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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Ilmiah Farmasi Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science Indonesian Journal of Business Intelligence (IJUBI) PREPOTIF : Jurnal Kesehatan Masyarakat JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Research of Social Science, Economics, and Management Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Keilmuan dan Aplikasi Teknologi Industri
Fudholi, Dhomas Hatta
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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A Mobile Deep Learning Model on Covid-19 CT-Scan Classification Susanto, Prastyo Eko; Kurniawardhan, Arrie; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.607 KB) | DOI: 10.29099/ijair.v6i1.257

Abstract

COVID-19 pandemic is currently happening in the world. Previous studies have been done to diagnose COVID-19 by identifying CT-scan images through the development of the novel Joint Classification and Segmentation System models that work in real-time. In this study, the author focuses on a different motivation and innovation focused on the development of mobile deep learning. Mobile Net, a deep learning model as a method for classifying the disease COVID-19, is used as the base model. It has a good level of efficiency and reliability to be implemented on devices that have small memory and CPU specifications, such as mobile phones. The used data in this study is a CT-scan image of the lungs with a horizontal slice that has been classified as positive or negative for COVID-19. To give a broader analysis, the author compares and evaluates the model against other architectures, such as MobileNetV3 Large, MobileNetV3 Small, MobilenetV2, ResNet101, and EfficientNetB0. In terms of the developed mobile architecture model, the classification of COVID-19 using MobileNetV2 obtained the best result with 0.81 accuracy.
TEMPORAL SPATIAL PROPERTY PROFILING AND IDENTIFICATION OF EARTHQUAKE PRONE AREAS USING ST-DBSCAN AND K-MEANS CLUSTERING Samsudin, Angga Radlisa; Fudholi, Dhomas Hatta; Iswari, Lizda
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1293

Abstract

Indonesia is a country located at the confluence of three major tectonic plates, namely Indo-Australia, Eurasia, and the Pacific so that earthquakes often occur, one of which is in West Nusa Tenggara Province. One way to accelerate the disaster mitigation process is to analyze earthquake occurrence based on spatial temporal aspects. This study uses data from BMKG NTB Province during 2018 with a total of 3,699 earthquake events which are then analyzed using ST-DBSCAN and K-Means. ST-DBSCAN analysis was used to determine earthquake prone areas based on the date and location of the event, while k-means used the depth and magnitude of the earthquake. The results show that the distribution pattern of earthquakes in the NTB region has a stationary pattern and there are similar prone areas based on the location and time of occurrence as well as the strength and depth of the earthquake. The ST-DBSCAN method using latitude and longitude attributes produces one cluster that covers 96.33% of the total data. Meanwhile, K-Means using the depth and magnitude attributes produced four clusters. The four clusters were obtained from the cluster density using the silhouette score value between -1 and 1. The K-means analysis used a silhouette score result of 18.527 which was found in cluster 1. Earthquake prone areas in the distribution of earthquakes or types of earthquakes are located in Gangga and Bayan sub-districts of North Lombok and in Sambelia and Sembalun sub-districts of East Lombok. The sub-district with the most frequent earthquakes is Sambelia sub-district with 112 earthquakes. Then the strength of the largest earthquakes on average occurred in Gangga sub-district with magnitudes of 4 to 6.2 SR with shallow earthquake types. The prone area is located at the foot of the mountain and directly adjacent to the ocean.ith shallow earthquake types. The Prone area is at the foot of a mountain and directly adjacent to the ocean.
Implementation Of Deep Learning For Fake News Classification In Bahasa Indonesia Widhi, Eko Prasetio; Fudholi, Dhomas Hatta; Hidayat, Syarif
Journal Research of Social Science, Economics, and Management Vol. 3 No. 2 (2023): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v3i02.546

Abstract

Fake news has become a serious threat in the digital information era. This research aims to develop a model for detecting fake news in Bahasa Indonesia using a deep learning approach, combining the Long Short-Term Memory (LSTM) method with word representations from Word2vec Continuous Bag of Words (CBOW) to achieve optimal results. Our main model is LSTM, optimized through hyperparameter tuning. This model can process information sequentially from both directions, allowing for a better understanding of the news context. The integration of Word2vec CBOW enriches the model's understanding of word relationships in news text, enabling the identification of important patterns for news classification. The evaluation results show that our model performs very well in detecting fake news. After the tuning process, we achieved an F1-Score of 97.30% and an Accuracy of 98.38%. 10-fold cross-validation yielded even better results, with an F1-Score and Accuracy reaching 99%.
Implementation of Semi-Supervised Learning with YOLOv11 for On-Shelf Availability Detection of Retail Avilba, Pandu; Kurniawardhani, Arrie; Fudholi, Dhomas Hatta
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10881

