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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnas Nasional Teknologi dan Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Panrita Abdi - Jurnal Pengabdian pada Masyarakat JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Komputer Terapan JURNAL ILMIAH INFORMATIKA ILKOM Jurnal Ilmiah Computer Based Information System Journal JSiI (Jurnal Sistem Informasi) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Informasi dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) Ihsan: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Multidisiplin Altruis: Journal of Community Services Journal of Applied Data Sciences Jurnal Mandiri IT Jurnal Puan Indonesia Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Medani : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Ibnu Sina Jurnal Pengabdian Barelang Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
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Penerapan Metode Fuzzy Sugeno Untuk Menentukan Kelayakan Pengiriman Limbah Barang Berbahaya Dan Beracun Alfannisa Annurrallah Fajrin; Tukino Tukino
Prosiding Vol 5 (2023): SNISTEK
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/psnistek.v5i.8118

Abstract

PT Semesta Citra Alam Batam is engaged in waste management and is located in the city of Batam. The waste collected by PT Semesta Citra Alam Batam is gathered at a waste collection warehouse located in the Industrial Waste Management Area of Batam City. The collected waste must be transported to waste utilization or hazardous waste disposal facilities. The data collection technique in this research is through observation and interviews conducted at PT Semesta Citra Alam Batam. The data analysis method used is fuzzy logic with the Sugeno method, which involves four stages: fuzzification, forming a fuzzy knowledge base, inference engine, and defuzzification. The research results from the application of fuzzy logic to determine the shipment of hazardous waste (B3 waste) at PT Semesta Citra Alam Batam involve both input and output variables. The input variables consist of load, content, and the output variable is a decision made using Matlab. These variables will generate rules that will be used to determine whether to send the waste or not.
Pelatihan Membangun Keunggulan Bersaing UMKM Melalui Pemasaran Online Di Kota Batam Tukino Tukino; Erlin Elisa; Algifanri Maulana; Yvonne Wangdra; Ronald Wangdra; Suvianto Wangdra
Prosiding Vol 5 (2023): SNISTEK
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/psnistek.v5i.8133

Abstract

The implementation of community service activities will take the form of Empowerment of E-Commerce-Based Marketing located at Rindang Garden Block B2 No. 04, Buliang Village, Batu Aji District, Batam. Based on the field interview results, the Small and Medium-sized Enterprises (UMKM) Rafflesia face challenges in marketing their products. In general, UMKM businesses have limitations in mastering information technology facilities and marketing media that are not well-known to the public. Conventional business owners feel that accounting recording is a cumbersome task. The training method used is to provide training through an online website. The methods used in the development of UMKM Rafflesia include survey methods, lecture methods, discussion methods, and training methods. The sustainability of the results of this coaching activity is expected to enable UMKM Rafflesia to manage online marketing effectively.
Analysis of the Use of Distance Learning Technology in Universities in the Riau Islands Province with the Technology Acceptance Model (TAM) Sama, Hendi; Wibowo, Tony; Tukino
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.5836

Abstract

The problem that is the focus of the research is the use of distance learning technology at universities in the Riau Islands Province using the Technology Acceptance Model approach. This research aims to analyze user perceptions on ease of use, usability, usage behavior, and use of distance learning technology. This research aims to determine user perceptions of distance learning technology in universities in the Riau Islands Province. Apart from that, this research also aims to evaluate the influence of the ease of use perspective, usability perspective, and usage behavior on the use of distance learning technology. The research results show that research respondents, who are academics and staff at universities in the Riau Islands Province, have a high perception of distance learning technology. From the test results, it was found that there was no significant influence between the ease of se perspective and the usability perspective on the use of distance learning technology. However, usage behavior has a significant influence on the use of distance learning technology. Apart from that, these three variables together have an influence of 69.70% on the use of distance learning technology.
Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision Akhiyar, Dinul; Tukino, Tukino; Defit, Sarjon
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2253.34-43

