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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 171 Documents
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Audit Energi dan Analisis Peluang Penghematan Konsumsi Energi di PT. Mitra Globalindo Nusantara Riza Ria Wirasari; Rani Zaina Azmy; Supriono Supriono
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.v7i1.9028

Abstract

Energi listrik memiliki peranan penting pada kegiatan operasional di suatu perusahaan, instansi, universitas, rumah sakit, hotel dan yang lainnya. Seiring bertambahnya kebutuhan listrik setiap tahun, ketersediaan sumber energi listrik menjadi semakin berkurang. Oleh karena itu, audit energi merupakan salah satu metode konservasi untuk mengoptimalkan penggunaan energi.  Audit energi juga merupakan langkah yang tepat untuk menganalisa konsumsi energi, menemukan peluang penghematan serta meningkatkan efisiensi penggunaan energi listrik. PT. Mitra Globalindo Nusantara adalah perusahaan yang bergerak pada usaha pengadaan jaringan infrastruktur dan maintenance jaringan telekomunikasi fiber optik. Penelitian ini menggunakan studi literatur tentang konservasi energi dan melakukan observasi melalui pengamatan dan pengukuran seara langsung pada bangunan PT. Mitra Globalindo Nusantara. Proses pengukuran meliputi pencahayaan, suhu ruangan, kelembapan udara. Sedangkan proses perhitungan konsumsi energi listrik dengan melakukan pengamatan secara langsung berdasarkan penggunaan energi listrik dengan durasi waktu 1x24 Jam dan selama 12 bulan. Perhitungan nilai Intensitas Konsumsi Energi (IKE) untuk ruangan AC dan NON AC untuk setiap ruangan dan setiap lantai pada bangunan. Hasil dari penelitian menyatakan nilai IKE untuk ruangan NON AC memilki nilai rata-rata 3.9 kWh/m²/bulan, yang termasuk ke dalam kategori efisien. Sedangkan ruangan AC memiliki nilai IKE  rata-rata 57, 2 kWh/m²/bulan yang termasuk ke dalam kategori boros. Konsumsi energi paling besar pada bangunan adalah penggunaan AC yang mencapai 47% dari total keseluruhan penggunaan energi/hari. 
Fundamental Dan Implementasi Big Data Dalam Transformasi Digital Haykal Tito Setiawan; Angga Reksa; Rizky Pratama Ramadani Ritonga; Sahat Parulian Sitorus
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.v7i1.8894

Abstract

Digital transformation has encouraged organizations to optimally utilize information technology to manage data. Big data has become a key element that plays a crucial role in supporting the digitalization process in various sectors. The theoretical basis of this study discusses the concepts of big data, digital transformation, information systems, data governance, and digital human resources. These theories form the basis for understanding the relationship between technology and organizational performance. The research method used is descriptive qualitative, using a literature review and case study approach. Data was obtained from various reliable sources and systematically analyzed to obtain valid results. The results show that implementing big data can improve operational efficiency and the quality of decision-making. In addition, big data also drives innovation and strengthens organizational competitiveness. The research discussion emphasizes that the success of big data implementation is influenced by the readiness of human resources, infrastructure, and management support. A holistic approach is necessary for digital transformation to be sustainable. The study concludes that big data is a strategic asset in the digital era. Optimal utilization of big data can support organizational growth, innovation, and sustainability in the future.
Etika Bisnis Islam Pada Pedagang Kuliner Di Pusat Pasar Kota Medan Nurhalimah Sibuea; Subaktiar Subaktiar; Adriansyah Adriansyah; Mukti Hakim
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.v7i1.9031

Abstract

This study used a qualitative approach with field research. The subjects were food and beverage vendors at the Medan City Market Center. Data sources consisted of primary and secondary data, with data collection techniques using interviews, observation, and documentation. The results of the study indicate that 1). Food vendors at the market center are able to implement three aspects of Islamic business axioms: equilibrium, free will, and responsibility, but have not been able to implement the aspect of unity. 2). Obstacles faced by food vendors in implementing Islamic business ethics include the idea that trading is more important than fulfilling obligations as Muslims. Furthermore, there are obstacles in inadequate waste management regulations, so good cooperation is expected between food vendors, buyers, and the relevant government. 3). The implementation of Islamic business ethics among food vendors at the market center aims to increase education about the importance of trading based on Islamic business ethics, thus enabling food vendors to conduct transactions properly and in accordance with Islamic law.
Analisis Tren Pendaftaran Siswa Alwashliyah Marbau Menggunakan Big Data Mhd Aftiansyah Putra; Marchelius Mulawarman; Abdul Aziz; Sahat Parulian Sitorus
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.v7i1.8865

