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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL PENDIDIKAN TAMBUSAI Jurnal Mantik Informatika Dinasti International Journal of Digital Business Management U-NET Jurnal Teknik Informatika Jurnal Teknologi Informasi dan Komunikasi Jurnal Minfo Polgan (JMP) Journal of Computer Science and Information Systems (JCoInS) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Masyarakat Gemilang (JPMG) Journal Pengabdian Kepada Masyarakat Balakka Paluta Jurnal Bima Telecommunications, Networks, Electronics, and Computer Technologies (TELNECT)
Sitorus, Sahat Parulian
Universitas Labuhanbatu
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Evaluasi Efektivitas Mekanisme Enkripsi End-to-End Dalam Mengurangi Resiko Kebocoran Data Pada Layanan Komunikasi Berbasis Cloud Lia, Trisalia Purba; Sitorus, Sahat Parulian; Sartika, Dea Putri; Pratiwi, Ajeng; Hasibuan, Zakaria
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15744

Abstract

Keamanan data merupakan komponen utama dari layanan komunikasi berbasis cloud, karena data bisa bahkan sering kali hilang dan disimpan di beberapa server. Enkripsi End-to-End (E2EE) muncul sebagai mekanisme perlindungan yang memungkinkan hanya penerima dan pengirim yang bisa dapat mengakses konten komunikasi dari server cloud, sedangkan pengguna ketiga tidak bisa mengakses data cloud tersebut. Artikel ini mengevaluasi efektivitas E2EE dalam mengurangi risiko kerusakan data dalam layanan komunikasi berbasis cloud dengan tinjauan literatur, analisis teori keamanan, dan simulasi konseptual vektor ancaman seperti akses tidak otentik, penyadapan, dan kerusakan metadata. Hasil menunjukkan bahwa E2EE secara substansial menurunkan kemungkinan kebocoran data jika diimplementasikan dengan manajemen kunci dan endpoint security yang memadai. Namun, ada tantangan signifikan: metadata dan informasi kontekstual tetap berisiko bocor, serta performa sistem dapat terpengaruh. Temuan ini menekankan perlunya penerapan E2EE disertai kebijakan keamanan menyeluruh dan edukasi pengguna.
Sistem Informasi Manajemen di Era IoT dan Cloud Audya, Anggi; Pefrianti, Lenni; Saroni, Habi; Putri, Pujawati Kurnia; Indriani, Vivi; Srikandy, Yuyun Lili; Sitorus, Sahat Parulian
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.v7i1.8816

Abstract

The rapid development of Internet of Things (IoT) and cloud computing technologies has significantly transformed the way organizations manage information. Management Information Systems (MIS) are no longer limited to data recording and reporting functions but have evolved into integrated systems capable of providing real-time information to support strategic decision-making. This article aims to examine the role, benefits, and challenges of implementing Management Information Systems in the era of IoT and cloud computing. The research method employed is a literature review, drawing on relevant journals, books, and scientific publications. The results indicate that the integration of IoT and cloud computing into MIS can enhance operational efficiency, data accuracy, and system flexibility. However, several challenges remain, particularly related to data security, privacy issues, and the readiness of human resources.
Implementation of Convolutional Neural Network (CNN) Method in Determining the Level of Ripeness of Mango Fruit Based on Image Mei Wita Sari; Sahat Parulian Sitorus; Rohani; Rahmadani Pane
Jurnal Penelitian Pendidikan IPA Vol 11 No 5 (2025): May
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i5.11436

Abstract

This study aims to classify the ripeness level of mango fruit using a Convolutional Neural Network (CNN) model based on digital images. This classification is important to help the automatic sorting process in the agricultural industry that relies on accuracy in determining fruit quality. Based on the literature review, CNN has been widely used in image-based object recognition because of its ability to extract visual features automatically. Previous studies have shown that CNN is effective in image classification, but the results are highly dependent on the quality of the data and the model parameters used. This research method involves collecting mango fruit images at three levels of ripeness (raw, half-ripe, ripe), which are then processed and analyzed using the Orange application with CNN architecture. Model evaluation was carried out using accuracy metrics, AUC, confusion matrix, and visualization through box plots and scatter plots to see the distribution and differences in data between classes. The results showed that the CNN model obtained an accuracy of 53.3% and an AUC value of 0.717, which indicates the model's initial ability to distinguish ripeness categories but with a fairly high level of misclassification. There is still overlapping data between classes, especially between the raw and half-ripe classes, which indicates the need for additional features and parameter refinement. In conclusion, CNN has the potential to be used in classifying the ripeness level of mango fruit, but its performance can be improved through feature development and deeper model tuning. 
IMPLEMENTASI BIG DATA DALAM ANALISIS SENTIMEN ULASAN PENGGUNA TOKOPEDIA BERBASIS APLIKASI WEB MENGGUNAKAN METODE NAIVE BAYES Lubis, Febri Ananda; Siregar, Iskandar Muda; Azry, Aldipan; Simbolon, Khalil Elhamdi; Sitorus, Sahat Parulian
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7190

