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All Journal JURNAL SISTEM INFORMASI BISNIS Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Computer Based Information System Journal Wacana: Jurnal Ilmiah Ilmu Komunikasi JURIKOM (Jurnal Riset Komputer) Journal on Education Jurnal Mantik Prosiding National Conference for Community Service Project Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Information System and Technology (JOINT) JOINCS (Journal of Informatics, Network, and Computer Science) Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Community Engagement Project (Concept) Conference on Business, Social Sciences and Technology (CoNeScINTech) Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK) Jurnal Informatika
Suwarno Liang
Department Of Information System, Universitas Internasional Batam, Jl. Gajah Mada, Tiban Indah, Kec. Sekupang, Kota Batam, Kepulauan Riau 29426
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities 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 Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analysis of Rebranding the X Application on User Loyalty in Batam City Suwarno Suwarno; Mangapul Siahaan; Annisya Putri Nadhia
WACANA: Jurnal Ilmiah Ilmu Komunikasi Volume 22, No. 2 December 2023
Publisher : Universitas Prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/wacana.v22i2.3408

Abstract

The global market is currently experiencing very fierce competition. Companies compete to implement all marketing strategies to be superior in surviving this competition, one of which is a rebranding strategy by changing the brand image of the X application which was formerly known as Twitter. This study aims to determine whether the effect of rebranding can affect the loyalty of X’s users by assessing brand trust, brand prestige, and brand love. This research method uses a mixed method which is divided into two approaches, namely quantitative and qualitative using linear regression analysis. The results show brand image does not have a big influence on brand trust, brand prestige, and brand love. Furthermore, brand trust, brand prestige, and brand love have a positive influence on brand loyalty meaning that users are not too affected by the rebranding of X, but they will remain loyal to using the application.
Analisis Kesuksesan Aplikasi M-Paspor di Kota Batam dengan Menggunakan Model Delone dan Mclean Suwarno Liang; Mangapul Siahaan; Jocelyn Jocelyn
Jurnal Sistem Informasi Bisnis Vol 14, No 1 (2024): Volume 14 Nomor 1 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss1pp38-45

Abstract

M-Paspor is a new version of the APAPO application launched at the end of 2021. Since the launch of the M-Paspor application, Indonesian citizens have been directed to submit passport queue applications through the M-Paspor. Batam City is a city that is on international shipping routes and borders directly with Singapore and Malaysia. With these advantages, many Indonesian citizens travel through the City of Batam, causing passport applications in the City of Batam to increase. This research aims to analyze the success of the M-Passport application in the City of Batam using the DeLone and McLean model. The method used in this research is a mixed method of qualitative and quantitative, with 30 qualitative data and 500 quantitative data collected. This research utilizes SPSS and AMOS technology to test validity, reliability, SEM, descriptive statistics, and R Square. The results obtained from this research show that all dependent variables have a positive effect on the independent variables with R Square values obtained as much as 61.3%, 58.9%, and 61.9%. By conducting this research, it is hoped that it can help M-Paspor application developers and the TPI Batam Class I Immigration Office to pay more attention to the quality of information, systems, and services from M-Paspor to improve increase public user satisfaction in public services.
Rancang Bangun Marketplace Jasa Desain Dengan Menggunakan Metode Content-Based Filtering Suwarno Suwarno; Tedy Fernando
Prosiding Vol 4 (2022): SNISTEK
Publisher : LPPM Universitas Putera Batam

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

Abstract

The public's need for design services is getting higher, it opens up opportunities for freelancers to offer the design services. Marketplace is a solution to connect the service between users and freelancers. Therefore, the design of the service marketplace needs to be designed smart enough to be able to provide appropriate recommendations and accordance with the user needs. This study builds a web-based design service marketplace that implements a recommendation system with a content-based filtering method with a cosine similarity algorithm that is applied to two objects, namely tags defined by freelancers and users. The results of the study indicate that the design service marketplace system has provided recommendation results in the form of a list of freelancers according to user tags.
Perancangan Sistem Informasi UMKM De’Sate Batam melalui Analisis Pengendalian Internal Menggunakan COSO Framework Suwarno Suwarno; Verren Calystania; Veni Sisca; Jessica Novia; Vira Vira; Stephanie Stephanie
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.8071

Abstract

With advances in technology in the current era, technology can make it easier for MSMEs to record more accurate financial reports so they can continue to survive and adapt to various conditions. The purpose of the research conducted was to analyze the state of MSME De'Sate through internal control using the COSO framework and then to design an information system through the UML Class Diagram and UI Layout Form modeling. The research approach was used by conducting direct observations and interviews with De'Sate MSME owners, with the types of data being primary data and secondary data obtained through literature studies. The research results obtained are as follows: (1) In maintaining the continuity of its business, De'Sate implements internal control with the COSO concept, (2) system design has been made using use case diagrams, UML class diagrams, and the creation of UI Layout forms and systems that made accompanied by a Point Of Sales (POS) so that De'Sate can improve the efficiency of the customer service process.
Semi-Supervised Bullying Detection in Narrative Student Counselling Reports Using a Hybrid CNN-LSTM with Pseudo-Labelling Suwarno Suwarno; Muthia Andini; Mangapul Siahaan
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11512

