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All Journal Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology JUTECH : Journal Education and Technology Abdi Jurnal Publikasi International Journal of Integrative Sciences AMMA : Jurnal Pengabdian Masyarakat
Badriah Nursakinah
Universitas Pamulang
Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Social Sciences Other

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Implementasi Sistem Pakar Diagnosa Artificial Intelligance Addcition Berbasis Web Menggunakan Metode Certainty Factor Studi Kasus HIMAGIRI UNS Riatma Putera, Bagas; Nursakinah, Badriah
Jurnal Informatika Universitas Pamulang Vol 10 No 4 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i4.53717

Abstract

The development of modern Artificial Intelligence (AI) technology has enhanced human interaction with intelligent systems in daily life; however, excessive use of AI can lead to AI Addiction, particularly among university students. This study aims to design and develop a web-based expert system using the Certainty Factor (CF) method to identify early symptoms of AI addiction and calculate the likelihood level of dependence based on user input. The case study was conducted on students of the Informatics Student Association (HIMAGIRI), Universitas Sebelas Maret (UNS), with symptom data obtained from the adaptation of the AI Addiction Scale (AIAS-21) and interviews with psychological experts specializing in addiction, anxiety, and mood disorders. Testing using Black Box Testing and White Box Testing demonstrated that all system functions operated properly and produced consistent diagnostic calculations. From 77 respondents, addiction tendencies were dominated by the Continued Use Despite Harm category (38%), followed by Compulsive Use/Loss of Control (33%) and Withdrawal (29%). These results indicate that the Certainty Factor method is effective in detecting AI addiction tendencies and providing relevant treatment recommendations, making this expert system a useful early detection tool as well as an educational medium to increase students’ self-awareness of their dependence on AI. Keywords: Expert System; Certainty Factor; Artificial Intelligence Addiction; Diagnosis, Web-based; Students; HIMAGIRI UNS
Analisis Penerapan Metode Moora dalam Seleksi Penerimaan Beasiswa Mahasiswa Badriah Nursakinah; Habib Nurfaizal; Khairudin
Jurnal ICT: Information Communication & Technology Vol. 25 No. 2 (2025): JICT-IKMI, December , 2025
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v25i2.318

Abstract

Scholarship acceptance is an important instrument in supporting the success of higher education, both to motivate outstanding students and to help those who are less fortunate. However, the selection process is often constrained by the objectivity, transparency, and complexity of decision-making because it involves various criteria. This research offers a solution by applying the Multi-Objective Optimization based on Ratio Analysis (MOORA) method, which is able to process multicriteria data by distinguishing benefit and cost criteria so as to produce an objective ranking. The purpose of this study is to analyze the application of the MOORA method in the selection of student scholarship admissions and assess its effectiveness in supporting decision-making. The study used five criteria, namely GPA, parental income, achievement, number of family dependents, and organizational activeness. The calculations are done using the Python programming language. The results of the study show that Rina Amelia ranks first with the highest MOORA score. These findings prove that the MOORA method is effective, transparent, and can be an alternative solution for universities in the scholarship selection process.
Predict Goods Demand Using the XGBoost Method Based on Sales Historical Data Badriah Nursakinah; Nurhalimah; Yuda Samudra
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9584

Abstract

Predicting the demand for goods is an important aspect of inventory management and operational planning because inaccurate predictions can lead to overstock or shortages of goods. This study aims to predict the demand for goods using the Extreme Gradient Boosting (XGBoost) algorithm based on historical sales data. The dataset used contains information on the transaction date, number of sales, stock, price, and time index, which is then processed through the preprocessing and feature engineering stages, including the formation of temporal features and sales lag features. Data sharing is carried out using a time series split approach to maintain the chronological order of the data. The XGBoost model is optimized using GridSearchCV with the TimeSeriesSplit validation scheme. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results showed that the model produced an MAE score of 54.13 and an RMSE of 77.60, while a SMAPE score of 43.13% showed an acceptable relative error rate in highly fluctuating sales data. Feature importance analysis shows that previous period (lag_1) sales and weekly patterns are the most dominant factors in demand predictions. These results prove that XGBoost is effectively used for historical data-driven demand prediction of goods and has the potential to support inventory management decision-making.
Co-Authors Bella Putri Amelia Dede Sunandar Fahlapi, Fasha Fitri Nurlaela Habib Nurfaizal Khairudin NURHALIMAH Nurhayati Octaviana Anugrah Ade Purnama Raditia Vindua Riatma Putera, Bagas Safitri, Andin Eka Sri Rama Putri Syafitri, Khairunnisa Vindua, Raditia Wijaya, Michelle Aniela Wildan Salim Yeskarwani Gulo Yuda Samudra