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All Journal Jupiter Aquacoastmarine Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik Jusikom : Jurnal Sistem Komputer Musirawas JURNAL PENGABDI SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknologi Sistem Informasi dan Aplikasi Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Jurnal Abdimas Mandiri Cendekia : Jurnal Pengabdian Masyarakat JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Perangkat Lunak BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Information Technology Ampera Journal of Computer and Information Systems Ampera Journal of Software Engineering Ampera Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Pengabdian Mandiri Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Pancasila (JPP) Innovative: Journal Of Social Science Research AQUACOASTMARINE: Journal of Aquatic & Fisheries Sciences Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) E-Amal: Jurnal Pengabdian Kepada Masyarakat
Febriyanti Panjaitan
Universitas Bina Darma
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Journal : Journal of Information Systems and Informatics

 1 
Penerapan pengolahan data pada Network Attached Storage (NAS) menggunakan metode Freenas di Kantor Dinas Kebudayaan dan Pariwisata Provinsi Sumatera Selatan Herikson Nainggolan; Febriyanti Panjaitan; Susan Dian Purnamasari
Journal of Information System and Informatics Vol 3 No 3 (2021): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v3i3.196

Abstract

Ilmu jaringan komputer mempunyai manajemen yang akan mengatur masalah keamanan, optimasi, dan sumber daya jaringan komputer. Salah satu kegiatan di dalam manajemen adalah pengolahan data sumber daya jaringan komputer, dimana jaringan komputer yang di dilakukan pengolahan data harus tetap terjaga layanan dan kualitasnya. Kantor dinas kebudayaan dan pariwisata provinsi sumatera selatan memiliki banyak data-data yang sangat penting bagi perusahaan, sistem pengolahan data sangatlah penting dengan cara mem-backup data itu sendiri untuk berjaga-jaga apabila terjadi kehilangan data. Oleh sebab itu digunakan network attached storage (NAS) sebagai sistem penyimpanan media eksternal berbasis FreeNAS sehingga memberikan alternatif bagi perusahaan dalam pengolahan data dan sumber daya jaringan komputer. Pembuatan sistem penyimpanan data yang baru melalui beberapa tahap yaitu dalam melakukan penginstalan dalam bentuk virtualisasi menggunakan VirtualBox, kemudian setelah melakukan penginstalan maka tindakan selanjutnya melakukan konfigurasi terhadap Freenas supaya nantinya mendapatkan IP agar bisa masuk kedalam sistem Freenas, konfigurasi yang dilakukan yaitu melakukan setting pada menu ZFS, Change Permission, dan NFS. Setelah melakukan konfigurasi dan mendapatkan IP atau hak akses masuk kedalam sistem Freenas maka sistem yang telah dibuat tadinya akan langsung tampil pada file exploler dengan memasukkan IP yang telah didapatkan sebelumnya, dan telah berhasil dibangun sebuah sistem penyimpanan data sebagai tempat untuk menjaga keamanan data dan back-up data yang berfungsi untuk berjaga-jaga jika terjadinya kehilangan data.
Tourism Application Information System Security Audit Using Cobit 5 Framework on Palembang City Febriyanti Panjaitan; Susan Dian Purnamasari; Windi Melisa
Journal of Information System and Informatics Vol 4 No 1 (2022): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v4i1.235

Abstract

Information technology is important in organizations so that the goals and expectations of the objectives of implementing IT can be achieved. The tourism office of the city of Palembang is an institution that has used information technology to help the continuity of their work. The tourism office of the city of Palembang has not conducted an audit of improving the security of the existing information system. Therefore, to avoid events that do not require an information security system audit. Cobit 5 framework that can be used as a reference for information system governance to achieve the expected goals. This study only discusses the security management process which includes the DSS05 (Delivery, service, and security) domain by collecting data using a questionnaire. The value of the level of security improvement to be achieved in the security system should reach level 5 and can provide recommendations that will be followed up by the institution to make improvements in the future.
Implementation of the Least Square Method for The Application of Population Growth Rate Prediction in Air Sugihan District Rizqi Prasetyo; Andri Andri; Susan Dian Purnamasari; Febriyanti Panjaitan
Journal of Information System and Informatics Vol 4 No 2 (2022): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v4i2.252

