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All Journal Jurnal Buana Informatika Jurnal Pendidikan dan Kajian Seni Sistemasi: Jurnal Sistem Informasi Techno Nusa Mandiri : Journal of Computing and Information Technology JURIKOM (Jurnal Riset Komputer) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Minda Baharu JURSIMA (Jurnal Sistem Informasi dan Manajemen) Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Digit : Digital of Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jutsi: Jurnal Teknologi dan Sistem Informasi Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer J-Com (Journal of Computer) J-Intech (Journal of Information and Technology) BEES: Bulletin of Electrical and Electronics Engineering Jurnal Komtekinfo JURSIMA Inspiration: Jurnal Teknologi Informasi dan Komunikasi JCOME (Journal of Computing and Multimedia Enginering Bulletin of Informatics and Data Science Jurnal Sistem Informasi dan Manajemen Scientific literacy Innovation and Technology Journal JoDENS: Journal of Digital Ecosystem for Natural Sustainability Al'Adzkiya International of Computer Science and Information Technology Journal Journal of Information Technology
Pernando, Yonky
Universitas Universal
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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ANALISIS SENTIMEN ULASAN E-COMMERCE SHOPEE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES angreyani, jeny; Pernando, Yonky
J-Com (Journal of Computer) Vol. 5 No. 1 (2025): MARET 2025
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v5i1.3570

Abstract

Abstract: In this study, an analysis of the use of the Naive Bayes algorithm for sentiment analysis of reviews from Shopee app users on the Google Play Store was conducted, with classification divided into three categories: positive, negative, and neutral. To improve data quality, a preprocessing process was carried out with stages of cleaning, case folding, normalization, stop word removal, stemming, and tokenizing. Next, the text is formatted using the TF-IDF method to facilitate classification. For this data, the Naive Bayes model is used, which has an accuracy rate of 87% in detecting sentiment. Positive and negative categories can be easily identified compared to neutral sentiments due to the smaller amount of neutral data. Overall, the Naive Bayes algorithm successfully analyzed user sentiments well. The research can be developed with other algorithm methods, such as SVM, K-NN, or Decision Tree, in order to compare the performance of various algorithms.Keywords: sentiment analysis; naive bayes; user reviews; e-commerce; shopee Abstrak: Dalam penelitian ini dilakukan analisis penggunaan algoritma Naive Bayes untuk analisis sentimen review dari pengguna aplikasi Shopee di Google Play Store, klasifikasi dibagai menjadi 3 kategori yaitu positif, negatif, dan netral. Untuk meningkatkan kualitas data, dilakukam proses preprocessing dengan tahap cleanimg, case folding, normalisasi, stopword removal, stemming, dan tekonezing. Selanjutnya, teks diformat menggunakan metode TF-IDF untuk memudahkan klasifikasi. Untuk data ini, model Naive Bayes digunakan, yang memiliki tingkat akurasi 87% dalam mendeteksi sentimen. Kategori positif dan negatif dapat dengan mudah diidentifikasi dibadingkan sentiemen netral karena jumlah data netral yang lebih sedikit. Secara keseluruhan, algoritma Naive Bayes berhasil menganalisis perasaan pengguna dengan baik. Penelitian dapat dikembangkan dengan algoritma metode lain, seperti SVM, K-NN, atau Decision Tree, guna membandingkan kinerja berbagai algoritma.Kata kunci: analisis sentiment; naive bayes; ulasan pengguna; e-commerce; shopee
Perancangan Aplikasi Manajemen Proyek Pada PT. Sintech Berkah Abadi Berbasis Web Yuni Roza; Yonky Pernando; Raymond Erz Saragih; Kaharuddin Kaharuddin; Ihsan Verdian
J-INTECH ( Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.868

Abstract

Project management is the activity of organizing, leading, and controlling company resources to achieve desired goals. PT. Sintech Berkah Abadi greatly needs such a system to support and improve services for the satisfaction of both employees and customers in their company's project activities. As a company operating in the software provider industry in the global market, offering business solutions with advanced technology, they face various challenges in data processing. These challenges include data errors or losses occurring in various stages such as application creation, data processing, approval processes, and project reporting. Therefore, PT. Sintech Berkah Abadi requires a web-based project management system application. The data collection methods used in this research are observation, interviews, and literature review, while the analysis method used is SWOT. The implementation of this project management system application can provide a solution for the company, making work processes more effective and efficient.
Simulasi Rumah Pintar Berbasis IOT Menggunakan Aplikasi Cisco Paket Tracer Mickhel; Anthony; Thionuartha, Kevin; Pernando, Yonky
Journal of Digital Ecosystem for Natural Sustainability Vol 5 No 2 (2025): Desember 2025
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v5i2.278