Abstract

The application of deep learning in various sectors continues to grow due to its ability to provide efficient and effective solutions to complex problems. One significant implementation is in object detection, such as identifying Indonesian rupiah banknotes. This innovation aims to assist individuals with visual impairments in using money more effectively. At present, visually impaired individuals rely on conventional methods, such as identifying banknotes by touch, folding them in specific ways, or seeking assistance from others. However, these methods are often time-consuming, prone to error, and lack practicality in everyday situations. In this project, a system was developed using the Convolutional Neural Network (CNN) architecture combined with the YOLO (You Only Look Once) algorithm. YOLO is renowned for its speed and accuracy in real-time object detection, making it an ideal choice for detecting banknotes in moving images. The training dataset included 1,260 images, and the model underwent 7,000 iterations during training. As a result, the system achieved a high mean Average Precision (mAP) score of 97.65%, demonstrating its robustness and precision. For validation, 140 test images were utilized, which yielded an impressive mAP value of 97.5%. To further evaluate the system's reliability, tests were conducted under varying conditions, such as banknotes with creases, folds, or different lighting scenarios. These tests resulted in an mAP score of 88%, showcasing the system's adaptability to real-world conditions. This system provides significant benefits for individuals with visual impairments by offering a practical, efficient, and accurate solution for recognizing banknotes. With this technology, visually impaired users can interact with currency independently, reducing their reliance on others and traditional, less practical methods. This innovation not only enhances their autonomy but also fosters inclusivity in financial transactions. By integrating this system into mobile applications or wearable devices, its accessibility and usability can be further improved, paving the way for a broader societal impact.
Iris Identification Using Resnet Iris Feature Extraction Architecture For Better Biometric Security Sama, Hendi; Tukino, Tukino; Siahaan, Mangapul; Titoni, Erica
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1166

Abstract

Iris recognition is widely acknowledged as one of the most reliable biometric modalities due to its high uniqueness, rich textural patterns, and long-term stability. Unlike other biometric traits, iris characteristics resist forgery, aging effects, and environmental variations, making it suitable for high-security applications. Recently, convolutional neural networks (CNNs) have been extensively applied in iris recognition to improve feature representation and classification accuracy. However, many CNN-based approaches still depend on conventional segmentation and handcrafted features, which reduce robustness under noisy data, illumination variations, occlusions, or unconstrained environments. To address these limitations, this study proposes an enhanced iris identification framework combining a modified T-Net for precise segmentation with deep residual feature extraction for improved discrimination. Unlike conventional systems focus mainly on classification, the proposed approach emphasizes segmentation-driven feature consistency, ensuring extracted features originate from accurately localized iris regions. This design enhances stability and reliability, particularly under challenging imaging conditions. The framework leverages transfer learning and efficient representation learning strategies, enabling high accuracy even with a limited labelled data. Evaluations on three benchmark datasets CASIA-IrisV4, IITD Iris Database, and UBIRIS.v2 covering both controlled and less-constrained acquisition scenarios. Results show that it achieves classification accuracy of up to 98.35%, while maintaining computational efficiency suitable for deployment. The proposed architecture offers a robust, data-efficient, and scalable solution for secure biometric authentication, with strong potential for real-world applications such as access control, identity verification, and high-security authentication systems.
Web-Based Warehouse Inventory System Using the Waterfall Method: A Case Study at Satria Wholesale Mart Melisa; Tukino; Agustia Hananto; April Lia Hananto
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.610

Abstract

In the digital era, manual warehouse inventory management is still challenging for many business people, including Satria Wholesale Mart. The main problems also faced are irregularities in recording incoming and outgoing goods, low accuracy of stock data, delays in reporting, and difficulties in tracking stock in real time. This finding aims to design and build an efficient and effective web-based warehouse inventory system using the Waterfall method. The finding method used is applied findings with a descriptive qualitative approach, which also aims to describe in detail and systematically the phenomena that also occur in the field and how new systems can be developed to solve these problems. The findings show that applying the waterfall method in developing a web-based inventory information system at PT Herso Ticep Indonesia has also yielded satisfactory results. The system that has also been developed has succeeded in meeting the needs of companies in inventory management, improving operational efficiency, and optimizing inventory management. These findings imply that companies can improve their operational efficiency and optimize inventory management by implementing this information system. The findings could also guide other companies that want to develop similar systems.
Text Data Classification Using the SVM Model on the LMDB Minecraft Dataset Bayu Yoga Astario; Tukino; Agustia Hananto; Fitria Nurapriani; Elfina Novalia
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.620

