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Nasution, Marnis
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.v6i3.7934

Abstract

This study aims to analyze and predict the total category of Motor Vehicle Tax (PKB) payments based on payment attributes and vehicle types, which is important to improve the effectiveness of tax management and support more appropriate decision making in related agencies; within the theoretical framework, classification models such as Decision Tree and Random Forest are used to predict data categories by utilizing historical patterns in the dataset, because these algorithms are able to capture interactions between attributes and provide logical interpretations of the prediction results; the research methodology is carried out using secondary data of PKB payments for 2024 from Samsat Balige, which is divided into training data and test data for the classification process and its performance is evaluated using accuracy, precision, recall, and F1-Score metrics through the Performance operator in RapidMiner; the results of the study show that Random Forest produces a more balanced prediction distribution with 100% accuracy, while Decision Tree has 96% accuracy but tends to be biased towards the “Low” category, and analysis of important attributes such as Fines, Total Amount, and the number of Jeep and Truck type vehicles shows a significant influence on the PKB payment category; Thus, the research conclusion confirms that Random Forest is proven to be more effective and stable than Decision Tree in predicting the total PKB payment category, is able to capture complex patterns between attributes, and provides accurate predictions on relatively small datasets, making it the optimal choice for PKB data classification.
Analisa Perancangan Sistem Informasi Pada Toko Rafatar Berbasis Web Menggunakan PHP MySQL Pane, Dinda Nurinayah; Masrizal, Masrizal; Harahap, Syaiful Zuhri; Munthe, Ibnu Rasyid
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.v6i3.7893

Abstract

Rafatar stores today still rely on manual sales systems that are prone to human error, inefficient, and make inventory management difficult. This study aims to design and build a website-based sales system to overcome these problems. The system was developed using the PHP programming language and MySQL database to improve operational efficiency, data accuracy, and expand market reach. For inventory management, this study applies the FIFO method (First-In, First-Out) to ensure accurate stock management and minimize the risk of loss. The development of this website is expected to increase the competitiveness of Rafatar stores in a competitive market.
Analisis Kepuasan Masyarakat Terhadap Kinerja Bupati Labuhanbatu Selatan Periode 2021-2024 Menggunakan Metode Decision Tree dan Naive Bayes Ramadhani, Ramadhani; Harahap, Syaiful Zuhri; Suryadi, Sudi; Masrizal, Masrizal
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.v6i3.7971

Abstract

This study was conducted to analyze the level of customer satisfaction with services by comparing the performance of two classification methods, namely Decision Tree and Naive Bayes, so that an accurate model can be obtained to assist decision making. This problem is important because understanding customer satisfaction patterns can be a strategic basis in improving service quality and maintaining loyalty. The theoretical basis used refers to the concept of machine learning classification, where Decision Tree forms a branching rule-based model based on attributes, while Naive Bayes relies on probability calculations based on Bayes' theorem with the assumption of independence between features. The research methodology includes data collection stages, pre-processing to ensure data quality, model training with both methods, and performance evaluation using Test & Score and Confusion Matrix. Based on the classification results, the Decision Tree method produces fairly good accuracy, precision, and recall, but the Naive Bayes method shows higher performance with an accuracy of 91.67%, a precision of the "Satisfied" class of 98.11%, and a recall of 92.86%, which indicates a very good level of prediction accuracy especially for the majority class. Evaluation of both methods shows that Naive Bayes excels in capturing existing data patterns, although Decision Tree still has good interpretability for classification rule analysis. In conclusion, both methods are capable of classifying customer satisfaction data with adequate performance, but Naive Bayes is recommended as the primary model due to its higher and more consistent evaluation results, while Decision Tree can be used as an alternative when model interpretation is a priority.
Model Prediktif Kepuasan Pelanggan Pada Hotel Platinum Menggunakan Motode K-Means Clustering Siregar, Siti Kholijah; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Munthe, Ibnu Rasyid
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.v6i3.7935

Abstract

Customer satisfaction is a key pillar of success in the competitive hospitality industry, directly impacting loyalty and profitability. Recognizing this, Platinum hotels need the ability to predict guest satisfaction in order to refine their service strategies. This study focuses on the development of predictive models of customer satisfaction at Platinum hotel using the K-Means Clustering method. This method was chosen because of its effectiveness in grouping complex data into homogeneous segments based on common characteristics. Customer Data will be grouped by attributes of their stay to identify different segments of customers with unique levels of satisfaction and preferences. It is hoped that this model can provide deep insights into customer profiles, reveal hidden patterns, and predict future guest expectations. The results of this study will contribute to improving the quality of Service and strategic decision-making at Platinum hotels and can be a reference for the hospitality industry in implementing a data-driven approach.
Implementasi Sistem Manajemen Hotspot Berbasis Mikrotik untuk Optimalisasi Akses Internet di Pesantren Anshori, Anshori; Rifa’i, M. Ali
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.v6i3.7892

Abstract

This study aims to examine the implementation of a Mikrotik-based hotspot management system to optimize internet access at Pondok Pesantren An-Nur, Ogan Komering Ilir, South Sumatra. The central issue addressed is the limited and uneven internet management within a boarding school environment. A qualitative case study approach was employed to explore both the technical and social dimensions of the system's implementation. Data were collected through semi-structured interviews, participatory observations, and document analysis, involving informants such as the network administrator, teachers, boarding school administrators, and active students. Data analysis revealed three main themes: (1) the design of a Mikrotik RB750-based network system with centralized authentication and proportional bandwidth allocation; (2) users’ perceptions and responses to access control and connection stability; and (3) the digital cultural transformation within the pesantren resulting from structured technological regulation. The findings indicate that the system not only improves network performance but also promotes digital discipline and content supervision in alignment with pesantren values. This research contributes to the understanding of technology integration in value-based educational institutions and proposes a practical, efficient, and contextual network management model. The practical implications are relevant for pesantren managers and policymakers in digital education. Further research is recommended to explore similar implementations across different religious institutions using a comparative approach.
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.
Evolusi Ilmiah Visualisasi Interaktif Untuk Eksplorasi Data Dinamis Saepuloh, Aep; Zaliluddin, Dadan; Kusdinar, Erlangga
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.v6i3.7379

Abstract

Dengan menggunakan metodologi bibliometrik, penelitian ini meneliti kemajuan penelitian ilmiah tentang visualisasi interaktif. Data diperoleh dari database Google Scholar melalui perangkat lunak Publish or Perish, dengan rentang waktu publikasi dari 2013 hingga 2024. Perangkat lunak VOSviewer digunakan untuk menganalisis 1.000 dokumen untuk mengidentifikasi tren publikasi, kolaborasi penulis, persebaran institusi, dan visualisasi jaringan kata kunci yang sering digunakan. Hasil analisis menunjukkan bahwa visualisasi interaktif telah meningkat dalam dekade terakhir, terutama dalam bidang ilmu komputer, pendidikan, dan sains data. Kata kunci yang sering muncul antara lain visualisasi data, interaksi pengguna, dan analitis visual. Selain itu, ditemukan bahwa sejumlah penulis dan institusi mendominasi publikasi yang membahas topik ini. Hasil ini memberikan gambaran mendalam tentang peta penelitian visualisasi interaktif dan membuka peluang untuk penelitian lebih lanjut di bidang visualisasi data interaktif, yang semakin penting di era big data dan transformasi digital

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