Abstract

The development of Internet of Things (IoT) technology has driven the creation of smart home systems, which enhance comfort, security, and energy efficiency for occupants. This study aims to simulate an IoT-based smart home system using Cisco Packet Tracer as a visualization and network testing tool. In this simulation, various smart devices—such as automated lights, temperature sensors, fans, and digital doors—were configured and integrated using IoT communication protocols. The simulation results demonstrate that Cisco Packet Tracer can be effectively used to model interactions between devices in a smart home network and test automated responses based on input from installed sensors. This simulation is expected to serve as a foundation for developing more complex smart home systems and as a learning medium for understanding basic IoT concepts in home automation
Performance Analysis of YOLO11 for Welding Defect Detection Under Low-Light Conditions Yonky Pernando; Raymond Erz Saragih; Masparudin Masparudin; Agus Suwandi; Ihsan Verdian; Fazlul Rahman; Ilwan Syafrinal
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v7i1.370

Abstract

This study aims to analyze the impact of image enhancement techniques on welding defect detection performance using a deep learning-based YOLO11L model. The dataset consists of 1392 welding images categorized into four classes: Good, Crack, Porosity, and Bad, with a significant class imbalance. Five image enhancement methods were evaluated, namely Zero-DCE, RETINEX, CLAHE, Supervision, and Gamma Correction, and compared against a no-enhancement baseline. Image quality was assessed using SSIM, and PSNR, while detection performance was evaluated using Precision, Recall, F1-Score, and mAP50. The results show that Gamma Correction achieves the best image quality improvement, with an average SSIM of 0.569, and a PSNR of 18.862 dB. However, contrasting results are observed at the detection stage, where 0.7772 and 0.6969, respectively, for the Gamma Correction-based model while for the baseline model without enhancement outperforms the enhanced model, achieving a mAP50 of 0.7098 and an F1-Score of 0.6965. This finding reveals a paradox where improved visual image quality does not necessarily lead to better object detection performance. This study highlights the importance of end-to-end evaluation in computer vision systems, particularly in industrial inspection applications, and demonstrates that original images, which are closer to the pretrained data distribution, may yield better detection results than heavily enhanced images.
Enhancing Air Traffic Forecasting Accuracy at Hang Nadim Airport Using ARIMA-Neural Network Masparudin Masparudin; Abdullah Abdullah; Raymond Erz Saragih; Yonky Pernando; Ilwan Syafrinal
SISTEMASI Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6265

Abstract

Passenger traffic fluctuations at Hang Nadim International Airport exhibit extreme volatility influenced by the unique characteristics of the Free Trade Zone (FTZ). Single statistical methods often fail to capture non-linear patterns in this high-variability data. Therefore, this study proposes a Hybrid ARIMA-Neural Network model to enhance forecasting accuracy. The primary variable used is the total monthly passenger volume (arrivals and departures). The research stages began with data preprocessing (80:20 train-test ratio), linear component modeling using ARIMA, residual extraction, and non-linear component modeling using Multi-Layer Perceptron (MLP) to correct residual errors on a one-step-ahead basis. Evaluation results show that the standalone ARIMA model is slow to anticipate extreme surges, resulting in a Mean Absolute Percentage Error (MAPE) of 23.75%. The hybrid model integration proved successful in compensating for these weaknesses, reducing the MAPE value to 12.51%. This achievement represents a 47.33% error reduction from the baseline. In terms of novelty, this hybrid approach provides a highly reliable computational solution for airport management with dual characteristics (tourism and industry) in mitigating uncertainty in capacity planning.
Mango and Banana Ripeness Detection based on Lightweight YOLOv8 Raymond Erz Saragih; Akhmad Rezki Purnajaya; Ilwan Syafrinal; Yonky Pernando; Yodi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.8895

