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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Journal of Innovation and Future Technology (IFTECH) Jurnal Teknik Informatika C.I.T. Medicom Jurnal Sistem Informasi Indonesian Journal Computer Science (ijcs) International Journal of Enterprise Modelling
Pujiastuti, Lise
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Pengembangan Sistem Deteksi Objek Botol Real-Time dengan YOLOv8 untuk Aplikasi Vision Triyanto, Dedi; Zidan, Muhammad; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6070

Abstract

Plastik daur ulang berperan penting dalam menanggulangi masalah limbah lingkungan sekaligus mendukung praktik keberlanjutan. Penelitian ini bertujuan mengembangkan sistem deteksi botol plastik dan kaleng daur ulang secara real-time menggunakan algoritma YOLOv8 yang terkenal akan kecepatan dan akurasinya. Dengan memanfaatkan dataset yang terdiri dari 2.900 gambar dan melatih model melalui Google Colab selama 25 epoch, penelitian ini berhasil menunjukkan performa luar biasa dari YOLOv8, dengan hasil mAP sebesar 99,5%, precision 99,7%, dan recall 99,5%. Model ini terbukti sangat efektif dalam mendeteksi objek daur ulang, memberikan prediksi yang tepat tanpa kesalahan negatif pada confusion matrix. Untuk penelitian lanjutan, disarankan menambah variasi kelas objek seperti botol kaca dan karet serta memperluas dataset guna meningkatkan generalisasi model. Selain itu, pengujian dalam kondisi nyata sangat diperlukan untuk memastikan kinerja optimal dalam lingkungan yang lebih kompleks. Pendekatan serupa dalam penelitian sebelumnya juga telah membuktikan kinerja unggul dalam deteksi real-time, menjadikan metode ini salah satu yang terdepan dalam pengembangan teknologi berbasis YOLO.
Deteksi dan Prediksi Cerdas Penyakit Paru-Paru dengan Algoritma Random Fores Kurniawan, Deny; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6071

Abstract

Penyakit paru-paru, seperti COPD, kanker paru-paru, dan asma, adalah masalah kesehatan global yang menyebabkan lebih dari tujuh juta kematian setiap tahun. Teknologi canggih, termasuk model deep learning dan algoritma Random Forest, telah digunakan secara efektif untuk mendeteksi dan mengklasifikasikan penyakit paru-paru dari data pencitraan dengan akurasi tinggi. Penelitian ini bertujuan menunjukkan efektivitas algoritma Random Forest dalam memprediksi penyakit paru-paru. Dataset yang digunakan terdiri dari 30.000 data dengan 11 atribut, diperoleh dari Kaggle dan diproses menggunakan perangkat lunak Orange versi 3.36.2. Algoritma Random Forest diimplementasikan dengan 10 pohon keputusan dan enam atribut yang dipertimbangkan pada setiap pembagian data. Model ini diuji menggunakan validasi silang dengan 10 lipatan, dan hasil pengujian menunjukkan nilai AUC sebesar 0,993, yang mengindikasikan tingkat akurasi yang sangat tinggi. Matriks kebingungan digunakan untuk mengevaluasi kinerja model, dengan mengukur akurasi, presisi, recall, F1-Score, dan AUC. Model ini menunjukkan akurasi yang tinggi, dengan nilai ROC AUC 0,453 untuk prediksi adanya penyakit paru-paru dan 0,547 untuk prediksi ketiadaan penyakit paru-paru. Hasil ini menunjukkan bahwa algoritma Random Forest dapat menjadi alat yang efektif dalam mengidentifikasi penyakit paru-paru. Penelitian ini berkontribusi pada pengembangan teknik diagnostik yang lebih akurat dan efisien, yang dapat membantu tenaga medis dalam mendiagnosis penyakit paru-paru pada pasien. Dengan pemahaman yang lebih baik tentang penerapan algoritma ini dalam dunia kesehatan, diharapkan dapat meningkatkan kualitas diagnosis dan perawatan pasien secara signifikan.
Privacy-Preserving machine learning in edge computing environments Kurniawan, Deni; Triyanto, Dedi; Wahyudi, Mochamad; Pujiastuti, Lise
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.621.pp118-125

