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All Journal IPTEK Journal of Proceedings Series Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) KOMNET : Jurnal Komputer, Jaringan dan Internet
I Made Candra Girinata
Department of Management Technology, Institut Teknologi Sepuluh Nopember
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Other

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Strategic Planning for Systems & Information Technology of XYZ Hospital Using Ward and Peppard Method I Made Candra Girinata; Erma Suryani
IPTEK Journal of Proceedings Series No 5 (2019): The 1st International Conference on Business and Management of Technology (IConBMT)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (685.771 KB) | DOI: 10.12962/j23546026.y2019i5.6392

Abstract

XYZ Hospital is one of the busiest private hospitals in Badung, Bali which has implemented systems and information technology handled by its IT division staff. However, the implementation of systems and information technology has not been equipped with strategic planning so that it has not been directed and aligned with the company's business strategy, only based on demand. The impacts of there's no strategic planning systems and information technology is the companies are difficult to invest appropriately based on system requirements and information technology that can support the company's business strategy. Increasing the competitive advantage of utilizing systems and information technology still not optimal because the development of IS / IT lack directional and appropriate. Therefore, to overcome the problems, in this research is making strategic planning of systems and information technology of XYZ hospital, which carried out with Ward and Peppard method which serves to produce an IS /IT strategic plan that can help the company to run their process business more effectively as well as add its business value. The analysis techniques used are Value Chain, PEST, Porter 's Five Forces, SWOT, BSC, CSF, gap analysis and Mc Farlan's Strategic Grid. From the results of the analysis of the current condition of the company shows that XYZ Hospital is in the position of quadrant 1 which indicates the company should be focused on an aggressive strategy where reduce weaknesses and avoid threats to get maximum benefit. The recommendations for future application portfolios obtained are three applications in the high potential quadrant, one application in the strategic quadrant and two applications in the key operational quadrant. The results of this study aim to provide an overview to management in making decisions relating to investment, implementation and management policies of IS / IT that can support the company's vision and mission
Prediksi Pergerakan Harga Ethereum Menggunakan Machine Learning dengan Algoritma Random Forest dan XGBoost Girinata, I Made Candra; Styawan, Budi; Saputra, Arwin Wahyu; Arif, M Aidil; Dahur, Arnoldus Janssen
Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Vol 4 No 2 (2025): Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Publisher : STMIK Amika Soppeng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70247/jumistik.v4i2.222

Abstract

ABSTRAK Perkembangan aset kripto yang pesat, khususnya Ethereum, menuntut adanya model prediksi harga yang akurat untuk mendukung strategi investasi dan manajemen risiko. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua algoritma machine learning ensemble, yaitu Random Forest (RF) dan XGBoost, dalam memprediksi harga harian Ethereum. Dataset historis ETH/USD sebanyak 3.423 observasi dari periode September 2016 hingga Juli 2025 diperoleh dari platform Bitfinex. Setelah melalui tahap pra-pemrosesan data dan rekayasa fitur temporal, dataset dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Model dievaluasi menggunakan metrik Root Mean Square Error (RMSE) dan Koefisien Determinasi (R²). Hasil eksperimen menunjukkan bahwa XGBoost secara signifikan mengungguli Random Forest, dengan nilai RMSE 134.63 dan R² 0.958. Sebagai perbandingan, Random Forest menghasilkan RMSE 208.45 dan R² 0.899. Temuan ini mengindikasikan bahwa mekanisme boosting pada XGBoost lebih efektif dalam menangkap kompleksitas dan volatilitas data pasar kripto. Kata kunci: Prediksi Harga, Ethereum, Machine Learning, XGBoost, Random Forest.
Network Intrusion Detection Using Machine Learning in Network Intrusion Detection Systems (NIDS) Jansen, Arnoldus; Yuswanto, Dery; Styawan, Budi; Girinata, I Made Candra
KOMNET : Jurnal Komputer, Jaringan dan Internet Vol. 4 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengabdian Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/yt59ac51

Abstract

Computer network security has become a crucial aspect as dependence on network-based services increases. One important mechanism in maintaining network security is the Network Intrusion Detection System (NIDS), which functions to detect suspicious activity or attacks on network traffic. The traditional signature-based approach has limitations in detecting new attacks (zero-day attacks). Therefore, this study proposes the application of Machine Learning and Deep Learning methods to improve network intrusion detection capabilities. The CIC-IDS2017 dataset was used as the data source because it represents various types of modern network attacks. The research stages included data pre-processing, feature selection, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The models used include Random Forest as a representation of Machine Learning and Long Short-Term Memory (LSTM) as a representation of Deep Learning. The results show that the Deep Learning approach is capable of providing better detection performance on complex attacks compared to conventional Machine Learning methods. This research is expected to serve as a reference in the development of adaptive and accurate network intrusion detection systems.
Co-Authors Arif, M Aidil Dahur, Arnoldus Janssen Erma Suryani Jansen, Arnoldus Saputra, Arwin Wahyu Styawan, Budi Yuswanto, Dery