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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 149 Documents
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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
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.
Klasifikasi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Nearest Neighbor (K-NN) Pada Data Akademik Perguruan Tinggi Efendi, Davina Rizky; Irmayani, Deci; Sihombing, Volvo
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.v6i3.8041

Abstract

Higher education is an important factor in scoring quality human resources, where one indicator of success is the graduation rate of students on time. This study aims to classify the graduation rate of students using the algorithm K-Nearest Neighbor (K-NN) based on academic data which includes GPA, number of credits, frequency of repetition of courses, and attendance. The results of the classification showed that 30% of students successfully graduated on time, while the rest had delays. With the k-NN approach, it is expected that this model can help universities in predicting student graduation more accurately and optimizing academic interventions to improve graduation efficiency.
Implementasi Alat Ukur Suhu Dan PH Air Untuk Budidaya Lobster Dengan Algoritma Fuzzy Logic Berbasis IoT Rahmi, Zulaida; Maulana, Rizky; Rialva, M. Nabil Risky
Journal of Computer Science and Information System(JCoInS) Vol 6, No 4: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i4.8347

Abstract

Freshwater lobster cultivation has quite good business prospects, but the growth of the existing lobster cultivation business has not been in line with demand, this can be seen from the low production level and quality of aquaculture products. The quality of temperature and pH of the water can affect the activity in freshwater lobster cultivation, because one of the factors that affects the level of molting frequency and cannibalism. In order to overcome this problem, research was carried out to create a tool using fuzzy logic algorithms and combined with Internet of Things technology. Based on the results of the study by conducting tests 10 times, the average error value on the temperature sensor was 0.40% and the average error value on the pH sensor was 0.22%.

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