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All Journal JURNAL SISTEM INFORMASI BISNIS CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JIKO (Jurnal Informatika dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Computer Based Information System Journal Wacana: Jurnal Ilmiah Ilmu Komunikasi JURIKOM (Jurnal Riset Komputer) Journal on Education KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Mantik Prosiding National Conference for Community Service Project JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Information System and Technology (JOINT) Bulletin of Information Technology (BIT) 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) INOVTEK Polbeng - Seri Informatika 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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Analisa Penerimaan Aplikasi Mypertamina dengan Model Unified Theory of Acceptance and Use of Technology 2 dan Information Systems Success Model Mangapul Siahaan; Suwarno Suwarno; Chintya Lorenz
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6712

Abstract

This study aims to analyze the factors that influence the acceptance of the MyPertamina application in Batam City using the UTAUT2 model approach and the IS Success Model. This research model includes variables such as performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, information quality, system quality, service quality, behavioral intention, user satisfaction, and use behavior. Data were collected from 177 respondents who used the MyPertamina application in Batam City and analyzed using the PLS-SEM and SEM (Amos) methods. The results showed that factors such as performance expectancy, effort expectancy, supporting conditions, information quality, service quality, user satisfaction, and behavioral intention had a significant influence on the acceptance of the MyPertamina application. In contrast, hedonic motivation and system quality did not show a significant influence. This study provides important insights into the factors that influence the acceptance of technology-based applications and provides recommendations for the development of similar applications in the future.
Yolov12N: Implementation and Measuring an Ingredient-Detection Recipe App for Household Food-Waste Reduction Suwarno Suwarno; Jackson Jackson; Deli Deli
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16494

Abstract

Household food waste remains pervasive, driven by suboptimal meal planning and the underuse of available ingredients, with particularly acute impacts in Indonesia. This study presents a mobile application using YOLOv12n for multi-ingredient detection that translates recognized items into actionable recipes, and it evaluates user acceptance through the Technology Acceptance Model. On this implementation, the detection module attains mAP at 0.5 of 0.579 and mAP from 0.5 to 0.95 of 0.331. This study implements a Flutter application with YOLOv12n multi-ingredient detection integrated with TheMealDB and observes 219 users. The instrument validity is established and reliability is strong. While, Structural Equation Modelling supports 3 hypotheses, meanwhile Perceived Usefulness to Behavior Intention is not significant, indicating an indirect pathway via attitude. In conclusion, the solution is feasible for daily use, and strengthening perceived usefulness and ease of use appears to be a promising route to increase adoption and help reduce household waste.
Examining the Impact of Software Testing Practices on Software Quality in Batam Software Houses Suwarno; Syaeful Anas Aklani; Nellsen Purwandi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5bmdas03

Abstract

This research aimed to investigate the impact of software testing practices on software quality in software companies in Batam, Indonesia. It focused on identifying key factors such as software testing knowledge, software testing approach, and software testing complexity and analysing their correlation with software quality. Data was collected from 48 respondents, including project managers, developers, and QA teams, using a questionnaire distributed via Google Forms and convenience sampling. The questionnaire was designed based on related studies to ensure relevance to the respondents’ roles. Regression analysis identified significant impacts of testing complexity, approach (p = 0.000), and knowledge (p = 0.003) on software quality. The F-test result (F = 32.622) confirmed a strong relationship between testing practices and software quality. These findings emphasise the critical role of robust testing strategies in enhancing software quality. For companies in Batam, the study offers actionable insights, including adopting structured frameworks and preferable action on testing approaches. Implementing these strategies can help organisations improve software outcomes and maintain competitiveness in the evolving software development landscape.
Implementasi Machine Learning berbasis Browser Extension untuk Deteksi URL Phishing menggunakan Logistic Regression Classification Suwarno Suwarno; Vincent Capricornness; Yefta Christian
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18882

Abstract

Phishing is an increasingly prevalent cyber threat that is difficult to counter with traditional methods such as blacklists, which often fail to swiftly recognize new phishing websites. This research addresses the issue by developing a machine learning-based phishing URL detection system implemented as a browser extension called PhishBonk, using Logistic Regression for classification. The development process encompassed the collection of a dataset consisting of 134,850 legitimate URLs and 100,945 phishing URLs, data preprocessing, URL feature extraction, model training and evaluation, and finally integrating the trained Logistic Regression model into the PhishBonk extension to enable automatic real-time detection. Experimental results demonstrate that the Logistic Regression model achieved an accuracy of approximately 99.53% in distinguishing phishing URLs from legitimate ones. Furthermore, a System Usability Scale (SUS) evaluation yielded an average score of 81%, indicating that the PhishBonk extension is user-friendly and well-received. These findings suggest that the proposed machine learning-based browser extension effectively provides real-time, accurate phishing detection while ensuring a positive user experience.
Machine Learning-Based Prediction of Muscle Hypertrophy: A Random Forest Approach Using Training and Nutrition Data Suwarno Suwarno; Alex Winarli; Herman Herman
JURNAL INFORMATIKA DAN KOMPUTER Vol 10, No 1 (2026): February 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v10i1.2426