Abstract

Fruits like bananas and mangoes are harvested after reaching a specific ripeness stage. Traditionally, farmers rely on manual inspection to determine ripeness, a process that can be tedious, time-consuming, expensive, and subjective. This work proposes an automatic bananas and mangoes ripeness detector utilizing computer vision technology. The detected bananas and mangoes fall into two classes: ripe and unripe. The state-of-the-art YOLOv8 architecture serves as the core of the detector. Three YOLOv8 variants, YOLOv8n, YOLOv8s, and YOLOv8m, were investigated for their performance. Results show that YOLOv8s achieved the highest overall performance, 0.9991 recall, and a mean Average Precision (mAP) of 0.8897. While YOLOv8m achieved the highest precision of 0.9995, YOLOv8n is the most miniature model, making it suitable for deployment on devices with limited resources.
Visual Interaktif Di Panggung Untuk Edukasi 3R: Implementasi Animasi Berbasis Mapping Menggunakan Resolume Arena Meidianto song; Yonky Pernando
J-Com (Journal of Computer) Vol. 6 No. 1 (2026): MARET 2026
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v6i1.4654

Abstract

Abstract: Environmental education plays a crucial role in raising public awareness of sustainability issues. The 3R concept (Reduce, Reuse, Recycle) has long been a fundamental strategy for waste management and the promotion of sustainable lifestyles; however, its dissemination is often presented in a textual manner that is less engaging for the general public. This study proposes an interactive visual approach utilizing projection mapping technology through Resolume Arena software to create an immersive educational stage performance. The animated visuals are narratively designed to enhance audience understanding of the 3R concept. The simulation results indicate that this approach has strong potential to improve audience engagement and information retention across diverse age groups. Keywords: 3R; interactive animation; environmental education; resolume arena; visual mapping. Abstrak: Pendidikan lingkungan menjadi salah satu pilar penting dalam membentuk kesadaran masyarakat akan isu keberlanjutan. Konsep 3R (Reduce, Reuse, Recycle) telah lama menjadi strategi utama dalam mengelola sampah dan mendukung gaya hidup berkelanjutan. Namun, penyampaian informasi mengenai 3R masih sering bersifat tekstual dan kurang menarik bagi masyarakat umum. Dalam studi ini, dikembangkan pendekatan visual interaktif melalui teknologi projection mapping menggunakan perangkat lunak Resolume Arena untuk menciptakan pertunjukan panggung edukatif yang imersif. Visual animasi dirancang secara naratif untuk menstimulasi pemahaman audiens terhadap konsep 3R. Hasil simulasi menunjukkan bahwa pendekatan ini berpotensi besar dalam meningkatkan keterlibatan dan retensi informasi pada audiens dari berbagai usia. Kata kunci: 3R; animasi interaktif; edukasi lingkungan; resolume arena; visual mapping.
Lightweight CNNs for Eggplant Leaf Disease Classification on a Balanced Dataset: A Comparative Study of MobileNetV3 and EfficientNetV2-B2 Masparudin Masparudin; Yonky Pernando; Ihsan Verdian; Fazlul Rahman
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.v5i1.166

Abstract

Eggplant (Solanum melongena) is a vital agricultural commodity, but its yield is highly vulnerable to foliar diseases. Early and accurate detection using deep learning is essential for effective crop management. However, deploying automated detection in real-world agricultural settings faces two primary challenges: (1) severe classification bias caused by complex background noise and data imbalance, and (2) extreme computational constraints that hinder the deployment of conventional deep learning models on farmers' edge devices. This study presents a robust methodology for classifying four conditions of eggplant leaves (Healthy, Leaf Spot, Mosaic Virus, and Insect Pest) by implementing an automated background removal technique and targeted data augmentation, resulting in a perfectly balanced dataset of 1,400 images. Furthermore, this research conducts a comparative analysis between two distinct categories of lightweight Convolutional Neural Networks (CNNs): MobileNetV3-Large (representing ultra-lightweight architectures with 224x224 input resolution) and EfficientNetV2-B2 (representing medium-lightweight architectures with 260x260 input resolution). The models were evaluated based on their accuracy, loss convergence, and computational efficiency using an 80:20 data split and early stopping callbacks to prevent overfitting. Experimental results demonstrate that both models achieved exceptional performance. EfficientNetV2-B2 exhibited superior stability and precision, achieving a peak validation accuracy of 97.50% and a validation loss of 0.073. Meanwhile, MobileNetV3-Large reached a validation accuracy of 96.07% with significantly faster training iterations. These findings indicate that while EfficientNetV2-B2 is highly recommended for precision-critical agricultural diagnostics, MobileNetV3-Large remains a formidable alternative for deployment on edge devices with extreme computational constraints
Efektivitas Integrasi Augmented Reality Dalam Pembelajaran Tematik Kelas 3 Siswa Sekolah Dasar Menggunakan Model ARCS Kaharuddin Kaharuddin; Musliadi KH; Ilwan Syafrinal; Yonky Pernando
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.v6i2.6070

