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All Journal Jurnal Ilmu Pengetahuan dan Teknologi (IPTEK) Jurnal Kecerdasan Buatan dan Teknologi Informasi
Muhamad Ramli
Institut Teknologi Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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PENDEKATAN TEORI GRAF UNTUK ANALISIS JARINGAN INTERAKSI PROTEIN-PROTEIN Yustina Sri Suharini; Muhamad Ramli; Sulistyowati; Endang R.D.
JURNAL ILMU PENGETAHUAN DAN TEKNOLOGI (IPTEK) Vol. 7 No. 2 (2023): Jurnal Ilmu Pengetahuan dan Teknologi (IPTEK)
Publisher : Institut Teknologi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31543/jii.v7i2.258

Abstract

Jaringan interaksi protein-protein merupakan hal penting pada setiap proses yang terjadi dalam sel biologis karena dapat digunakan untuk mempelajari kondisi fisiologis sel ketika berada dalam keadaan normal atau tidak normal. Di sisi lain, infrastruktur komputasi telah berada di era yang cukup memadai untuk menyimpan data hasil eksperimen dari berbagai tempat dan waktu. Namun data yang terkumpul perlu diolah dan dianalisis dengan cara yang tepat agar menghasilkan pengetahuan atau wawasan baru yang bermanfaat. Penelitian ini bertujuan melakukan pendekatan agar data jaringan interaksi protein-protein yang terkumpul di database menjadi informasi yang bermakna. Pendekatan dilakukan menggunakan teori graf dengan studi kasus data protein virus SARS-Cov-2. Metode yang digunakan adalah metode in-silico dengan data sekunder berasal dari database bereputasi yang dapat diakses publik. Hasil penelitian berupa daftar protein-protein paling berpengaruh pada virus SARS-Cov-2 berdasarkan parameter-parameter umum yang digunakan dalam ilmu jaringan.
WALMART PRICE PREDICTION USING HOLT-WINTERS FORECASTING Melani Indriasari; Muhamad Soleh; Muhamad Ramli; Sunarto; Sumiarti Andri
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.438

Abstract

Stock price prediction remains a complex challenge due to the volatile, noisy, and nonlinear nature of financial markets. This study aims to evaluate the effectiveness of the Holt-Winters Exponential Smoothing (HWES) method in forecasting the stock price of Walmart Inc. (WMT) and its application in investment decision-making. Historical monthly closing price data from January 2020 to December 2024 were collected and used to build an additive Holt-Winters model. The model was validated using out-of-sample data from January to February 2025, achieving RMSE of 4.535 USD and MAE of 4.801 USD, indicating good short-term predictive performance. The model was then used to forecast stock prices from March 2025 to December 2026, revealing a consistent upward trend with moderate seasonal fluctuations. However, deviations between predicted and actual values were observed during periods of market volatility, particularly in late 2025. To further evaluate performance, the Holt-Winters model was compared with the ARIMA model. Results show that ARIMA outperformed Holt-Winters in short-term forecasting with lower RMSE (4.71), MAE (4.26), and MAPE (4.21%), while Holt-Winters was more effective in capturing seasonal patterns. An investment simulation using a Dollar Cost Averaging (DCA) strategy combined with technical analysis indicators produced a total return of 3.45%, supported by both capital gains and dividend income. These findings suggest that while Holt-Winters provides a simple and interpretable approach for long-term forecasting, its performance can be improved by integrating adaptive models and external factors such as market sentiment and macroeconomic conditions for more robust predictions.
Co-Authors Endang R.D. Melani Indriasari Muhamad Soleh SUHARINI, YUSTINA SRI Sulistyowati Sumiarti Andri Sunarto