Abstract

Pesatnya perkembangan e-commerce mendorong meningkatnya jumlah ulasan pengguna yang memuat opini dan pengalaman terhadap layanan yang digunakan. Data ulasan tersebut memiliki karakteristik Big Data, seperti volume yang besar, variasi bahasa, dan pertumbuhan data yang cepat, sehingga analisis manual menjadi tidak efisien. Penelitian ini bertujuan mengimplementasikan konsep Big Data dalam analisis sentimen ulasan pengguna Tokopedia berbasis aplikasi web menggunakan algoritma Multinomial Naive Bayes. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan tahapan pengumpulan data, preprocessing teks, ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), serta klasifikasi sentimen ke dalam kategori positif, negatif, dan netral. Dataset yang digunakan terdiri dari 4.000 ulasan pengguna yang dibagi menjadi data latih dan data uji. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode Naive Bayes mampu mengklasifikasikan sentimen ulasan dengan performa yang baik. Visualisasi hasil analisis dalam bentuk dashboard aplikasi web memudahkan interpretasi distribusi sentimen. Dominasi sentimen negatif mengindikasikan adanya tingkat ketidakpuasan pengguna terhadap layanan pada data yang dianalisis. Kontribusi penelitian ini terletak pada penerapan analisis sentimen berbasis Big Data yang terintegrasi dengan aplikasi web sebagai alat evaluasi layanan e-commerce yang bersifat aplikatif dan skalabel.
Memahami Teknologi Big Data dan Mengatasi Tantangan Implementasinya Hasanah, Nurfanny; Sakina, Hardianur; Nst, Sonya Ardila; Sitorus, Sahat Parulian
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.36882

Abstract

Big Data memainkan peran penting dalam mendukung pengambilan keputusan di berbagai sektor, seperti industri mode, keuangan, dan kesehatan. Pemanfaatan data skala besar memungkinkan organisasi untuk memahami perilaku konsumen, memprediksi tren, dan merumuskan strategi berbasis bukti secara lebih efektif. Studi ini bertujuan untuk meneliti potensi Big Data dalam meningkatkan efisiensi dan efektivitas pengambilan keputusan serta mengidentifikasi tantangan dalam implementasinya. Metode penelitian yang digunakan adalah tinjauan pustaka dengan pendekatan deskriptif kualitatif melalui analisis jurnal ilmiah, buku akademik, dan sumber terpercaya. Hasil penelitian menunjukkan bahwa teknologi pendukung Big Data, seperti Hadoop, Apache Spark, komputasi awan, dan kecerdasan buatan serta pembelajaran mesin, memainkan peran penting dalam pengolahan dan analisis data. Namun, implementasinya masih menghadapi tantangan seperti kualitas dan keragaman data, interoperabilitas sistem yang terbatas, kurangnya sumber daya manusia yang kompeten, dan masalah keamanan dan privasi data. Oleh karena itu, diperlukan strategi manajemen data terintegrasi, peningkatan kompetensi sumber daya manusia, dan implementasi peraturan perlindungan data yang jelas agar Big Data dapat dimanfaatkan secara optimal dan berkelanjutan.
Pemanfaatan Teknologi Big Data Dalam Pengambilan Keputusan Dan Inovasi Di Era Digital Harefa, Arnes Dian Putri; Julianti, Julianti; Nahampun, Kessia Inriani; Ritonga, Putri; Sitorus, Sahat Parulian
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.v7i1.8866

Abstract

Big Data is an information technology designed to manage data with extremely high volume, velocity, and variety that cannot be effectively processed using traditional approaches. This technology provides solutions to enhance decision-making processes, predict behavioral patterns, and foster service innovation across various sectors, including industry, education, and healthcare. This study conducts a comprehensive review of the evolution of Big Data technology, its main characteristics, and its impact on digital transformation through a literature review of recent scientific publications from the period 2021–2025. The results indicate that the adoption of infrastructures such as Hadoop, Spark, and real-time analytics platforms contributes to improved operational efficiency and the implementation of data-driven decision making. However, challenges related to data privacy, data quality, and human resource competencies still require appropriate mitigation strategies. The findings of this study highlight the importance of integrating Big Data with artificial intelligence and cloud computing architectures to address future analytical demands.
Pemanfaatan Big Data dalam Meningkatkan Daya Saing UMKM Hasibuan, Muhammad Ridho; Adriansyah, Wahyu; Akmal, Zahri; Tarigan, Oktari; Sitorus, Sahat Parulian
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.v7i1.8875

