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All Journal Creative Information Technology Journal SISFOTENIKA CogITo Smart Journal Jurnal ICT : Information Communication & Technology Technologia: Jurnal Ilmiah E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Manajemen Informatika dan Sistem Informasi Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia EXPLORE Journal of Informatics, Electrical and Electronics Engineering Jurnal Informatika Polinema (JIP)
Asro Nasiri
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science

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PEMANFAATAN DEEP LEARNING UNTUK SEGMENTASI PARU-PARU DARI CITRA X-RAY DADA Putranto, Dinar Wakhid; Andi Sunyoto; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.114

Abstract

Chest or thoracic X-ray examination is the most commonly used supporting examination in the diagnosis of lung diseases. In addition to being quick, X-rays are more economical than CT scans or laboratory blood tests. Before determining the disease that appears from the lung image in the chest X-ray image, the doctor first determines the boundaries of the lung area. Not every chest X-Ray image has a normal lung image, some display abnormal images that look white haze or morphological changes due to lung disease processes. As one of the CNN architectures that can be used in segmenting the lungs is UNET which is an encode-decoder architecture. This research tries to train a deep learning model with U-Net CNN architecture. From the results of our experiments, the proposed model can show the ability to recognize lung boundaries even though there are abnormal or foggy lung images. The performance of the model is calculated by measuring the pixel accuracy value and the overlap value with Jaccard Index (IoU), the values of both are 98.25% and 94.54% respectively.
Perancangan Model Manajemen (Tata Kelola) Data Menggunakan Domain APO14 COBIT 2019 Prasetya, Rendra; Alva Hendi Muhammad; Asro Nasiri
Jurnal Informatika Polinema Vol. 10 No. 3 (2024): Vol. 10 No. 3 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i3.5135

Abstract

Manajemen data memainkan peran krusial dalam operasional organisasi, pentingnya manajemen (tata kelola) data yang tepat untuk mendukung operasional dan pengambilan keputusan yang cermat. COBIT 2019, terutama melalui domain APO14, memberikan kerangka kerja penting bagi organisasi dalam mengoptimalkan manajemen data mereka. Penelitian ini bertujuan untuk menganalisis penerapan COBIT 2019 dalam meningkatkan tata kelola di organisasi, dengan fokus pada Fakultas Syariah di IAIN Ponorogo sebagai studi kasus. Manajemen data yang baik tidak hanya mempengaruhi operasional internal organisasi tetapi juga dampaknya pada keputusan strategis, inovasi, dan daya saing di pasar. Pentingnya tata kelola data yang tepat tercermin dalam praktik seperti perencanaan, pengorganisasian, penyimpanan, dan pemanfaatan data secara cermat. Namun, masih ada tantangan dalam mengintegrasikan secara holistik di berbagai unit dalam organisasi, seperti yang terjadi di Fakultas Syariah IAIN Ponorogo. Dengan pencapaian nilai capability level tata kelola data sebesar 3,14 menurut domain APO14 COBIT 2019, Fakultas Syariah telah mencapai tingkat memadai dalam pengelolaan data. Meski demikian, terdapat kebutuhan untuk meningkatkan aspek-aspek tertentu dalam subdomain seperti APO14.02, APO14.03, APO14.04, APO14.06, APO14.07, APO14.08, APO14.09, dan APO14.10. Penelitian ini berkontribusi pada pemahaman dan pengembangan COBIT 2019, khususnya domain APO14 dalam konteks manajemen data, dan memberikan pandangan yang jelas serta rekomendasi praktis dalam meningkatkan tata kelola data organisasi.
PERANCANGAN TATA KELOLA DATA MENGGUNAKAN COBIT 2019 UNTUK PENINGKATAN KETERSEDIAAN DAN KEHANDALAN DATA AKADEMIK Amrullah; Alva Hendi Muhamad; Asro Nasiri
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 1 (2025): MISI Januari 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i1.1407