Abstract

Air Sugihan is one of the 18 sub-districts in Ogan Komering Ilir Regency, South Sumatra Province which has 19 villages. After observation, in the annual program policy planning carried out by the Air Sugihan District, almost all development plans need to have an information base for future time estimates, namely predictions of population growth rates. Therefore, this study aims to contribute to the Air Sugihan sub-district by conducting a predictive analysis of the population growth rate with the least square method and implementing it into an application. The use of the least square method is a suitable method for predicting the rate of population growth. From the results of the analysis of prediction calculations for 2021, the same results were obtained with the details of the birth value of 762 with MAD errors (77.04) and MAPE (11.78 %), the death value of 460 with MAD errors (65.41) and MAPE (20.41 %), the Migration-coming value of 637 with MAD errors (190.67) and MAPE (81.55 %) and the Migration-away value of 877 with errors MAD (169.99) and MAPE (45.35 %). With the implementation into the application, it facilitates the process of managing population growth rate data in determining the results of predictions or forecasting and conclusions can be drawn from the prediction results for which factors or variables are more specific to affect the rate of population growth in the future.
Detection of Inorganic Waste Using Convolutional Neural Network Method Riduan, Achmad; Panjaitan, Febriyanti; Rizal, Syahril; Huda, Nurul; Purnamasari, Susan Dian
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.662

Abstract

Waste, encompassing both domestic and industrial materials, presents a significant environmental challenge. Effectively managing waste requires accurate identification and classification. Convolutional Neural Networks (CNNs), particularly the Residual Network (ResNet) architecture, have shown promise in image classification tasks. This research aims to utilize ResNet to identify types of waste, contributing to more efficient waste management practices. The ResNet101 architecture, comprising 101 layers, is employed in this study for waste classification. The dataset consists of 2527 images categorized into six classes: Cardboard, Glass, Metal, Paper, Plastic, and Trash. The ResNet model is pre-trained, leveraging existing knowledge to enhance classification accuracy. The dataset is divided into training and testing sets to evaluate the model's performance. The testing results, evaluated using a Confusion Matrix, demonstrate strong performance in waste classification. The ResNet101 model achieves 92% accuracy in detecting inorganic waste objects within the training dataset and maintains a high accuracy of 90% on the testing dataset. This indicates the effectiveness of the ResNet architecture in accurately identifying various types of waste, contributing to improved waste management efforts. he utilization of ResNet101 for waste classification yields promising results, with high accuracy rates observed across both training and testing datasets. By effectively identifying types of waste, this approach facilitates more efficient waste management practices, enabling better resource allocation and environmental conservation. Further research and application of CNN architectures in waste management could lead to enhanced sustainability efforts and improved waste-handling strategies.
Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques Panjaitan, Febriyanti; Ce, Win; Oktafiandi, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Putra, Vito Hafizh Cahaya
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1019

Abstract

Sentiment analysis is crucial for understanding public opinion, especially in political contexts like the 2024 South Sumatra gubernatorial election. Social media platforms such as Twitter and YouTube provide key sources of public sentiment, which can be analyzed using machine learning to classify opinions as positive, neutral, or negative. However, challenges such as data imbalance and selecting the right model to improve classification accuracy remain significant. This study compares five machine learning algorithms (SVM, Naïve Bayes, KNN, Decision Tree, and Random Forest) and examines the impact of data balancing on their performance. Data was collected via Twitter crawling (140 entries) and YouTube scraping (384 entries), and text features were extracted using CountVectorizer. The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. The Decision Tree and Random Forest models achieved the highest accuracies of 79.22% and 75.32% on imbalanced data, respectively. However, they also exhibited overfitting, as indicated by their near-perfect training performance. Naïve Bayes, on the other hand, demonstrated the lowest accuracy at 54.55% despite achieving high precision, suggesting frequent misclassification, particularly for the minority class. SVM and KNN also struggled with imbalanced data, recording accuracies of 58.44% and 63.64%, respectively. Significant improvements were observed after applying data balancing techniques. The accuracy of SVM increased to 71.43%, and KNN improved to 66.23%, indicating that these models are more stable and effective when class distributions are even. These findings highlight the substantial impact of data balancing on model performance, particularly for methods sensitive to class distribution. While tree-based models achieved high accuracy on imbalanced data, their tendency to overfit underscores the importance of balancing techniques to enhance model generalization.
Sentiment Analysis on Coretax Data Using SVM and Random Forest with SMOTE and Tomek-Link Oktafiandi, Hery; Winarnie, Winarnie; Ramadhan, M. Fajar; Panjaitan, Febriyanti
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1279