Abstract

On-Shelf Availability (OSA) is a critical aspect of retail operations that affects customer satisfaction and potential sales. Computer vision–based systems have emerged as a promising solution to monitor product availability on store shelves. However, their implementation faces the challenge of limited labeled data, which requires time-consuming manual annotation with precise bounding boxes. This study proposes a semi-supervised learning approach based on pseudo-labeling using the YOLOv11n architecture to address the scarcity of labeled data. We utilized a dataset of 918 retail product images with 174 classes, divided into four proportions of labeled data (20%, 40%, 60%, and 80%). The research stages included training a teacher model, generating pseudo-labels with a confidence threshold of 0.5, and training a student model using a combination of labeled and pseudo-labeled data. Experimental results show that this approach effectively improves detection performance. With 60% labeled data, the model achieved an mAP50 of 0.931 and an mAP50-95 of 0.864, along with high-quality pseudo-labels (F1-Score 0.727; IoU 0.819). This significant improvement indicates that pseudo-labels can enrich data variation without introducing excessive noise. The study demonstrates that semi-supervised learning can reduce dependence on large labeled datasets while offering a practical and efficient solution for OSA detection systems in retail environments
PERMODELAN PENGETAHUAN KESIAPAN PENANGANAN BENCANA DI RUMAH SAKIT Hardiyanti, Mawar; Fudholi, Dhomas Hatta
Indonesian Journal of Business Intelligence (IJUBI) Vol 4 No 2 (2021): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v4i2.1934

Abstract

Bencana alam adalah peristiwa yang umumnya membawa dampak negatif. Indonesia adalah salah satu negara rawan bencana. Rumah sakit merupakan tempat rujukan pertama saat korban bencana membutuhkan perawatan. Pada dasarnya, berbagai studi yang menghasilkan pengetahuan sudah cukup banyak tersedia. Namun untuk penggunaannya pada bencana alam belum dikelola dan diterapkan dengan baik. Berdasarkan hal tersebut maka kami akan membangun medel pengetahuan kesiapan penanganan bencana di rumah sakit. Penelitian ini mengembangkan sebuah model pengetahuan berbasis ontologi untuk kesiapan rumah sakit pada penanganan bencana berdasarkan konsep tenaga kesehatan, institusi terkait, rencana darurat, dan alat. Proses permodelan ontologi pada penelitian ini terdiri dari tiga fase yaitu Konseptualisasi, Implemetasi dan Evaluasi. Pembangunan ontologi didasarkan dari hasil kuisioner yang telah diisi oleh pengurus TIM Bencana dari tiga rumah sakit di Jawa Tengah. Hasil yang didapatkan dari pengukuran ontologi yang dibuat untuk Relationship Richness sebesar 0.68, Inheritance Richness sebesar 0.18, dan Attribute Richness sebesar 0.04. Sedangkan hasil pengujian query yang dilakukan menggunakan DL Query Panel adalah sistem dengan kemapuan memberi sebuah jawaban dari gabungan ekspresi Class, object property untuk mendapatkan instancedari data Individual.
Prediksi Pencapaian Target Kerja untuk Optimasi Manajemen Proyek Menggunakan Metode Long Short Term Memory dan Random Forest Achmad Zahra, Rizqatasyaa; Fudholi, Dhomas Hatta
Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Vol. 25 No. 2 (2025): Regular Issue
Publisher : SEKOLAH TINGGI TEKNOLOGI INDUSTRI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36275/8m9pg216