Abstract

Bullying incidents in schools are often documented in narrative student counselling reports containing informal language, emotional expressions, and contextual dependencies, which pose challenges for automated text classification, particularly under limited labeled data conditions. This study aims to develop a bullying detection model for narrative student counselling reports using a Hybrid CNN-LSTM architecture combined with a pseudo-labelling-based semi-supervised learning approach. The proposed model is trained through a two-stage process, consisting of pre-training on approximately 70,000 publicly available abusive-language texts and fine-tuning using 1,000 anonymized student counselling reports validated by guidance counsellors. Pseudo-labelling is employed to expand the training data while preserving domain relevance and adhering to ethical considerations. Experimental results show that the proposed model achieves an accuracy of 0.8698, a recall of 0.8570, and an F1-score of 0.7951. Although the precision value (0.7415) is relatively lower, higher recall is prioritized to reduce the risk of overlooking potential bullying cases in the school counselling context. Comparative analysis with Logistic Regression and Linear SVM indicates that the Hybrid CNN-LSTM model demonstrates more stable performance when processing longer narrative inputs that require contextual interpretation. This study contributes empirical evidence on the effectiveness of semi-supervised deep learning for bullying detection in low-resource, narrative student counselling data, a setting that remains underexplored in prior work.
Development of an Automated Attendance System Based on Facial Recognition Using Convolutional Neural Networks (CNN) for Kaca Super Jaya MSME: Pengembangan Sistem Kehadiran Otomatis Menggunakan Pengenalan Wajah Menggunakan Convolutional Neural Network (CNN) terhadap UMKM Kaca Super Jaya Syaeful Anas Aklani; Jetset; Suwarno Suwarno
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1692

Abstract

Attendance management is a critical component of human resource administration, yet conventional methods such as manual sign-in sheets and card-based systems are often inefficient, error-prone, and vulnerable to manipulation. This study aims to design and implement an automatic attendance system based on face recognition using Convolutional Neural Networks (CNN) for UMKM Kaca Super Jaya. The proposed system replaces manual attendance by enabling real-time, contactless, and automated attendance recording through facial identification. An applied research approach with qualitative methods was employed, involving system development, direct observation, and structured interviews with users. The CNN model was trained using facial image datasets under various conditions, including different lighting levels, facial expressions, and viewing angles, to improve robustness and accuracy. The system architecture integrates a camera as input, a CNN-based face recognition model, a backend server, and a web-based dashboard for attendance monitoring and reporting. Experimental results show that the system achieved an average face recognition accuracy of 96%, demonstrating reliable performance even under suboptimal lighting and non-frontal face angles. The implementation significantly reduced attendance processing time, minimized human error, and lowered the potential for fraudulent practices such as proxy attendance. These findings indicate that CNN-based face recognition is an effective and practical solution for enhancing attendance management efficiency and accuracy in small and medium enterprises.
Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming Suwarno Suwarno; Anderson Arvando; Davina Davina; Brain Gantoro; Hendi Sama; Deli Deli
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.v7i2.1141

Abstract

Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.
Co-Authors Afandi Afandi Afandi Alviana Alviana Amalia Putri Yulandi Anderson Arvando Andry Andry Annisya Putri Nadhia Annisya Putri Nadhia Ari Firmansah Arief Fernando Brain Gantoro Caca Natasya Calvin Chin Chris Tan Christian, Yefta Daniel Adventus Davina Davina Deli Deli Derrick Derrick Dessy Amelia Dimas Firmansyah Nasution Dirson Wiratama Edi Santoso Evi Yanti Felix King Lie Fenky Fenky Gracea Venice Hendi Hendi Jeffrey Rustandi Jervis William Jesen Jeverlino Jessica Christina Jessica Novia Jetset Jocelyn Jocelyn Jocelyn Jocelyn Jon Susanto Jonathan Jonathan Jonathan Jonathan Joyslin Joyslin Julianto Julianto Juven Gautama Juven Gautama Kevin Gautama Kevin Indra Bhaskara Kevin kevin Kristianti Kristianti Kristianti Kristianti Malvin Huang Marvin Christian Marvin Christian Melna Caintan Melvan Melvan Melvin Melvin Melvy Devalia Mike Sonobe Pangihutannasa Moch Ihda Farhan Effendi Muhammad Faiz Mungkap Mangapul Siahaan Muthia Andini Philander Alvando Davian Ratu Olivia Ricky Fernando Rio Fernando Rio Riferro Lim Roma Sabet Manurung Roma Sebet Manurung Ryo Kusnadi Sama, Hendi Stephanie Stephanie Syaeful Anas Aklani, Syaeful Teddy Sanjaya Tedy Fernando Valene Fortuna Lim Vanessa Riarta Atmaja Vendryan Vendryan Veni Sisca Verren Calystania Vicco Leonardo Vicky Tantri Vincent Eng Vincent Vincent Vinson Vinson Violen Anjeli Anggraini Violin Anjeli Anggraini Vionna Vionna Vira Vira Wendy Wendy William Surya Jaya William Surya Jaya Yudi Hartanto Yudi Hartanto