Abstract

Di era digital, institusi pendidikan tinggi menghadapi tantangan dalam pengelolaan data akademik, terutama dalam memastikan ketersediaan, keandalan, dan keamanan data. Penelitian ini bertujuan untuk merancang kerangka tata kelola data akademik di STMIK Lombok dengan menggunakan kerangka kerja COBIT 2019. Metodologi yang digunakan meliputi identifikasi kesenjangan tata kelola, analisis kebutuhan institusional, dan implementasi solusi berbasis lima domain utama COBIT 2019: APO07, BAI03, DSS01, DSS05, dan EDM01. Hasil penelitian menunjukkan peningkatan signifikan dalam integrasi data, aksesibilitas real-time, dan keamanan. Implementasi sistem berbasis cloud meningkatkan efisiensi operasional, sementara protokol keamanan seperti enkripsi data dan backup otomatis mengurangi risiko kebocoran data. Kerangka tata kelola yang dirancang mampu menciptakan prosedur yang terstandar dan menyelaraskan operasional TI dengan tujuan strategis institusi, dengan tingkat kematangan pada kategori “Largely Achieved” untuk semua domain yang dievaluasi. Penelitian ini membuktikan aplikasi praktis COBIT 2019 dalam mengatasi tantangan pengelolaan data akademik. Temuan ini memberikan kontribusi penting bagi literatur tata kelola TI dengan menyediakan model yang dapat direplikasi di institusi lain. Penelitian selanjutnya disarankan untuk mengeksplorasi dampak jangka panjang penerapan COBIT 2019 serta integrasi teknologi canggih untuk meningkatkan efektivitas tata kelola. Penelitian ini menegaskan peran penting tata kelola TI yang terstruktur dalam meningkatkan efisiensi dan keamanan institusi pendidikan tinggi.
Efektivitas Pelatihan Awal Berbasis Domain Spesifik Legal-BERT Untuk Natural Language Processing Hukum: Replikasi Dan Perluasan Studi Casehold Zakiri, Hasani; Alva Hendi Muhammad; Asro Nasiri
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2610

Abstract

Abstract?The emergence of domain-specific language models has demonstrated significant potential across various specialized fields. However, their effectiveness in legal natural language processing (NLP) remains underexplored, particularly given the unique challenges posed by legal text complexity and specialized terminology. Legal NLP has practical applications such as automated legal precedent search and court decision analysis that can accelerate legal research from weeks to hours. This study evaluates the CaseHOLD dataset to provide comprehensive empirical validation of domain-specific pretraining benefits for legal NLP tasks with focus on data efficiency and context complexity analysis. We conducted systematic experiments using the CaseHOLD dataset containing 53,000 legal multiple-choice questions. We compared four models: BiLSTM, BERT-base, Legal-BERT, and RoBERTa across varying data volumes (1%, 10%, 50%, 100%) and context complexity levels. Paired t-tests with 10-fold cross-validation and Bonferroni correction ensure robust methodology that guarantees finding reliability. Legal-BERT achieved the highest macro-F1 score of 69.5% (95% CI: [68.0, 71.0]), demonstrating a statistically significant improvement of 7.2 percentage points over BERT-base (62.3%, p < 0.001, Cohen's d= 1.23). RoBERTa showed competitive performance at 68.9%, nearly matching Legal-BERT. The most substantial improvements occurred under limited data conditions with 16.6% improvement at 1% training data. Context complexity analysis revealed an inverted-U pattern with optimal performance on 41-60 word texts. The introduced Domain Specificity Score (DS-score) showed strong positive correlation (r = 0.73, p < 0.001) with pretraining effectiveness, explaining 53.3% of performance improvement variance. These findings provide empirical evidence that domain-specific pretraining offers significant advantages for legal NLP tasks, particularly under data-constrained conditions and moderate-high context complexity. The key distinction of this research is the development of a predictive DS-score framework enabling benefit estimation before implementation, unlike previous studies that only evaluated post-hoc performance. The results have practical implications for developing legal NLP systems in resource-limited environments and provide optimal implementation guidance for Legal-BERT.
Co-Authors Acihmah Sidauruk Alva Hendi Muhamad Alva Hendi Muhammad Amrullah Andi Oktafiqurahman Andi Sunyoto Arga Fiananta Arief Setyanto Arif Laksito Bambang Soedijono WA Eka Wahyu Pujiharto Eka Wahyu Sholeha Elvis Pawan Ema Utami Kusrini Kusrini Kusrini Kusrini Kusrini _ Maykel Sonobe Mohamad Syafri Lamato Muhamad Rodi Prasetya, Rendra Putranto, Dinar Wakhid Rizfi Syarif Tohir Ismail Zakiri, Hasani