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All Journal Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Brahmana : Jurnal Penerapan Kecerdasan Buatan JOINT (Journal of Information Technology Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Puan Indonesia Tedc Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Journal of Informatics and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics Journal of New Trends in Sciences
Sugianto, Castaka Agus
Politeknik TEDC Bandung
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Optimasi Algoritma K-Means Menggunakan Metode Elbow Pada Data Penerima Program Keluarga Harapan (PKH) Sugianto, Castaka Agus; Wanaziana, Keny Kirana
Informatics and Digital Expert (INDEX) Vol. 6 No. 1 (2024): INDEX, Mei 2024
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v6i1.1739

Abstract

Program Keluarga Harapan (PKH) merupakan program pemberian bantuan sosial bersyarat kepada keluarga miskin yang ditujukan kepada keluarga penerima manfaat PKH. Melalui PKH, keluarga miskin didorong untuk memiliki akses dan memanfaatkan pelayanan sosial dasar di bidang kesehatan, pendidikan, pangan dan gizi, perawatan, serta pendampingan. Pada kelurahan Cibabat dan kelurahan Cipageran kecamatan Cimahi Utara terdapat 387 data penerima bantuan PKH pada tahun 2022. Namun belum adanya pengolahan data penerima bantuan PKH tersebut sehingga dalam melakukan pendampingan penerima bantuan PKH belum mendapatkan penglompokan yang sesuai dengan riwayat pendidikan. Berdasarkan latar belakang masalah yang telah dijabarkan, penulis tertarik untuk melakukan Clustering menggunakan Algoritma K-means pada data penerima bantuan PKH. Berdasarkan pengujian metode elbow pada algoritma k-means didapat nilai k yang optimal adalah k=3. Pengelompokan dataset yang digunakan menjadi 3 kelompok cluster, diantranya cluster_0 sebanyak 257 data, cluster_1 sebanyak 75 data, dan cluster_2 sebanyak 55 data. Pada cluster_0 di dominasi oleh peserta lulusan SD sebanyak 173 data, untuk cluster_1 di dominasi oleh peserta tidak sekolah sebanyak 40 data, dan untuk cluster_2 di dominasi peserta tidak sekolah sebanyak 48 data. Pada cluster tersebut didapatkan nilai performa berdasarkan rata-rata avg. within centroid distance_cluster_0 adalah 6.720, avg. within centroid distance_cluster_1 adalah 14.373, avg. within centroid distance_cluster_2 adalah 8.496 dan Davies Bouildin Index adalah 0.816. Hasil penelitian ini diharapkan menjadi acuan bagi pengurus sekretariat PPKH dalam melaksanakan pendampingan masyarakat penerima Program Keluarga Harapan.
Pengembangan Media Pembelajaran Virtual Reality untuk Meningkatkan Pemahaman Konsep Fisika pada Siswa SMA Dini Rohmayani; Castaka Agus Sugianto
Journal of New Trends in Sciences Vol. 2 No. 1 (2024): Februari: Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i1.783

Abstract

The learning of physics, particularly mechanics, poses significant challenges for high school students. Concepts such as Newton’s laws, energy, and three-dimensional vectors are often difficult to grasp using traditional teaching methods. Virtual Reality (VR) has emerged as a promising solution by providing an immersive and interactive learning environment. This study aims to evaluate the effectiveness of VR-based learning media in enhancing students’ understanding of physics concepts, with a specific focus on mechanics. An experimental design was employed, consisting of two groups: an experimental group using VR for learning and a control group receiving traditional instruction. Pre-test and post-test assessments were used to measure the improvement in students' conceptual understanding of physics. The findings indicate that students in the experimental group demonstrated a significant improvement in their understanding of complex physics concepts, such as projectile motion, force, and Newton’s laws, compared to the control group. Students in the experimental group also exhibited higher levels of engagement and motivation, with VR's immersive nature encouraging active participation in learning. The study concludes that VR is an effective tool for enhancing students’ comprehension of abstract and complex physics concepts, improving their visualization and problem-solving skills. Furthermore, VR-based learning provides students with opportunities to conduct virtual experiments and simulations that may not be possible in traditional classroom settings. The implications of this study suggest that VR should be integrated into the physics curriculum to improve learning outcomes, especially in schools with access to the necessary technology. Educators and curriculum developers are encouraged to explore VR’s potential in fostering a more engaging and effective physics education.
Analisis Big Data dalam Deteksi Dini Wabah Penyakit Menular untuk Mendukung Sistem Kesehatan Publik Ayu Hendrati Rahayu; Castaka Agus Sugianto; Dini Rohmayani
Journal of New Trends in Sciences Vol. 2 No. 1 (2024): Februari: Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i1.785

