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All Journal Jurnal Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNOLOGI DAN OPEN SOURCE IJID (International Journal on Informatics for Development) Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Responsif : Riset Sains dan Informatika Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Abdimas Komunikasi dan Bahasa Profitabilitas Indonesian Journal of Networking and Security - IJNS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi J-Intech (Journal of Information and Technology) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY JEECS (Journal of Electrical Engineering and Computer Sciences) JURSIMA Sinergi: Jurnal Pengabdian Kepada Masyarakat Journal of Accounting Information System TEKNOSIA Bulletin of Informatics and Data Science Jurnal Sistem Informasi dan Manajemen Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Help: Journal of Community Service (HJCS) Media Teknologi dan Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika) Journal of Information Technology Jurnal Teknoinfo
Riska Aryanti
Universitas Bina Sarana Informatika, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : bulletin of informatics and data science

 1 
Sistem Pendukung Keputusan Penilaian Kinerja Tenaga Honorer Menerapkan Metode Weighted Product (WP) dan Complex Proportional Assessment (COPRAS) dengan Kombinasi Pembobotan Rank Order Centroid (ROC) Yarimani Laia; Mesran Mesran; I Gede Iwan Sudipa; Darma Setiawan Putra; Perani Rosyani; Riska Aryanti
Bulletin of Informatics and Data Science Vol 2, No 1 (2023): May 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Honorary workers are someone who is appointed by a Personnel Development Officer or other officials in the government to carry out certain tasks in government agencies whose salary is paid by the APBN or APBD. The performance evaluation of honorary workers is aimed at planning the development of human resources and training and increasing the utilization of honorary workers in accordance with their duties and functions. The process of assessing the performance of honorary workers who are unfair can have an adverse effect on the honorary workers themselves. In the process of evaluating honorary staff, an efficient system is needed to see the performance results of the honorary staff, a Decision Support System is needed. The method applied in this study is the Weighted Product (WP) and Complex Proportional Assessment (COPRAS) methods applying Rank Order Centroid (ROC) weighting using these two methods, it is expected to provide effective results in evaluating honorary workers. The final results obtained in calculating the best alternative value in the calculation of the Weighted Product (WP) method, namely A4 on behalf of "Karin" with a value of Vi = 0.246 and in the calculation of the Complex Proportional Assessment method (COPRAS), namely A6 in the name of "Love" with a value of Ui = 100
Selection of the Best E-Commerce Platform Based on User Ratings using a Combination Entropy and SAW Methods Faruk Ulum; Junhai Wang; Setiawansyah Setiawansyah; Riska Aryanti
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.92

Abstract

Choosing the right e-commerce platform has a crucial role for consumers and business actors. For consumers, a reliable and user-friendly platform provides a safe, convenient, and efficient shopping experience. Considering various aspects of choosing the right e-commerce platform is a strategic investment that can provide long-term added value for all parties involved in the digital ecosystem. The purpose of this study is to identify and determine the best e-commerce platforms based on user experience and assessment with an objective and structured decision-making approach using a combination of Entropy and SAW methods. The results of the ranking of the best e-commerce platform selection determined through the combination of the Entropy and SAW methods, obtained that Shopee ranked first with the highest preference value of 0.9819, followed by Tokopedia in second place with a value of 0.973. Furthermore, Blibli is in third place with a score of 0.9401, followed by Lazada with a score of 0.9305, and the last is Bukalapak with a score of 0.9021. This research makes a significant contribution to multi-criteria decision-making by applying a combination of Entropy and SAW methods to evaluate and determine the best e-commerce platform based on user assessments. The results of this research can be used as a practical reference as a basis for strategic decision-making in choosing the e-commerce platform that best suits market needs
Waste Classification using EfficientNetB3-Based Deep Learning for Supporting Sustainable Waste Management Agustiani, Sarifah; Junaidi, Agus; Aryanti, Riska; Kamil, Anton Abdul Basah
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.108

