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All Journal Prosiding Semnastek Jurnal Ilmiah KOMPUTASI JURTEKSI JIKA (Jurnal Informatika) Jutech: Jurnal Teknologi Informasi Jurnal Sistem Informasi Journal of Computer Science Advancements Journal of Loomingulisus ja Innovatsioon
Sestri, Elliya
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Law, Crime, Criminology & Criminal Justice Other

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RANCANG BANGUN SISTEM INFORMASI BANK SAMPAH BERBASIS WEB MENGGUNAKAN METODE WATERFALL (STUDI KASUS: BANK SAMPAH DESA PAMEGARSARI) Khairullah, Muhammad; Syamsu, Muhajir; Sestri, Elliya
Jurnal Sistem Informasi (JUSIN) Vol 5 No 1 (2024): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v5i1.2452

Abstract

This research aims to design and develop a web-based waste bank information system using the waterfall method, focusing on the case study of the waste bank in Pamegarsari Village. The conventional waste management system in many villages relies heavily on manual processes, leading to inefficiencies in data recording, reporting, and management. The utilization of a web-based information system offers a potential solution to enhance waste management practices by enabling efficient data recording, processing, and reporting. The waterfall method is chosen for its sequential approach, which involves distinct phases including requirements gathering, system design, implementation, testing, and maintenance. By applying the waterfall method, this study seeks to provide a systematic and structured framework for developing the waste bank information system tailored to the specific needs and context of Pamegarsari Village. The findings of this research are expected to contribute to the improvement of waste management practices at the village level, promoting environmental sustainability and community engagement.
PENGELOMPOKKAN DATA MAHASISWA MENGGUNAKAN CLUSTERING UNTUK OPTIMALISASI PENERIMAAN MAHASISWA BARU Yusuf, Diana; Sestri, Elliya; Razi, Fahrul
Jurnal Informatika Vol 8, No 4 (2024): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v8i4.12637

Abstract

Salah satu tahapan penting dalam pengelolaan perguruan tinggi yakni proses penerimaan mahasiswa baru, dimana proses ini akan mempengaruhi kualitas dan kuantitas mahasiswa yang diterima di perguruan tinggi. Mengoptimalkan proses ini memerlukan pendekatan yang efektif untuk menganalisis data potensi mahasiswa. Dimana akan dilakukan pengelompokkan data mahasiswa menggunakan algoritma clustering K-Means untuk menemukan pola dan karakteristik yang dapat mengoptimalkan penerimaan mahasiswa baru. Penerapan algoritma K-Means vabel-variabel seperti program studi, IPK, kelurahan, kota, provinsi, dan jenis sekolah. Hasil pengelompokkan diharapkan dapat memberikan wawasan lebih dalam mengenai segementasi calon mahasiswa, sehingga perguruan tinggi dapat menyusun strategi penerimaan yang lebih tepat sasaran. Diharapkan dapat memberikan dasar bagi pengambilan keputusan yang lebih berbasis data untuk meningkatkan kualitas penerimaan mahasiswa pada masa mendatang.
Prediksi Cacat Lempeng Baja Menggunakan Algoritma Bagging: Pendekatan Machine Learning untuk Peningkatan Kualitas Produksi Digdoyo, Aji; Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Sestri, Elliya; Fitriansyah, Reza
Jurnal Ilmiah Komputasi Vol. 24 No. 1 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 1, Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.1.3654