Abstract

Edge computing has transformed data processing by moving computation closer to the source, enabling real-time analysis and decision-making. Edge devices are decentralized, which creates privacy and confidentiality concerns, especially when applying machine learning algorithms to sensitive data. Privacy-preserving machine learning methods for edge computing are examined in this research. Federated learning, homomorphic encryption, differential privacy, and secure aggregation are examined as data protection methods for network edge machine learning. A thorough study of these methods shows the challenges of balancing privacy, computational economy, and model correctness. Federated learning has promise for collaborative model training without raw data sharing, but communication overhead and convergence speed remain. A fictional healthcare use case shows how federated learning may be used to train collaborative models across many edge devices while protecting patient data. The case study stresses the necessity for sophisticated optimizations to overcome edge device limits and regulatory compliance. Federated learning algorithms, privacy-preserving procedures, and ethics must be improved, according to the research. Future directions include improving heterogeneous edge algorithms, addressing data ownership and consent ethics, and increasing model decision-making openness. This paper presents essential insights on privacy-preserving machine learning in edge computing and advocates for robust techniques for different edge environments. The paper emphasizes the importance of technological advances, ethical frameworks, and regulatory compliance for secure and privacy-aware machine learning in decentralized edge computing
Explainable artificial intelligence (XAI) for trustworthy decision-making Kurniawan, Deni; Triyanto, Dedi; Wahyudi, Mochamad; Pujiastuti, Lise
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.622.pp240-246

Abstract

This research delves into the optimization of loan approval decisions by integrating the Trustworthy Decision Making (TDM) framework into a mathematical model. The study aims to strike a balance between maximizing loan approvals and ensuring fairness, transparency, and accountability in AI-driven decision-making processes. Leveraging principles of transparency, fairness, and accountability, the mathematical model seeks to optimize loan approvals while adhering to ethical considerations. The formulation emphasizes the importance of interpretable models to maintain transparency in decision explanations, ensuring alignment with trustworthy AI practices. Implementation results demonstrate the efficacy of the model in achieving a balanced approval rate across demographic groups while providing transparent explanations for decisions. This study highlights the significance of ethical considerations and mathematical formulations in fostering responsible AI implementations. However, continual refinement and adaptation of such models remain essential to align with evolving ethical standards and societal expectations. Overall, this research contributes to the discourse on responsible AI by showcasing a methodological approach that integrates ethical principles and mathematical formulations to promote fairness, transparency, and accountability in AI-driven decision-making.
Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance Wahyudi, Mochamad; Sintagel br Sianipar, Imeldi; Pujiastuti, Lise; Solikhun, Solikhun; Kurniawan, Deny
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2363.44-53

Abstract

Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-Medoids method with a quantum computing approach to predict AIDS based on clinical and demographic data. K-Medoids is chosen to group large amounts of data using a clustering technique that determines the center point (medoid) of each cluster, minimizing the overall distance between data in a cluster. The Manhattan distance is used because it is easier to process data. The quantum computing approach is used to overcome the limitations of classical computing when processing large-scale medical data. This study shows that the application of quantum algorithms to the K-Medoids method allows for faster and more accurate predictions in the diagnosis of AIDS. The tests carried out showed that the prediction accuracy of classical and quantum methods was comparable, namely 85%. The results support the great potential of quantum computing to improve the efficiency of medical predictions. The research involves converting data into quantum format, processing it with the K-Medoids algorithm, and evaluating its performance based on metrics such as intercluster distance and computation time. The research will also identify patterns and risk factor for the spread of AIDS that can be used to develop more effective health interventions. The conclusion of the research is that integrating the K-Medoids techniques can only increase the speed of data processing but also provide competitive accuracy compared to traditional techniques. This research opens up new possibilities in medical data analysis, especially when managing large and complex data sets. The bottom line is that these findings can help make better medical decisions and strategically support AIDS prevention and treatment efforts.
Co-Authors Ade Christian Adi Supriyatna ahmad yani Dedi Triyanto Deni Kurniawan, Deni DENY KURNIAWAN Firmansyah Firmansyah HARIYANTO HARIYANTO Hengki Tamando Sihotang Herman Mawengkang Lita Sari Marita Mochamad Wahyudi Rahmansyah Siregar, Muhammad Ruhul Amin Sintagel br Sianipar, Imeldi Siregar, Muhammad Rahmansyah Solikhun Solikhun Solikhun, Solikhun Sumanto Sumanto Ummu Radiyah, Ummu Zidan, Muhammad