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All Journal Justek : Jurnal Sains Dan Teknologi Building of Informatics, Technology and Science Idealis : Indonesia Journal Information System Journal of Applied Data Sciences
Santoso, Cahyono Budy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics

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Enhancing Apple Leaf Disease Detection with Deep Learning: From Model Training to Android App Integration Santoso, Cahyono Budy; Singadji, Marcello; Purnama, Denny Ganjar; Abdel, Saimam; Kharismawardani, Aqila
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.507

Abstract

This study presents an innovative approach to enhance apple leaf disease detection using deep learning by comparing three models: ReXNet-150, EfficientNet, and Conventional CNN (ResNet-18). The objective is to identify the most accurate and efficient model for real-world deployment in resource-constrained environments. Utilizing a dataset of 1,730 high-quality images, the models were trained using transfer learning, achieving significant results. ReXNet-150 outperformed other models with an F1-score of 0.988, precision of 0.989, and recall of 0.989. EfficientNet and ResNet-18 demonstrated competitive performances with F1-scores of 0.966 and 0.977, respectively. The integration of the ReXNet-150 model into a TensorFlow Lite-based Android application ensures real-time detection, enabling farmers and researchers to capture or upload images for immediate classification. The findings highlight ReXNet-150's robustness, achieving a test accuracy of 98.9% and minimal misclassification, making it ideal for practical agricultural applications. The novelty lies in bridging advanced deep learning with mobile deployment, addressing real-world constraints. Future work could extend this framework to multi-crop disease detection and real-time video analysis, providing scalable solutions for precision agriculture.
ANALISIS TREN LOWONGAN PEKERJAAN SOFTWARE ENGINEERING DI INDONESIA DENGAN CLUSTERING DAN SOCIAL NETWORK ANALYSIS Harahap, Tiara Amanda Jullet; Santoso, Cahyono Budy
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 2 (2025): Jurnal IDEALIS Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3518

Abstract

Pertumbuhan ekonomi digital di Indonesia mendorong permintaan tinggi terhadap tenaga kerja software engineering. Namun, distribusi spasial dan keterkaitan antar entitas dalam ekosistem lowongan kerja digital belum dianalisis secara komprehensif. Penelitian ini bertujuan untuk menjawab permasalahan kurangnya pemetaan struktur dan tren lowongan pekerjaan software engineering di Indonesia. Untuk itu, digunakan dua pendekatan analitik: K-Means Clustering untuk segmentasi spasial dan fungsional berdasarkan atribut lokasi dan posisi, serta Social Network Analysis (SNA) untuk menganalisis struktur hubungan antara lokasi, perusahaan, dan posisi pekerjaan. Data dikumpulkan melalui web scraping dari 12 platform daring terkemuka di Indonesia, menghasilkan 5.272 entri yang disaring menjadi 2.303 entri unik. Hasil clustering menunjukkan bahwa posisi Software Engineer (85 koneksi), Data Engineer, dan Frontend Developer mendominasi di Jakarta dan Tangerang. Jakarta tercatat sebagai lokasi dengan Degree Centrality tertinggi (1.396), menandakan dominasinya sebagai pusat rekrutmen digital nasional. Dari sisi perusahaan, PT Telkom Indonesia (degree = 62) dan Shopee (degree = 58) merupakan aktor strategis. Posisi Product Manager dan DevOps Engineer memiliki nilai betweenness tertinggi, menunjukkan fungsi lintas tim yang krusial. Penelitian ini memberikan kontribusi terhadap pemahaman spasial dan struktural pasar tenaga kerja digital Indonesia. Temuan ini dapat dimanfaatkan untuk strategi rekrutmen, perencanaan pendidikan vokasional, dan pengembangan karier. Penelitian selanjutnya disarankan untuk mengintegrasikan dimensi temporal dan variabel keahlian atau gaji guna memperluas cakupan analisis.
Implementasi Convolutional Neural Network dengan SMOTE+ENN untuk Klasifikasi Kualitas Udara Berdasarkan Data Deret Waktu Polutan Santoso, Cahyono Budy; Kesya Makarena, Maria Rachel
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8057

Abstract

The degradation of air quality in metropolitan areas, such as Jakarta, constitutes a significant environmental and public health challenge, contributing directly to an elevated risk of various diseases. The primary objective of this study is to develop and evaluate the effectiveness of an air quality classification model based on a Convolutional Neural Network (CNN), with a specific focus on addressing class imbalance using the hybrid resampling technique SMOTE+ENN. Utilizing a historical dataset from the HI Jakarta Station spanning 2010-2021, the model leverages key pollutant parameters (PM10, SO₂, CO, O₃, and NO₂) to classify air quality according to the Indonesian Air Quality Index (ISPU) standard. To mitigate the inherent challenge of class imbalance within the dataset, this study conducts a comparative analysis between a baseline CNN model and an optimized model enhanced with the hybrid resampling technique, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbours (SMOTE + ENN). The dataset was partitioned into an 80% training set and a 20% testing set. Empirical results demonstrate that the application of SMOTE + ENN yields a substantial improvement in performance. The final optimized model achieves a superior accuracy of 98.98%, significantly outperforming the baseline model. This outcome confirms that integrating CNN with the SMOTE + ENN strategy produces a highly effective and robust framework for air quality classification in Jakarta. Nonetheless, subsequent validation on more diverse datasets is recommended to ascertain the model's generalization capabilities and long-term reliability.
Clustering of Data on Vegetable Crop Production in the City of Bandung Using the K-Means Algorithm Saputra, Ifano Rangga; Santoso, Cahyono Budy
Justek : Jurnal Sains dan Teknologi Vol 8, No 4 (2025): December
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v8i4.35555

Abstract

Vegetable farming supports urban food security, and Bandung City is one of West Java's main horticultural centers. However, vegetable production remains unevenly distributed across its sub-districts. This study analyzes production patterns from 2018–2023 using the K-Means Clustering algorithm. The dataset includes 12 major commodities, and the analysis involves data preprocessing, determining the optimal number of clusters using the Elbow Method and Silhouette Score, applying K-Means, and visualizing results through heatmaps and PCA. The findings reveal three clusters: Cluster 0 dominated by potatoes and the "others" category; Cluster 1 dominated by kale; and Cluster 2 dominated by shallots and petsai. These patterns indicate concentrated and specialized production across specific sub-districts. The study concludes that K-Means effectively identifies multi-commodity production similarities and provides strategic insight for Business Intelligence applications in agricultural planning and policy development.
Co-Authors Abdel, Saimam Harahap, Tiara Amanda Jullet Kesya Makarena, Maria Rachel Kharismawardani, Aqila Marcello Singadji Purnama, Denny Ganjar Saputra, Ifano Rangga