Abstract

Dalam era digitalisasi, pengelolaan sumber daya manusia dalam proyek teknologi informasi menghadapi tantangan signifikan, khususnya dalam memperkirakan kebutuhan mandays secara akurat. Mandays merupakan satuan yang merepresentasikan total hari kerja yang dibutuhkan satu atau lebih tenaga kerja untuk menyelesaikan suatu tugas atau proyek. Ketidaktepatan estimasi mandays dapat menyebabkan risiko under allocation dan over allocation, yang berdampak pada pemborosan anggaran, ketidakseimbangan beban kerja, serta keterlambatan proyek. Permasalahan serupa masih dijumpai pada berbagai perusahaan berbasis proyek, baik di sektor teknologi informasi, konstruksi, maupun manufaktur, karena proses estimasi umumnya masih dilakukan secara manual dan belum didukung sistem prediktif berbasis data historis. Penelitian ini mengusulkan model prediktif berbasis kombinasi metode LSTM dan Random Forest dengan pendekatan error reciprocal, yaitu pemberian bobot lebih besar pada model dengan tingkat kesalahan prediksi (MAE) yang lebih rendah. Prediksi mandays dilakukan berdasarkan faktor-faktor proyek yang meliputi jenis proyek, status, durasi, jumlah SDM, dan nilai proyek. LSTM digunakan untuk mengenali pola temporal pada data historis proyek, sedangkan Random Forest untuk menangani hubungan non-linear dan menghasilkan prediksi yang lebih stabil. Hasil pengujian menunjukkan model tunggal LSTM memiliki performa yang lebih baik dibandingkan RF, dengan nilai MAE masing-masing sebesar 286.21 dan 369.57. Namun, model kombinasi LSTM-RF memberikan performa terbaik dengan nilai MAE sebesar 279.32 dan MAPE 17.65%. Evaluasi tambahan menggunakan metrik MMRE < 25% dan PRED(25) ≥ 75%, menunjukkan bahwa model kombinasi berada dalam kategori prediksi yang baik serta mampu meningkatkan akurasi prediksi sekitar 4% dibandingkan model tunggal. Dengan demikian, model LSTM-RF efektif dalam memprediksi pencapaian target kerja dan mendukung pengambilan keputusan dalam manajemen proyek TI.
Hybrid DBSCAN - K-Means Clustering for Financial Loss Identification in INA-CBG Claims Based on Medical Treatment Patterns Dianqori, Muhammad Fajar; Fudholi, Dhomas Hatta; Utomo, Galih Aryo; Paputungan, Irving Vitra
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9483

Abstract

Hospital financial deficits due to INA-CBG claim discrepancies pose a critical challenge to healthcare sustainability in Indonesia. The difference between hospital operating costs and INA-CBG rates often results in significant financial deficits, which can threaten the sustainability of healthcare providers, especially hospitals. However, existing studies lack a systematic approach to identify distinct patterns of financial losses based on clinical treatment characteristics. This study aims to identify clusters of patients with different financial loss characteristics using a hybrid DBSCAN-K-Means clustering approach based on medical procedure frequency patterns. The DBSCAN algorithm was employed to detect and separate noise from data, while K-Means was used to identify medical treatment patterns. The data were obtained from electronic medical records of inpatients during the 2023–2024 period at a private hospital (N = 6,021 cases). The final clustering results revealed two main clusters with a highly significant difference in deficits between clusters (p = 6.21 × 10⁻³⁸, Cliff's Delta = −0.216). Cluster 0 represents patients with intensive care who have a higher frequency of routine procedures, with an average deficit of 1.51 times (51.3% greater) and an average length of stay of 1.76 times (76% longer) than Cluster 1. Cluster 1 represents patients with a focus on obstetrics/neonatology with a predominance of Doppler procedures. Meanwhile, the noise cluster (13.39%) represents more extreme cases with an average loss of −7.82 million IDR and high mortality. Of the total 315 treatment features, 114 were confirmed to be statistically significant. This study contributes a novel hybrid clustering framework for identifying financial loss patterns in INA-CBG claims, providing actionable insights for hospital management to optimize service utilization, adjust procedure fees for complex cases, and strengthen financial risk management strategies.
Co-Authors Achmad Zahra, Rizqatasyaa Ahmad Fathan Hidayatullah, Ahmad Fathan Al-Faruq, Umar Abdul Aziz Al-Sabahi, Abdullah Yahya Moqbel Alhanafi, Ahmad Azzam Andaru, Gabriel Imam Arifin, Dimastyo Muhaimin Arrie Kurniawardhani Arrochman, Ibnu Fajar Avilba, Pandu Bagus W, Alan Tusa Basbeth, Faishal Bauravindah, Achmad Chairullah Naury Dewa, Chandra Kusuma Dianqori, Muhammad Fajar Eka Handayani Eko Prasetio Widhi Fahmi, Yunizar Fikri, Khairul Hardiyanti, Mawar Kurniawardhan, Arrie Lizda Iswari Manoppo, Tommy Nugraha Mustofa, M Najmudin, Nabil Naury, Chairullah Nayoan, Royan Abida N Nayoan, Royan Abida N. Nur Iman Pailus, Muhammad Paputungan, Irving Vitra Ridho Rahmadi Rima Tri Wahyuningrum Saefudin Saefudin Satyatama, Mochamad Rezky Septia Rani Siti Mutmainah Suci Hanifah Suryatin, Suryatin Susanto, Prastyo Eko Suyuti, Maghfirah Syarif Hidayat Utomo, Galih Aryo Wijayanti, Shofia Yasmin, Saarah Muthiah Yoga Sahria Yudi prayudi Yurio Windiatmoko, Yurio Zahra, Annisa