Abstract

Text classification is a fundamental task in Natural Language Processing (NLP) aimed at categorizing text data into predefined classes. This study implements a Support Vector Machine (SVM) model to classify text data from the LMDB Minecraft Dataset, which contains user reviews of the Minecraft movie. The research involves text preprocessing, TF-IDF feature extraction, and SVM model training. The classification results are evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. The comment data is also analyzed based on the timing of their appearance in the movie. All processes are visualized in diagrams; the final results are saved in Excel format. The SVM model performs adequately on informal and domain-specific language data, providing a foundation for future research in similar text classification contexts.
Inventory control system using threshold method for automotive industry Fadillah, Arya; Priyatna, Bayu; Nurapriani, Fitria; Tukino, Tukino
Jurnal Mandiri IT Vol. 14 No. 4 (2026): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i4.528

Abstract

Inventory control is a critical aspect in manufacturing companies to ensure the availability of materials and support smooth production processes. Ineffective inventory management can lead to stock shortages or overstock conditions, which may disrupt operational activities. This study aims to develop a web-based inventory control system using the Reorder Point (ROP) method to optimize stock management in a manufacturing environment. The system is designed to monitor stock levels, calculate Average Daily Usage (ADU), safety stock, and reorder points automatically. When stock reaches a predefined threshold, the system provides notifications to assist decision-making in replenishment processes. The development method used in this study is the Waterfall model, including analysis, design, implementation, and testing stages. The system is implemented using the CodeIgniter framework and MySQL database. Testing results show that the system can accurately calculate ROP values and effectively provide early warnings for low stock conditions. Therefore, the proposed system can improve efficiency, reduce the risk of stock shortages, and support better inventory management in manufacturing companies. The system achieved an accuracy rate of 100% in calculating ROP values based on Black Box testing results, and successfully generated real-time notifications for all critical stock conditions. The novelty of this study lies in the integration of threshold-based logic with automated notification features in a web-based system, which provides a more responsive and practical solution compared to previous inventory control approaches that rely on manual monitoring.
Co-Authors Abdul Latif Alfannisa Annurrallah Fajrin alfannisa annurrallah fajrin Alfannisa Annurrullah Fajrin Alfannisa Annurullah Fajrin Alfannisa Fajrin Algifanri Maulana, Algifanri Algifari Maulana Ali Abrar Amrizal . Amrizal Amrizal Amrizal Amrizal Anggia Arista Anggia Dasa Putri April Lia Hananto Argo Putra Prima Arif Rahman Hakim Arif Rahman Hakim Arnomo, Sasa Ani Bahariandi Aji Prasetyo Baru Harahap Bayu Yoga Astario Citra Indah Asmarawati Dian Efriyenti Dian Efriyenti Dinul Akhiyar Djumhadi Djumhadi Elisa, Erlin Elsya Tarigan Paskaria Loyda Tarigan Elva Susanti Erlin Elisa Fadillah, Arya Fitria Nurapriani Hananto, Agustia Handoko, Koko Harman, Rika Melisa Muhammad Taufik Syastra Mungkap Mangapul Siahaan NANDA HARRY MARDIKA Nofriani Fajrah, Nofriani Novalia, Elfina Nurapriani, Fitria Priyatna, Bayu Realize, Realize Rizki Prakasa Hasibuan Rohman Nurafan Putra Pratama Ronald Wangdra Sama, Hendi Sarjon Defit Steven Famy Suvianto Wangdra Syaeful Akbar Syahril Effendi Titoni, Erica Tony Wibowo, Tony Winda Yohanna Siahaan Young, Filbert Yvonne Wangdra