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ANALISIS FAKTOR EKSTERNAL DAN FAKTOR INTERNAL YANG MEMPENGARUHI TERJADINYA NON PERFORMING LOANS PADA BANK PEMBANGUNAN DAERAH (BPD) DI INDONESIA PERIODE 2011 - 2015 Sari, Dewi Permata
Jurnal Ilmiah Mahasiswa FEB Vol. 5 No. 1
Publisher : Fakultas Ekonomi dan Bisnis Universitas Brawijaya

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Abstract

Bank merupakan lembaga keuangan yang memiliki fungsi intermediasi yakni menghimpun dana masyarakat yang berupa simpanan dan menyalurkan kembali dana tersebut dalam bentuk kredit. Jumlah kredit yang disalurkan oleh bank berpotensi memiliki resiko gagal bayar dari debitur dan Non Performing Loans mencerminkan indikator awal terjadinya resiko kredit. Karena semakin tinggi kredit yang disalurkan oleh bank, maka semakin besar potensi kredit bermasalah yang harus ditanggung oleh bank. Penelitian ini bertujuan untuk mengetahui faktor-faktor eksternal dan faktor internal yang mempengaruhi terjadinya Non Performing Loans pada bank pembangunan daerah (BPD) di Indonesia periode tahun 2011-2015. Penelitian ini dilakukan dengan purposive sampling. Sampel yang digunakan adalah 4 BPD di Indonesia dengan jumlah asset tertinggi dan terendah. Metode analisis data menggunakan metode data panel untuk mengetahui pengaruh Inflasi, BI Rate, Produk Domestik Bruto, Bank Size, Biaya Operasional Pendapatan Operasional, dan Loan to Deposit Ratio terhadap Non Performing Loans pada Bank Pembangunan Daerah. Hasil pembahasan menunjukkan bahwa secara simultan variabel-variabel independen (Inflasi, BI Rate, PDB, Bank Size, BOPO, dan LDR) berpengaruh terhadap NPL. Sedangkan secara parsial dengan uji t menunjukkan bahwa PDB dan BOPO berpengaruh siginifikan sedangkan variabel Inflasi, BI Rate, Bank Size, dan LDR tidak berpengaruh terhadap terjadinya Non Performing Loans pada kelompok Bank Pembangunan Daerah di Indonesia. Kata Kunci : Non Performing Loan (NPL), Inflasi, BI Rate, PDB, Bank Size, BOPO, LDR
Laporan Kasus: Perlemakan Hati Akut pada Kehamilan Winarta Wahjudi, Jeffy; Ngutamani, Sulih Yekti; Khaerul Putri, Olyvia Yulyani; Sari, Dewi Permata
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i12.52257

Abstract

Perlemakan hati akut pada kehamilan atau Acute Fatty Liver of Pregnancy (AFLP) adalah penyakit obstetri yang ditandai dengan disfungsi hati ibu. AFLP terjadi pada trimester tiga kehamilan dan merupakan kasus kegawatdaruratan obstetri yang jarang terjadi. Laporan kasus ini bertujuan untuk mengkaji bagaimana mendiagnosis hingga tatalaksana yang tepat pada pasien AFLP yang didapat pada kasus AFLP RS Paru Jember. Penegakan diagnosis AFLP adalah dengan kriteria Swansea yang didapatkan dari anamnesis, pemeriksaan fisik, dan pemeriksaan penunjang. Tatalaksana AFLP yaitu dengan stabilisasi dan terminasi kehamilan. Diagnosis dan tatalaksana yang cepat dan tepat akan meningkatkan prognosis ibu dan janin lebih baik.
Pengaruh Penerapan IFRS, Kemampuan Manajerial, dan Komisaris Independen Terhadap Manajemen Laba Perusahaan Manufaktur Sub Sektor Makanan dan Minuman Periode 2020-2022 Maharani, Raisya Zuhra; Nabella, Septa Diana; Sari, Dewi Permata
Jurnal Mirai Management Vol 10, No 1 (2025)
Publisher : STIE AMKOP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/mirai.v10i1.8728

Abstract

Penelitian ini bertujuan untuk mengetahui serta menganalisis pengaruh penerapan IFRS (International Financial Reporting Standards), kemampuan manajerial, dan komisaris independen terhadap manajemen laba pada perusahaan manufaktur sub sektor makanan dan minuman yang terdaftar di Bursa Efek Indonesia periode 2020–2022. Penelitian ini didasarkan pada praktik manajemen laba yang masih sering terjadi di perusahaan, yang dapat merugikan stakeholders dan menurunkan kredibilitas laporan keuangan. Metode yang digunkaan pada penelitian ini adalah metode kuantitatif dengan pendekatan analisis regresi linear berganda. Data yang digunakan merupakan data sekunder yang diperoleh dari laporan keuangan tahunan perusahaan yang terdaftar di Bursa Efek Indoneisa dengan jumlah sampel sebanyak 22 perusahaan yang dipilih menggunakan metode purposive sampling selama tiga tahun, dengan 66 observasi. Hasil penelitian menunjukkan bahwa penerapan IFRS, kemampuan manajerial, dan komisaris independen secara simultan tidak berpengaruh terhadap manajemen laba. Secara parsial, variabel penerapan IFRS berpengaruh positif namun tidak signifikan terhadap manajemen laba, sedangkan kemampuan manajerial dan komisaris independent tidak berpengaruh terhadap manajemen laba. Penelitian ini diharapkan akan memberikan kontribusi teoritis dan praktis untuk meningkatkan kualitas laporan keuangan dan mengawasi praktik manajemen laba.
PENGARUH UMUR PERUSAHAAN, OPINI AUDIT, REPUTASI KAP, DAN KOMPLEKSITAS OPERASI TERHADAP KETEPATAN WAKTU PENYAMPAIAN LAPORAN KEUANGAN (STUDI EMPIRIS PADA PERUSAHAAN ENERGI YANG TERDAFTAR DI BEI 2021-2023) Sari, Dewi Permata; Indrawati, Novita; Afifah, Ulfa
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Vol 12, No 1 (2025): : (Januari - Juni)
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi

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Abstract

This study was conducted to determine the effect of company age, audit opinion, reputation ofpublic accounting firms, and complexity of operations on the timeliness of financial reportsubmission in energy sector companies listed on the Indonesia Stock Exchange. This study applieda quantitative study using secondary data. The population in this study were all energy companieslisted on the Indonesia Stock Exchange (IDX) in the 2021-2023 period. The research sample wasselected using the purposive sampling method, so that the sample obtained was 55 samples. Datawere obtained from annual financial reports obtained through the official website of the IndonesiaStock Exchange and other related websites. The data analysis technique used in this study wasdescriptive statistics with the help of SPSS (Statistic Package for Social Science) software version25. The results of the study showed that the independent variables, namely company age,reputation of public accounting firms, and complexity of operations had no significant effect onthe timeliness of financial report submission. Meanwhile, the audit opinion variable showed asignificant effect on the timeliness of financial report submission.Keywords: Company Age, Audit Opinion, Public Accounting Firm Reputation, OperationalComplexity, and Timeliness of Financial Report Submission
Development of a Raspberry Pi 4-Powered Internet of Things System for Acne-Prone Skin Health Monitoring Kurniawan, Aprila; Sari, Dewi Permata; Wijanarko, Yudi; Sabara, Gally
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36997

Abstract

This research developed an Internet of Things (IoT)-based facial skin health monitoring system, with a focus on acne-prone skin. Facial skin is categorized into three main types: normal, oily, and dry, as well as four types of acne: blackheads, papules, pustules, and nodules. The system is designed to enhance the accuracy of skin condition monitoring through facial image analysis, utilizing a dataset of 4,092 images. The high number of acne cases, especially in 12-24 year olds with 40-50 million cases in the United States, is the background of this research. Conventional skin analyzers are considered less capable of providing accurate quantitative data. Therefore, a Smart Skin Analyzer Detector was developed that uses a Raspberry Pi as a data processor. Images are taken through a webcam, analyzed, and then the results are sent to the cloud. The system is also integrated with Telegram to provide users with real-time notifications regarding their skin type and acne condition. This approach enables more effective, faster, and more affordable skin monitoring. The results demonstrate that IoT technology has significant potential in enhancing personalized and sustainable skin care.
Comparative Study: Performance Comparison of You Only Look Once and Convolutional Neural Networks Algorithms in Human Object Detection Sari, Dewi Permata; Ramadhani, M. Akbar Tri; Abdurrahman, Abdurrahman
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37676

Abstract

The evolution of object identification technologies, particularly for person detection applications, has increasingly accelerated due to the merger of deep learning and artificial intelligence with computer vision. This study intends to test the efficacy of two object detection algorithms, YOLOv8n and CNN MobileNetSSD, in identifying human objects in digital photos. A dataset of 12,334 human-labeled photos from the Roboflow platform was utilized to train the YOLOv8n model, while performance results for the CNN MobileNetSSD model were acquired from a prior article. The precision, recall, and F1-score of each model were examined. Experimental results reveal that YOLOv8n attains 94% precision, 92% recall, and a 92.9% F1-score, representing a considerable enhancement over MobileNetSSD. Conversely, MobileNetSSD got an F1-score of 85.2%, with a precision of 86.5% and a recall of 84.1%. The findings show that CNN MobileNetSSD is more ideal for non-time-sensitive or resource-limited scenarios; however, YOLOv8n is preferable for real-time human identification tasks due to its greater accuracy and faster inference. This comparative analysis is important for differentiating object detection models matched to certain application needs.
Acne Skin Detection System Using You Only Look Once (YOLOV8) Based on Artificial Intelligence Sabara, Gally; Abdurrahman, Abdurrahman; Sari, Dewi Permata; Kurniawan, Aprila
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37217

Abstract

Acne is one of the most common skin problems among teenagers and young adults, and early detection is essential to prevent progression and long-term skin damage. This study aims to develop a real-time acne detection system utilizing the YOLOv8 deep learning algorithm, integrated with a Raspberry Pi and webcam, and supported by Telegram-based notifications for user monitoring. The dataset comprises 4,092 annotated facial images representing three types of acne: papule, pustule, and nodule. Model training was conducted in Google Colab with appropriate hyperparameter adjustments. The evaluation results show that the model performs well in detecting papule and pustule acne types, with correct predictions of 258 and 222 samples, respectively, in the confusion matrix, although misclassification remains high for comedones and background classes. The Precision–Confidence Curve indicates that the model achieves a perfect precision score of 1.00 at a confidence threshold of 0.929, while the F1–Confidence Curve shows an optimal F1-score of 0.73 at a confidence level of 0.39, demonstrating the best balance between precision and recall. Real-time testing further confirms that the system can detect papules with high confidence (88%), but confidence levels for comedones (31%) and nodules (29%) remain low due to visual similarity and non-ideal lighting conditions. Overall, the results indicate that the YOLOv8-based system is capable of performing real-time acne detection with acceptable accuracy. However, further improvements in dataset diversity and annotation quality are required to enhance performance, particularly for comedone detection.