Abstract

This study is motivated by the increasing adoption of digital tax platforms in Indonesia, particularly Coretax, which enables online tax reporting and payment. Understanding user sentiment is crucial for evaluating system effectiveness and identifying areas for improvement. However, sentiment data is often imbalanced, making it challenging to detect the sentiments of the minority class. This research evaluates the performance of Support Vector Machine (SVM) and Random Forest (RF) in classifying sentiment from Coretax related reviews collected between March and September 2025 from Twitter, YouTube, and the DJP application. Lexicon-based labeling and preprocessing were applied, followed by class balancing using Tomek-Link, SMOTE, and SMOTE-Tomek techniques. On the original data, SVM achieved an accuracy of 98.56%, while Random Forest reached 98.43%, both performing strongly on the majority class. However, minority class detection was improved through SMOTE and SMOTE-Tomek, albeit with a slight decrease in overall accuracy due to the risk of overfitting. The novelty of this study lies in its focus on Coretax 2025 data and a comparative analysis of multiple resampling techniques, providing practical insights into improving sentiment analysis performance on imbalanced digital tax data.
Co-Authors Aan Restu Mukti Ade Putra Adha Oktarini Saputri, Nurul Ahmad Haidar Mirza Ahmad Muhtadi Ahmad Siddiq Ajuan Ritonga Aldrison Aprilo Almira, Della Amalia, Ghina Andari, Ella Andri Andri Andri, Andri Ari Muzakir Bangkit Seandi Taroreh Bastam, Mukhlis Nahriri Candra Buana Ce, Win Christin Evasari Nainggolan Darius Antoni Debby Sinta Devi Della Silvana Desi Efriyani Desi Kusmindari, Ch. Devi Wahyu Pratama, Sri Dian Purnamasari, Susan Dinda Mustika Ariani6 Dwiandari Rumanti Edi Supratman Evi Yuliangsih Evi Yulianingsih Fadilah, Amalia Fatmasari Fatmasari Fatmasari Fatmasari Febriandi, Yusuf Fikriliani, Cindy Fino Charli Firamon Syakti, Firamon Gumanti, Gita Hadi Syaputra Hafizh Cahaya Putra, Vito Haidar Mirza, Ahmad Hamim, Sumi Amariena Harahap, Muhamad Haikal Firyaldi Helda Yudiastuti Herikson Nainggolan Hery Oktafiandi Hutrianto Ilham Muammar Choiri Irwansyah Ibrahim Izman Herdiansyah, Muhammad Jemakmun, Jemakmun Kanugrahan, Ghanim Kembaren, Natasyha Pebrina Br Sembiring Kurniawan, Tri Basuki Leon Andretti Abdillah M. Iqbal Ramdhani Maria Ulfa Maria Ulfa Maser, Wahyu Haryati Mellan Fratama Muhammad Akbar Muhammad Andhika Pratama Muhammad Iqbal Ramdhani Muhammad Taufik Mukhlis Nahriri Bastam Mutia Mawardah Nababan, David Novita Sari Nurmala Sari Nurmeyliandari, Revianty Nurul Adha Nurul Huda Oktafiandy, Hery Oktaviani, Nia Pasaribu, Rihan Addilah Permai, Antika Pramadona, Adelia Putri Puji Agustini, Eka Puji Agustin Purba, Theresia Widi Astuti Putra, M. Soekarno Qoriani Widayati Raihan, Mohammad Ramadhan, M. Fajar Revianty Nurmeyliandari Revianty Nurmeyliandari Nurhendi Riduan, Achmad Rini Octavianti Rizki Amalia Rizky Amalia Rizqi Prasetyo Rolia Wahasusmia Rusmin Syafari, Rusmin Rusti Simatupang Saputra, Rahjan Satria, Femas Sauda, Siti Silfani Oktaviani Sinurat, Ries Ruth Rytond Siti Sauda Suryayusra, Suryayusra Susan Dian Purnamasari Susan Dian Purnamasari, Susan Dian Syahril Rizal Syahril Rizal, Syahril Syaputra, Hadi Tri Basuki Kurniawan Tri Noviana Triska Amalia Sari Usman Ependi Usman Ependi Wahyu Pranata Wahyu Wijaya Widya Cholil Wijaya, Satrio Wijaya, Wahyu Winarnie Windi Melisa Yomi Rosadi Yudi Ramdhani