Abstract

The quality of education in Indonesia has become a serious problem in recent years. Various problems such as poor education management, inequality of facilities between urban and rural areas, and traditional learning methods that do not follow the development of modern technology make the quality of education low. This study aims to evaluate the effectiveness of the application of Augmented Reality (AR) in Theme 3 Thematic learning in Elementary Schools. The research method uses the MDLC (Multimedia Development Life Cycle) approach, with testing based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) Motivation Model. In addition, a test of students' level of understanding was carried out using the Student Activity Sheet (LKPD). Data was obtained from 32 grade 3 elementary school students. The test results showed that the AR application had an excellent interpretation/very effective interpretation score of 86%, with details of 90% for Attention, 82% for Relevance, 81% for Confidence, and 84% for Satisfaction. The level of students' understanding of the material is also high, with an average score of 85. So it can be concluded that the Augmented Reality application is very feasible to be applied in the Theme 3 Thematic learning process in Elementary Schools because it is able to effectively increase students' interest and understanding.
Aplikasi Pembelajaran Al-Qur’an "Madina" Memanfaatkan Teknologi Digital Pada Anak Usia Dini Berbasis Android Menggunakan Metode Rapid Application Development Musliadi KH; Kaharuddin Kaharuddin; Yuni Roza; Yonky Pernando
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.v6i2.6102

Abstract

Learning the Qur'an is the most important aspect in shaping a person's personality and morality, including in early childhood, especially those who adhere to Islam. Currently, the level of interest in learning the Qur'an in early childhood has decreased in various circles due to the influence of technological developments. Technological developments have changed children's behavior and attitudes in everyday life, especially in the use of smartphones which are more often used to play and watch movies via YouTube, Instagram, and Facebook than used to learn to read the Qur'an so that knowledge about reading the Qur'an is eliminated due to playing and watching movies. Along with the increasing influence of technology among children, there needs to be a breakthrough that can be utilized in learning the Qur'an in early childhood without changing the behavior of using smartphones. To design the "Madina" Qur'an learning, an appropriate, fast, and effective method is needed as a benchmark in the design cycle. The Rapid Application Development method is one of the many methods that are most often used to design Android-based applications, this is because this method only consists of four main cycles and focuses on the use of a short time in the design process. The results of the research and evaluation conducted obtained 54 respondents who were dominated by parents as the main companions in using smartphones. Children who use this application the most are in the age range under 4 years where on average they use the application for approximately 15-30 minutes under parental supervision. The Al-Qur'an learning application "Madina" can be accepted by users and parents, this is indicated by the evaluation results being at an average percentage of 86% with very strong criteria.
Co-Authors Abdullah Abdullah Adam, Steffi Adinda Tria Suci Adrianus Gultom Agus Suwandi Akhmad Rezki Purnajaya Aldi Setiawan, Aldi Amsar Amsar Andhika Anggeliani, Cynthia angreyani, jeny Anthony Anton, Oey Axel Arya Maitri Cundana Axel Arya Maitri Cundana Bahar, Hardi Candli, Candli Delvin Desyanti Fazlul Rahman Fazlul Rahman Febrianti, Eka Lia Givi Ofgivia Gusnardi Fernandus Gago Hendra, Doni Febri Herdiansya Herdiansya Huang Wijaya, Jacky Ihsan Verdian Ilwan Syafrinal Ilwan Syafrinal Ilwan Syafrinal Ismail, Asriyani Joko Kuswanto Jordan Hue Kaharuddin Kaharuddin Kaharuddin Kaleb Yefune Sun Kelvin Falencio Keven Leonando Hong Kurniawan, Ade Lika, Sudiharyanto lim, sheren LUBIS, SYARIF HIDAYAH M. Ibnu Safari Marfuah, Marfuah Masparudin Masparudin Masparudin, Masparudin Meidianto song Meidianto, Meidianto Metta Paramita Mickhel Musliadi K H Nursaka Putra Ofgivia, Givi Prativi, Martina Purnajaya, Akhmad Rezki Raymond Erz Saragih Robi Robi Roni Zeki Roza, Yuni Ryan Prayoga Samsul Lutfi Saragih, Raymond Erz Simanjuntak, Nurchaya Sintia Sintia song, Meidianto Thionuartha, Kevin Titus Kristanto Verdian, Ihsan Vinson Deslian Wijaya Massie, Alamsyah Yanto, Endri Yodi Yodi Yuni Roza Yuni Roza Yuni Roza