Abstract

Waste management is a critical issue in sustainable development, particularly in large urban areas that generate a high volume of waste daily. One of the main challenges is the absence of a fast, accurate, and efficient waste sorting system. This study aims to develop a waste classification model using deep learning based on the EfficientNetB3 architecture to support more sustainable waste management. The model was trained on a dataset obtained from a Kaggle repository, consisting of 4,650 images evenly distributed across six waste categories: batteries, glass, metal, organic, paper, and plastic (775 images per class). The training and evaluation were conducted using a supervised image classification approach. The model achieved an overall accuracy of 93%, with average precision, recall, and F1-score values of 93%. Among all categories, organic waste achieved the highest F1-score (0.99), followed by paper (0.97) and batteries (0.97), while plastic and metal categories obtained F1-scores of 0.89. These results demonstrate that the EfficientNetB3 architecture is effective in performing multi-class waste classification. This model has the potential to be implemented in camera-based waste sorting systems such as smart bins or automated recycling units, thereby contributing to the reduction of unprocessed waste and supporting the achievement of Sustainable Development Goal (SDG) 12: responsible consumption and production
Explainable Machine Learning for Multi-Class Classification of Internet Firewall Traffic Titik Misriati; Riska Aryanti
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.163

Abstract

The increasing diversity and scale of network traffic introduce significant challenges in performing accurate and interpretable firewall analysis. This research aims to bridge the gap between predictive performance and model transparency by developing an explainable machine learning framework for multi-class firewall traffic classification. The study utilizes the Internet Firewall Data dataset consisting of 65,532 network traffic instances distributed across four firewall action classes and evaluates seven classification algorithms, including Decision Tree, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbors, Naïve Bayes, and Logistic Regression. The dataset was partitioned using a stratified 80:20 hold-out approach to preserve the original class distribution and the experimental process involves data preprocessing, normalization, and validation on an independent test set using accuracy, precision, recall, and F1-score metrics. The findings reveal that XGBoost achieves the highest performance, reaching an accuracy of 99.81%, followed by Decision Tree and Random Forest. This indicates that ensemble and tree-based approaches are highly effective in modeling complex and non-linear traffic patterns. To improve interpretability, this study incorporates explainable artificial intelligence techniques, including feature importance and SHAP analysis. The results show that traffic-related attributes significantly influence classification outcomes, providing meaningful insights into firewall decision behavior
Co-Authors Agus Junaidi Agustiani, Sarifah Aldian Mauluda Alif Rizqi Mulyawan Andi Saryoko Andika Bayu Hasta Yanto Andreas Roy Prasetya Ari Sulistiyawati Arifin, Yosep Tajul Arman Ramadhani Asistyasari, Ayuni Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Atang Saepudin Atang Saepudin Atang Saepudin Azis, Munawar Abdul Bayu Kusuma Ilyasa Universitas Bina Sarana Informatika Dahlia Dahlia Darma Setiawan Putra Dede Firmansyah Dede Firmansyah Saefudin Dedi Darwis Deni Gunawan Desyanti Desyanti Diah Puspitasari Dian Ardiansyah Dian Ardiansyah Dyah Ayu Megawaty Eka Dyah Setyaningsih Eka Fitriani Eka Fitriani Eka Fitriani Eka Fitriyani Elah Nurlelah Fachri, Muhamad Farah Diba Azkia Faruk Ulum Faruk Ulum Fuad Nur Hasan Haliza Ramadhanti, Pristya Harefa, Kristine Haryani Hasan, Fuad Nur Henny Leidiyana Herdian Pratama Hetty Rohayani I Gede Iwan Sudipa Irfan Ridwan Jananto Watori Junhai Wang Junhai Wang Junhai Wang Junhai Wang Kamil, Anton Abdul Basah KOMALASARI, YULI Kristine Harefa Martenia, Rina Masngud Megawaty, Dyah Ayu Mesran, Mesran Mochamad Wahyudi Munawar Abdul Azis Oktaviyani Oktaviyani Palupiningsih, Pritasari Pasaribu, A. Ferico Octaviansyah Perani Rosyani Pristya Haliza Ramadhanti Pristya Haliza Ramadhanti Rachilsyah Ramdhani Efendi Raditya Rimbawan Oprasto Rahmat Hidayat Ramadhani, Arya Richardus Eko Indrajit Rifky Permana Rifqi Rizaldi Rina Martenia Rizqi Nur Esmeralda Rosiun Universitas Bina Sarana Informatika Roy Prasetya, Andreas Royadi - Royadi Royadi Royadi, Royadi Ryan Randy Suryono Salman Alfarizi SALMAN ALFARIZI Samudi Sari Dewi Universitas Bina Sarana Informatika PSDKU Pontianak Setiawansyah Setiawansyah Siti Khotimatul Wildah Siti Marlina Siti Marlina, Siti Sopiyan Dalis Sumanto Sumanto Titik Misriati Tri Wahyuni Tri Wahyuni tri wahyuni Ulum, Faruk Wahyudi, Agung Deni Walim Walim Wang, Junhai Yarimani Laia Yosep Nuryaman