Abstract

Industri baja memiliki peran krusial dalam berbagai sektor, menjadi faktor kunci dalam memastikan integritas struktural produk akhir. Penelitian ini bertujuan untuk mengatasi masalah ini dengan menerapkan algoritma Bagging dalam prediksi cacat lempeng baja. Hasil model training dengan kurva ROC dengan nilai AUC 99% dab logloss 0,14. Pengukuran precision, recall, dan f1 score untuk 7 jenis cacat baja memperoleh prosentase yang sangat baik (lebih dari 90%). Confusion Matrix menunjukan korelasi yang kuat antara jenis cacat ke 6 dan ke 5. Sedangkan validasi, antara jenis cacat ke 4 dan ke 0 terdapat hubungan yang sangat kuat. Classification report menunjukan nilai precision, recall, dan f1 score terbaik (lebih dari 80%) untuk jenis cacat ke 1, 2, dan 3. Nilai AUC yang cukup baik yaitu 88% dan Logloss yang cukup besar yaitu 3,13. Penelitian selanjutnya dapat fokus untuk meningkatkan nilai logloss yang masih harus diperbaiki untuk proses validasi.
Desain Komunikasi Visual Berbasis Segmentasi Pelanggan untuk H&M Terisia, Vany; Hastomo, Widi; Sestri, Elliya; Syamsu, Muhajir; Novitasari, Lyscha; Putra, Yoga Rarasto; Fiqhri, Zul; Sudarwanto, Pantja; Daruningsih, Kukuh
Prosiding Semnastek PROSIDING SEMNASTEK 2025
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk merancang strategi komunikasi visual berdasarkan segmentasi pelanggan pada industri fashion retail, studi kasus pada H&M Group. Data diambil dari dataset H&M Personalized Fashion Recommendations di Kaggle dan diolah dengan pendekatan RFM (Recency, Frequency, Monetary) serta algoritma K-Means clustering untuk mengidentifikasi tipe pelanggan. Hasil analisis menunjukkan tiga klaster utama: pelanggan bernilai tinggi, sedang, dan rendah. Berdasarkan hasil tersebut, dirancang pendekatan visual yang berbeda untuk setiap segmen, baik dalam desain iklan digital maupun visual merchandising. Penelitian ini memberikan kontribusi dalam pengambilan keputusan pemasaran visual yang berbasis data untuk meningkatkan retensi pelanggan.
The Role of Business Incubators in Facilitating Startup Growth in Indonesia Sestri, Elliya; Anis, NIna; Farah, Rina
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i2.1974

Abstract

Business incubators have emerged as vital institutions in fostering startup growth, providing support, resources, and networking opportunities to early-stage ventures. In Indonesia, a rapidly growing startup ecosystem, incubators play a crucial role in helping entrepreneurs navigate the challenges of scaling their businesses. However, the extent to which these incubators contribute to startup success remains underexplored, particularly in the Indonesian context.. The research aims to identify the specific support mechanisms provided by incubators and assess how these contribute to the scalability and sustainability of startups in the country. This study employs a mixed-methods approach, combining quantitative surveys of startup founders who have participated in incubator programs and qualitative interviews with incubator managers and industry experts. The data collected were analyzed to identify key factors that contribute to startup growth, such as access to funding, mentorship, networking opportunities, and business development services. The findings indicate that business incubators significantly enhance startup growth by providing access to crucial resources, including seed funding, mentoring, and networking with industry professionals. Startups that participated in incubator programs demonstrated higher survival rates and faster growth compared to those that did not. Mentorship and access to strategic partnerships were particularly important for long-term sustainability. Business
STUDENT GRADUATION PREDICTION USING DECISION TREE ALGORITHM WITH CRISP-DM METHOD (CASE STUDY: ITB AHMAD DAHLAN) Husni, Kholilah; Sestri, Elliya; Terisia, Vany
Journal of Computer Science Advancements Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i5.2429

Abstract

On-time graduation is an important indicator of higher education effectiveness; however, delays in student graduation are still observed at ITB Ahmad Dahlan Jakarta. This study develops a student graduation prediction system using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Decision Tree algorithm based on historical academic data. The model was built through six CRISP-DM stages, including problem understanding, data preparation, modeling, and evaluation. Testing results indicate high performance with an Accuracy of 97.44%, Precision of 97.14%, Recall of 100%, and F1-Score of 98.55%. This system has the potential to support strategic decision-making to enhance academic quality through data-driven approaches.
Penerapan YOLO dan OpenCV dalam Klasifikasi Kendaraan pada Lalu Lintas Kota Depok pamungkas, aldo; arbekti, shevti; sestri, elliya
Jurnal Teknologi Informasi (JUTECH) Vol 6 No 2 (2025): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v6i2.3207

