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All Journal JOURNAL OF ICT APLICATIONS AND SYSTEM Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Putra, Wahyu Eka
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Visualization of Covid-19 Data in Indonesia in 2022 through the Google Data Studio Dashboard Putra, Wahyu Eka; Yanto, Budi; Erwis, Fauzi
JOURNAL OF ICT APLICATIONS AND SYSTEM Vol 2 No 2 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i2.238

Abstract

The COVID-19 pandemic has presented significant challenges to governments, researchers and the general public in understanding and monitoring the spread of this disease. In an effort to analyse the spread of COVID-19 disease in Indonesia effectively, this study uses Google Data Studio as a tool for data visualization and better understanding. This review is based on collecting data on the spread of COVID-19 disease in Indonesia which has been collected from various reliable sources. , including the World Health Organization (WHO) and national health agencies. This data is then processed and processed using Google Data Studio to produce informative visualizations. The results of the study show that Google Data Studio can be used effectively to analyse the spread of the COVID-19 disease in Indonesia. Through the use of available features, such as interactive graphs, maps, and tables, researchers can easily describe patterns of disease spread, infection rates, and recovery rates from an area or country. The quality of data collected from different sources may vary, and this can affect the accuracy and reliability of the resulting visualizations. Elements of the Scorecard that displays some important information related to the Covid-19 pandemic from 1 January 2019 to 31 January 2022. Information regarding the Covid-19 displayed on the Scorecard is as follows. The total survivors of the Covid-19 disease are 3,234,336,858 people. This indicates the number of people who have successfully recovered and recovered from infection with the Covid-19 virus during the period in question. The total number of deaths due to Covid-19 is 89,398,496 people. This reflects the number of people who died due to complications caused by the Covid-19 virus in that period.
Klasifikasi Sentimen Masyarakat di Twitter terhadap Puan Maharani dengan Metode Modified K-Nearest Neighbor Putra, Wahyu Eka; Fikry, Muhammad; Yusra; Yanto, Febi; Cynthia, Eka Pandu
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1211

Abstract

This study aims to address the challenges in classifying sentiment on Twitter regarding Puan Maharani by implementing the Modified K-Nearest Neighbor (MK-NN) method, supplemented with feature weighting and feature selection techniques. This method is designed to improve accuracy by assigning higher weights to important features and reducing data dimensions to avoid overfitting. Data is collected using a crawling technique on Indonesian-language tweets, which are then manually labeled and processed through a preprocessing stage. The testing results using the modified K-Nearest Neighbor (MK-NN) method with confusion matrices show the model's performance at three different values of K (3, 5, and 7) and data ratios of 90:10, 80:20, and 70:30. With a 90:10 data ratio and K=3, the method achieved the highest accuracy of 89.0%. These results indicate that the combination of MK-NN and related techniques is highly effective in sentiment classification, offering an innovative solution to the limitations of conventional methods. These findings have potential applications in public opinion analysis, particularly for supporting data-driven strategic decision-making.
EVALUASI KAPABILITAS TATA KELOLA DAN MANAJEMEN TI DENGAN COBIT 2019 PADA PT. XYZ Kusuma, Muhammad Wira Ade; Marpaung, Muhammad Nazaruddin; Putra, Wahyu Eka; Megawati, Megawati
Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi Vol. 2 No. 7 (2024): Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi
Publisher : Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/neraca.v2i7.1985

Abstract

Penelitian ini bertujuan untuk menilai kapabilitas proses Teknologi Informasi (TI) di PT. XYZ menggunakan kerangka kerja COBIT 2019 dan model Capability Level, dengan fokus pada tiga domain: BAI11 (Manage Projects), EDM04 (Ensure Resource Optimization), dan BAI05 (Manage Organizational Change). Penelitian menggunakan pendekatan studi kasus kualitatif dengan pengumpulan data melalui wawancara semi-terstruktur, observasi langsung, dan analisis dokumen terkait. Hasil penilaian menunjukkan bahwa kapabilitas proses TI di PT. XYZ untuk domain BAI05 berada pada level 2 dengan kategori "Largely Achieved" (80,93%), domain BAI11 berada pada level 4 dengan kategori "Fully Achieved" (83,33%), dan domain EDM04 berada pada level 3 dengan kategori "Largely Achieved" (83,33%). Analisis kesenjangan mengidentifikasi gap sebesar 2 tingkat untuk BAI05 dan 1 tingkat untuk EDM04 dibandingkan dengan target kapabilitas yang diharapkan. Rekomendasi diberikan untuk meningkatkan kapabilitas pada domain BAI05 dan EDM04 agar PT. XYZ dapat mencapai target kapabilitas yang lebih tinggi. Peningkatan ini diharapkan dapat mendukung tujuan strategis perusahaan secara lebih efektif, serta meningkatkan kinerja dan efisiensi secara keseluruhan.
Convolutional Neural Networks Using EfficientNetB0 Architecture and Hyperparameters on Skin Disease Classification Khairunnisa, Putri; Putra, Wahyu Eka; Yitong, Wu; Jufrizal, Abni; Makmum, Muhammad Nur Aflah
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1569

Abstract

Skin diseases are often caused by air temperature, environmental hygiene and personal hygiene, with symptoms such as itching, pain and redness. Contributing factors include exposure to chemicals, sunlight, infections, a weak immune system, microorganisms, and poor personal hygiene. This study uses Convolutional Neural Networks (CNN) with EfficientNetB0 model and hyperparameter optimization for skin disease classification. The dataset consists of 1158 images that have been divided into eight categories, with 80% for training data and 20% for test data. Data augmentation is applied to increase the variety of training data. Various combinations of hyperparameters such as learning rate, optimizer (Adamax and AdamW), and activation function (ReLU and LeakyReLU) were tested in 16 training scenarios. The best results was obtained from the third scenario with the original dataset, Adamax optimizer, ReLU activation function, and 0.01 learning rate, which gave a testing accuracy of 95.70%. The model also showed good generalization and low loss values. Confusion matrix analysis and classification report showed high accuracy in skin disease classification. This study concludes that EfficientNetB0 with proper hyperparameter optimization can improve the accuracy and effectiveness of skin disease diagnosis.
Co-Authors Budi Yanto, Budi Eka Pandu Cynthia, Eka Pandu Erwis, Fauzi Febi Yanto Jufrizal, Abni Khairunnisa, Putri Kusuma, Muhammad Wira Ade Makmum, Muhammad Nur Aflah Marpaung, Muhammad Nazaruddin Megawati - Muhammad Fikry Yitong, Wu Yusra