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All Journal JISTECH: Journal of Information Science and Technology SMATIKA Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining EDUMATIC: Jurnal Pendidikan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Naibaho, Julius Panda Putra
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Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering

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SISTEM INFORMASI PENYULUH PERTANIAN DAN KELOMPOK TANI DI BALAI PENYULUH PERTANIAN MANOKWARI BARAT MENGGUNAKAN FRAMEWORK LARAVEL 9 DAN BOOTSTRAP 4.6 : AGRICULTURAL EXTENSION AND FARMER GROUP INFORMATION SYSTEM AT THE WEST MANOKWARI AGRICULTURAL EXTENSION CENTER USING THE Laravel 9 AND BOOTSTRAP 4.6 FRAMEWORKS Rumbarar, Natalia Christi; Naibaho, Julius Panda Putra; Sanglise, Marlinda
JISTECH: Journal of Information Science and Technology Vol 14 No 2 (2025): Volume 14 Nomor 2 Tahun 2025
Publisher : Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jistech.v14i2.974

Abstract

This study discusses the design of a web-based information system utilizing Laravel 9 and Bootstrap 4.6 for the West Manokwari Agricultural Extension Center (BPP). This system aims to optimize information management, increase public access, and accelerate data management. The research method uses a waterfall approach with stages of requirements, design, implementation, verification, and maintenance. The main features developed include managing extension schedules, farmer group data, work area data, and agency profiles. Testing using black-box testing shows that all functions run as required. This system is expected to improve the efficiency, transparency, and accountability of data management within the West Manokwari BPP.
Real time weather forcasting with conditional CNN and TCN-BiLSTM Ensamble at Manokwari Lie, Ilham Tatayo; Naibaho, Julius Panda Putra; Kweldju, Alex De
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15999

Abstract

Short-term weather forecasting is fundamentally critical for disaster mitigation in dynamic tropical maritime regions. However, conventional numerical approaches suffer from high computational latency, and spatial deep learning models frequently experience severe performance degradation during nocturnal conditions due to the absence of illumination. This study aims to develop an adaptive, real-time multimodal weather nowcasting system that effectively prevents nocturnal predictive failure through a dynamic conditional ensemble architecture. The proposed framework integrates a Convolutional Neural Network (CNN) to extract optical features from a dataset of 2,515 localized sky images with a Temporal Convolutional Network and Bidirectional Long Short-Term Memory (TCN-BiLSTM) pipeline to process 15,111 corresponding meteorological time-series records from BMKG. To address visual ambiguity, the system strictly employs a day-night gating mechanism, deactivating the CNN at night to rely solely on thermodynamic data. Finally, the optimized model was deployed via TensorFlow.js for decentralized client-side browser inference. Experimental evaluations explicitly demonstrate that the conditional ensemble significantly outperformed all standalone models, achieving a peak accuracy of 92.49% and a Macro F1-score of 0.913 while successfully preserving a robust recall rate for precipitation events. Furthermore, the browser-based deployment completely eliminated server transmission bottlenecks, achieving sub-second warm-start inference latency across heterogeneous consumer devices. Ultimately, the conditional day-night modality gating mechanism effectively mitigates nocturnal visual degradation, proving that implementing this integrated architecture as a client-side web application is highly feasible for delivering instantaneous public weather warnings.
Classification of Wild Edible Plants Using InceptionV3 with Transfer Learning and Metadata Integration as a Decision Support System Fauzi, Ridho Nur; Naibaho, Julius Panda Putra; De Kweldju, Alex
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.39091

Abstract

Deep learning has advanced intelligent systems for plant identification; however, distinguishing edible wild plants remains challenging due to limited datasets and the need for contextual information beyond visual classification. This study develops a Convolutional Neural Network (CNN) framework that integrates metadata as a decision support system to enhance food safety and strengthen community-based food security. A dataset of 16,076 images across 34 classes of edible wild plants was collected and enriched with metadata containing plant descriptions, consumption status, and nutritional values. The dataset was split into 75% training, 20% validation, and 5% testing to ensure reliable evaluation. The proposed solution employs InceptionV3 with transfer learning as the primary model, chosen for its ability to capture complex visual features in limited datasets, while MobileNetV3-Large serves as a lightweight comparative architecture. Results show that InceptionV3 achieved superior performance with a test accuracy of 0.87 and F1-score of 0.88, whereas MobileNetV3-Large obtained only 0.03 accuracy, indicating poor generalization. This highlights the importance of selecting architectures with sufficient depth for domains characterized by high visual variability. Metadata integration enhanced the system’s role as a decision support tool, providing contextual information such as edibility status and nutritional content. The novelty of this research lies in combining CNN-based classification with metadata integration, transforming the system into a practical framework for safe consumption decisions. Limitations include the dataset containing only edible plants. Future work should incorporate non-edible classes, evaluate performance under real-world conditions, and explore advanced architectures and explainable AI techniques to improve robustness, transparency, and accessibility.
Classification of Online Gambling Spam Comments on YouTube Using Support Vector Machine Pariamalinya, Umbu Anaagung; Limbong, Josua Josen A.; Naibaho, Julius Panda Putra
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.39193

Abstract

While digital transformation has established YouTube as a major communication platform, the site has also become vulnerable to online gambling spam in Indonesia. This study investigates the effectiveness of the Support Vector Machine (SVM) algorithm for automated spam detection as an alternative to manual moderation. A total of 9,169 comments were collected from gaming, education, and entertainment channels using the YouTube Data API v3 and were used to train and evaluate the model with an 80:20 data split. The experimental results show that SVM achieved an accuracy of 99.62% and an F1-score of 0.996, demonstrating strong capability in identifying spam comments written in informal and modified promotional language. The main contribution of this study is the development of a highly accurate and practical spam detection approach for Indonesian YouTube comments, which can support more efficient moderation systems. However, the model still has limitations in detecting sarcastic content. Therefore, future research should explore deep learning models such as BERT to improve contextual understanding and strengthen automated moderation in digital environments.
Co-Authors Absur, Eka Saputri aprillia atmaja, cindy Artahsasta, Yosia Candra Darmawan, Candra Christian Dwi Suhendra, Christian Dwi De Kweldju, Alex Fauzi, Ridho Nur Furima, Yobel Agustina Indriawan, Muhammad Fikri jamila, Nur Jayanto, Gunawan Yudi kala’lembang, Thesiana Kweldju , Alex De Kweldju, Alex De Leatemia, Novelia Lie, Ilham Tatayo Limbong, Josua Josen A. Mahmud, Nurul Ahdiati Mambraku, Cony Marini, Lion Ferdinand Masturah, Emil Maulana, Muhamad Fijai Ningsih, Ari Nur Safitri Prameti Paiki, Fridolin F. Pariamalinya, Umbu Anaagung Parma Hadi Rantelinggi Pilihantra, Ragil Ratte, Sety Richiansyah, Yohanes Alfian Rumbarar, Natalia Christi Rumere, Meike Agnes Rumkabu, Sara Nelince Sabandar, Ogelyn Miranda Sanglise, Marlinda Setiawan, Tommy Eko Suciati, Fitri Eka Budi Suhendra, Cristian Dwi sutimin sutimin Syahputra, Prasetya Dwi Syahriza, Alfi Wahyu Akbar Yom, Isaiah Carlietias Kusuma Wanal