Abstract

The growth in the number of vehicles in Depok City has driven the need for an accurate and efficient traffic monitoring system. This study implements the You Only Look Once (YOLO) version 8 algorithm to automatically detect and classify vehicles based on Python and OpenCV. The focus of the study is on four types of vehicles, namely motorcycles, private cars, buses, and trucks. The dataset was obtained from CCTV recordings and field documentation, then annotated using LabelImg and processed into YOLO format. The training process was carried out using the pretrained YOLOv8 model, while the system testing was conducted on videos of Depok City roads. Model performance was evaluated using the metrics of mAP@0.5 and mAP@0.5:mAP95, precision, recall, and F1 score. The evaluation results show that the model achieved a mAP@0.5 of 91% and a mAP@0.5:mAP95 of 75.1%, precision of 88.5%, recall of 85.2%, and an F1-score of 86.8%. With these results, the model is capable of detecting and classifying vehicles in real time with high accuracy under various lighting conditions and camera angles. Additionally, this system is integrated with a web interface using Flask for direct visualization of detection results. This research contributes to supporting smart transportation systems in urban environments and provides a potential solution for data-based traffic management.
EFFICIENTNET MODEL FOR BONE AGE PREDICTION Hastomo, Widi; Sestri, Elliya; Ningsih, Silvia
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4355

Abstract

Abstract: Accurate bone age estimation is essential for monitoring pediatric growth, diagnosing endocrine disorders, and supporting clinical decision-making. Although deep learning has improved prediction accuracy, limited studies have systematically examined how increasing model depth affects performance and reliability. This study evaluates the effectiveness of progressively deeper convolutional neural networks, specifically EfficientNet variants B0 to B5, for bone age estimation from hand radiographs. Experiments were conducted using 12,611 hand X-ray images from the RSNA Pediatric Bone Age Challenge dataset on Kaggle. To ensure fair comparison, all models were trained using a unified and consistent training pipeline. Model performance was evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), and Pearson correlation coefficient. The results show a consistent improvement in prediction accuracy as model depth increases. Among the evaluated models, EfficientNet-B5 achieved the best performance, with an MAE of 21.5 months, MAPE of 6.23%, CCC of 0.9148, and Pearson’s r of 0.9203. These findings confirm that model scaling plays a critical role in enhancing prediction robustness and clinical reliability. Future work should emphasize external validation across diverse populations and incorporate interpretability techniques, such as Grad-CAM, to improve clinical transparency and trust. Keywords: bone age prediction; deep learning; model evaluation; clinical validation Abstrak: Estimasi usia tulang yang akurat sangat penting untuk memantau pertumbuhan anak, mendiagnosis gangguan endokrin, dan mendukung pengambilan keputusan klinis. Meskipun pembelajaran mendalam telah meningkatkan akurasi prediksi, studi yang secara sistematis meneliti bagaimana peningkatan kedalaman model memengaruhi kinerja dan keandalan masih terbatas. Studi ini mengevaluasi efektivitas jaringan saraf konvolusional yang semakin dalam, khususnya varian EfficientNet B0 hingga B5, untuk estimasi usia tulang dari radiografi tangan. Eksperimen dilakukan menggunakan 12.611 gambar sinar-X tangan dari dataset RSNA Pediatric Bone Age Challenge di Kaggle. Untuk memastikan perbandingan yang adil, semua model dilatih menggunakan alur pelatihan yang terpadu dan konsisten. Kinerja model dievaluasi menggunakan Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), dan koefisien korelasi Pearson. Hasil menunjukkan peningkatan yang konsisten dalam akurasi prediksi seiring dengan peningkatan kedalaman model. Di antara model yang dievaluasi, EfficientNet-B5 mencapai kinerja terbaik, dengan MAE sebesar 21,5 bulan, MAPE sebesar 6,23%, CCC sebesar 0,9148, dan Pearson’s r sebesar 0,9203. Temuan ini menegaskan bahwa penskalaan model memainkan peran penting dalam meningkatkan optimasi prediksi dan keandalan klinis. Penelitian selanjutnya dapat menekankan validasi eksternal di berbagai populasi dan menggabungkan teknik interpretasi, seperti Grad-CAM, untuk meningkatkan transparansi dan kepercayaan klinis. Kata kunci: prediksi usia tulang; deep learning; evaluasi model; validasi klinis
Co-Authors Anis, Nina arbekti, shevti Bayangkari Karno, Adhitio Satyo Daruningsih, Kukuh Digdoyo, Aji Fahrul Razi Farah, Rina Fiqhri, Zul Husni, Kholilah Khairullah, Muhammad Lyscha Novitasari pamungkas, aldo Putra, Yoga Rarasto Reza Fitriansyah Silvia Ningsih Sudarwanto, Pantja Syamsu, Muhajir Vany Terisia Widi Hastomo YUSUF, DIANA