Abstract

Muscle hypertrophy is defined as the enlargement of muscle fibers through resistance training stimulation, and remains a primary goal for many individuals engaged in structured exercise programs. However, response to training may vary significantly due to genetic, physiological, and lifestyle factors. As a result, many individuals struggle to predict whether a given training program will effectively support their muscle growth goals, often leading to inefficient training strategies and suboptimal outcomes. The lack of predictive tools that can estimate muscle hypertrophy outcomes based on mesurable variables highlights the need for data driven approaches in personal fitness planning. Therefore, this study aims to develop a Muscle Hypertrophy prediction model using the Random Forest algorithm. The data used are synthetic data generated from ChatGPT, which consists of 500 samples with 5 relevant features. Models were developed with 80% train 20% test using R2 Metrics. The results showed that the optimized Random Forest model achieved an accuracy of 79% on R2. These findings indicate that the method used can help predicting the muscle growth by measurable metrics. So it can be used as a tool in Personal Fitness goal. 
Sentiment Analysis of Instagram Reviews: Exploring Neutral Sentiments using Support Vector Machines Hendi Sama; Inov Santoso; Suwarno Suwarno
JURNAL INFORMATIKA DAN KOMPUTER Vol 10, No 1 (2026): February 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v10i1.2515

Abstract

The rapid growth of mobile applications necessitates a better understanding of user feedback via app store reviews. This study analyzes 1,000 Indonesian-language Google Play Store reviews of the Instagram application using the Support Vector Machine (SVM) algorithm to classify sentiments into positive, negative, and neutral categories. The primary objectives are to evaluate the effectiveness of SVM and investigate the impact of neutral sentiments on model performance. The methodology involved preprocessing steps such as text cleaning, slang normalization, stop-word removal, and stemming with Sastrawi, followed by TF-IDF feature extraction. A LinearSVC model was optimized using GridSearchCV and five-fold cross-validation. The model achieved 64.5% accuracy and a Macro-F1 score of 0.4701, outperforming Multinomial Naïve Bayes and Logistic Regression baselines. Further analysis revealed that neutral sentiments significantly affect performance; removing this class increased Macro-F1 to 0.6965. Additionally, probability thresholding and class-weight balancing improved neutral-class recognition, raising its F1-score from 0.1429 to 0.1778. These findings indicate that while SVM is effective for Indonesian app-review sentiment analysis, neutral sentiment remains a classification challenge requiring specific handling strategies.
Co-Authors Afandi Afandi Afandi Alex Winarli 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 Chintya Lorenz Chris Tan Christian, Yefta Daniel Adventus Davina Davina Davina Deli Deli Derrick Derrick Dessy Amelia Dimas Firmansyah Nasution Dirson Wiratama Edi Santoso Endrico Endrico Erwin Evi Yanti Felix King Lie Fenky Fenky Gracea Venice Hendi Hendi Herman Herman Herman Inov Santoso Jackson Jackson Jeffrey Rustandi Jervis William Jesen Jeverlino Jessica Christina Jessica Novia Jetset Jetset Jetset Jocelyn Jocelyn Jocelyn Jocelyn Joen Lie Jon Susanto Jonathan Jonathan Jonathan Jonathan Josua Yoprisyanto Joyslin Joyslin Julianto Julianto Juven Gautama Juven Gautama Kevin Gautama Kevin Indra Bhaskara Kevin kevin Kristianti Kristianti Kristianti Kristianti Leon Salim 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 Nellsen Purwandi 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 Syahputra, Bayu Teddy Sanjaya Tedy Fernando Valene Fortuna Lim Vanessa Riarta Atmaja Vendryan Vendryan Veni Sisca Verren Calystania Vicco Leonardo Vicky Tantri Vincent Capricornness Vincent Eng Vincent Vincent Vinson Vinson Violen Anjeli Anggraini Violin Anjeli Anggraini Vionna Vionna Vira Vira Wendy Wendy Wesly Wesly Wibowo, Tony William Surya Jaya William Surya Jaya Yudi Hartanto Yudi Hartanto