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All Journal Bulletin of Electrical Engineering and Informatics SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Al-Irsyad: Jurnal Pendidikan dan Konseling CCIT (Creative Communication and Innovative Technology) Journal Jurnal Mantik Bahtera Inovasi FARABI: Jurnal Matematika dan Pendidikan Matematika
Sutarman Sutarman
Department Of Mathematics, Universitas Sumatera Utara, Medan, 20155, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Application of Fuzzy Time Series Method Cheng Model in Forecasting Stock Prices PT Bukit Asam Tbk Alya Nadhira Nur; Esther SM Nababan; Parapat Gultom; Sutarman Sutarman
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 21, No 1 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v21i1.22910

Abstract

Investment in stocks is one type of investment that can get huge profits, but there are also great risks. So it is necessary to analyze in advance before starting an investment in stocks, in order to avoid losses. One way is to forecast the stock price using fuzzy time series Cheng. The data used is weekly period stock price data from PTBA in January 2020 - December 2022, which can be categorized as a form of time series. From this research, the forecasting value for the next period is Rp. 3797. Which results in a MAPE of 4.2%, which means that FTS Cheng method is very good to use in forecasting the share price of PT Bukit Asam Tbk, because it produces a MAPE value <10%, and produces an RMSE of 158 rupiah, which means the average of the difference between actual and forecast values.
PERFORMANCE OF ROBUST SUPPORT VECTOR MACHINE CLASSIFICATION MODEL ON BALANCED, IMBALANCED AND OUTLIERS DATASETS Muhammad Ardiansyah Sembiring; Herman Saputra; Riki Andri Yusda; Sutarman Sutarman; Erna Budhiarti Nababan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5272

Abstract

In the realm of machine learning, classification models are important for identifying patterns and grouping data. Support Vector Machine (SVM) and Robust SVM are two types of models that are often used. SVM works by finding an optimal hyperplane to separate data classes, while Robust SVM is designed to deal with uncertainty and noise in the data, making it more resistant to outliers. However, SVM has limitations in dealing with class imbalance and outliers in the dataset. Class imbalance makes the model tend to predict the majority class, and outliers can interfere with model formation. This research compares the performance of SVM and Robust SVM on normal, unbalanced and outlier datasets. The software uses Python and Scikit-learn for implementation and comparison of the two models. Key features include automatic data preprocessing, model training, and evaluation with metrics such as accuracy, precision, recall, and F1 score. The results show that Robust SVM is superior in accuracy on normal datasets and is very effective in dealing with class imbalance, achieving a maximum accuracy of 100%. On datasets with outliers, Robust SVM maintains stable accuracy, demonstrating its robustness to outliers. This research contributes to correspondence management by providing more reliable classification models, improving data processing accuracy, and supporting more informed decision making in software development
Automating Internet Distribution with Script-Driven Provisioning and Load Balancing Methods Albadri, Aldhi; Nasution, Mahyuddin K. M.; Sutarman, Sutarman
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3321

Abstract

The utilization of software-based automation technology in the internet network distribution process is currently relatively expensive, while conventional configuration methods cause inefficient use of time, cost, and energy. The time spent is about 5 minutes for each configuration process. The waiting time for a queue of 5 customers with 1 technician is 20 minutes. This problem can be solved by applying the concept of network automation using the Zero Touch Provisioning method, which can increase time efficiency to 5 seconds for each configuration process. Additionally, the use of Priority and Round-Robin algorithms is very helpful in overcoming queue management problems, allowing the server to work according to the desired process logic. The results showed an average wait time of 7.6 seconds with a quantum value of 10. This value was obtained in the process of 5 customer queues with 1 server.
Co-Authors Ade Candra, Ade Afrizal . Albadri, Aldhi Alya Nadhira Nur Anni Rotua Aritonang Efendi, Syahril Erna Budhiarti Nababan Esther SM Nababan Fajar Muhajir Heni Pujiastuti Herman Mawengkang Herman Saputra Husain Husain Husain Husain Ibnu Jaki Lubis Isfenti Sadalia Iskandarini Iwada Grawilser Talunohi JefrY Sudono Halim Mahyuddin K. M Nasution Mochamad Wahyudi Muhammad Ardiansyah Sembiring Parapat Gultom Poltak Sihombing Pratiwi, Risdy Absari Indah Riki Andri Yusda Rismayani . Saib Suwilo Sarah Ayatun Nufus