Abstract

The rapid development of information technology has driven the growth of data in massive volumes, commonly referred to as Big Data. The utilization of Big Data offers strategic opportunities for Micro, Small, and Medium Enterprises (MSMEs) to enhance their competitiveness amid increasingly intense business competition. This study aims to examine the role of Big Data in supporting decision-making, understanding consumer behavior, and improving the operational efficiency of MSMEs. The research method employed is a literature review of relevant journals, books, and research reports. The findings indicate that the application of Big Data enables MSMEs to conduct market analysis, personalize products, and optimize digital marketing strategies. However, challenges such as limited human resources, technological infrastructure, and data security remain significant barriers. Therefore, support from the government and related stakeholders is necessary to encourage the optimal adoption of Big Data in the MSME sector.
Visualisasi Data Perkara Tindak Pidana Umum Menggunakan Python (Studi Kasus : Case Management System Kejaksaan Negeri Labuhanbatu Tahun 2023-2024) Adryan, Ahmad; Andre, Ivo; Rambe, Fani Wulandari; Sitorus, Sahat Parulian
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.v7i1.8887

Abstract

This study examines general criminal cases at the Kejaksaan Negeri Labuhanbatu for the years 2023–2024, focusing on three main aspects: case types, monthly trends, and the status of case handling. The data were analyzed and visualized using Python with pandas and matplotlib, producing bar and line charts that facilitate the identification of patterns, fluctuations, and case distribution. The analysis shows a decrease in Narcotics and Theft cases, while other case types, including Child Protection and Fraud, remained relatively stable. Monthly trends revealed that the highest number of cases occurred in November 2023, whereas the lowest was observed in April 2024, indicating potential seasonal effects on case occurrences. Regarding case handling, most cases successfully reached the stages of File Receipt and Complete File, although the number of cases achieving execution decreased compared to the previous year. These visualizations provide a clear and comprehensive view of the workflow from case initiation to execution, highlighting stages that require more attention and resources. Overall, this study demonstrates that data visualization can significantly improve understanding of complex datasets, support strategic planning, and assist the District Attorney’s Office in prioritizing case management for general criminal cases.
Fundamental Dan Implementasi Big Data Dalam Transformasi Digital Setiawan, Haykal Tito; Reksa, Angga; Ritonga, Rizky Pratama Ramadani; Sitorus, Sahat Parulian
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.
Analisis Tren Pendaftaran Siswa Alwashliyah Marbau Menggunakan Big Data Putra, Mhd Aftiansyah; Mulawarman, Marchelius; Aziz, Abdul; Sitorus, Sahat Parulian
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.
Co-Authors Abdul Aziz Adrian, Doni Adriansyah, Wahyu Adryan, Ahmad Ahmad Hidayat Sutawijaya Ajeng Pratiwi Akmal, Zahri Andre, Ivo Angga Putra Juledi Aritonang, Putri Audya, Anggi Ayuda, Muhammad Irfan Azry, Aldipan Bangun, Budianto Budiman, Hafidz Dini Hariyati Adam Elysa Rohayani Hasibuan Ferdy Ardiansyah, Ferdy Fitri Aini Nasution Harefa, Arnes Dian Putri Hasanah, Nurfanny Hasibuan, Elysa Rohayani Hasibuan, Muhammad Ridho Hasibuan, Zakaria Indrayani, Puput Indriani, Vivi Iskandar Muda Siregar, Iskandar Muda Julianti Julianti, Julianti Khairunnisya Ritonga Lia, Trisalia Purba Lubis, Febri Ananda Lucky Suriyah Ningsih M. Zarlis Manoppo, Aisyah Rahmadiyah Marnis Nasution Mei Wita Sari Muhammad Efendi, Muhammad Mulawarman, Marchelius Nahampun, Kessia Inriani Ningsih, Andini Yulistia Nst, Sonya Ardila Nurandani, Dhea Pane, Rahmadani Pasaribu, Rohani Pefrianti, Lenni Pratiwi, Devi Anassafila Putra, Mhd Aftiansyah Putri, Pujawati Kurnia Rahmad Aditiya Rahmi, Eriski Aulia Rambe, Fani Wulandari Reksa, Angga ritonga, Putri Ritonga, Rizky Pratama Ramadani Rohani Rohani Rohani Sakina, Hardianur samsir Samsir Sari, Wulan Inda Saroni, Habi Sartika, Dea Putri Setiawan, Haykal Tito Siagian, Ayu Febri Simbolon, Khalil Elhamdi Siti Sarah, Siti Srikandy, Yuyun Lili Suherman Ph.D. Tarigan, Oktari Volvo Sihombing Yanris, Gomal Juni Yustina, Meiliya Cahya