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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika dan Teknik Elektro Terapan JUTIK : Jurnal Teknologi Informasi dan Komputer JURIKOM (Jurnal Riset Komputer) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) INFOKUM Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Dananjaya, Md Wira Putra
Universitas Pendidikan Nasional
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Klasifikasi Kualitas Tanah Berdasarkan Kandungan pH, Kelembapan, dan Suhu Menggunakan Algoritma K-Nearest Neighbors Md Wira Putra Dananjaya; Gede Humaswara Prathama; I Gusti Ngurah Darma Paramartha; Putu Gita Pujayanti
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 4 (2025): OCTOBER-DECEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i4.4049

Abstract

This study aims to analyze soil quality using the K-Nearest Neighbors (KNN) algorithm based on environmental parameters such as temperature, humidity, pH, and nutrient content (N, P, K). The dataset used consists of 660 entries covering 22 different classes describing soil types with varying characteristics. The KNN model was applied to classify soil quality, and the results were evaluated using the Confusion Matrix and Classification Report. The accuracy of the model obtained was around 61%, indicating potential improvements in the classification of some more difficult soil classes. The model performed better on certain classes such as kidney beans, chickpeas, and grapes, but was less than optimal on other classes such as watermelon and pomegranate. These results indicate class alignment in the dataset that affects model performance. This study contributes to the application of machine learning algorithms in agriculture, especially for soil quality monitoring. In the future, this study opens up opportunities for further improvements by using parameter optimization techniques and other more complex algorithms. Thus, the results of this study can be used as a basis for developing intelligent systems for more effective and efficient soil management.
Implementasi Clean Architecture pada Chatbot Cuaca Hiper-Lokal Berbasis Hybrid NLP di Wilayah Bali Md Wira Putra Dananjaya
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2938

Abstract

Accurate meteorological information is crucial for the tourism and agriculture sectors in Bali Province. However, global weather Application Programming Interfaces (APIs) often have limitations in recognizing hyper-local areas and lack natural language interaction capabilities. This study aims to develop the Bali Weather Bot, an intelligent Telegram-based assistant using Clean Architecture and Hybrid Natural Language Processing (NLP). The methodology combines the Gemini 1.5 Flash Large Language Model (LLM) for entity extraction with a Context-Aware Dictionary Mapping fallback mechanism to standardize local abbreviations and Indonesian temporal metaphors. Evaluation was conducted using a dataset of 250 test query scenarios evaluated through a confusion matrix and latency benchmarking. System evaluation demonstrates that this hybrid architecture successfully extracts spatial and temporal parameters with an overall accuracy of 82.80%. Quantitative evaluation shows a Precision of 92.00%, Recall of 89.22%, and an F1-score of 90.58%, while maintaining a 0% system crash rate. In conclusion, the hybrid approach effectively mitigates AI hallucination and enhances hyper-local data retrieval reliability in conversational interfaces.
Pengembangan Website Klasifikasi Jenis Sampah pada Anak menggunakan Metode CNN dengan Arsitektur MobileNetV2 I Gede Agus Perdana Putra; Md. Wira Putra Dananjaya
Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Vol. 5 No. 3 (2026): September : Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupsim.v5i3.8012

Abstract

Waste management in Indonesia, particularly the limited ability to distinguish and sort organic and inorganic waste from an early age, poses a challenge to achieving sustainable development goals. This study aims to develop a web-based learning system that integrates a Convolutional Neural Network (CNN) model with a transfer learning approach using the MobileNetV2 architecture to classify images of organic and inorganic waste as an interactive educational medium for elementary school students aged 6–12 years. A dataset comprising 3,000 images (1,500 organic and 1,500 inorganic) was obtained from publicly available sources and divided into training, validation, and testing sets at a ratio of 70:15:15. The preprocessing stage included data augmentation, normalization, and image resizing to 224 × 224 pixels. The model was trained for 20 epochs using the Adam optimizer and Binary Crossentropy loss function. The evaluation results demonstrated a test accuracy of 92%, with precision, recall, and F1-score values of 0.92 for both classes. The model was subsequently integrated into a Flask-based website featuring image upload functionality, educational articles, and interactive quizzes. The main contribution of this study lies in integrating a lightweight CNN-based image classification model into a child-oriented educational web platform, thereby supporting efforts to improve waste-sorting awareness from an early age among elementary school students in Denpasar City.
Co-Authors Alicia Alfia Asokawati Darmaastawan, Kadek Gede Humaswara Prathama Gede Indrawan Gusti Agung Ayu Putri I Gede Agus Perdana Putra I Gusti Ngurah Darma Paramartha Ni Luh Putu Ika Candrawengi Pujayanti, Putu Gita Putu Gita Pujayanti Putu Gita Pujayanti Putu Gita Pujayanti Putu Wira Buana Sariyasa . Yadhurani Dewi Amritha