Abstract

This study aims to analyze student enrollment trends at the Alwashliyah Marbau Education Foundation over the past five years, focusing on the MTS, MAS, SMK-1, and SMK-2 levels. The analysis shows that the SMK-1 vocational program has seen a 15% increase in enrollment annually, while the MAS program has seen a significant decline of up to 20% in the last year. The majority of enrollees come from the Marbau area (70%), indicating a certain geographic dominance in student recruitment. Correlation tests identified a positive relationship between digital promotion and enrollment growth at the SMK level. Key recommendations include increasing the intensity of digital promotion, adjusting the curriculum based on job market needs, and evaluating promotional strategies for programs with declining trends. The resulting data visualization also provides insights to support recruitment strategy optimization.
Pengaruh Corporate Social Responsibility, Profitabilitas dan Leverage Terhadap Nilai Perusahaan Pada Perusahaan Manufaktur Yang Terdaftar di Bursa Efek Indonesia Periode 2021-2023 Putri Awaliyah Rahma Pulungan; Suriana Suriana; Widia Wardani
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.v7i1.8923

Abstract

This study aims to determine the effect of Corporate Social Responsibility (CSR), Profitability, and Leverage on Company Value, both partially and simultaneously, in companies that are the research samples. The method used is multiple linear regression analysis with a sample size of 54 companies. The independent variables in this study are CSR, Profitability, and Leverage, while the dependent variable is Company Value. The results of the t-test analysis show that: (1) the CSR variable has a significant negative effect on company value with a calculated t value of -2.293 < t table 1.675 and a significance of 0.026 (< 0.05); (2) the Profitability variable does not have a significant effect with a calculated t value of 0.594 < t table 1.675 and a significance of 0.555 (> 0.05); and (3) the Leverage variable has a negative but not significant effect with a calculated t value of -0.300 < t table 1.675 and a significance of 0.765 (> 0.05). Simultaneously, the F-test results show a calculated F-value of 1.929 < F-table 2.79 with a significance level of 0.137 (> 0.05), indicating that CSR, Profitability, and Leverage collectively have no significant effect on firm value. The Adjusted R² value of 0.019 indicates that the variation in firm value can only be explained by the three independent variables by 1.9%, while the remaining 98.1% is explained by other factors outside this research model. This study implies that companies need to manage CSR and debt use more wisely and improve profitability quality to positively impact firm value.
Analisis Efektivitas Transformasi Digital Marketing Berbasis Software as a Service (SaaS) terhadap Daya Saing Visual UMKM Desa Bandar Kumbul Rinda Puspita; Budianto Bangun; Rahma Muti Ah; Syaiful Zuhri Harahap
Journal of Computer Science and Information System(JCoInS) Vol 7, No 2: JCoIns | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i2.9211

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy, but they still face challenges in promotion, particularly in utilizing digital media. MSMEs in Bandar Kumbul Village generally still use traditional methods and are unable to create engaging promotional content. This activity aims to improve MSMEs' understanding and skills in using the Canva application as a digital promotional tool. The method used is community service through preparation, implementation, and evaluation stages using the 70/20/10 learning model. Data was collected through observation, interviews, and pre- and post-tests. The results show an increase in participants' knowledge and skills in creating promotional content such as posters, banners, and social media, as well as an increase in digital media utilization and understanding of branding. Therefore, Canva is effective in improving the quality of promotions, expanding market reach, and enhancing the competitiveness of MSMEs.
Analisis Prediksi Jumlah Kunjungan Pasien di Puskesmas Aek Kota Batu Menggunakan Metode Regresi Linear Adinda Ayu Lestari Nasution; Marnis Nasution; Asriani Hasibuan; Ibnu Rasyid Munthe
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.v7i3.9711

Abstract

Patient visit numbers at the Aek Kota Batu Community Health Center (Puskesmas) fluctuate over time; therefore, a method is required to predict visit volumes for future periods to inform healthcare service planning. This study aims to analyze historical patient visit patterns and develop a prediction model using linear regression. Monthly patient visit data from 2025 to 2026 were processed using the Knowledge Discovery in Databases (KDD) framework, comprising data selection, preprocessing, transformation, data mining, and evaluation. Analysis was conducted using POM-QM for Windows software, incorporating five independent variables based on time-series lags (lag-5 through lag-1). The results demonstrate that linear regression can generate a prediction model based on the relationship between historical data and future patient visit volumes. Model performance was evaluated using R², MAE, RMAE, MSE, RMSE, and MAPE metrics to assess prediction accuracy. The resulting model can serve as a decision-support tool for planning healthcare personnel, facilities, and services at the Aek Kota Batu Community Health Center, thereby enhancing the effectiveness and efficiency of service delivery.
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.

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