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All Journal Syntax Jurnal Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sistem dan Informatika JIKO (Jurnal Informatika dan Komputer) Informatika Pertanian Jurnal Sains dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) JUTIM (Jurnal Teknik Informatika Musirawas) J-SAKTI (Jurnal Sains Komputer dan Informatika) EDUMATIC: Jurnal Pendidikan Informatika MULTINETICS JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Ilmiah Wahana Pendidikan International Journal of Basic and Applied Science Computer Science (CO-SCIENCE) J-SAKTI (Jurnal Sains Komputer dan Informatika) CEBONG Journal International Journal of Enterprise Modelling Jurnal Informatika Polinema (JIP) Jurnal Sistem Informasi dan Aplikasi
Budi Arif Dermawan
Universitas Pembangunan Nasional Veteran Jakarta
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Journal : journal of applied informatics and computing

 1 
Penerapan Metode Regresi Linear Berganda untuk Prediksi Kerugian Negara Berdasarkan Kasus Tindak Pidana Korupsi Alfanda Novebrian Maharadja; Iqbal Maulana; Budi Arif Dermawan
Journal of Applied Informatics and Computing Vol 5 No 1 (2021): July 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i1.3184

Abstract

Tindak pidana korupsi merupakan kegiatan yang dapat mengakibatkan kerugian keuangan negara atau perekonomian negara serta dapat menghambat pembangunan nasional. Semenjak penindakan kasus korupsi 2013-2020, pada tahun 2014 merupakan angka tertinggi dalam jumlah kasus, yaitu sebanyak 629 kasus, sedangkan pada tahun 2020 negara mengalami kerugian tertinggi sebesar Rp. 18,6 Triliun. Adanya permasalahan tersebut perlu dilakukan kebijakan yang tepat serta antisipasi dalam meminimalisir kerugian negara pada tahun selanjutnya. Oleh karena itu penelitian ini melakukan prediksi kerugian negara berdasarkan tindak pidana korupsi dengan menggunakan regresi linear berganda. Regresi linear berganda merupakan salah satu metode statistik yang digunakan untuk menelusuri pola hubungan antara variabel terikat dengan dua atau lebih variabel bebas. Pembelajaran regresi linear berganda dalam penelitian ini menghasilkan model regresi yang dimana menghasilkan nilai konstanta yaitu 284645.5891073216 serta nilai koefisien yaitu -139837.38007863 dan 363493.06049751. Kemudian penelitian ini melakukan pengukuran performa model regresi linear dengan kondisi pembagian data 80% untuk data training dan 20% untuk data testing. Dari kondisi pembagian data tersebut memperoleh nilai RMSE sebesar 8447373.485 untuk data training dan 9769609.026 untuk data testing. Sedangkan untuk nilai koefesien determinasi memperoleh nilai sebesar 0.579 untuk data training yang tingkat hubungan antar variabelnya cukup kuat dan 0.662 untuk data testing yang berarti tingkat hubungan antar variabelnya kuat. Dengan melakukan prediksi menggunakan metode regresi linear berganda dapat memberikan informasi yang membantu pemerintah dalam mengambil kebijakan yang tepat terahadap permasalahan kasus korupsi serta meminimalisir dan mengantisipasi kerugian negara yang lebih besar untuk tahun selanjutnya.
Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method Permatasari, Ismi Aprilianti; Dermawan, Budi Arif; Maulana, Iqbal; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.3279

Abstract

The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.
Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan Ikhsal, Muhammad Fachry; Dermawan, Budi Arif; Adam, Riza Ibnu
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5562

Abstract

The failure of the accident detection system on CCTV cameras can affect the increase in the death rate on the highway. The use of the CNN method in the construction of CCTV accident detection systems has been widely used before. However, common problems that are often encountered are dirty lenses and varifocal zooms that don't automatically focus, causing the quality of the resulting CCTV images to decrease, thus affecting system performance. In this research, a model was developed to detect accidents on CCTV images using the MobileNetV2 pre-trained model which was optimized by upscaling the dataset using the Real-ESRGAN model to produce more optimal performance. This study uses a CCTV image dataset totaling 989 and consisting of 2 types of prediction classes including accident and non-accident. The results showed that the MobileNetV2 model succeeded in producing 94% testing accuracy and an average inference time of 3.33 seconds in the GT test scenario. During the testing process, it was found that the model was not optimal if it identified new data with clustered objects. In addition, based on the test scenarios X2, X4, X8 it was found that the image quality calculated based on PSNR and SSIM values greatly influences classification performance such as accuracy, precision, recall, and AUC score.
Co-Authors Ade Hikma Tiana Adhi Rizal Agung Siswoyo Aini Lailla Asri, Nur Alfafisabil, Wahyu Alfanda Novebrian Maharadja Aliffia Kulsumarwati Alrasyid, Wildan Anggara, Jerry Anis Fitri Nur Masruriyah Anis Masruriyah Apriade Voutama Ashari, Aliffia Novsiyanti Betha Nurina Sari Carudin Cika Oktavia Daffa Rafif Agustian Dwi Ely Kurniawan Eddy Ryansyah Erliza Hambali Fuza Putra Arwanda Galih Prakoso Rizky A Galih Prakoso Rizky A Haerudin Hannie, Hannie Hengki Tamando Sihotang Ibnu Adam, Riza Ikhsal, Muhammad Fachry Intan Purnamasari Iqbal Maulana Iqbal Maulana Isy Karima Fauzia LILIK BUDIPRASETYO Loemongga Oktaria Sihombing Maesha Ayu Syaharani Muhammad Hilmi Syarif Nana Mulyana Maghfur Natcha Kwintarini Suparman Nur Padilah, Tesa Nurul Afifah Arifuddin Permatasari, Ismi Aprilianti Putri Dwi Rahayu Putri, Dyas Rahma Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda, Rasenda Renaldy Permana Sidiq Rijal Abdulhakim Riza Ibnu Adam Riza Ibnu Adam, Riza Ibnu Rizky A, Galih Prakoso Rohanah, Aan Sanggi Bayu Ardika Sofi Defiyanti Sukarno Wijaya, Nanda Susanto Susanto Susanto Susilawati Susilawati Syafi'i, Muhammad Tesa Nur Padilah Tesa Nur Padilah Theresia Aurelly Claudia Budianto Wahyu Alfafisabil Wildan Alrasyid Yuyun Umaidah