Abstract

The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.
Interpretable Deep Learning Model for Grape Leaf Disease Classification Based on EfficientNet with Grad-CAM Visualization Castaka Agus Sugianto; Dini Rohmayani; Jhoanne Fredricka; Mohamed Doheir
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2745

Abstract

Grape leaf diseases pose a significant threat to agricultural productivity, especially in regions with fluctuating climatic conditions that create favorable environments for pathogen growth. Early and accurate disease detection is essential for preventing severe crop losses. Traditional manual inspection methods are inefficient and prone to human error, highlighting the need for an automated approach. This study proposes a computer vision-based solution using Convolutional Neural Networks (CNN) improved by EfficientNetB0 to classify grape leaf diseases. The model was trained on a publicly available dataset from Kaggle, which consists of 9,027 images in four classes: ESCA, Leaf Blight, Black Rot, and Healthy. Each image has a resolution of 300 × 300 pixels with a 24-bit color depth, ensuring sufficient detail for analysis. To enhance model performance, data augmentation and hyperparameter tuning were applied. The EfficientNetB0 model was employed due to its strong feature extraction capabilities and computational efficiency. The proposed model achieved 99.36% accuracy, with evaluation metrics including precision (99%), recall (99%), and F1-score (99%), demonstrating its reliability in distinguishing disease categories. Further analysis using a confusion matrix and Grad-CAM visualization provided insights into the model’s decision-making process. The results indicate that this deep learning-based approach is highly effective for grape leaf disease classification. Future research can explore real-time field data collection, attention mechanisms, and self-supervised learning to further improve classification accuracy and model generalization for large-scale agricultural applications.
Co-Authors Aditia Gusman Aldy Noer Tjahyo Apnena, Riri Damayanti Ayu Hendrati Rahayu Candra Irawan Christy Atika Sari Dadan Saepul Ramdan Dahlan Dahlan Dini Rohmayani Dini Rohmayani Eko Hari Rachmawanto Faishal Fachruddin Febianto, Aldi Kristiawan Fitrah, La Ode Muhammad Fredricka, Jhoanne Hidayah, Hamimah Nur Iqna Aulia Iskandar, Rachmat Juniardi, Trianda Kosasih, Heldi Akbar Lela Kesmasari Maulana, Firdi Rizky Melani Nur Astita Mohamed Doheir Nadya Selvi Pujiyanita Nafea, Mohammed Mansoor Nia Ekawati, Nia Pebriana, Sela Perdana, Rangga Satria Pujiyanita, Nadya Selvi Rahmawan, Septiadi Ramdan, Dadan Saepul Riyan Riyan Rizqy Dimas Monica Rohmayani, Dini Safira, Rifa Sari, Putri Nurlaela Shandy Tresnawati Sukmawati, Dini Syams, Fawwaz Muhammad Syifa Fauzia Tahir Tahir, Syifa Fauzia Tati Ernawati Tresnawati, Shandy Tri Herdiawan Apandi Tri Pratiwi Olivia Riska Bokings Wanaziana, Keny Kirana Zulkipli ., Zulkipli Zundi, Teguh Muhammad